This problem appears in most of information systems . This concept is based on Service Oriented Architecture ( SOA ) to improve the connection between business services and software services . If companies can reach this On Demand Business context they could improve the software development process by applying standards like Model Driven Architecture ( MDA ) , to separate business logic from software and technological platforms , and Business Process Management ( BPM ) to define business processes . a lot of research involves in the area of e-learning with service-oriented architecture . In e-learning systems , the challenges are increase of the complexity and more interoperability between systems in distributed environment . An Operating System Designed to Connect Preparing to Connect Administrative Connections Using NT Networking Features Keeping Connected Connecting to the World with TCP/IP Enterprise Connections Microsoft Connections Novell Connections Other Connections Client/Server , Distributed Computing , and the Future of Windows NT Appendices Index . As distributed computing becomes increasingly prevalent , driving a demand for security and privacy in distributed communication , cryptographic protocols are becoming increasingly valuable components of computational environments . While informal understandings of " security and privacy " abound , the task of provably ensuring that these properties hold necessitates a more systematic approach . Due to the advent of technologies and large resource intensive applications , a large scale distributed and heterogeneous system like grids have emerged as popular platforms . Grid Computing is a kind of distributed computing that involves the integrated and collaborative use of geographically-dispersed resources . Hence , reliable resource sharing is required to process the huge amount of computational jobs across system . Enterprises have to be more dynamic in terms of collaboration with partners and even competitors . The service oriented architecture is a promising distributed computing paradigm offering solutions that are extendible , flexible and compatible with legacy systems . This paper proposes and investigates the use of SOA in the construction of collaborative services . Cloud computing is the fastest growing distributed computing paradigm that provides online IT resources on demand by following a pay-as-you-go billing model . The success of this computing paradigm enables cloud providers to offer an extensive collection of parallel computing resources to deal with Big Data workflow scheduling problems . Mobile computing represents a major point of departure from the traditional distributed computing paradigm . The potentially very large number of independent computing units , a decoupled computing style , frequent disconnections , continuous position changes , and the location-dependent nature of the behavior and communication patterns present designers with unprecedented challenges in the areas of modularity and dependability . The context-awareness of things that belong to IoT networks have to be considered in a distributed computation paradigm . In the paper we suggest the use of graph transformations and temporal logic as a formal framework for a knowledge representation of user/inhabitant behaviors in multi-agent systems . Hence , the Situated Computing Framework ( SCF ) research project at Siemens Corporate Research ( SCR ) aims to develop a ubiquitous computing infrastructure that facilitates nomadic users to access rich multimedia contents using small screen devices . This paper describes a new distributed computing concept , the Small Screen/Composite Device ( SS/CD ) framework , which offers mobile users new classes of ubiquitous and mobile multimedia services without to limit the diversity and the richness of the provided services . This paper calls attention to the existence of not only the objective nature , but also the subjective nature of time in the organizational change surrounding the implementation of new information systems . This paper draws its empirical material from an on-going study of the implementation and use of distributed computing-based systems at a mid-sized university ( MSU ) . Implementing client/server networks illuminates three themes that contribute to the dual objective/subjective nature of time at MSU : ( 1 ) client/ server computing is a complex web of technologies and involves people who are struggling to reach a stable , productive state ; ( 2 ) the development of a client/server project is a discontinuous process ; and ( 3 ) because of the number of stakeholders involved in the implementation of client/server systems , there are “ temporal asymmetries ” — that is , differences in how these people themselves perceive and experience time . Directing when typified proficiently is useful in improving the general QOS of the distributed computing framework . A distributed computing based system is particularly set apart by its ability of rendering a remote association between very versatile hubs even without any foreordained foundation . Without any system distributed computing hubs work as switches exchanging information through different jumps . This real data could be obtained by either manual counting at traffic junctions , or using the probes such as loop inductors , CCTV cameras etc . This distributed computing based approach works to find the best traffic simulation corresponding to the real data in hand , using evolutionary computing technique . The result gives us a new lower bound of $ \widetilde{\Omega } ( \sqrt[3]{n\delta^2}+\sqrt{n } ) $ on the number of rounds required for computing the diameter $ \delta$ of any $ n$-node network with quantum messages of size $ O(\log n)$ in the CONGEST model . We draw a connection between the distributed computing scenario above and a new model of query complexity . In this model , an algorithm computing a bi-variate function $ f$ has access to the inputs $ x$ and $ y$ through two separate oracles $ O_x$ and $ O_y$ , respectively . We describe these results , discuss their efficiency , and demonstrate their relevance to privacy preserving computation of data mining algorithms . Note that we consider here a distributed computing scenario , rather than a scenario where all data is gathered in a central server , which then runs the algorithm against all data . ( The central server scenario introduces interesting privacy issues , too , but they are outside the scope of this paper . ) The distributed computing scenario is rapidly evolving for integrating self-organizing and dynamic wireless networks . Unreliable failure detectors are classical mechanisms which provide information about process failures and can help systems to cope with the high dynamism of these networks . A Distributed Computing Perspective . Software agents that are autonomous , communicative , and possibly intelligent processes raise new questions for developers of distributed systems . A Distributed computing Perspective ( 2010 JGI/ANL HPC Workshop ) . David Konerding from Google , Inc. gives a presentation on " High Performance Computational Biology : A Distributed Computing Perspective " at the JGI/Argonne HPC Workshop on January 26 , 2010 . Expert perspectives are provided by authoritative researchers and practitioners from around the world , discussing research developments and emerging trends , presenting case studies on helpful frameworks and innovative methodologies , and suggesting best practices for efficient and effective data analytics . Features : reviews a framework for fast data applications , a technique for complex event processing , and agglomerative approaches for the partitioning of networks ; introduces a unified approach to data modeling and management , and a distributed computing perspective on interfacing physical and cyber worlds ; presents techniques for machine learning for big data , and identifying duplicate records in data repositories ; examines enabling technologies and tools for data mining ; proposes frameworks for data extraction , and adaptive decision making and social media analysis . The overall purpose of these products is to allow physicians , nurses and staff to work anywhere , at anytime and from any computer on the Internet without any software installation . Finally , we can conclude that since WWW is becoming widespread and is significantly changing the current computer industry , we are entering a distributed computing era with ubiquitous web browsers and servers . Presently , web software has become almost indispensable . The rapid growth of networked high-performance workstations is driving the market toward a distributed model of computing . The focus of this this model is to make a network of workstations work together on problems without dependence on operating system or architecture . Fog computing is a technology that brings computing and storage resources near to the end user . Being in its infancy , fog computing lacks standardization in terms of architectures and simulation platforms . This has restricted the growth of IoT in certain sectors . This paper investigates the fog-computing paradigm as an alternative for IoT applications . There is a need to systematically review and synthesize fog computing concerns or challenges for IoT applications . The existing solutions , their strengths and weaknesses are considered . A new approach to conducting data mining within the fog computing concept and its application to the Internet of Things is proposed . The application to fuzzy clustering is described as well . A problem of workload distribution within the heterogeneous computing environment is not new and has been solved frequently and successfully . However , optimization problem models neglect such aspects of fog computing concept as : the computation nodes are unequal in terms of desirable workload distribution ; the cloud layer is the mandatory participant of the computing process . Fog computing leverages the computational resources at the network edge to meet the increasing demand for latency-sensitive applications in large-scale industries . In this article , we study the computation offloading in a fog computing network , where the end users , most of the time , offload part of their tasks to a fog node . Nevertheless , limited by the computational and storage resources , the fog node further simultaneously offloads the task data to the neighboring fog nodes and/or the remote cloud server to obtain the additional computing resources . This increases the efficiency of the entire system , as it not only reduces the amount of data that is transported back and forth between the cloud and the sensors but also eliminates the risk that a data center failure bears with it . We also analyze the security and deployment issues of this fog computing layer . In order to solve the coupling resource management problem , we propose a fog computing model and extend the Hungarian algorithm to manage the coupling resource which can get smaller delay to realize effective and sustainable services . The fog computing layer acts as a buffer and controller between CPS layer and cloud layer which can handle malicious attacks to build highly sustainable systems . Experimental results and theoretical analysis show that our method can reduce the coupling computing and increase the resource utilization to make systems more effectively . This raises a complexity for battery operated smart cameras , as they would be required to perform intensive image processing operations on large volumes of data , within energy consumption constraints . By using intelligence partitioning we analyse the effects of different partitioning scenarios for the processing tasks between the smart camera node , the fog computing layer and cloud computing , in the node energy consumption as well as the real time performance of the WVSN ( Wireless Vision Sensor Node ) . The results obtained show that traditional design space exploration approaches are inefficient for WVSN , while intelligence partitioning enhances the energy consumption performance of the smart camera node and meets the timing constraints . In the proposed system the weighted majority game theory is used for selecting fog device in indoor and outdoor regions . Simulation results demonstrate that the proposed fog computing based system reduces the average delay , average jitter and energy consumption by approximately 15 % , 20 % and 15 % respectively than the existing cloud only health care system . In this paper , we propose employing dual energy sources for supporting the fog nodes , where solar power is the primary energy supply and grid power is the backup supply . Based on that , we present a comprehensive analytic framework for incorporating green energy sources to support the running of IoT and fog computing-based systems , and to handle the tradeoff in terms of average response time , average monetary , and energy costs in the IoT. This paper describes an online algorithm , Lyapunov optimization on time and energy cost ( LOTEC ) , based on the technique of Lyapunov optimization . LOTEC is a quantified near optimal solution and is able to make control decision on application offloading by adjusting the two-way tradeoff between average response time and average cost . The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of the client . This paper developed a Fog Computing based framework named FogGIS for mining analytics from geospatial data . It has been built a prototype using Intel Edison , an embedded microprocessor . Allocation and selection mechanisms in such environment fall into a class of multi-criteria decision-making ( MCDM ) problems . The existing MCDM methods may not be suitable for fog computing environment as these problems are distributed , scalable and dynamic . We propose an adaptive multi-criteria decision-making ( A-MCDM ) model to obtain an optimal ranking of alternatives in dynamic and scalable environments . The Grid is a distributed computing infrastructure that enables coordinated resource sharing within dynamic organizations consisting of individuals , institutions , and resources . The Grid extends the distributed and parallel computing paradigms allowing resource negotiation and dynamical allocation , heterogeneity , open protocols and services . Grid environments can be used both for compute intensive tasks and data intensive applications as they offer resources , services , and data access mechanisms . The grid is a distributed computing infrastructure that enables coordinated resource sharing within dynamic organizations consisting of individuals , institutions , and resources . The grid extends the distributed and parallel computing paradigms allowing for resource negotiation and dynamical allocation , heterogeneity , open protocols , and services . Grid environments can be used both for compute-intensive tasks and data intensive applications by exploiting their resources , services , and data access mechanisms . Cloud computing provides more reliable and flexible access to IT resources , which differentiates it from other distributed computer paradigms . Managing the applications efficiently in cloud computing motivates the challenge of provisioning and allocating resource on demand in response to dynamic workloads . The growth of the Internet connected devices population has fuelled the emergence of new distributed computer paradigms ; one of these paradigms is the so-called Fog-to-Cloud ( F2C ) computing , where resources ( compute , storage , data ) are distributed in a hierarchical fashion between the edge and the core of the network . This new paradigm has brought new research challenges , such as the need for a novel framework intended to controlling and , more in general , facilitating the interaction among the heterogeneous devices conforming the environment at the edge of the network and the available resources at cloud . Next we describe the usage of network sampling in community discovery , where the goal is to reduce the number of nodes and/or edges while retaining the network ’s underlying community structure . Finally , we review research efforts that leverage various parallel and distributed computing paradigms in community discovery , which can facilitate finding communities in tera- and peta-scale networks . In this paper we introduce generic workers , a programming model for JavaScript unifying parallel and