processes is provided. Finally, under energy overflow and outage constraints, the *average data service (transmission) rate* over the wireless channel is obtained and the *effective capacity* of the system, which characterizes the maximum data arrival rate at the transmitter buffer under quality-of-service (QoS) constraints imposed on the data buffer overflow probability, is derived.'
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title: On the Energy and Data Storage Management in Energy Harvesting Wireless Communications
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Energy harvesting, energy overflow, energy outage, effective capacity, queueing theory, large deviation theory, Markov process.

Introduction
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Due to the unprecedented growth in the number of wireless devices and systems, requiring an ever-increasing amount of energy, research efforts in recent years have focused on more sophisticated energy management techniques. In particular, energy harvesting (EH) technology has attracted significant interest from the research community as a means both to reduce the carbon footprint of communication networks and to provide increased autonomy to wireless devices [@energy_harvesting_ulukus_2015; @ozel2015fundamental; @gunduz2014designing; @tadayon2013power]. The challenges in these studies arise from the complexity in modeling practical energy storage technologies, variable energy consumption patterns, and the stochastic aspects of natural energy sources, such as wind and solar. Researchers have 