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Optimal load balancing in cloud computing
- 2. Abstract Introduction Algorithm & Flow Diagram Experimental Setup Result & Analysis Conclusion Future Enhancement References
- 3. Load balancing is the major concern in the cloud computing environment. Cloud comprises of many hardware and software resources and managing these will play an important role in executing a client's request. This present situation the load balancing algorithms built should be very efficient in allocating the request and also ensuring the usage of the resources in an intelligent way so that underutilization of the resources will not occur in the cloud environment. In the present work, a novel VM-assign load balance algorithm is proposed which allocates the incoming requests to the all available virtual machines in an efficient manner.
- 4. Distributed computing leads to a new technology called Cloud computing which is used to store and retrieve the files. The main issue is scheduling incoming request efficiently. Many algorithms provides minimum response time and distributed load, but communication delays and efficient load balancing were not addressed. Load balancing is essential because every node in the cloud does the same amount of work throughout, therefore increasing the throughput and minimizing the response time.
- 5. The goals of load balancing are to: • Improve the performance • Maintain system stability • Build fault tolerance system • Increase the availability • Increase the user satisfaction • Improve the resource utilization ratio
- 6. This algorithm focuses mainly on finding out the least loaded virtual machine and how incoming jobs are allocated intelligently. VM-assign load balancer algorithm maintains an index/assign table of virtual machines and also the load of VMs. Proposed algorithm employs a method for selecting a VM for processing client's request. It checks for least loaded VM.
- 7. Input: No of incoming jobs x1, x2 . . . . . . .. xN Available VM y1, y2. . . . . . yN Output: All incoming jobs x1, x2 . . . .. xN are allocated least loaded virtual machine among the available y1,y2········ yN. 1: Initially all the VM's have 0 allocations. 2: VM-assign load balancer maintains the index / assign table of VMs which has no. of requests currently allocated to each VM. 3: When requests arrive at the data center it passes to the load balancer. 4: Index table is parsed and least loaded VM is selected for execution.
- 8. Case I: if found a. Check whether the chosen least loaded VM is used immediately in the last iteration. if YES goto step 4 to find next least VM if NO Least loaded VM is chosen 5: VM-assign load balancer returns the VM id to the data center. 6: Request is assigned to the VM. Data center notifies the VM-assign load balancer about the allocation. 7: VM-assign load balancer updates the requests hold by each VM.
- 9. 8: When the VM finishes the processing the request, data center receives the response. 9: data center notifies the VM-assign load balancer for the VM de- allocation and VM-assign load balancer updates the table. 10: Repeat from step 2 for the next request.
- 11. For the experimentation Cloudsim based Cloud Analyst simulator has been used. Architecture of CloudAnalyst Simulator
- 12. Results are analyzed w.r.t efficient utilization of the virtual machines by avoiding the under or overloading conditions. If we use the proposed VM-assign load balance algorithm all the VMs are utilized completely and properly. The algorithm is tested for initially with five VM and later 25 VM. In both cases our proposed algorithm balances the load on all available VMs in an efficient way.
- 13. Table 1. VMs Usage with 5 VMs
- 14. In this paper, an efficient algorithm is designed which manages the load at the server by considering the current status of the all available VMs for assigning the incoming requests intelligently. The VM-assign load balancer mainly focuses on the efficient utilization of the resources/VMs. Hence, the proposed algorithm optimally distributes the load and hence under / over utilization (VMs) situations will not arise.
- 15. As a future scope the proposed algorithm can still be improved by taking some more dynamic situations of the incoming requests and how the algorithm responses if we mix both static and dynamic loads.
- 16. Kallithea Al Nuaimi, Nader Mohamed, Mariam Al Nuaimi and Jameela AI-Jaroodi, "A Survey of Load Balancing in Cloud Computing: Challenges and Algorithms", in Second Symposium on Network Cloud Computing and Applications, IEEE 2012. Zhen Xiao, Weijia Song and Qi Chen, "Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment" IEEE transactions on parallel and distributed systems, Vol. No. 24 6, june 2013. Hemant S. Mahalle, Parag R. Kaveri and Vinay Chavan, "Load Balancing On Cloud Data Centres" in International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, issue 1, January 2013.
- 17. THANK YOU……