Name
Abstract | ||
|---|---|---|
The development of cloud computing makes it possible to dynamically adjust resources for software services on demand. However, different software services have different QoS requirements, and their external environment is changing all the time. Thus, self-adaptive ability is needed for software services because engineers manage is difficult in the cloud resources. This paper proposes a self-adaptive resource management framework for software services in Cloud. First, for a given software services, its iterative QoS model is trained on historical data, which is capable to predict a QoS value of one management operation by using the information on current running workload, allocated resources, real QoS value and an operation of resource allocation. Then, we employ PSO-based runtime decision algorithm together with the predicted QoS value to determine future resource allocation operations. Last, a feedback control loop is introduced to gradually allocate resource through feedback and iteration. The loops iterate until the PSO-based algorithm suggests no further improvement over the current resource allocation. Such self-adaptive management programs can adapt to the change in the outer environments and allocate cloud resource effectively. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00223 | ISPA/BDCloud/SocialCom/SustainCom |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haijiang Wang | 1 | 0 | 0.34 |
| Yun Ma | 2 | 216 | 20.25 |
| Xianghan Zheng | 3 | 0 | 0.34 |
| Xing Chen | 4 | 55 | 6.16 |
| Longkun Guo | 5 | 0 | 0.34 |