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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pop, F. Cristea, V. Bessis, N. Sotiriadis, S. |
| Copyright Year | 2013 |
| Description | Author affiliation: Sch. of Comput. & Math., Univ. of Derby, Derby, UK (Bessis, N.; Sotiriadis, S.) || Comput. Sci. Dept., Univ. Politeh. of Bucharest, Bucharest, Romania (Pop, F.; Cristea, V.) |
| Abstract | Evolutionary computing offers different methods to solve NP-hard problems, finding a near-optimal solution. Task scheduling is a complex problem for large environments like Clouds. Genetic algorithms are a good method to find a solution for this problem considering multi-criteria constrains. This is also a method used for optimization. In these type of environments service providers want to increase the profit and the customers (end-users) want to minimize the costs. So, its all about money and we have minimum two optimization constrains. On the other hand, a good technique to ensure the QoS is to use the reputation of resources offered. This aspect is very important for service providers because represents a ranking method for them. We present in this paper a reputation guided genetic scheduling algorithm for independent tasks in inter-Clouds environments. The reputation is considered in the selection phase of genetic algorithm as an evolutionary criteria for the algorithm. We evaluate the proposed solution considering load-balancing as a way to measure the optimization impact for providers and maxspan as a metric for user performance. |
| Starting Page | 772 |
| Ending Page | 776 |
| File Size | 778443 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781467362399 |
| e-ISBN | 9780769549521 |
| DOI | 10.1109/WAINA.2013.206 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-03-25 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Measurement Schedules Program processors Processor scheduling Cloud Computing Independent Tasks Optimal scheduling Genetic Algorithm Scheduling Reputation Biological cells Genetic algorithms |
| Content Type | Text |
| Resource Type | Article |
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