Dynamic resource orchestration for multi-task application in heterogeneous mobile cloud computing

2016 ◽  
Author(s):  
Qi Qi ◽  
Jianxin Liao ◽  
Jingyu Wang ◽  
Qi Li ◽  
Yufei Cao
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Resource Orchestration ◽  
Dynamic Resource

scholarly journals Software Defined Resource Orchestration System for Multitask Application in Heterogeneous Mobile Cloud Computing

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Qi Qi ◽  
Jianxin Liao ◽  
Jingyu Wang ◽  
Qi Li ◽  
Yufei Cao
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Performance Metrics ◽  
Integrated Management ◽  
Access Network ◽  
Particle Swarm Algorithm ◽  
Pareto Optimum ◽  
Mobile Cloud ◽  
Resource Orchestration ◽  
And Migration

The mobile cloud computing (MCC) that combines mobile computing and cloud concept takes wireless access network as the transmission medium and uses mobile devices as the client. When offloading the complicated multitask application to the MCC environment, each task executes individually in terms of its own computation, storage, and bandwidth requirement. Due to user’s mobility, the provided resources contain different performance metrics that may affect the destination choice. Nevertheless, these heterogeneous MCC resources lack integrated management and can hardly cooperate with each other. Thus, how to choose the appropriate offload destination and orchestrate the resources for multitask is a challenge problem. This paper realizes a programming resource provision for heterogeneous energy-constrained computing environments, where a software defined controller is responsible for resource orchestration, offload, and migration. The resource orchestration is formulated as multiobjective optimal problem that contains the metrics of energy consumption, cost, and availability. Finally, a particle swarm algorithm is used to obtain the approximate optimal solutions. Simulation results show that the solutions for all of our studied cases almost can hit Pareto optimum and surpass the comparative algorithm in approximation, coverage, and execution time.

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