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Latency based group discovery algorithm for network aware cloud scheduling
Affiliation:1. Marine-Integrated Bionics Research Center, Pukyong National University, Busan 608-737, Republic of Korea;2. Department of Biomedical Engineering, Center for Marine-Integrated Biomedical Technology (BK21 Plus) Pukyong National University, Busan 608-737, Republic of Korea;3. Department of Anatomy and Cell Biology, Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada;4. Division of Bioscience and Korea Nokyong Research Center, Konkuk University, Chungju 380-701, Republic of Korea;5. Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA;6. Global Bioresources Research Center, Korea Institute of Ocean Science and Technology, Ansan 426-744, Republic of Korea;7. Department of Marine-bio Convergence Science and Marine Bioprocess Research Center, Pukyong National University, Busan 608-737, Republic of Korea;8. Department of Food Science and Biotechnology, Kunsan National University, Kunsan 573-701, Republic of Korea;9. Department of Chemistry, Pukyong National University, Busan 608-737, Republic of Korea;10. Department of Food Science and Nutrition, Dankook University, Gyeonggi 448-701, Republic of Korea;11. Department of Marine Biotechnology, Gangneung-Wonju National University 210-720, Republic of Korea;12. Department of Marine Life Sciences, Jeju National University, Jeju 690-756, Republic of Korea
Abstract:Cloud computing is a big paradigm shift of computing mechanism. It provides high scalability and elasticity with a range of on-demand services. We can execute a variety of distributed applications on cloud’s virtual machines (computing nodes). In a distributed application, virtual machine nodes need to communicate and coordinate with each other. This type of coordination requires that the inter-node latency should be minimal to improve the performance. But in the case of nodes belonging to different clusters of the same cloud or in a multi-cloud environment, there can be a problem of higher network latency. So it becomes more difficult to decide, which node(s) to choose for the distributed application execution, to keep inter-node latency at minimum. In this paper, we propose a solution for this problem. We propose a model for the grouping of nodes with respect to network latency. The application scheduling is done on the basis of network latency. This model is a part of our proposed Cloud Scheduler module, which helps the scheduler in scheduling decisions on the basis of different criteria. Network latency and resultant node grouping on the basis of this latency is one of those criteria. The main essence of the paper is that our proposed latency grouping algorithm not only has no additional network traffic overheads for algorithm computation but also works well with incomplete latency information and performs intelligent grouping on the basis of latency. This paper addresses an important problem in cloud computing, which is locating communicating virtual machines for minimum latency between them and group them with respect to inter-node latency.
Keywords:Cloud scheduling  Latency grouping  Group partitioning  Network measurement
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