Virtual Machine Customization Using Resource Using Prediction for Efficient Utilization of Resources in IaaS Public Clouds

The main cause of energy wastage in cloud data centres is the low level of server utilization. Low server utilization is a consequence of allocating more resources than required for running applications. For instance, in Infrastructure as a Service (IaaS) public clouds, cloud service providers (CSPs) deliver computing resources in the form of virtual machines (VMs) templates, which the cloud users have to choose from. More often, inexperienced cloud users tend to choose bigger VMs than their application requirements. To address the problem of inefficient resources utilization, the existing approaches focus on VM allocation and migration, which only leads to physical machine (PM) level optimization. Other approaches use horizontal auto-scaling, which is not a visible solution in the case of IaaS public cloud. In this paper, we propose an approach of customizing user VM’s size to match the resources requirements of their application workloads based on an analysis of real backend traces collected from a VM in a production data centre. In this approach, a VM is given fixed size resources that match applications workload demands and any demand that exceeds the fixed resource allocation is predicted and handled through vertical VM auto-scaling. In this approach, energy consumption by PMs is reduced through efficient resource utilization. Experimental results obtained from a simulation on CloudSim Plus using GWA-T-13 Materna real backend traces shows that data center energy consumption can be reduced via efficient resource utilization

Energy-Aware and Proactive Host Load Detection in Virtual Machine Consolidation

With the expansion and enhancement of cloud data centers in recent years, increasing the energy consumptionand the costs of the users have become the major concerns in the cloud research area. Service quality parametersshould be guaranteed to meet the demands of the users of the cloud, to support cloud service providers,and to reduce the energy consumption of the data centers. Therefore, the data center's resources must be managedefficiently to improve energy utilization. Using the virtual machine (VM) consolidation technique is animportant approach to enhance energy utilization in cloud computing. Since users generally do not use all thepower of a VM, the VM consolidation technique on the physical server improves the energy consumption andresource efficiency of the physical server, and thus improves the quality of service (QoS). In this article, a serverthreshold prediction method is proposed that focuses on the server overload and server underload detectionto improve server utilization and to reduce the number of VM migrations, which consequently improves theVM's QoS. Since the VM integration problem is very complex, the exponential smoothing technique is utilizedfor predicting server utilization. The results of the experiments show that the proposed method goes beyondexisting methods in terms of power efficiency and the number of VM migrations.

Cost-Aware Ant Colony Optimization Based model for Load Balancing in Cloud Computing

: Balancing the incoming data traffic across the servers is termed as Load balancing. In cloud computing, Load balancing means distributing loads across the cloud infrastructure. The performance of cloud computing depends on the different factors which include balancing the loads at the data center which increase the server utilization. Proper utilization of resources is termed as server utilization. The power consumption decreases with an increase in server utilization which in turn reduces the carbon footprint of the virtual machines at the data center. In this paper, the cost-aware ant colony optimization based load balancing model is proposed to minimize the execution time, response time and cost in a dynamic environment. This model enables to balance the load across the virtual machines in the data center and evaluate the overall performance with various load balancing models. As an average, the proposed model reduces carbon footprint by 45% than existing methods.

Performance Analysis of Batch Jobs in Cloud Environment

The complexity of running batch jobs in cloud environment is increasing day by day due to huge volume of back end data increasing dynamically in different formats, growing number of online or offline requests from various environments or availability of system resources. This paper explores the various behaviors of batch jobs from production cluster and the following observations are made: (i) The number of user jobs submitted at offline are high thereby increasing the wait time and processing time. (ii) Each job submitted with huge volume of data causing increase in queue size, job failure and errors on completion. (iii) Server utilization is unbalanced during the peak time thereby causing the server crash.