Dynamic service migration and workload scheduling in edge-clouds

被引:192
|
作者
Urgaonkar, Rahul [1 ]
Wang, Shiqiang [3 ]
He, Ting [2 ]
Zafer, Murtaza [4 ]
Chan, Kevin [5 ]
Leung, Kin K. [3 ]
机构
[1] IBM TJ Watson Res Ctr, Cloud Based Networks Grp, Yorktown Hts, NY 10598 USA
[2] IBM TJ Watson Res Ctr, Network Analyt Res Grp, Yorktown Hts, NY USA
[3] Univ London Imperial Coll Sci Technol & Med, Dept Elect & Elect Engn, London SW7 2AZ, England
[4] Nyansa Inc, Analyt Portfolio Co, Palo Alto, CA USA
[5] US Army Res Lab, Computat & Informat Sci Directorate, Adelphi, MD USA
关键词
Edge-clouds; Service migration; Stochastic optimization; Markov decision processes; PLACEMENT;
D O I
10.1016/j.peva.2015.06.013
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Edge-clouds provide a promising new approach to significantly reduce network operational costs by moving computation closer to the edge. A key challenge in such systems is to decide where and when services should be migrated in response to user mobility and demand variation. The objective is to optimize operational costs while providing rigorous performance guarantees. In this paper, we model this as a sequential decision making Markov Decision Problem (MDP). However, departing from traditional solution methods (such as dynamic programming) that require extensive statistical knowledge and are computationally prohibitive, we develop a novel alternate methodology. First, we establish an interesting decoupling property of the MDP that reduces it to two independent MDPs on disjoint state spaces. Then, using the technique of Lyapunov optimization over renewals, we design an online control algorithm for the decoupled problem that is provably cost-optimal. This algorithm does not require any statistical knowledge of the system parameters and can be implemented efficiently. We validate the performance of our algorithm using extensive trace-driven simulations. Our overall approach is general and can be applied to other MDPs that possess a similar decoupling property. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:205 / 228
页数:24
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