Resource scheduling algorithm with load balancing for cloud service provisioning
Cloud computing uses scheduling and load balancing for virtualized file sharing in cloud infrastructure. These two have to be performed in an optimized manner in cloud computing environment to achieve optimal file sharing. Recently, Scalable traffic management has been developed in cloud data center...
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| Main Authors: | , , |
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| Format: | Article |
| Published: |
2019
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059462538&doi=10.1016%2fj.asoc.2018.12.021&partnerID=40&md5=fc57bfedf7dab7c8601b5e5f758c8d8e http://eprints.utp.edu.my/22121/ |
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| Institution: | Universiti Teknologi Petronas |
| Summary: | Cloud computing uses scheduling and load balancing for virtualized file sharing in cloud infrastructure. These two have to be performed in an optimized manner in cloud computing environment to achieve optimal file sharing. Recently, Scalable traffic management has been developed in cloud data centers for traffic load balancing and quality of service provisioning. However, latency reducing during multidimensional resource allocation still remains a challenge. Hence, there necessitates efficient resource scheduling for ensuring load optimization in cloud. The objective of this work is to introduce an integrated resource scheduling and load balancing algorithm for efficient cloud service provisioning. The method constructs a Fuzzy-based Multidimensional Resource Scheduling model to obtain resource scheduling efficiency in cloud infrastructure. Increasing utilization of Virtual Machines through effective and fair load balancing is then achieved by dynamically selecting a request from a class using Multidimensional Queuing Load Optimization algorithm. A load balancing algorithm is then implemented to avoid underutilization and overutilization of resources, improving latency time for each class of request. Simulations were conducted to evaluate the effectiveness using Cloudsim simulator in cloud data centers and results shows that the proposed method achieves better performance in terms of average success rate, resource scheduling efficiency and response time. Simulation analysis shows that the method improves the resource scheduling efficiency by 7 and also reduces the response time by 35.5 when compared to the state-of-the-art works. © 2018 Elsevier B.V. |
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