Cloud broker service‐oriented resource management model. Issue 2 (23rd April 2015)

• Record Type: Journal Article
• Authors: Aazam, Mohammad; Huh, Eui‐Nam
• Other Names: Xiang Wei guestEditor.; Zheng Kan guestEditor.; Niyato Dusit guestEditor.; Militano Leonardo guestEditor.; Araniti Giuseppe guestEditor.
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 28:Issue 2(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2015-04-23
• Subjects: Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.2937
• Languages: English
• ISSNs: 2161-5748

Abstract

Abstract: With the increasing digital media content, cloud computing's importance is scaling up very fast. It provides ease of management for the growing media content. Besides this, features like ubiquitous access, service creation, service discovery and resource provisioning play a significant role. Cloud arena is going beyond this now, and the next era is of cloud federation. Services would be brought up together to the user through multiple clouds, known as cloud federation or intercloud computing. Although still in its infancy, intercloud computing is meant to provide more scalable, efficient and better managed services. Cloud broker is an important feature of intercloud computing, which plays its role in terms of resource management, service discovery, service‐level agreement negotiation and match making between service provider(s) and customer(s). The already performed studies have trivially addressed brokerage in intercloud environment, and no complete resource management model is presented so far. In this study, we present a detailed service‐oriented dynamic resource management model, which covers the issues of resource prediction, customer type‐based resource estimation and reservation, advanced reservation, pricing, refunding and acquired quality of service‐based refunding. We have implemented our system in Java/NetBeans 8.0 (Oracle Corporation) and evaluated through CloudSim 3.0.3 (The CLOUDS Lab, The University of Melbourne, Australia) toolkit, on 11 parameters. Our methodology was modelled on Google Cluster trace of 12 000 machines. The results and discussion show the validity and performance of our system. Copyright © 2015 John Wiley & Sons, Ltd. Abstract : This work presents cloud broker resource management model. Implemented on Google Cluster trace of 12 000 machines, the model presents resource allocation, pricing and refunding according to the historical record of cloud customers.