Poster Session

Poster Session

11:30 AM — 12:10 PM JST
Jun 25 Fri, 10:30 PM — 11:10 PM EDT

MoDEMS: Optimizing Edge Computing Migrations for User Mobility

Taejin Kim (Carnegie Mellon University, USA); Siqi Chen (University of Colorado Boulder, USA); Youngbin Im (Ulsan National Institute of Science and Technology, Korea (South)); Xiaoxi Zhang (Sun Yat-sen University, China); Sangtae Ha (University of Colorado Boulder, USA); Carlee Joe-Wong (Carnegie Mellon University, USA)

Edge computing systems benefit from knowledge of short-term mobility from 5G technologies, as tasks offloaded from user devices can be placed at the edge to reduce their latencies. However, as devices move, they will need to offload to different edge servers, which may require migrating data from one edge server to another. In this paper, we introduce MoDEMS, a system architecture through which we provide a rigorous theoretical framework to study the challenges of such migrations to minimize the service provider cost and user latency. We show that this cost minimization problem can be expressed as an integer linear programming problem, which is challenging to solve due to resource constraints at the servers and unknown user mobility patterns. We show that finding the optimal migration plan is in general NP-hard, and we propose alternative heuristic solution algorithms. We finally validate our results with realistic user mobility traces.

High-QoE DASH live streaming using reinforcement learning

Bo Wei (Waseda University, Japan); Hang Song (The University of Tokyo, Japan); Jiro Katto (Waseda University, Japan)

With the live video streaming becomes more and more common in daily life such as live meeting and live video call, it is an urgent task to ensure high-quality and low-delay live video streaming service. High user quality of experience (QoE) should be ensured to satisfy the requirement of user, for which latency is one of the important factors. In this paper, a high-QoE live streaming method is proposed with reinforcement learning. Experiments are conducted to evaluate the proposed method. Results demonstrate that the proposal shows the best performance with highest QoE compared with conventional methods in three network conditions. In Ferry case, the QoE is almost twice of the QoE of other methods.

Joint Optimization of Multi-user Computing Offloading and Service Caching in Mobile Edge Computing

Zhang Zhenyu and Huan Zhou (China Three Gorges University, China); Dawei Li (Montclair State University, USA)

This paper jointly considers the optimization of multi-user computing offloading and service caching in Mobile Edge Computing (MEC), and formulates the problem as a Mixed-Integer Non-Linear Program (MINLP), aiming to minimize the task cost of the system. The original problem is decomposed into an equivalent master problem and sub-problem, and a Collaborative Computing Offloading and Resource Allocation Method (CCORAM) is proposed to solve the optimization problem, which includes two low-complexity algorithms. Simulation results show that CCORAM with low time complexity is very close to the optimal method, and performs much better than other benchmark methods.

Adaptive Search Area Configuration for Location-based P2P Networks

Hiroki Hanawa, Takumi Miyoshi, Taku Yamazaki and Thomas Silverston (Shibaura Institute of Technology, Japan)

This paper proposes an adaptive peer search method that considers the dynamic users' mobility in location-based peer-to-peer (P2P) networks. In the previous study, the neighbor peer search is periodically executed, and the search area is constantly fixed as a circle regardless of the peers' moving speed. Therefore, it is difficult to properly acquire on neighbor peers, especially in the situation when peers are moving at high speed. In this paper, we suggest a method to determine the peer search interval and the search area size adaptively depending on the peers' locations and moving speed. Simulation results show that the proposed method enables peers to search neighbor peers at appropriate intervals and to obtain nearby information efficiently.

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