dc.contributor.advisor | Begnum, Kyrre | |
dc.contributor.advisor | Nichele, Stefano | |
dc.contributor.author | Azab, Eman Moustafa | |
dc.date.accessioned | 2021-12-01T11:53:04Z | |
dc.date.available | 2021-12-01T11:53:04Z | |
dc.date.issued | 2021 | |
dc.identifier.uri | https://hdl.handle.net/11250/2832340 | |
dc.description.abstract | Cloud computing provides more reliable web services due to its flexibility
for accessing resources on-demand and self-managed services. However,
cloud computing faces new challenges when managing a massive amount
of services in an environment full of uncertainties. Moreover, the
customer´s variations of services requirements make them more complex.
All of these factors increase the difficulty of managing the services on the
cloud.
The two keys for providing a reliable web service are service performance
and resources utilization. Service performance grantees providing
reliable service in terms of response time, but that should be achieved
without wasting the limited amount of available resources. Therefore,
providing an optimized service without under- or over-provisioning is a
requirement. There is a need to develop an autonomous service to adapt
itself based on the surrounding environment.
This thesis explores introducing a learning automaton algorithm for
developing an autonomous web service. The result is aSpace machine
that has been built and developed through a set of phases and shows
a progression from an autonomous scalable service towards the aSapce
machine. The aSapce machine optimizes the service performance in terms
of self-provision.
The results indicate the ability of the aSapce machine to manage the
different workloads effectively by providing an elastic web service. Further
exploration for this can pave the way towards handling more complex
situations. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | OsloMet - storbyuniversitetet | en_US |
dc.relation.ispartofseries | ACIT;2021 | |
dc.subject | Cloud computing | en_US |
dc.subject | Self-adaptive service | en_US |
dc.subject | Reinforcement learning | en_US |
dc.subject | Artificial intelligence | en_US |
dc.title | Investigate AI-based learning for cloud services for adaptive autonomous behavior | en_US |
dc.type | Master thesis | en_US |
dc.description.version | publishedVersion | en_US |