distributed computing paradigms , that allows the same application to run well on a variety of clients while utilizing the available resources in the best possible way . We describe the design and implementation of an infrastructure supporting our programming model and evaluate performance of selected applications running on devices with differing computational capabilities . CATSA consists of the following components : Task Estimation Deadline Division , Task Sequencing , Resource Matching Decision , and Task Scheduling to solve the scheduling problem . These components schedule tasks onto geographically distributed computing paradigms , in such a way that decreases the makespan of all applications . Performance evaluation results determine that the suggested framework outperforms about the makespan of mobile workflow applications as compared to existing task scheduling algorithms . Often data must be used in several steps of a computation to complete a full task . Composing default data transformation operators with the standard Hadoop MapReduce runtime is very convenient . However , the current strategy of using high-level languages to support iterative applications with Hadoop MapReduce relies on an external wrapper script in other languages such as Python and Groovy , which causes significant performance loss when restarting mappers and reducers between jobs . The manager is capable of getting aware of the overloading or under-loading situations with the resources allocated to its user community . For the former , it takes an action of requesting more resources from , for example , the batch system of a High Performance Computing ( HPC ) cluster or Computing Clouds and integrates the additional resources , in case of acquisition , into the Hadoop MapReduce runtime . For the latter , the manager gives the free resources back to its source . We propose Multi-pass MapReduce Duplicate Count Strategy ( MultiMR-DCS++ ) , a MR-based approach for multi-pass adaptive SNM , aiming to increase even more the performance of the SNM . The evaluation results based on real-world datasets and cluster infrastructure show that our approach increases the performance of MapReduce-based SNM regarding the EM execution time and detection quality . A new approach for the Entity Matching task parallelization is proposed . We propose MapReduce Duplicate Count Strategy ( MR -- DCS + + ) , an efficient MapReduce-based approach for the adaptive SNM , aiming to increase even more the performance of SNM . The evaluation results based on real-world datasets and cloud infrastructure show that our approach increases the performance of MapReduce-based SNM by providing better results for the EM execution time . The invention discloses a MapReduce frame-based internet of things system , which comprises a Map job , a MapReduce job , a Reduce work and a distributed data file ; one MapReduce job has one or more Reduce jobs ; one Reduce work is in charge of reading and processing one data set , the conditions that how many sets are generated and each Map output result is distributed to which set are decided by a final hash function at a Map stage , and each data set is remotely read to a machine where the corresponding Reduce job is at ; the Reduce job performs integration processing and analysis on those data and outputs a final data result . The MapReduce frame-based internet of things system can effectively timely process data , can fast accurately analyze a distribution condition of user data and helps a user to fast know data features and make a business/political decision . This paper proposes a heterogeneity model to predict the shared resource contention between the simultaneously running tasks of a MapReduce application when heterogeneous computing resources ( e.g. CPUs and GPUs ) are allocated . To support the approach , we present a heterogeneous MapReduce framework , Hadoop+ , which enables CPUs and GPUs to process big data coordinately , and leverages the heterogeneity model to assist users in selecting the computing resources for different purposes . Our experimental results show three benefits . Performing data mining tasks such as clustering can be very complex due to the high dimensionality and volume of data being mined . This paper proposes an approach for data clustering using the Symbiotic Organisms Search algorithm ( SOS ) developed in the MapReduce parallel architecture . Also , the cluster quality evolution is analysed using the purity measured considering four different fitness metrics . We propose to extend MapReduce with EFind , an Efficient and Flexible index access solution , to better support this class of ap- plications . EFind introduces a standard index access interface to MapReduce so that ( i ) developers can easily and flexibly express index access operations without unnatural code , and ( ii ) the EFind enhanced MapReduce system can automatically optimize the in- dex access operations . We propose and analyze a number of in- dex access strategies that utilize caching , re-partitioning , and index locality to reduce redundant index accesses . This paper uses MapReduce parallel programming mode to make the Ant Colony Optimization ( ACO ) algorithm parallel and bring forward the MapReduce-based improved ACO for Multi-dimensional Knapsack Problem ( MKP ) . A variety of techniques , such as change the probability calculation of the timing , roulette , crossover and mutation , are applied for improving the drawback of the ACO and complexity of the algorithm is greatly reduced . This paper describes what is big data with its characteristics , concept of Association rules with the Apriori algorithm in big data , problems in the existing MapReduce base Apriori algorithm . We propose new improve MapReduce approach base Apriori algorithm for mining on a relationship with the help of given one suitable example where Reduce function runs only one time after running on Map function and this proposed algorithm run on any type of database . In the Hadoop platform , we use FCM based on the iterative MapReduce and MapReduce basic models , respectively . The algorithm is used to diagnose the transformer ; the experimental results show that the diagnostic speed of FCM algorithm based on iterative mapreduce is more than 12 times of the MapReduce basic model algorithm , the misjudgment rate is lower 12 to 15 percent , and the diagnostic efficiency of FCM algorithm is improved effectively . MapReduce alternates between computation and communication intensive phases with bursty workloads . The challenge to make execution of MapReduce green , lies in controlling server and network resources simultaneously . The related work offers various good solutions for homogenous systems , with the central theme of packing tasks into as small number of servers as possible and thus overlooking the possibility to " sleep " servers and network components . The results of this procedure usually highlight disagreements between the compared products which should be further analyzed . The presented work has the aim to address this need by proposing an innovative crowdsourcing-based game that engages citizens in validating disagreements between land cover datasets . The game was played during the Free and Open Source Software for Geospatial ( FOSS4 G ) Europe Conference 2015 by the conference participants and allowed to evaluate the disagreements between the GlobeLand30 and the DUSAF land cover datasets on the Como city area ( Italy ) . For effective sentiment analysis , it is essential to build emotional word polarity lexicon . This paper introduces a crowdsourcing-based game that we have developed for efficiently building a polarity lexicon in Korean . First , we collected a corpus from the relating Internet communities using a crawler , and we classified them into words using the Twitter POS analyzer . A key metric of crowdsourcing is data accuracy , which relies on the quality of the participating workers ’ data ( e.g. , the probability that the data are equal to the ground truth ) . However , the data quality of a worker can be its own private information ( which the worker learns , e.g. , based on its location ) that it may have incentive to misreport , which can , in turn , mislead the crowdsourcing requester about the accuracy of the data . This issue is further complicated by the fact that the worker can also manipulate its effort made in the crowdsourcing task and the data reported to the requester , which can also mislead the requester . A key metric of crowdsourcing is data accuracy , which relies on the quality of the participating workers ' data ( e.g. , the probability that the data is equal to the ground truth ) . However , the data quality of a worker can be its own private information ( which the worker learns , e.g. , based on its location ) that it may have incentive to misreport , which can in turn mislead the crowdsourcing requester about the accuracy of the data . This issue is further complicated by the fact that the worker can also manipulate its effort made in the crowdsourcing task and the data reported to the requester , which can also mislead the requester . Fingerprinting-based indoor localization suffers from its time-consuming and labor-intensive site survey . As a promising solution , sample crowdsourcing has been recently promoted to exploit casually collected samples for building offline fingerprint database . However , crowdsourced samples may be annotated with erroneous locations , which raises a serious question about whether they are reliable for database construction . To avoid energy consumption resulted by blockchain consensus , Edgence proposes a random but verifiable way to elect a masternode to generate each new block . The potential of the tailored Edgence is shown by examples of decentralized crowdsourcing and AI training . This method uses a tree to arrange crowd ’s ideas and enable flexible modifications of prior ideas . A demonstration system named Sketchfans is developed , and the results of a crowdsourcing sketching experiment using this system are presented . The experimental results validate that this method is effective ; participants rely heavily on the idea tree for inspiration , and the best ideas appear around the ends of branches on the idea tree . The crowdsourcing movement has spawned a host of successful efforts that organize large numbers of globally-distributed participants to tackle a range of tasks , including crisis mapping ( e.g. , Ushahidi ) , translation ( e.g. , Duolingo ) , and protein folding ( e.g. , Foldit ) . Alongside these specialized systems , we have seen the rise of general-purpose crowdsourcing marketplaces like Amazon Mechanical Turk and Crowdflower that aim to connect task requesters with task workers , toward creating new crowdsourcing systems that can intelligently organize large numbers of people . However , these positive opportunities have a sinister counterpart : what we dub “ Weaponized Crowdsourcing ” . Lane-based road network information , such as the number and locations of traffic lanes on a road , has played an important role in intelligent transportation systems . In this paper , we propose a Collecting Lane-based Road Information via Crowdsourcing ( CLRIC ) method , which can automatically extract detailed lane structure of roads by using crowdsourcing data collected by vehicles . First , CLRIC filters the high-precision GPS data from the raw trajectories based on region growing clustering with prior knowledge . Crowdsourced delivery offers greater flexibility and requires less capital investment than traditional outsourcing approaches . This study deals with the planning of the last-mile delivery with partial crowdsourcing integration , in which crowdsourcing helps to manage the delivery from a transfer point to final customer locations . The decision involves several basic questions , including which customer is to be outsourced , by which outsourcing partner , and at which transfer point . Theoretical model is created to derive the performance improvement ( evaluated in term of average file downloading time ) achieved by our strategy , which is further validated via simulation . This paper not only provides system designers a more efficient content replication solution , but also can push forward the development of the crowdsourcing-based CDNs . Unlike other methods , we do not discard dissenting votes , but incorporate them into a richer and more continuous representation of truth . CrowdTruth is a widely used crowdsourcing methodology1 adopted by industrial partners and public organizations such as Google , IBM , New York Times , Cleveland Clinic , Crowdynews , Sound and Vision archive , Rijksmuseum , and in a multitude of domains such as AI , news , medicine , social media , cultural heritage , and social sciences . The goal of this tutorial is to introduce the audience to a novel approach to crowdsourcing that takes advantage of the diversity of opinions and perspectives that is inherent to the Web , as methods that deal with disagreement and diversity in crowdsourcing have become increasingly popular .
This problem appears in most of information systems . This concept is based on Service Oriented Architecture ( SOA ) to improve the connection between business services and software services . If companies can reach this On Demand Business context they could improve the software development process by applying standards like Model Driven Architecture ( MDA ) , to separate business logic from software and technological platforms , and Business Process Management ( BPM ) to define business processes .
a lot of research involves in the area of e-learning with service-oriented architecture . In e-learning systems , the challenges are increase of the complexity and more interoperability between systems in distributed environment .
An Operating System Designed to Connect Preparing to Connect Administrative Connections Using NT Networking Features Keeping Connected Connecting to the World with TCP/IP Enterprise Connections Microsoft Connections Novell Connections Other Connections Client/Server , Distributed Computing , and the Future of Windows NT Appendices Index .
As distributed computing becomes increasingly prevalent , driving a demand for security and privacy in distributed communication , cryptographic protocols are becoming increasingly valuable components of computational environments . While informal understandings of " security and privacy " abound , the task of provably ensuring that these properties hold necessitates a more systematic approach .
Due to the advent of technologies and large resource intensive applications , a large scale distributed and heterogeneous system like grids have emerged as popular platforms . Grid Computing is a kind of distributed computing that involves the integrated and collaborative use of geographically-dispersed resources . Hence , reliable resource sharing is required to process the huge amount of computational jobs across system .
Enterprises have to be more dynamic in terms of collaboration with partners and even competitors . The service oriented architecture is a promising distributed computing paradigm offering solutions that are extendible , flexible and compatible with legacy systems . This paper proposes and investigates the use of SOA in the construction of collaborative services .
Cloud computing is the fastest growing distributed computing paradigm that provides online IT resources on demand by following a pay-as-you-go billing model . The success of this computing paradigm enables cloud providers to offer an extensive collection of parallel computing resources to deal with Big Data workflow scheduling problems .
Mobile computing represents a major point of departure from the traditional distributed computing paradigm . The potentially very large number of independent computing units , a decoupled computing style , frequent disconnections , continuous position changes , and the location-dependent nature of the behavior and communication patterns present designers with unprecedented challenges in the areas of modularity and dependability .
The context-awareness of things that belong to IoT networks have to be considered in a distributed computation paradigm . In the paper we suggest the use of graph transformations and temporal logic as a formal framework for a knowledge representation of user/inhabitant behaviors in multi-agent systems .
