Novel algorithms and equivalence optimisation for resource allocation in cloud computing

In this paper, we model the optimisation of the resource allocation in cloud computing as a constraint satisfaction problem considering three types of resources CPU, RAM and bandwidth and design a Choco-Based algorithm CB for VM resource allocation in virtualised cloud data centres. We also propose an Improved First-Fit Decreasing Algorithm IFFD and an Improved Best-Fit Decreasing Algorithm IBFD and conduct performance evaluation experiments using Choco. The experimental results show that CB has better results, whereas its solution time is longer than IFFD and IBFD in resource allocation. Moreover, to reduce the complexity of solving the problem of CSP-based resource allocation, we propose an equivalence optimisation which can greatly reduce the search space for resource allocation by making tree pruning with resource equivalence. Then, a resource allocation algorithm based on Equivalent Optimisation EO is designed. Experimental results also show that compared with CB, EO greatly reduces the time of allocating resource of cloud computing.

[1]  Deyu Qi,et al.  A Threshold-based Dynamic Resource Allocation Scheme for Cloud Computing , 2011 .

[2]  Albert Y. Zomaya,et al.  Energy efficient utilization of resources in cloud computing systems , 2010, The Journal of Supercomputing.

[3]  Bu-Sung Lee,et al.  Optimal virtual machine placement across multiple cloud providers , 2009, 2009 IEEE Asia-Pacific Services Computing Conference (APSCC).

[4]  Xavier Lorca,et al.  Entropy: a consolidation manager for clusters , 2009, VEE '09.

[5]  Chris Rose,et al.  A Break in the Clouds: Towards a Cloud Definition , 2011 .

[6]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[7]  Rajkumar Buyya,et al.  Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers , 2011, J. Parallel Distributed Comput..

[8]  Manish Parashar,et al.  Energy-efficient application-aware online provisioning for virtualized clouds and data centers , 2010, International Conference on Green Computing.

[9]  Anand Sivasubramaniam,et al.  Xen and Co.: Communication-Aware CPU Management in Consolidated Xen-Based Hosting Platforms , 2009, IEEE Transactions on Computers.

[10]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[11]  César A. F. De Rose,et al.  Server consolidation with migration control for virtualized data centers , 2011, Future Gener. Comput. Syst..

[12]  Jean-Marc Menaud,et al.  SLA-Aware Virtual Resource Management for Cloud Infrastructures , 2009, 2009 Ninth IEEE International Conference on Computer and Information Technology.

[13]  Rajkumar Buyya,et al.  Bandwidth‐aware divisible task scheduling for cloud computing , 2014, Softw. Pract. Exp..

[14]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[15]  Kurt Maly,et al.  Analysis of Energy Efficiency in Clouds , 2009, 2009 Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns.

[16]  Naixue Xiong,et al.  A game-theoretic method of fair resource allocation for cloud computing services , 2010, The Journal of Supercomputing.

[17]  Anand Sivasubramaniam,et al.  Xen and co.: communication-aware CPU scheduling for consolidated xen-based hosting platforms , 2007, VEE '07.

[18]  Fangzhe Chang,et al.  Optimal Resource Allocation in Clouds , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[19]  Rajkumar Buyya,et al.  Energy Efficient Resource Management in Virtualized Cloud Data Centers , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[20]  J. Wenny Rahayu,et al.  Global parallel index for multi-processors database systems , 2004, Inf. Sci..

[21]  Chen Liang,et al.  Novel Resource Allocation Model and Algorithms for Cloud Computing , 2013, 2013 Fourth International Conference on Emerging Intelligent Data and Web Technologies.

[22]  Jean-Marc Menaud,et al.  Performance and Power Management for Cloud Infrastructures , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[23]  David Taniar,et al.  Ontology as a Service (OaaS): extracting and replacing sub-ontologies on the cloud , 2012, Cluster Computing.

[24]  David Taniar,et al.  Ontology as a Service (OaaS): a case for sub-ontology merging on the cloud , 2011, The Journal of Supercomputing.

[25]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[26]  Gautam Kar,et al.  Application Performance Management in Virtualized Server Environments , 2006, 2006 IEEE/IFIP Network Operations and Management Symposium NOMS 2006.

[27]  Cong Wang,et al.  Efficient verifiable fuzzy keyword search over encrypted data in cloud computing , 2013, Comput. Sci. Inf. Syst..

[28]  Xianghua Xu,et al.  RAS-M: Resource Allocation Strategy Based on Market Mechanism in Cloud Computing , 2009, 2009 Fourth ChinaGrid Annual Conference.

[29]  David Taniar,et al.  High Performance Parallel Database Processing and Grid Databases , 2008 .

[30]  Jean-Marc Menaud,et al.  Autonomic virtual resource management for service hosting platforms , 2009, 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing.

[31]  Vasileios Pappas,et al.  Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement , 2010, 2010 Proceedings IEEE INFOCOM.

[32]  Martin Bichler,et al.  Capacity Planning for Virtualized Servers , 2007 .

[33]  David Taniar,et al.  Replica synchronisation in grid databases , 2005, Int. J. Web Grid Serv..

[34]  Xavier Lorca,et al.  Choco: an Open Source Java Constraint Programming Library , 2008 .

[35]  Gregor von Laszewski,et al.  Efficient resource management for Cloud computing environments , 2010, International Conference on Green Computing.