Improved Virtual Machine (VM) based Resource Provisioning in Cloud Computing
| dc.contributor.author | Md. Mahfuzur, Rahman | |
| dc.contributor.examiningcommittee | Irani, Pourang (Computer Science) Diamond, Jeff (Electrical and Computer Engineering) Abawajy, Jemal (Science, Engineering and Built Environment) | en_US |
| dc.contributor.supervisor | Graham, Peter (Computer Science) | en_US |
| dc.date.accessioned | 2016-10-13T17:09:26Z | |
| dc.date.available | 2016-10-13T17:09:26Z | |
| dc.date.issued | 2016 | |
| dc.degree.discipline | Computer Science | en_US |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | en_US |
| dc.description.abstract | To achieve “provisioning elasticity”, the cloud needs to manage its available resources on demand. A-priori, static, VM provisioning introduces no runtime overhead but fails to handle unanticipated changes in resource demands. Dynamic provisioning addresses this problem but introduces runtime overhead. To avoid sub-optimal provisioning my PhD thesis adopts a hybrid approach that combines static and dynamic provisioning. The idea is to adapt an initial static placement of VMs in response to evolving load characteristics. My work is focused on broadening the applicability of clouds by looking at how the infrastructure can be more effectively used to support historically atypical applications (e.g. those that are interactive in nature with tighter QoS constraints). To accomplish this I have developed a family of related algorithms that collectively improve resource sharing on physical machines to permit load variation to be better addressed and to lessen the probability of VM interference due to resource contention. The family includes three core dynamic provisioning algorithms. The first algorithm provides for the short-term, controlled sharing of resources between co-hosted VMs, the second identifies pairs (and by extrapolation larger groups) of VMs that are predicted to be "compatible" in terms of the resources they need. This allows the cloud provider to do co-location to make the first algorithm more effective. The final, third, algorithm deals with under-utilized physical machines by re-packing the VMs on those machines while also considering their compatibility. This final algorithm both addresses the possibility of the second algorithm creating underutilized machines as a result of pairing and migration and also handles underutilization arising from “holes” left by the termination of short-duration VMs (another form of atypical VM application). I have also created a surprisingly simple static provisioning algorithm that considers compatibility to minimize VM interference that can be used before my dynamic algorithms. My evaluation is primarily simulation-based though I have also implemented the core algorithms on a small test-bed system to ensure correctness. The results obtained from my simulation experiments suggest that hybrid static and dynamic provisioning approaches are both feasible and should be effective supporting a broad range of applications in cloud environments. | en_US |
| dc.description.note | February 2017 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/31887 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Resource Provisioning, Cloud Computing, Optimization | en_US |
| dc.title | Improved Virtual Machine (VM) based Resource Provisioning in Cloud Computing | en_US |
| dc.type | doctoral thesis | en_US |