Hence , the Situated Computing Framework ( SCF ) research project at Siemens Corporate Research ( SCR ) aims to develop a ubiquitous computing infrastructure that facilitates nomadic users to access rich multimedia contents using small screen devices . This paper describes a new distributed computing concept , the Small Screen/Composite Device ( SS/CD ) framework , which offers mobile users new classes of ubiquitous and mobile multimedia services without to limit the diversity and the richness of the provided services .
This paper calls attention to the existence of not only the objective nature , but also the subjective nature of time in the organizational change surrounding the implementation of new information systems . This paper draws its empirical material from an on-going study of the implementation and use of distributed computing-based systems at a mid-sized university ( MSU ) . Implementing client/server networks illuminates three themes that contribute to the dual objective/subjective nature of time at MSU : ( 1 ) client/ server computing is a complex web of technologies and involves people who are struggling to reach a stable , productive state ; ( 2 ) the development of a client/server project is a discontinuous process ; and ( 3 ) because of the number of stakeholders involved in the implementation of client/server systems , there are “ temporal asymmetries ” — that is , differences in how these people themselves perceive and experience time .
Directing when typified proficiently is useful in improving the general QOS of the distributed computing framework . A distributed computing based system is particularly set apart by its ability of rendering a remote association between very versatile hubs even without any foreordained foundation . Without any system distributed computing hubs work as switches exchanging information through different jumps .
This real data could be obtained by either manual counting at traffic junctions , or using the probes such as loop inductors , CCTV cameras etc . This distributed computing based approach works to find the best traffic simulation corresponding to the real data in hand , using evolutionary computing technique .
The result gives us a new lower bound of $ \widetilde{\Omega } ( \sqrt[3]{n\delta^2}+\sqrt{n } ) $ on the number of rounds required for computing the diameter $ \delta$ of any $ n$-node network with quantum messages of size $ O(\log n)$ in the CONGEST model . We draw a connection between the distributed computing scenario above and a new model of query complexity . In this model , an algorithm computing a bi-variate function $ f$ has access to the inputs $ x$ and $ y$ through two separate oracles $ O_x$ and $ O_y$ , respectively .
We describe these results , discuss their efficiency , and demonstrate their relevance to privacy preserving computation of data mining algorithms . Note that we consider here a distributed computing scenario , rather than a scenario where all data is gathered in a central server , which then runs the algorithm against all data . ( The central server scenario introduces interesting privacy issues , too , but they are outside the scope of this paper . )
The distributed computing scenario is rapidly evolving for integrating self-organizing and dynamic wireless networks . Unreliable failure detectors are classical mechanisms which provide information about process failures and can help systems to cope with the high dynamism of these networks .
A Distributed Computing Perspective . Software agents that are autonomous , communicative , and possibly intelligent processes raise new questions for developers of distributed systems .
A Distributed computing Perspective ( 2010 JGI/ANL HPC Workshop ) . David Konerding from Google , Inc. gives a presentation on " High Performance Computational Biology : A Distributed Computing Perspective " at the JGI/Argonne HPC Workshop on January 26 , 2010 .
Expert perspectives are provided by authoritative researchers and practitioners from around the world , discussing research developments and emerging trends , presenting case studies on helpful frameworks and innovative methodologies , and suggesting best practices for efficient and effective data analytics . Features : reviews a framework for fast data applications , a technique for complex event processing , and agglomerative approaches for the partitioning of networks ; introduces a unified approach to data modeling and management , and a distributed computing perspective on interfacing physical and cyber worlds ; presents techniques for machine learning for big data , and identifying duplicate records in data repositories ; examines enabling technologies and tools for data mining ; proposes frameworks for data extraction , and adaptive decision making and social media analysis .
The overall purpose of these products is to allow physicians , nurses and staff to work anywhere , at anytime and from any computer on the Internet without any software installation . Finally , we can conclude that since WWW is becoming widespread and is significantly changing the current computer industry , we are entering a distributed computing era with ubiquitous web browsers and servers . Presently , web software has become almost indispensable .
The rapid growth of networked high-performance workstations is driving the market toward a distributed model of computing . The focus of this this model is to make a network of workstations work together on problems without dependence on operating system or architecture .
Fog computing is a technology that brings computing and storage resources near to the end user . Being in its infancy , fog computing lacks standardization in terms of architectures and simulation platforms .
This has restricted the growth of IoT in certain sectors . This paper investigates the fog-computing paradigm as an alternative for IoT applications . There is a need to systematically review and synthesize fog computing concerns or challenges for IoT applications .
The existing solutions , their strengths and weaknesses are considered . A new approach to conducting data mining within the fog computing concept and its application to the Internet of Things is proposed . The application to fuzzy clustering is described as well .
A problem of workload distribution within the heterogeneous computing environment is not new and has been solved frequently and successfully . However , optimization problem models neglect such aspects of fog computing concept as : the computation nodes are unequal in terms of desirable workload distribution ; the cloud layer is the mandatory participant of the computing process .
Fog computing leverages the computational resources at the network edge to meet the increasing demand for latency-sensitive applications in large-scale industries . In this article , we study the computation offloading in a fog computing network , where the end users , most of the time , offload part of their tasks to a fog node . Nevertheless , limited by the computational and storage resources , the fog node further simultaneously offloads the task data to the neighboring fog nodes and/or the remote cloud server to obtain the additional computing resources .
This increases the efficiency of the entire system , as it not only reduces the amount of data that is transported back and forth between the cloud and the sensors but also eliminates the risk that a data center failure bears with it . We also analyze the security and deployment issues of this fog computing layer .
In order to solve the coupling resource management problem , we propose a fog computing model and extend the Hungarian algorithm to manage the coupling resource which can get smaller delay to realize effective and sustainable services . The fog computing layer acts as a buffer and controller between CPS layer and cloud layer which can handle malicious attacks to build highly sustainable systems . Experimental results and theoretical analysis show that our method can reduce the coupling computing and increase the resource utilization to make systems more effectively .
