{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:34:39Z","timestamp":1754156079236,"version":"3.41.2"},"reference-count":34,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2016,4,18]],"date-time":"2016-04-18T00:00:00Z","timestamp":1460937600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJWIS"],"published-print":{"date-parts":[[2016,4,18]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>This paper aims to provide a method for media resource allocation in Cloud systems for supporting green computing policies, as well as attempting to improve the overall performance of system by optimizing the communication latencies.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>A common method for resource allocation is using resource agent that takes the budgets\/prices of applicants\/resources and creates a probability matrix of allocation according to the policies of system. Two general policies for optimization are latency optimization and green computing. Presented heuristic for latencies is so that the average latencies of communication between applicant and resource are measured, and they will affect the next decision. For gaining green computing, it is attempted to consolidate the allocated resources on smaller number of physical machines. So calculation formula of the price of each resource is modified to decrease the probability of allocating the resources on the machine with least allocated resources.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Results of proposed method indicates its success in both green computing and improving the performance. Experiments show decreasing 21.4 per cent of response time simultaneously with increasing tasks in the tested range. The maximum and minimum of saved energy is acceptable and reported as 79.2 and 16.8 per cent.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title>\n<jats:p>Like other centralized solutions, the proposed method suffers from the limitations of centralized resource agent, like bottle neck. But the implementation of distributed resource agent is postponed to future work.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>Proposed method presents heuristics for improving the performance and gaining green computing. The key feature is formulating all the details and considering pitch variables for controlling the policies of system.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijwis-10-2015-0031","type":"journal-article","created":{"date-parts":[[2016,5,27]],"date-time":"2016-05-27T05:39:19Z","timestamp":1464327559000},"page":"2-17","source":"Crossref","is-referenced-by-count":7,"title":["Energy efficient and latency optimized media resource allocation"],"prefix":"10.1108","volume":"12","author":[{"given":"Masoud","family":"Nosrati","sequence":"first","affiliation":[]},{"given":"Ronak","family":"Karimi","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"first-page":"963","article-title":"Network aware resource allocation in distributed clouds","year":"2012","key":"key2020121622195412900_ref013"},{"issue":"3","key":"key2020121622195412900_ref033","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1145\/857166.857167","article-title":"Developing a bidding agent for multiple heterogeneous auctions","volume":"3","year":"2003","journal-title":"ACM Transactions on Internet Technology"},{"year":"2010","key":"key2020121622195412900_ref024","article-title":"Energy efficient resource management in virtualized cloud data centers"},{"year":"2010","key":"key2020121622195412900_ref025","article-title":"Energy efficient allocation of virtual machines in cloud data centers"},{"year":"2010","key":"key2020121622195412900_ref022","article-title":"Energy-Efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges"},{"year":"2001","key":"key2020121622195412900_ref017","article-title":"Managing energy and server resources in hosting centers"},{"issue":"1","key":"key2020121622195412900_ref034","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1002\/spe.995","article-title":"CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms","volume":"41","year":"2011","journal-title":"Software Practice and Experience"},{"issue":"10","key":"key2020121622195412900_ref027","article-title":"A survey of energy efficient data centers in a cloud computing environment\u201d","volume":"2","year":"2013","journal-title":"International Journal of Advanced Research in Computer and Communication Engineering"},{"issue":"1","key":"key2020121622195412900_ref006","first-page":"1","article-title":"Different modalities in first stage enhancement of labor","volume":"2","year":"2015","journal-title":"General Health and Medical Sciences"},{"key":"key2020121622195412900_ref002","unstructured":"Emmerich, W. 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