Joint Service Caching and Task Offloading for Mobile Edge Computing in Dense Networks

Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the network edge, thereby meeting the latency requirements of many emerging mobile applications and saving backhaul network bandwidth. Although many existing works have studied computation offloading policies, service caching is an equally, if not more important, design topic of MEC, yet receives much less attention. Service caching refers to caching application services and their related databases/libraries in the edge server (e.g. MEC-enabled BS), thereby enabling corresponding computation tasks to be executed. Because only a small number of application services can be cached in resource-limited edge server at the same time, which services to cache has to be judiciously decided to maximize the edge computing performance. In this paper, we investigate the extremely compelling but much less studied problem of dynamic service caching in MEC-enabled dense cellular networks. We propose an efficient online algorithm, called OREO, which jointly optimizes dynamic service caching and task offloading to address a number of key challenges in MEC systems, including service heterogeneity, unknown system dynamics, spatial demand coupling and decentralized coordination. Our algorithm is developed based on Lyapunov optimization and Gibbs sampling, works online without requiring future information, and achieves provable close-to-optimal performance. Simulation results show that our algorithm can effectively reduce computation latency for end users while keeping energy consumption low

Energy-Efficient Context-Aware Matching for Resource Allocation in Ultra-Dense Small Cells

With the explosive growth of mobile data traffic and rapidly rising energy price, how to implement caching at small cells in an energy-efficient way is still an open problem and requires further research efforts. In this paper, we study the energy-efficient context-aware resource allocation problem, which falls into the category of mixed integer nonlinear programming (MINLP) and is NP-hard. To provide a tractable solution, the MINLP problem is decoupled and reformulated as a one-to-one matching problem under two-sided preferences, which are modeled as the maximum energy efficiency that can be achieved under the expected matching. An iterative algorithm is developed to establish preference profiles by employing nonlinear fractional programming and Lagrange dual decomposition. Then, we propose an energy-efficient matching algorithm based on the Gale-Shapley algorithm, and provide the detailed discussion and analysis of stability, optimality, implementation issues, and algorithmic complexity. The proposed matching algorithm is also extended to scenarios with preference, indifference, and incomplete preference lists by introducing some tie-breaking and preference deletion rules. The simulation results demonstrate that the proposed algorithm achieves significant performance and satisfaction gains compared with the conventional algorithms

Integrated and Heterogenous Mobile Edge Caching (MEC) Networks

The recent phenomenal growth of the global mobile data traffic, mainly caused by intelligent Internet of Things (IoTs), is the most significant challenge of wireless networks within the foreseeable future. In this context, Mobile Edge Caching (MEC) has been recognized as a promising solution to maintain low latency communication. This, in turn, improves the Quality of Service (QoS) by storing the most popular multimedia content close to the end-users. Despite extensive progress in MEC networks, however, there are still limitations that should be addressed. Through this Ph.D. thesis, first, we perform a literature review on recent works on MEC networks to identify challenges and potential opportunities for improvement. Then, by highlighting potential drawbacks of the reviewed works, we aim to not only enhance the cache-hit-ratio, which is the metric to quantify the users’ QoS, but also to improve the quality of experience of caching nodes. In this regard, we design and implement a Deep Reinforcement Learning (DRL)-based connection scheduling framework [1] to minimize users’ access delay by maintaining a trade-off between the energy consumption of Unmanned Aerial Vehicles (UAVs) and the occurrence of handovers. We also use D2D communication [2] to increase the network’s capacity without adding any infrastructure. Another approach to effectively use the limited storage capacity of caching nodes is to increase the content diversity by employing the coded caching strategies in cluster-centric networks. Despite all the researches on the cluster-centric cellular networks, there is no framework to determine how different segments can be cached to increase the data availability in a UAV-aided cluster-centric cellular network. Moreover, to date, limited research has been performed on UAV-aided cellular networks to provide high QoS for users in both indoor and outdoor environments. Through this thesis research, we aim to address these gaps [3,4]. In addition, another goal of this thesis is to design real-time caching strategies [5–9] to predict the upcoming most popular content to improve the users’ access delay. Last but not least, capitalizing on recent advancements of indoor localization frameworks [10–14], we aim to develop a proactive caching strategy for an integrated indoor/outdoor MEC network