This raises a complexity for battery operated smart cameras , as they would be required to perform intensive image processing operations on large volumes of data , within energy consumption constraints . By using intelligence partitioning we analyse the effects of different partitioning scenarios for the processing tasks between the smart camera node , the fog computing layer and cloud computing , in the node energy consumption as well as the real time performance of the WVSN ( Wireless Vision Sensor Node ) . The results obtained show that traditional design space exploration approaches are inefficient for WVSN , while intelligence partitioning enhances the energy consumption performance of the smart camera node and meets the timing constraints .
In the proposed system the weighted majority game theory is used for selecting fog device in indoor and outdoor regions . Simulation results demonstrate that the proposed fog computing based system reduces the average delay , average jitter and energy consumption by approximately 15 % , 20 % and 15 % respectively than the existing cloud only health care system .
In this paper , we propose employing dual energy sources for supporting the fog nodes , where solar power is the primary energy supply and grid power is the backup supply . Based on that , we present a comprehensive analytic framework for incorporating green energy sources to support the running of IoT and fog computing-based systems , and to handle the tradeoff in terms of average response time , average monetary , and energy costs in the IoT. This paper describes an online algorithm , Lyapunov optimization on time and energy cost ( LOTEC ) , based on the technique of Lyapunov optimization . LOTEC is a quantified near optimal solution and is able to make control decision on application offloading by adjusting the two-way tradeoff between average response time and average cost .
The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of the client . This paper developed a Fog Computing based framework named FogGIS for mining analytics from geospatial data . It has been built a prototype using Intel Edison , an embedded microprocessor .
Allocation and selection mechanisms in such environment fall into a class of multi-criteria decision-making ( MCDM ) problems . The existing MCDM methods may not be suitable for fog computing environment as these problems are distributed , scalable and dynamic . We propose an adaptive multi-criteria decision-making ( A-MCDM ) model to obtain an optimal ranking of alternatives in dynamic and scalable environments .
The Grid is a distributed computing infrastructure that enables coordinated resource sharing within dynamic organizations consisting of individuals , institutions , and resources . The Grid extends the distributed and parallel computing paradigms allowing resource negotiation and dynamical allocation , heterogeneity , open protocols and services . Grid environments can be used both for compute intensive tasks and data intensive applications as they offer resources , services , and data access mechanisms .
The grid is a distributed computing infrastructure that enables coordinated resource sharing within dynamic organizations consisting of individuals , institutions , and resources . The grid extends the distributed and parallel computing paradigms allowing for resource negotiation and dynamical allocation , heterogeneity , open protocols , and services . Grid environments can be used both for compute-intensive tasks and data intensive applications by exploiting their resources , services , and data access mechanisms .
Cloud computing provides more reliable and flexible access to IT resources , which differentiates it from other distributed computer paradigms . Managing the applications efficiently in cloud computing motivates the challenge of provisioning and allocating resource on demand in response to dynamic workloads .
The growth of the Internet connected devices population has fuelled the emergence of new distributed computer paradigms ; one of these paradigms is the so-called Fog-to-Cloud ( F2C ) computing , where resources ( compute , storage , data ) are distributed in a hierarchical fashion between the edge and the core of the network . This new paradigm has brought new research challenges , such as the need for a novel framework intended to controlling and , more in general , facilitating the interaction among the heterogeneous devices conforming the environment at the edge of the network and the available resources at cloud .
Next we describe the usage of network sampling in community discovery , where the goal is to reduce the number of nodes and/or edges while retaining the network ’s underlying community structure . Finally , we review research efforts that leverage various parallel and distributed computing paradigms in community discovery , which can facilitate finding communities in tera- and peta-scale networks .
In this paper we introduce generic workers , a programming model for JavaScript unifying parallel and distributed computing paradigms , that allows the same application to run well on a variety of clients while utilizing the available resources in the best possible way . We describe the design and implementation of an infrastructure supporting our programming model and evaluate performance of selected applications running on devices with differing computational capabilities .
CATSA consists of the following components : Task Estimation Deadline Division , Task Sequencing , Resource Matching Decision , and Task Scheduling to solve the scheduling problem . These components schedule tasks onto geographically distributed computing paradigms , in such a way that decreases the makespan of all applications . Performance evaluation results determine that the suggested framework outperforms about the makespan of mobile workflow applications as compared to existing task scheduling algorithms .
Often data must be used in several steps of a computation to complete a full task . Composing default data transformation operators with the standard Hadoop MapReduce runtime is very convenient . However , the current strategy of using high-level languages to support iterative applications with Hadoop MapReduce relies on an external wrapper script in other languages such as Python and Groovy , which causes significant performance loss when restarting mappers and reducers between jobs .
The manager is capable of getting aware of the overloading or under-loading situations with the resources allocated to its user community . For the former , it takes an action of requesting more resources from , for example , the batch system of a High Performance Computing ( HPC ) cluster or Computing Clouds and integrates the additional resources , in case of acquisition , into the Hadoop MapReduce runtime . For the latter , the manager gives the free resources back to its source .
We propose Multi-pass MapReduce Duplicate Count Strategy ( MultiMR-DCS++ ) , a MR-based approach for multi-pass adaptive SNM , aiming to increase even more the performance of the SNM . The evaluation results based on real-world datasets and cluster infrastructure show that our approach increases the performance of MapReduce-based SNM regarding the EM execution time and detection quality . A new approach for the Entity Matching task parallelization is proposed .
We propose MapReduce Duplicate Count Strategy ( MR -- DCS + + ) , an efficient MapReduce-based approach for the adaptive SNM , aiming to increase even more the performance of SNM . The evaluation results based on real-world datasets and cloud infrastructure show that our approach increases the performance of MapReduce-based SNM by providing better results for the EM execution time .
The invention discloses a MapReduce frame-based internet of things system , which comprises a Map job , a MapReduce job , a Reduce work and a distributed data file ; one MapReduce job has one or more Reduce jobs ; one Reduce work is in charge of reading and processing one data set , the conditions that how many sets are generated and each Map output result is distributed to which set are decided by a final hash function at a Map stage , and each data set is remotely read to a machine where the corresponding Reduce job is at ; the Reduce job performs integration processing and analysis on those data and outputs a final data result . The MapReduce frame-based internet of things system can effectively timely process data , can fast accurately analyze a distribution condition of user data and helps a user to fast know data features and make a business/political decision .
