Details

Autor(en) / Beteiligte

• Lei Yang
• Jiannong Cao
• Zhenyu Wang
• Weigang Wu

Titel

Network Aware Multi-User Computation Partitioning in Mobile Edge Clouds

Ist Teil von

• 2017 46th International Conference on Parallel Processing (ICPP), 2017, p.302-311

Ort / Verlag

IEEE

Erscheinungsjahr

2017

Quelle

IEL

Beschreibungen/Notizen

• Mobile edge cloud has been increasingly concerned by researchers due to its closer distance to mobile users than the traditional cloud on Internet. Offloading computations from mobile devices to the nearby edge cloud is an effective technique to accelerate the applications and/or save energy on the mobile devices. However, the mobile edge cloud usually has limited computation resources and constrained access bandwidth shared by multiple users in its proximity. Thus, allocation of resources and bandwidth among the users is significant to the overall application performance. In this paper, we study network aware multi-user computation partitioning problem in mobile edge clouds, i.e., to decide for each user which parts of the application should be offload onto the edge cloud, and which others should be executed locally, and meanwhile to allocate the access bandwidth among the users, such that the average application performance of the users is maximized. This problem is novel in that we consider the competition among users for both computing resources and bandwidth, and jointly optimizes the partitioning decisions with the allocation of resources and bandwidths among users, while most existing works either focus on the single user computation partitioning or study the multiple user computation partitioning without regard of the constrained network bandwidth. We first formulate the problem, and then transform it into the classic Multi-class Multi-dimensional Knapsack Problem and develop an effective algorithm, namely Performance Function Matrix based Heuristic (PFM-H), to solve it. Comprehensive simulations show that our proposed algorithm outperforms the benchmark algorithms significantly in the average application performance.