OP-MLB An Online VM Prediction based Multi-objective Load Balancing Framework for Resource Management at Cloud Datacenter
ABSTARCT :
The elasticity of cloud resources allow cloud clients to expand and shrink their demand of resources dynamically over time. However, fluctuations in the resource demands and pre-defined size of virtual machines (VMs) lead to lack of resource utilization, load imbalance and excessive power consumption. To address these issues and to improve the performance of datacenter, an efficient resource management framework is proposed, which anticipates resource utilization of the servers and balances the load accordingly. It facilitates power saving, by minimizing the number of active servers, VM migrations and maximizing the resource utilization. An online resource prediction system, is developed and installed at each VM, to minimize the risk of Service Level Agreement (SLA) violations and performance degradation due to under/overloaded servers. In addition, multi-objective VM placement and migration algorithms are proposed to reduce the network traffic and power consumption within datacenter. The proposed framework is evaluated by executing experiments on three real world workload datasets namely, Google Cluster Dataset, Planet Lab and Bitsbrain traces. The comparison of proposed framework with the state-of-art approaches, reveals its superiority in terms of different performance metrics. The improvement in power saving achieved by OP-MLB framework is upto 85.3% over the Best-Fit approach.
EXISTING SYSTEM :
? The ACO approach outperformed the existing Threshold Energy Saving Algorithm (TESA) approach considering energy consumption, number of migrations, and SLA violations.
? The efficient resource utilization of the existing Cloud resources is important to increase the service provisioning ratio and satisfy the SLA for the clients.
? The efficient resource utilization has several impacts like the maximal utilization of the existing resources, powering off the underutilized machines, and most importantly resulting in improved energy consumption.
? The results are compared with existing PSO, Levy Flight, and Particle Swarm Optimization (LEPSO) algorithms.
DISADVANTAGE :
? Task scheduling in a cloud environment is a problem of specifying tasks to an appropriate machine to finish their work.
? Therefore, the scheduling of task problem can be qualified as the method of finding out a model mapping for execution of user tasks with the aim of reaching the desired goals
? The suggested algorithm repaired the problem of the active balancer algorithm by using a reservation table among the phase of the chosen and assignment of VMs.
? Any optimization case is either maximization or minimization, which depends on the nature of the problem.
? To overcome this problem, a GA is incorporated with a Throttled to improve load balancing.
PROPOSED SYSTEM :
• In this paper, an Efficient Adaptive Migration Algorithm (EAMA) is proposed for effective migration and placement of VMs on the Physical Machines (PMs) dynamically.
• Numerous approaches have been proposed by researchers to improve the resource utilization with reduced energy consumption in CDCs.
• Dynamic VM consolidation and management approaches proposed in have shown improvements in the energy consumption.
• The proposed approach has two main features: first, selection of the location of the PM which has less delay as compared to the other PMs where the VMs are required to be migrated.
ADVANTAGE :
? Load balancing qualifies network resources for best response and performance and provides high gratifications to consumer.
? The proposed strategy was tested using cloud Sim, and the results proved that the performance of their strategy is better than other conventional algorithms.
? The efficient allocation of resources and scheduling is a vital task in cloud computing based on which the performance of the system is rated.
? After that, changes are made to the settings to evaluate the performance of the previous algorithms and the proposed DTG algorithm.
? Therefore, an efficient task scheduling algorithm aims to balance diverse and conflicting parameters together at the same time.
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