This paper proposes a heterogeneity model to predict the shared resource contention between the simultaneously running tasks of a MapReduce application when heterogeneous computing resources ( e.g. CPUs and GPUs ) are allocated . To support the approach , we present a heterogeneous MapReduce framework , Hadoop+ , which enables CPUs and GPUs to process big data coordinately , and leverages the heterogeneity model to assist users in selecting the computing resources for different purposes . Our experimental results show three benefits .
Performing data mining tasks such as clustering can be very complex due to the high dimensionality and volume of data being mined . This paper proposes an approach for data clustering using the Symbiotic Organisms Search algorithm ( SOS ) developed in the MapReduce parallel architecture . Also , the cluster quality evolution is analysed using the purity measured considering four different fitness metrics .
We propose to extend MapReduce with EFind , an Efficient and Flexible index access solution , to better support this class of ap- plications . EFind introduces a standard index access interface to MapReduce so that ( i ) developers can easily and flexibly express index access operations without unnatural code , and ( ii ) the EFind enhanced MapReduce system can automatically optimize the in- dex access operations . We propose and analyze a number of in- dex access strategies that utilize caching , re-partitioning , and index locality to reduce redundant index accesses .
This paper uses MapReduce parallel programming mode to make the Ant Colony Optimization ( ACO ) algorithm parallel and bring forward the MapReduce-based improved ACO for Multi-dimensional Knapsack Problem ( MKP ) . A variety of techniques , such as change the probability calculation of the timing , roulette , crossover and mutation , are applied for improving the drawback of the ACO and complexity of the algorithm is greatly reduced .
This paper describes what is big data with its characteristics , concept of Association rules with the Apriori algorithm in big data , problems in the existing MapReduce base Apriori algorithm . We propose new improve MapReduce approach base Apriori algorithm for mining on a relationship with the help of given one suitable example where Reduce function runs only one time after running on Map function and this proposed algorithm run on any type of database .
In the Hadoop platform , we use FCM based on the iterative MapReduce and MapReduce basic models , respectively . The algorithm is used to diagnose the transformer ; the experimental results show that the diagnostic speed of FCM algorithm based on iterative mapreduce is more than 12 times of the MapReduce basic model algorithm , the misjudgment rate is lower 12 to 15 percent , and the diagnostic efficiency of FCM algorithm is improved effectively .
MapReduce alternates between computation and communication intensive phases with bursty workloads . The challenge to make execution of MapReduce green , lies in controlling server and network resources simultaneously . The related work offers various good solutions for homogenous systems , with the central theme of packing tasks into as small number of servers as possible and thus overlooking the possibility to " sleep " servers and network components .
The results of this procedure usually highlight disagreements between the compared products which should be further analyzed . The presented work has the aim to address this need by proposing an innovative crowdsourcing-based game that engages citizens in validating disagreements between land cover datasets . The game was played during the Free and Open Source Software for Geospatial ( FOSS4 G ) Europe Conference 2015 by the conference participants and allowed to evaluate the disagreements between the GlobeLand30 and the DUSAF land cover datasets on the Como city area ( Italy ) .
For effective sentiment analysis , it is essential to build emotional word polarity lexicon . This paper introduces a crowdsourcing-based game that we have developed for efficiently building a polarity lexicon in Korean . First , we collected a corpus from the relating Internet communities using a crawler , and we classified them into words using the Twitter POS analyzer .
A key metric of crowdsourcing is data accuracy , which relies on the quality of the participating workers ’ data ( e.g. , the probability that the data are equal to the ground truth ) . However , the data quality of a worker can be its own private information ( which the worker learns , e.g. , based on its location ) that it may have incentive to misreport , which can , in turn , mislead the crowdsourcing requester about the accuracy of the data . This issue is further complicated by the fact that the worker can also manipulate its effort made in the crowdsourcing task and the data reported to the requester , which can also mislead the requester .
A key metric of crowdsourcing is data accuracy , which relies on the quality of the participating workers ' data ( e.g. , the probability that the data is equal to the ground truth ) . However , the data quality of a worker can be its own private information ( which the worker learns , e.g. , based on its location ) that it may have incentive to misreport , which can in turn mislead the crowdsourcing requester about the accuracy of the data . This issue is further complicated by the fact that the worker can also manipulate its effort made in the crowdsourcing task and the data reported to the requester , which can also mislead the requester .
Fingerprinting-based indoor localization suffers from its time-consuming and labor-intensive site survey . As a promising solution , sample crowdsourcing has been recently promoted to exploit casually collected samples for building offline fingerprint database . However , crowdsourced samples may be annotated with erroneous locations , which raises a serious question about whether they are reliable for database construction .
To avoid energy consumption resulted by blockchain consensus , Edgence proposes a random but verifiable way to elect a masternode to generate each new block . The potential of the tailored Edgence is shown by examples of decentralized crowdsourcing and AI training .
This method uses a tree to arrange crowd ’s ideas and enable flexible modifications of prior ideas . A demonstration system named Sketchfans is developed , and the results of a crowdsourcing sketching experiment using this system are presented . The experimental results validate that this method is effective ; participants rely heavily on the idea tree for inspiration , and the best ideas appear around the ends of branches on the idea tree .
The crowdsourcing movement has spawned a host of successful efforts that organize large numbers of globally-distributed participants to tackle a range of tasks , including crisis mapping ( e.g. , Ushahidi ) , translation ( e.g. , Duolingo ) , and protein folding ( e.g. , Foldit ) . Alongside these specialized systems , we have seen the rise of general-purpose crowdsourcing marketplaces like Amazon Mechanical Turk and Crowdflower that aim to connect task requesters with task workers , toward creating new crowdsourcing systems that can intelligently organize large numbers of people . However , these positive opportunities have a sinister counterpart : what we dub “ Weaponized Crowdsourcing ” .
