Name
Abstract | ||
|---|---|---|
In Cloud broker system, workflow application requests from different users are managed through workflow scheduling and resource provisioning. In workflow scheduling phase, most existing algorithms allocate each task on certain VM in serial. In general, single task does not fully utilize allocated resource such as CPU, memory, and so on. When multiple tasks are processed with same resource in parallel, the resource utilization is improved that leads to saving the cost. In order to solve this problem, the Parallel Task Merging scheme in the same VM is proposed, which saves the cost of execution while satisfying SLA deadline. After workflow scheduling, VM resource provisioning is required. Auto-scaling VM resources approach is proposed, which adjusts the number of VMs while the number of requests varies. In this paper, we do experiment the parallel task merging and auto-scaling approaches on different environments to observe on which conditions these two approaches are working well or not. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-38904-2_15 | Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering |
Keywords | Field | DocType |
Workflow scheduling,Virtual machine allocation,Cloud resource provisioning | Workflow technology,Workflow scheduling,Computer science,Cloud broker,Provisioning,Workflow application,Workflow engine,Workflow management system,Workflow,Distributed computing | Conference |
Volume | ISSN | Citations |
167 | 1867-8211 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongsik Yoon | 1 | 0 | 0.34 |
| Seong Hwan Kim | 2 | 334 | 31.76 |
| Dong-Ki Kang | 3 | 39 | 7.69 |
| Chan-hyun Youn | 4 | 238 | 42.68 |