Name
Abstract | ||
|---|---|---|
A service cloud could improve its QoS (Quality of Service) by partitioning jobs into multiple tasks and processing those tasks in parallel. In contrast to processing all jobs with the same degree of parallelism (DOP), dividing jobs into different groups and processing them with varying DOPs may achieve better performance results, especially focusing on those jobs which have a greater impact on performance of service clouds. In this paper, we describe a novel differentiated DOP policy, which divides jobs into several groups identified by jobs’ service time and sets proper DOPs for different groups of jobs. Then, we propose a parallel multi-queue and multi-station analytical model for service clouds with our differentiated DOP policy, to predict important performance metrics. Thus this model can guide cloud providers to determine optimal DOPs and resource allocation schemes for different groups to improve the total QoS of a service cloud. We also present a new metric, called Optimized Performance of Groups (OPG), to quantify the level of performance optimization of every group. The objective is to maximize the minimum OPG to ensure OPG within a certain range, thereby enforcing a fair trade-off between all groups. Through extensive experiments, we validate the effectiveness of the proposed differentiated DOP policy and analytical model. |
Year | Venue | Field |
|---|---|---|
2018 | ICSOC | Degree of parallelism,Computer science,Quality of service,Real-time computing,Resource allocation,Service cloud,Service time,Distributed computing,Cloud computing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
16 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiulin Li | 1 | 0 | 0.34 |
| Li Pan | 2 | 18 | 4.51 |
| Shijun Liu | 3 | 120 | 33.80 |
| Yuliang Shi | 4 | 5 | 3.47 |
| Xiangxu Meng | 5 | 308 | 60.76 |