Energy-Efficient Caching for Mobile Edge Computing in 5G Networks

Zhaohui Luo, Minghui LiWang, Zhijian Lin, Lianfen Huang, Xiaojiang Du, Mohsen Guizani
2017 Applied Sciences  
Mobile Edge Computing (MEC), which is considered a promising and emerging paradigm to provide caching capabilities in proximity to mobile devices in 5G networks, enables fast, popular content delivery of delay-sensitive applications at the backhaul capacity of limited mobile networks. Most existing studies focus on cache allocation, mechanism design and coding design for caching. However, grid power supply with fixed power uninterruptedly in support of a MEC server (MECS) is costly and even
more » ... asible, especially when the load changes dynamically over time. In this paper, we investigate the energy consumption of the MECS problem in cellular networks. Given the average download latency constraints, we take the MECS's energy consumption, backhaul capacities and content popularity distributions into account and formulate a joint optimization framework to minimize the energy consumption of the system. As a complicated joint optimization problem, we apply a genetic algorithm to solve it. Simulation results show that the proposed solution can effectively determine the near-optimal caching placement to obtain better performance in terms of energy efficiency gains compared with conventional caching placement strategies. In particular, it is shown that the proposed scheme can significantly reduce the joint cost when backhaul capacity is low. catch up with the rate requirements, backhaul capabilities have been regarded as a bottleneck for mobile cellular networks. One promising solution to meet the demand is edge caching, which brings video contents closer to the users, reduces data traffic going through the backhaul links, the time required for content delivery, as well as help to smooth the traffic during peak hours. In wireless edge caching, highly sought-after videos are cached in the cellular BSs or wireless access points so that demands from users to the same content can be accommodated easily without duplicate transmissions from remote servers. Specifically, local caching can be more effective when a fraction of requested contents has high popularity. Recently, Mobile Edge Computing (MEC) [4, 5] has been introduced as an emerging paradigm enabling a capillary distribution of cloud storage capabilities to the edge of the cellular radio access network (RAN). In particular, the MECSs are implemented directly at the BSs using generic-computing platforms, which enable context-aware services and caching deployment in close-proximity to the mobile users. As a consequence of this, MECS presents a unique opportunity to not only implement edge caching but also perform caching placement strategy design. With the benefits of avoiding potential network congestion and alleviating the backhaul links burden, caching popular content at MECSs for backhaul capacity-limited mobile networks has emerged as a cost effective solution [6, 7] . Recently, a good deal of works have been focused on big data analysis strategies for edge caching [8, 9] , context-aware caching deployment strategy design [10, 11] , and decentralized coded caching strategies [12, 13] . Nevertheless, the cache allocation mechanism, more specifically, the energy efficiency (EE) cache deployment, has received less attention. When the actual budget is given, the cache size deployed at MECS will not be arbitrarily large. Caching more content requires activating more MECS, which results in more energy consumption. Moreover, providing grid power supply with fixed power uninterruptedly in support of MECS is costly and even infeasible, especially when the load changes dynamically over time. Hence, the cost energy of MECS should be carefully investigated, and the EE of MECS within the 5G cellular network should be optimized. As a result, the interplay between the EE and backhaul capacity is supposed to be intensively studied. Recently, the issue of energy efficiency has received a lot of attention in the MEC system [14] . In [15] , user association and power allocation in millimeter-wave-based ultra-dense networks is considered with attention to load balance constraints, energy harvesting by base stations, user quality of service requirements, energy efficiency, and cross-tier interference limits. Literature [16] investigates the power control and sensing time optimization problem in a cognitive small cell network, where the mitigation of cross-tier interference, imperfect hybrid spectrum sensing, and energy efficiency are considered. As one of the most popular and efficient energy saving schemes [17, 18] , BS sleeping has been proposed and widely studied to realize substantial energy saving in cellular networks [19] [20] [21] [22] . However, integrating MEC with BSs significantly complicates the energy saving issue due to the fact that BSs now provide not only radio access services but also caching services. Furthermore, since caching resources on MECS are limited, downloading some content from the CN is inevitable. As a result, energy consumption couples the caching capacity and MECSs' sleeping decisions over time. It has been observed that the content popularity and caching capacity are two main factors affecting the MECSs' sleeping decisions. Literature [23] discussed the caching deployment problem with a given wireless transmission rate, and it also made an assumption that three factors of the backhaul transmission rate, MECSs' storage capacity and system energy consumption are fixed. However, in practical mobile networks, base stations should consider different wireless channel states and conditions as well as different types of backhaul links and system power. Thus, it is necessary and crucial for caching deployment and active MECS to consider the above three factors [7, 11] . As a consequence, how to design an optimal solution to minimize energy cost while guaranteeing high user's quality of experience (QoE) is a challenging issue. In this paper, we study the joint optimization of average download latency and average energy consumption in cellular networks with MEC integration in order to maximize the QoE for users while
doi:10.3390/app7060557 fatcat:nbol46adfzgifi3trptcd7xcja