Edge computing in the Internet of Things brings applications and content closer to the users by introducing an additional computational layer at the network infrastructure, between cloud and the resource-constrained data producing devices and user equipment. This way, the opportunistic nature of the operational environment is addressed by introducing computational power in location with low latency and high bandwidth. However, location-aware deployment of edge computing infrastructure requires careful placement scheme for edge servers. To provide the best possible Quality of Service for the user applications, their proximity needs to be optimized. Moreover, the deployment faces practical limitations in budget limitations, hardware requirements of servers and in online load balancing between servers. To address these challenges, we formulate the edge server placement as a capacitated location-allocation problem, while minimizing the distance between servers and access points of a real city-wide Wi-Fi network deployment. In our algorithm, we utilize both upper and lower server capacity constraints for load balancing. Furthermore, we enable sharing of workload between servers to facilitate deployments with low capacity servers. The performance of the algorithm is demonstrated in placement scenarios, exemplified by high capacity servers for edge computing and low capacity servers for Fog computing, with different parameters in a real-world data set. The data set consists of both dense deployment of access points in central areas, but also sparse deployment in suburban areas within the same network infrastructure. In comparison, we show that previous approaches do not sufficiently address such deployment. The presented algorithm is able to provide optimal placements that minimize the distances and provide balanced workload with sharing by following the capacity constraints.
Simulated data sets were utilized in @cite_15 @cite_32 , where the other studies utilized real-world data sets. The data set @cite_47 consists of geo-referenced phone call detail records over the city of Milan for three months' period, which was used in @cite_24 @cite_30 @cite_21 . The Shanghai Telecom data set contains the data of mobile users accessing 3000 base stations with 4.6 million call records and 7.5 million movement traces of 10 thousand users in six successive months @cite_0 . The data set was used in @cite_41 @cite_11 @cite_36 . The data set utilized in @cite_1 consists of thousands of Wi-Fi access points in New York City. In @cite_16 , the data set was obtained through the globally-distributed Planetlab nodes and the measurement nodes, deployed in China @cite_16 .
@cite_30 @cite_47 @cite_41 @cite_36 @cite_21 @cite_1 @cite_32 @cite_24 @cite_0 @cite_15 @cite_16 @cite_11
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Mobile edge computing (MEC) is an emerging technology that aims at pushing applications and content close to the users (e.g., at base stations, access points, and aggregation networks) to reduce latency, improve quality of experience, and ensure highly efficient network operation and service delivery. It principally relies on virtualization-enabled MEC servers with limited capacity at the edge of the network. One key issue is to dimension such systems in terms of server size, server number, and server operation area to meet MEC goals. In this paper, we formulate this problem as a mixed integer linear program. We then propose a graph-based algorithm that, taking into account a maximum MEC server capacity, provides a partition of MEC clusters, which consolidates as many communications as possible at the edge. We use a dataset of mobile communications to extensively evaluate them with real world spatio-temporal human dynamics. In addition to quantifying macroscopic MEC benefits, the evaluation shows that our algorithm provides MEC area partitions that largely offload the core, thus pushing the load at the edge (e.g., with 10 small MEC servers between 55 and 64 of the traffic stay at the edge), and that are well balanced through time. The study of socio-technical systems has been revolutionized by the unprecedented amount of digital records that are constantly being produced by human activities such as accessing Internet services, using mobile devices, and consuming energy and knowledge. In this paper, we describe the richest open multi-source dataset ever released on two geographical areas. The dataset is composed of telecommunications, weather, news, social networks and electricity data from the city of Milan and the Province of Trentino. The unique multi-source composition of the dataset makes it an ideal testbed for methodologies and approaches aimed at tackling a wide range of problems including energy consumption, mobility planning, tourist and migrant flows, urban structures and interactions, event detection, urban well-being and many others. In this article, we propose content-centric, in-network content caching and placement approaches that leverage cooperation among edge cloud devices, content popularity, and GPS trajectory information to improve content delivery speeds, network traffic congestion, cache resource utilization efficiency, and users' quality of experience in highly populated cities. More specifically, our proposed approaches exploit collaborative filtering theory to provide accurate and efficient content popularity predictions to enable proactive in-network caching of Internet content. We propose a practical content delivery architecture that consists of standalone edge cloud devices to be deployed in the city to cache and process popular Internet content as it disseminates throughout the network. We also show that our proposed approaches ensure responsive cloud content delivery with minimized service disruption. A device for selectively moistening the flap of envelopes has a moistening member and a pivoting moistening deflector selectively operable to moisten the flap or not. It further comprises a pivoting slide bar to the front of the moistening member and actuated between a protection position and a retracted position