A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

Load Balancing in Heterogeneous Cloud Environments by Using PROMETHEE Method

Abstract: Efficient Scheduling of tasks in a cloud environment improves resources utilization thereby meeting users' requirements. One of the most important objectives of a scheduling algorithm in cloud environment is a balanced load distribution over various resources for enhancing the overall performance of the cloud. Such a scheduling is complex in nature due to the dynamicity of resources and incoming application specifications. In this paper, we employ PROMETHEE decision making model to design a scheduling algorithm, called PROMETHEE Load Balancing (PLB).This paper formulates the load balancing issue as a multi-criteria decision making problem and aims to achieve well-balanced load across virtual machines for maximizing the overall throughput of the cloud. Extensive simulation results in CloudSim environment show that the proposed algorithm outperforms existing algorithms in terms of load balancing index (LBI), VM load variation, makespan, average execution time and waiting time

H-word: Supporting job scheduling in Hadoop with workload-driven data redistribution

The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-44039-2_21Today’s distributed data processing systems typically follow a query shipping approach and exploit data locality for reducing network traffic. In such systems the distribution of data over the cluster resources plays a significant role, and when skewed, it can harm the performance of executing applications. In this paper, we addressthe challenges of automatically adapting the distribution of data in a cluster to the workload imposed by the input applications. We propose a generic algorithm, named H-WorD, which, based on the estimated workload over resources, suggests alternative execution scenarios of tasks, and hence identifies required transfers of input data a priori, for timely bringing data close to the execution. We exemplify our algorithm in the context of MapReduce jobs in a Hadoop ecosystem. Finally, we evaluate our approach and demonstrate the performance gains of automatic data redistribution.Peer ReviewedPostprint (author's final draft