Cloud services have been becoming more and more popular and important as cloud computing and the SaaS model emerged. For providing complex and large-scale services, composite cloud services based on the SOA technologies are gaining attention in both research work and practical system development since they are promising in bringing lots of benefits, such as fast and flexible development as well as greater market opportunities for software components. To realize the benefits of composite cloud services, several research issues have to be addressed, including service discovery, service composition, service selection, security concerns, and so on. This thesis focuses on the service selection problem which concerns the QoS performance of an entire composite cloud service.
Most previous research works on service selection assumed deterministic QoS parameters and proposed solutions based on different optimization techniques. However, in practice we know that most performance parameters, such as service response time, are not always deterministic and rather follow a stochastic distribution. Therefore, this thesis deals with the service selection problem in a dynamic cloud environment with stochastic performance variation. The goal is to efficiently and effectively select among existing functionally equivalent services for putting them together to provide new composite cloud services at the minimal costs under some QoS constraints.
We propose two approaches to the service selection problem in this thesis. The first one is an iterative compound approach, with each iteration containing three steps: Integer Linear Programming (ILP) optimization, simulation of stochastic performance, and adaptation. The second approach is a one-step method based on the Chebyshev’s theorem and nonlinear
programming. It takes into consideration the stochastic performance in the objective function of a nonlinear programming formulation.
The advantage of the first approach is that it can reduce the total costs as effectively as the second nonlinear programming approach, but requires much less computational time.
However, although having higher computational complexity, the second approach has a unique advantage which is the capability of enforcing an upper bound on SLA violation while minimizing the costs. This capability can be useful since in many cases the SLA might contain the enforcement of QoS violation ratio over a specific period of time or a specific number of service requests. The proposed approaches were evaluated with a series of simulation experiments and compared to a previous method in the literature. The experimental results indicate that our approaches outperform the previous method significantly across different scenarios with different relationships among services’ QoS attributes and costs.
In this thesis, we focus on the cost and response time performance of composite cloud services. Therefore, among various QoS parameters we dealt with mean response time and the standard deviation of response time of each service. However, service availability is another factor which could affect the response time or even reliability of composite cloud services although current cloud computing platforms are in general reliable enough. It is a promising future research direction to take both response time and availability into consideration in developing service selection approaches.
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