RecCac: Recommendation Empowered Cooperative Edge Caching for Internet of Things
HAN Suning, LI Xiuhua, SUN Chuan, WANG Xiaofei, Victor C. M. LEUNG
[Introduction]Edge caching is an emerging technology for supporting massive content access in mobile edge networks to address rapidly growing Internet of Things (IoT) services and content applications. However, the edge server is limited with the computation/storage capacity, which causes a low cache hit. Cooperative edge caching jointing neighbor edge servers is regarded as a promising technique to improve cache hit and reduce congestion of the networks. Further, recommender systems can provide personalized content services to meet user’s requirements in the entertainment-oriented mobile networks. Therefore, we investigate the issue of joint cooperative edge caching and recommender systems to achieve additional cache gains by the soft caching framework. To measure the cache profits, the optimization problem is formulated as a 0–1 Integer Linear Programming (ILP), which is NP-hard. Specifically, the method of processing content requests is defined as server actions, we determine the server actions to maximize the quality of experience (QoE). We propose a cachefriendly heuristic algorithm to solve it. Simulation results demonstrate that the proposed framework has superior performance in improving the QoE.
Cost-Effective Task Scheduling for Collaborative Cross-Edge Analytics
ZHAO Kongyang,GAO Bin,ZHOU Zhi
[Introduction]Collaborative cross-edge analytics is a new computing paradigm in which Internet of Things (IoT) data analytics is performed across multiple geographically dispersed edge clouds. Existing work on collaborative cross-edge analytics mostly focuses on reducing either analytics response time or wide-area network (WAN) traffic volume. In this work, we empirically demonstrate that reducing either analytics response time or network traffic volume does not necessarily minimize the WAN traffic cost, due to the price heterogeneity of WAN links. To explicitly leverage the price heterogeneity for WAN cost minimization, we propose to schedule analytic tasks based on both price and bandwidth heterogeneities. Unfortunately, the problem of WAN cost minimization underperformance constraint is shown non-deterministic polynomial (NP)-hard and thus computationally intractable for large inputs. To address this challenge, we propose price- and performanceaware geo-distributed analytics (PPGA) , an efficient task scheduling heuristic that improves the cost-efficiency of IoT data analytic jobs across edge datacenters. We implement PPGA based on Apache Spark and conduct extensive experiments on Amazon EC2 to verify the efficacy of PPGA.