Abstract

In this study an Adaptive Load Balancing (ALB) approach is developed to effectively balance the load distributed across the cloud servers to minimize bandwidth and energy consumption on service provisioning. Cloud computing infrastructure has evolved as highly scalable services with massive computation power and storage capability with the resources being provided as service by the cloud environment and guarantees the Service Level Agreement (SLA). However, the needs of the subscribers have grown to an extent that there requires a big active platform for load balancing even if the resources are shared. Besides, the cloud computing paradigm also needs to optimally balance the load at the middle of the servers in order to avoid hotspot and improve resource utility. To perform energy conservation in cloud infrastructures, the use of chronological traffic data from data centers uses a service request prediction model. Collaborative provable data possession scheme adopt Homomorphic verifiable responses and hash index hierarchy but the drawback is that the match index structure are not matched properly with clustering model. Different level of power tariffs and requests made to the servers affect the decisions, where to serve the cluster needs. SLA Laws on privacy includes a factor that decides whether the loads can be moved in or out of a cluster, whereas they affect the overall energy consumption. ALB approach balances the load from every cluster group by minimizing the bandwidth and energy consumption. With repetitive query messaging, ALB collects the information about the current load of other group and then computes the average energy and bandwidth consumption of each group. The ALB Approach not only balances the energy consumption but also enhances the utilization of resources with minimal bandwidth usage. Extensive level of experimental studies is conducted to illustrate the efficiency and effectiveness of the proposed method. An experimental evaluation is accepted out to estimate the performance of the ALB approach with Virtual Machine (VM) energy-efficient cloud data centers. Performance metric for evaluation of ALB approach is measured in terms of energy consumption, bandwidth utilization rate, performance tradeoff and response time to service request, load balance factor and clustering efficiency.

Keywords:

Adaptive load balancing approach , bandwidth utilization , cloud infrastructure , data centers , query messaging , service level agreement, virtual machine,

References

1. Anbang, R. and M.A. Imad, 2013. Towards trustworthy resource scheduling in clouds. IEEE T. Inf. Foren. Sec., 8(6): 973-984.
   CrossRef    
2. Avinash, M., M. Mukesh, D. Sanket and R. Shrisha, 2011. Energy conservation in cloud infrastructures. Proceeding of the IEEE International System Conference, pp: 456-460.
   
3. Boyang, W., S.S.M. Chow, L. Ming and L. Hui, 2013. Storing shared data on the cloud via security-mediator. Proceeding of the IEEE 33rd International Conference on Distributed Computing Systems (ICDCS, 2013), pp: 124-133.
   
4. Chang, L., C. Jinjun, N. Surya, P. Suraj and Z. Xuyun, 2013. A privacy leakage upper-bound constraint based approach for cost-effective privacy preserving of intermediate datasets in cloud. IEEE T. Parall. Distr., 24(6): 1192-1202.
   CrossRef    
5. Chunming, Q., Y. Dantong, J. Tao and L. Xin, 2010. Application-specific resource provisioning for wide-area distributed computing. IEEE Network, 24(4): 25-34.
   CrossRef    
6. Cong, W., R. Kui, W. Jia and W. Qian, 2013. Harnessing the cloud for securely outsourcing large-scale systems of linear equations. IEEE T. Parall. Distr., 24(6): 1172-1181.
   CrossRef    
7. Cong, W., L. Jin, R. Kui, W. Qian and L. Wenjing, 2011. Enabling public auditability and data dynamics for storage security in cloud computing. IEEE T. Parall. Distr., 22(5): 847-859.
   CrossRef    
8. Gagare, G.J., P.P. Ghorpade, K.B. Jachak and S.K. Korde, 2012. Homomorphic authentication with random masking technique ensuring privacy and security in cloud computing. BIOINFO Security Inform., 2(2): 49-52.
   
9. Govindan, V.K., M.V. Haresh and S. Kaladyy, 2011. Agent based dynamic resource allocation on federated clouds. Proceeding of the IEEE Recent Advances in Intelligent Computational Systems (RAICS, 2011). Trivandrum, pp: 111-114.
   
10. Guojun, W., W. Jie and L. Qin, 2010. Hierarchical attribute-based encryption for fine-grained access control in cloud storage services. Proceeding of the 17th ACM Conference on Computer and Communications Security, pp: 735-737.
    
11. Jianfeng, Y. and L. Wen-Syan, 2009. Calibrating resource allocation for parallel processing of analytic tasks. Proceeding of the IEEE International Conference on e-Business Engineering, pp: 327-332.
    
12. Jing, F., K. Karthik, N. Yamini and L. Yung-Hsiang, 2011. Resource allocation for real-time tasks using cloud computing. Proceeding of IEEE 20th International Conference on Computer Communications and Networks (ICCCN, 2011), pp: 1-7.
    
13. Kazuki, M. and K. Shin-Ichi, 2011. Evaluation of optimal resource allocation method for cloud computing environments with limited electric power capacity. Proceeding of the IEEE 14th International Conference on Network-Based Information Systems (NBIS), pp: 1-5.
    
14. Kui, R., L. Ming, Y. Shucheng, L. Wenjing and Z. Yao, 2012. Scalable and secure sharing of personal health records in cloud computing using attribute-based encryption. IEEE T. Parall. Distr., 20(20): 20.
    
15. Lin, D., S. Sundareswaran and A.C. Squicciarini, 2012. Ensuring distributed accountability for data sharing in the cloud. IEEE T. Depend. Secure, 9(4): 556-568.
    CrossRef    
16. Muhammad, A.A., G. Rajesh and S. Ryo, 2012. Energy efficient geographical load balancing via dynamic deferral of workload. Proceeding of the IEEE 5th International Conference on Cloud Computing, pp: 188-195.
    
17. Rami, B. and N. Vivek, 2013. A decentralized self-adaptation mechanism for service-based applications in the cloud. IEEE T. Software Eng., 39(5): 591-612.
    CrossRef    
18. Selvarani, S. and G.S. Sadhasivam, 2010. Improved cost-based algorithm for task scheduling in cloud computing. Proceeding of the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp: 1-5.
    CrossRef    
19. Shanbiao, W. and Z. Yan, 2012. Secure collaborative integrity verification for hybrid cloud environments. Int. J. Coop. Inf. Syst., 21(3): 165-197.
    CrossRef    
20. Tomita, T. and S. Kuribayashi, 2011. Congestion control method with fair resource allocation for cloud computing environments. Proceeding of the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, pp: 1-6.
    CrossRef    

Competing interests

The authors have no competing interests.

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