Demand-driven task scheduling using 2D chromosome genetic algorithm in mobile cloud

Mobile cloud computing, which comes up in recent years, is a new computing paradigm. In mobile cloud, mobile users can access and schedule the resources or services in remote clouds via wireless networks, which we call mobile cloud task scheduling. They even can build mobile micro-cloud (MuCloud) wi...

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Veröffentlicht in:2014 IEEE International Conference on Progress in Informatics and Computing S. 539 - 545
Hauptverfasser: Zhiming Cai, Chongcheng Chen
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2014
Schlagworte:
2D chromosome
Biological cells
demand-driven model
genetic algorithm
Genetic algorithms
mobile cloud
Mobile communication
Mobile computing
Mobile handsets
Processor scheduling
Scheduling
task scheduling
ISBN:9781479920334, 1479920339
Online-Zugang:Volltext
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Zusammenfassung:Mobile cloud computing, which comes up in recent years, is a new computing paradigm. In mobile cloud, mobile users can access and schedule the resources or services in remote clouds via wireless networks, which we call mobile cloud task scheduling. They even can build mobile micro-cloud (MuCloud) with mobile device to provide lightweight service. However, unreliable wireless connection and dynamic join and quit of MuCloud make task scheduling in mobile cloud face more challenges than in wired cloud. Moreover, from both the users and service providers' perspective, task scheduling is a multi-objective optimization problem. Small makespan and load balancing are pursued by mobile users and cloud service providers respectively. In this paper, we advance a demand-driven task scheduling model and introduce an estimate method to predict warranty complete time of tasks in wireless network. An improved genetic algorithm using 2D chromosome (2DCGA) is presented to tackle multi-objective task scheduling. Simulation experiments show: 1) compared with Markov model, our estimate method has higher accuracy of prediction and more reasonable prediction results of probability of task scheduling failure; 2) 2DCGA has good performance for task scheduling. When compared with IGA, it has smaller makespan and lower deviation of load; 3) objective priority can be adjusted exactly by weights of fitness functions. It makes 2DCGA suitable for multi-objective optimization.
ISBN:9781479920334
1479920339
DOI:10.1109/PIC.2014.6972393