Lane-based road network information , such as the number and locations of traffic lanes on a road , has played an important role in intelligent transportation systems . In this paper , we propose a Collecting Lane-based Road Information via Crowdsourcing ( CLRIC ) method , which can automatically extract detailed lane structure of roads by using crowdsourcing data collected by vehicles . First , CLRIC filters the high-precision GPS data from the raw trajectories based on region growing clustering with prior knowledge .
Crowdsourced delivery offers greater flexibility and requires less capital investment than traditional outsourcing approaches . This study deals with the planning of the last-mile delivery with partial crowdsourcing integration , in which crowdsourcing helps to manage the delivery from a transfer point to final customer locations . The decision involves several basic questions , including which customer is to be outsourced , by which outsourcing partner , and at which transfer point .
Theoretical model is created to derive the performance improvement ( evaluated in term of average file downloading time ) achieved by our strategy , which is further validated via simulation . This paper not only provides system designers a more efficient content replication solution , but also can push forward the development of the crowdsourcing-based CDNs .
Unlike other methods , we do not discard dissenting votes , but incorporate them into a richer and more continuous representation of truth . CrowdTruth is a widely used crowdsourcing methodology1 adopted by industrial partners and public organizations such as Google , IBM , New York Times , Cleveland Clinic , Crowdynews , Sound and Vision archive , Rijksmuseum , and in a multitude of domains such as AI , news , medicine , social media , cultural heritage , and social sciences . The goal of this tutorial is to introduce the audience to a novel approach to crowdsourcing that takes advantage of the diversity of opinions and perspectives that is inherent to the Web , as methods that deal with disagreement and diversity in crowdsourcing have become increasingly popular .
An on Demand Business Context to Improve Software Development Process Based on Business Knowledge Service-Oriented Reference Architecture for Personalized E-learning Systems (SORAPES) Networking Windows NT 3.51 An adversary-centric logic of security and authenticity AHSWDG: An Ant Based Heuristic Approach to Scheduling and Workload Distribution in Computational Grids A service oriented architecture framework for collaborative services A dynamic VM provisioning and de-provisioning based cost-efficient deadline-aware scheduling algorithm for Big Data workflow applications in a cloud environment Transient data sharing among mobile programs Context-awareness of the IoT through the on-the-fly preference modeling A situated computing framework for mobile and ubiquitous multimedia access using small screen and composite devices Transitioning to client/server: using a temporal framework to study organizational change A Study on Distributed Computing Routing Optimization by using ZRP Technique Distributed computing approach to optimize road traffic simulation Quantum Distributed Complexity of Set Disjointness on a Line Privacy Preserving Data Mining by Cyptography A Failure Detector for Wireless Networks with Unknown Membership Responsible Agent Behavior: A Distributed Computing Perspective High Performance Computational Biology: A Distributed computing Perspective (2010 JGI/ANL HPC Workshop) Data Science and Big Data Computing: Frameworks and Methodologies Web-based distributed MIS application in health care DEC: A Distributed Enterprising Computing System FogNetSim++: A Toolkit for Modeling and Simulation of Distributed Fog Environment Fog Computing Challenges: A Systematic Review Fuzzy Clustering Based on Cloud and Fog Computing An Ontology-Based Approach to the Workload Distribution Problem Solving in Fog-Computing Environment Latency-Driven Parallel Task Data Offloading in Fog Computing Networks for Industrial Applications Fog Computing-Based IoT for Health Monitoring System Coupling resource management based on fog computing in smart city systems From the Sensor to the Cloud: Intelligence Partitioning for Smart Camera Applications FogIoHT: A weighted majority game theory based energy-efficient delay-sensitive fog network for internet of health things Adaptive Energy-Aware Computation Offloading for Cloud of Things Systems FogGIS: Fog Computing for geospatial big data analytics An adaptive model for resource selection and allocation in fog computing environment Data Mining Systems Using Grid Computing Service-oriented middleware for distributed data mining on the grid Dynamic Resource Provisioning in Cloud Computing: A Heuristic Markovian Approach A Hash-Based Naming Strategy for the Fog-to-Cloud Computing Paradigm Community Discovery: Simple and Scalable Approaches Generic workers: towards unified distributed and parallel JavaScript programming model Content Aware Task Scheduling Framework for Mobile Workflow Applications in Heterogeneous Mobile-Edge-Cloud Paradigms: CATSA Framework Integrating Pig with Harp to Support Iterative Applications with Fast Cache and Customized Communication Using adaptive resource allocation to implement an elastic MapReduce framework Towards the efficient parallelization of multi-pass adaptive blocking for entity matching Adaptive sorted neighborhood blocking for entity matching with MapReduce Mapreduce frame-based internet of things system Hadoop+: Modeling and Evaluating the Heterogeneity for MapReduce Applications in Heterogeneous Clusters Quality Analysis of Different Metrics for Data Clustering Using Bio-Inspired Algorithm and MapReduce Architecture Efficient and Flexible Index Access in MapReduce MapReduce-Based Ant Colony Optimization Algorithm for Multi-Dimensional Knapsack Problem Mining on Relationships in Big Data era using Improve Apriori Algorithm with MapReduce Approach Improved Iteration FCM Algorithm for MapReduce Research Green MapReduce for heterogeneous data centers A crowdsourcing-based game for land cover validation 한국어 극성 사전 구축을 위한 크라우드소싱 기반 감성 단어 극성 태깅 게임 Truthful Data Quality Elicitation for Quality-Aware Data Crowdsourcing Incentivizing Truthful Data Quality for Quality-Aware Mobile Data Crowdsourcing Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples Edgence: A blockchain-enabled edge-computing platform for intelligent IoT-based dApps Collaborative sketching in crowdsourcing design: a new method for idea generation Weaponized Crowdsourcing: An Emerging Threat and Potential Countermeasures CLRIC: Collecting Lane-Based Road Information Via Crowdsourcing A decision model for last-mile delivery planning with crowdsourcing integration Replicating Coded Content in Crowdsourcing-Based CDN Systems Crowdsourcing Inclusivity: Dealing with Diversity of Opinions, Perspectives and Ambiguity in Annotated Data
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