A Comprehensive Survey of Load Balancing Strategies Using Hadoop Queue Scheduling and Virtual Machine Migration

• Main Authors: Niladri Sekhar Dey, T. Gunasekhar
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Data center; load balancing; task scheduler; FIFO; FAIR; capacity
• Online Access: https://ieeexplore.ieee.org/document/8762113/

The recent growth in the demand for scalable applications from the consumers of the services has motivated the application development community to build and deploy the applications on cloud in the form of services. The deployed applications have significant dependency on the infrastructure available with the application providers. Bounded by the limitations of available resource pools on-premises, many application development companies have migrated the applications to third party cloud environments called data centers. The data center owners or the cloud service providers are entitled to ensure high performance and high availability of the applications and at the same time the desired scalability for the applications. Also, the cloud service providers are also challenging in terms of cost reduction and energy consumption reductions for better manageability of the data center without degrading the performance of the deployed applications. It is to be noted that the performance of the application does not only depend on the responsiveness of the applications rather also must be measured in terms of service level agreements. The violation of the service level agreements or SLA can easily disprove the purpose of application deployments on cloud-based data centers. Thus, the data center owners apply multiple load balancing strategies for maintaining the desired outcomes from the application owners at the minimized cost of data center maintainability. Hence, the demand of the research is to thoroughly study and identify the scopes for improvements in the parallel research outcomes. As the number of applications ranging from small data-centric applications coming with the demand of frequent updates with higher computational capabilities to the big data-centric application as big data analytics applications coming with efficient algorithms for data and computation load managements, the data center owners are forced to think for efficient algorithms for load managements. The algorithms presented by various research attempts have engrossed on application specific demands for load balancing using virtual machine migrations and the solution as the proposed algorithms have become application problem specific. Henceforth, the further demand of the research is a guideline for selecting the appropriate load balancing algorithm via virtual machine migration for characteristics-based specific applications. Hence, this paper presents a comprehensive survey on existing virtual machine migration and selection processes to understand the specific application-oriented capabilities of these strategies with the advantages and bottlenecks. Also, with the understanding of the existing measures for load balancing, it is also important to furnish the further improvement strategies, which can be made possible with a detailed understanding of the parallel research outcomes. Henceforth, this paper also equips the study with guidelines for improvements and for further study. Nonetheless, the study cannot be completed without the mathematical analysis for better understanding and experimental analysis on different standards of datasets for better conclusive decisions. Hence, this paper also presents the discussion on mathematical models and experimental result analysis for the conclusive decision on the improvement factors and the usability of the migration methods for various purposes. Finally, this paper is a comprehensive survey on the background of the research, recent research outcomes using mathematical modeling and experimental studies on various available datasets, and finally identify the scopes of improvements considering various aspects such as execution time, mean time before a VM migration, mean time before a host shutdown, number of node shutdowns, SLA performance degradation, VM migrations, and energy consumption.