Name
Abstract | ||
|---|---|---|
According to the dynamic, distribution and complexity of cloud computing, resource scheduling effectively with users' QoS demand and achieving maximum benefit is the unprecedented challenge. To solve the above problem, we propose to use genetic algorithm: design for the crossover operator and build a cloud resource optimization scheduling model that promised to address user needs while optimizing resource allocation. With the experiments, this paper verifies the superiority of models made in this paper. The results show that the use of genetic algorithm to optimize cloud resource scheduling has the rationality and feasibility. Meanwhile, using the genetic algorithm is useful for effectively scheduling of cloud resource meeting the users' QoS. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-39068-5_43 | ISNN (2) |
Keywords | Field | DocType |
crossover operator,optimizing resource allocation,resource scheduling,cloud resource optimization scheduling,genetic algorithm,cloud computing,qos demand,cloud resource scheduling,cloud resource,qos constraint,maximum benefit | Fair-share scheduling,Scheduling (computing),Computer science,Two-level scheduling,Genetic algorithm scheduling,Resource allocation,Rate-monotonic scheduling,Dynamic priority scheduling,Cloud computing,Distributed computing | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Wang | 1 | 6 | 1.48 |
| jinkuan wang | 2 | 94 | 33.64 |
| Cuirong Wang | 3 | 110 | 15.54 |
| Xin Song | 4 | 15 | 6.88 |