in front of the moistening member. The moistening deflector is adapted to cooperate with the slide bar and the moistening member and is coupled to the bar so that they are actuated virtually simultaneously. The device finds applications in automatic mail handling. Abstract Mobile edge computing is an emerging technology that provides services within the close proximity of mobile subscribers by edge servers that are deployed in each edge server. Mobile edge computing platform enables application developers and content providers to serve context-aware services (such as service recommendation) by using real time radio access network information. In service recommendation system, quality of service (QoS) prediction plays an important role when mobile devices or users want to invoke services that can satisfy user QoS requirements. However, user mobility (e.g., from one edge server to another) often makes service QoS prediction values deviate from actual values in traditional mobile networks. Unfortunately, many existing service recommendation approaches fail to consider user mobility. In this paper, we propose a service recommendation approach based on collaborative filtering and make QoS prediction based on user mobility. This approach initially calculates user or edge server similarity and selects the Top-K most-similar neighbors, predicts service QoS, and then makes service recommendation. We have implemented our proposed approach with experiments based on Shanghai Telecom datasets. Experimental results show that our approach can significantly improve on the accuracy of service recommendation in mobile edge computing. Remote clouds are gradually unable to achieve ultra-low latency to meet the requirements of mobile users because of the intolerable long distance between remote clouds and mobile users and the network congestion caused by the tremendous number of users. Mobile edge computing, a new paradigm, has been proposed to mitigate aforementioned effects. Existing studies mostly assume the edge servers have been deployed properly and they just pay attention to how to minimize the delay between edge servers and mobile users. In this paper, considering the practical environment, we investigate how to deploy edge servers effectively and economically in wireless metropolitan area networks. Thus, we address the problem of minimizing the number of edge servers while ensuring some QoS requirements. Aiming at more consistence with a generalized condition, we extend the definition of the dominating set, and transform the addressed problem into the minimum dominating set problem in graph theory. In addition, two conditions are considered for the capacities of edge servers: one is that the capacities of edge servers can be configured on demand, and the other is that all the edge servers have the same capacities. For the on-demand condition, a greedy based algorithm is proposed to find the solution, and the key idea is to iteratively choose nodes that can connect as many other nodes as possible under the delay, degree and cluster size constraints. Furthermore, a simulated annealing based approach is given for global optimization. For the second condition, a greedy based algorithm is also proposed to satisfy the capacity constraint of edge servers and minimize the number of edge servers simultaneously. The simulation results show that the proposed algorithms are feasible. We present @math , a decision support framework to provision edge servers for online services providers (OSPs). @math takes advantage of the increasingly flexible edge server placement, which is enabled by new technologies such as edge computing platforms, cloudlets and network function virtualization, to optimize the overall performance and cost of edge infrastructures. The key difference between @math and traditional server placement approaches lies on that @math can discover proper unforeseen edge locations which significantly improve the efficiency and reduce the cost of edge provisioning. We show how @math effectively identifies promising edge locations which are close to a collection of users merely with inaccurate network distance estimation methods, e.g., geographic coordinate (GC) and network coordinate systems (NC). We also show how @math comprehensively considers various pragmatic concerns in edge provisioning, such as traffic limits by law or ISP policy, edge site deployment and resource usage cost, over-provisioning for fault tolerance, etc., with a simple optimization model. We simulate @math using real network data at global and county-wide scales. Measurement-driven simulations show that with a given cost budget @math can improve user performance by around 10-45 percent at global scale networks and 15-35 percent at a country-wide scale network. Abstract With the rapid increase in the development of the Internet of Things and 5G networks in the smart city context, a large amount of data (i.e., big data) is expected to be generated, resulting in increased latency for the traditional cloud computing paradigm. To reduce the latency, mobile edge computing has been considered for offloading a part of the workload from mobile devices to nearby edge servers that have sufficient computation resources. Although there has been significant research in the field of mobile edge computing, little attention has been given to understanding the placement of edge servers in smart cities to optimize the mobile edge computing network performance. In this paper, we study the edge server placement problem in mobile edge computing environments for smart cities. First, we formulate the problem as a multi-objective constraint optimization problem that places edge servers in some strategic locations with the objective to make balance the workloads of edge servers and minimize the access delay between the mobile user and edge server. Then, we adopt mixed integer programming to find the optimal solution. Experimental results based on Shanghai Telecom’s base station dataset show that our approach outperforms several representative approaches in terms of access delay and workload balancing.