Artificial bee colony based live migration technique for cloud data centers

Cloud computing plays significant role in our society, because it provides wide range of applications by making use of Internet. Cloud Computing provide services to its users based upon “pay as you go” model. However, providing high availability to cloud resources to its users is still an open area of research. Cloud service provider utilizes workflow scheduling techniques to minimize the waiting time of users. But, majority of scheduling techniques are NP-Completer in nature. Therefore, in this paper artificial bee colony based workflow scheduling technique is proposed in this paper. Live migration is also done to balance the load between available virtual machines. Extensive experiments have been performed by considering the proposed techniques. Experimental results reveal that proposed technique outperforms others in terms of makespan, execution cost, efficiency, utilization, energy consumption, and speedup.

[1]  Shiyong Lu,et al.  Scheduling Scientific Workflows Elastically for Cloud Computing , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[2]  S. Sowmya Kamath,et al.  An hybrid bio-inspired task scheduling algorithm in cloud environment , 2014, Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[3]  B. Kruekaew,et al.  Virtual Machine Scheduling Management on Cloud Computing Using Artificial Bee Colony , 2014 .

[4]  Rajkumar Buyya,et al.  A Dynamic Critical Path Algorithm for Scheduling Scientific Workflow Applications on Global Grids , 2007, Third IEEE International Conference on e-Science and Grid Computing (e-Science 2007).

[5]  Kuo-Qin Yan,et al.  Towards a Load Balancing in a three-level cloud computing network , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[6]  Jianhua Gu,et al.  A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment , 2010, 2010 3rd International Symposium on Parallel Architectures, Algorithms and Programming.

[7]  Xiaoming Chen,et al.  Delay-cost tradeoff for virtual machine migration in cloud data centers , 2017, J. Netw. Comput. Appl..

[8]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems , 2007, IFSA.

[9]  A. Taleb-Bendiab,et al.  A Comparative Study into Distributed Load Balancing Algorithms for Cloud Computing , 2010, 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops.

[10]  Dan C. Marinescu,et al.  Energy-Aware Load Balancing and Application Scaling for the Cloud Ecosystem , 2017, IEEE Transactions on Cloud Computing.

[11]  Hai Jin,et al.  Performance and energy modeling for live migration of virtual machines , 2011, Cluster Computing.

[12]  Keqin Li,et al.  DataABC: A fast ABC based energy-efficient live VM consolidation policy with data-intensive energy evaluation model , 2017, Future Gener. Comput. Syst..

[13]  Rajkumar Buyya,et al.  Critical-path and priority based algorithms for scheduling workflows with parameter sweep tasks on global grids , 2005, 17th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD'05).

[14]  Zhen Xiao,et al.  Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment , 2013, IEEE Transactions on Parallel and Distributed Systems.

[15]  Gopi Bhatt,et al.  Load balancing in cloud computing using optimization techniques: A study , 2016, 2016 International Conference on Communication and Electronics Systems (ICCES).

[16]  Nader Mohamed,et al.  A Survey of Load Balancing in Cloud Computing: Challenges and Algorithms , 2012, 2012 Second Symposium on Network Cloud Computing and Applications.

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

[18]  Yu Xue,et al.  Discrete gbest-guided artificial bee colony algorithm for cloud service composition , 2014, Applied Intelligence.

[19]  Gaochao Xu,et al.  A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using Bayes Theorem , 2013, TheScientificWorldJournal.

[20]  Luiz Fernando Bittencourt,et al.  HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds , 2011, Journal of Internet Services and Applications.

[21]  Li-zhen Cui,et al.  A Multiple QoS Constrained Scheduling Strategy of Multiple Workflows for Cloud Computing , 2009, 2009 IEEE International Symposium on Parallel and Distributed Processing with Applications.