Survey on Resource Allocation Policy and Job Scheduling Algorithms of Cloud Computing1

Cloud computing is the product of the evolution of calculation. It is a new distributed computing model. As more and more people put into the research and applications on cloud computing, the technology of computing becomes more and more widely used. Cloud computing has a huge user group. It has to deal with a large number of tasks. How to make appropriate decisions when allocating hardware resources to the tasks and dispatching the computing tasks to resource pool has become the main issue in cloud computing. This paper is based on the current situation of resource allocation policy and job scheduling algorithms under cloud circumstance. It summarizes some methods to improve the performance, including dynamic resource allocation strategy based on the law of failure, dynamic resource assignment on the basis of credibility, ant colony optimization algorithm for resource allocation, dynamic scheduling algorithm based on threshold, optimized genetic algorithm with dual fitness and improved ant colony algorithm for job scheduling.

[1]  Yennun Huang,et al.  Software rejuvenation: analysis, module and applications , 1995, Twenty-Fifth International Symposium on Fault-Tolerant Computing. Digest of Papers.

[2]  Sarvapali D. Ramchurn,et al.  Continuous Double Auctions with Execution Uncertainty , 2009, AMEC/TADA.

[3]  Thomas J. Hacker,et al.  Using queue structures to improve job reliability , 2007, HPDC '07.

[4]  Marco Dorigo,et al.  The ant colony optimization meta-heuristic , 1999 .

[5]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[6]  Pan Yub Credibility-based Dynamic Resource Distribution Strategy Under Cloud Computing Environment , 2011 .

[7]  Mark S. Squillante,et al.  Failure data analysis of a large-scale heterogeneous server environment , 2004, International Conference on Dependable Systems and Networks, 2004.

[8]  Tian Guan,et al.  Reliable Resource Provision Policy for Cloud Computing , 2010 .

[9]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Jian Peng,et al.  Task scheduling algorithm based on improved genetic algorithm in cloud computing environment , 2011 .

[11]  Zheng Xiao Research Survey of Cloud Computing , 2011 .

[12]  Feng Gao,et al.  The Trend of Cloud Computing in China , 2011, J. Softw..

[13]  Zeng Zhou Study of grid resource scheduling strategy based on ant colony algorithm , 2007 .

[14]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[15]  Zheng Jun,et al.  Ant colony optimization algorithm for computing resource allocation based on cloud computing environment (Chinese) , 2010 .

[16]  Bruno Schulze,et al.  Using clouds to address grid limitations , 2008, MGC '08.

[17]  Bianca Schroeder,et al.  A Large-Scale Study of Failures in High-Performance Computing Systems , 2006, IEEE Transactions on Dependable and Secure Computing.

[18]  Kang G. Shin,et al.  Adaptive control of virtualized resources in utility computing environments , 2007, EuroSys '07.

[19]  David E. Irwin,et al.  Sharing Networked Resources with Brokered Leases , 2006, USENIX Annual Technical Conference, General Track.

[20]  Mark S. Squillante,et al.  Performance Implications of Failures in Large-Scale Cluster Scheduling , 2004, JSSPP.

[21]  Richard P. Martin,et al.  Improving cluster availability using workstation validation , 2002, SIGMETRICS '02.

[22]  Kuangnan Fang,et al.  Mass Data Analysis and Forecasting Based on Cloud Computing , 2012, J. Softw..

[23]  Dan Meng,et al.  Reliable Resource Provision Policy for Cloud Computing: Reliable Resource Provision Policy for Cloud Computing , 2010 .

[24]  Sanjay Ghemawat,et al.  MapReduce: simplified data processing on large clusters , 2008, CACM.