Name
Abstract | ||
|---|---|---|
One of the critical challenges facing the cloud computing industry today is to increase the profitability of cloud services. In this paper, we deal with the problem of scheduling parallelizable batch type jobs in commercial data centers to maximize cloud providers' profit. We propose a novel and efficient two-step on-line scheduler. The first step is to rank the arrival jobs to decide an eligible set based on their inherent profitability and pre-allocate resources to them; and the second step is to re-allocate resources between the waiting jobs from the eligible set, based on threshold profit-effectiveness ratio as a cut-off point, which is decided dynamically by solving an aggregated revenue maximization problem. The results of numerical experiments and simulations show that our approach are efficient in scheduling parallelizable batch type jobs in clouds and our scheduler can outperform other scheduling algorithms used for comparison based on classical heuristics from literature. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-39958-4_38 | WEB-AGE INFORMATION MANAGEMENT, PT II |
Keywords | Field | DocType |
Cloud, Resource allocation, Scheduling, Profit maximization | Parallelizable manifold,Data mining,Mathematical optimization,Computer science,Scheduling (computing),Real-time computing,Profitability index,Heuristics,Resource allocation,Job scheduler,Profit maximization,Cloud computing | Conference |
Volume | ISSN | Citations |
9659 | 0302-9743 | 3 |
PageRank | References | Authors |
0.40 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuo Zhang | 1 | 3 | 0.40 |
| Li Pan | 2 | 39 | 18.95 |
| Shijun Liu | 3 | 120 | 33.80 |
| Lei Wu | 4 | 73 | 17.47 |
| Xiangxu Meng | 5 | 308 | 60.76 |