This thesis studied the QoS-based web service composition problem in the cloud. The main objective of this research was to develop effective evolutionary algorithms to perform network-aware and QoS-based web service composition in a large scale environment. The problem is described as a constrained multi-objective optimization problem. This objective was successfully solved via a set of novel evolutionary algorithms which have been presented in this thesis.
Specifically, three major issues of QoS-based web service composition in the cloud were tackled. The first issue is how to accurately and efficiently estimate the end-to-end network distance (or network latency) of a composite service in the cloud. We defined this issue as a prediction problem where the aim is to estimate the unknown RTTs of a set of network paths given a subset of already known path RTTs. The issue was successfully addressed in Chapter 3 where a new RTT estimation algorithm was presented. The second issue is how to search for low latency and QoS optimal solutions in a large scale cloud environment. This issue was tackled in Chapter 4 via four new evolutionary algorithms. The third issue dealt involved how to find QoS and latency optimal solutions in a dynamic environment where QoS values are constantly changing. The issue was solved in Chapter 5 using a new genetic algorithm. The proposed algorithms were evaluated in a simulated environment to test their optimality, efficiency and scalability in different instances. Results
obtained from the evaluation demonstrated the competitiveness of the algorithms when compared to previous approaches.
Starting from Chapter 1, the thesis introduced the research objectives where we discussed the research motivation, challenges and research problem. At this point, the problem was defined as a NP-Hard combinatorial optimization problem. The chapter outlined crucial challenges to be addressed in the thesis such as type of QoS model, service composition algorithms and evaluation. Major contributions of this research were also presented such as a novel method for prediction end-to-end network latency, and a new set of evolutionary algorithms for performing network-aware service composition in the cloud. After the contributions were discussed, the outline of the thesis was then presented to close the chapter.
In Chapter 2, a comprehensive background and analysis on QoS-based web service composition techniques was presented. The chapter started by describing web services, their benefits and web service model. The chapter also described QoS and classified major QoS attributes such as cost, reputation, response time, reliability and availability. Concepts of QoS-based service composition were then introduced. The concepts discussed include workflows, service composition steps and factors that justify the NP-Hardness of the QoS-based service composition problem. The chapter then analysed the recent techniques developed to tackle the problem. Techniques were first classified into four categories; Intra-task approaches, inter-task approaches, approximation approaches and pareto-optimization approaches. Each category was explained in great detail including associated works, strengths and weaknesses. For instance, intra-task approaches such as dynamic programming, simple additive weighting are very efficient in large scale environment however they have poor optimality. Inter task approaches like linear integer programming have high optimality but are computationally inefficient in large scale scenarios. Approximation approaches e.g. particle
swarm and genetic algorithms are more efficient than other approaches in large environments but they mostly find sub-optimal solutions. Pareto-optimal approaches are similar to approximate methods except that they offer the service consumer with an alternative solution in the form of a Pareto set. The chapter then discusses techniques that perform service composition in dynamic environment. These techniques normally don’t have prior knowledge of QoS scores before the optimization process is performed. The techniques discussed include; Internal composition techniques that rebuild compositions from ground up or from point of failure e.g. AI planning and reinforced learning; External adaptation techniques that use adapters to bridge between composition and dynamic environment. From the analysis, it is observed that external adaptation methods are slower than internal adaptation methods, although they are able to find better quality solutions in a dynamic environment. The chapter then reviewed recent works that focused on solving QoS-based web service composition in the cloud. The techniques discussed adopt mainly evolutionary algorithms to find QoS-optimal compositions with minimal network cost to the cloud. Examples of methods discussed include ant colony algorithm, genetic algorithm, hierarchical task networks and finite state machines. Finally the chapter introduced network coordinate systems (NCS) due to their significance in aiding the proposed algorithms to solve the research problem. The operational procedure and benefits of NCS were discussed including an analysis of its main works.
In Chapter 3, a new method for predicting end-to-end network performance of a composite service is presented. The chapter first introduced end-to-end network performance with special focus on network latency due to the ease at which it can be obtained from the Internet. The importance of estimating network latency in the cloud discussed. Then the prediction problem was defined, followed by a brief description of current techniques used to solve the problem. Here, Euclidean distance (EDM) and non-negative matrix
factorization (NMF) methods were discussed. It was deduced that NMF provided more accurate RTT estimates than EDM. Thus an Enhanced NMF method known as LANMF was proposed to further improve the accuracy of NMF. LANMF uses learning automata concepts to enhance the general update strategy of NMF such that each web service node can employ its own coordinate update towards minimal prediction error. Finally, the LANMF algorithm is evaluated in a simulated large scale cloud environment of web service nodes. It was observed from the results that LANMF obtains more accurate RTT estimates than recent techniques based on NMF (DMF) and EDM. This is thanks to its unique automata-based update strategy which learns what path to take in updating a web service node’s coordinate to ensure minimum prediction error.
Chapter 4 studied the QoS-based web service composition in the cloud. Firstly, the chapter identified the challenges posed by QoS-based service composition problem. They include multiple conflicting QoS attributes, multiple QoS constraints and impact of network performance on composite service selection. A detailed description of our QoS model is then presented. The model consists of QoS attributes considered in this thesis e.g. cost, response time, execution time, and network latency. The chapter also discusses the significance of network latency during QoS optimization process in the cloud.. The research problem is then formulated as a constrained multi- objective optimization problem. To address the problem, we presented four new algorithm namely network-aware NSGA-II algorithm (INSGA), K-mean based NSGA-II (KNSGA) algorithm, multi-population PSO (NMPSO) algorithm and non-dominated sort-based fruit fly optimization algorithm (NFOA). INSGA employed unique ND-Crossover and ND-Mutation operators which retains compositions having good crowding distances and RTTs and alters solutions with poor RTTs and crowding distances into new children. KNSGA searches for QoS-optimal and low latency solutions with the aid of
K-mean based K-Mutation operator. NMPSO separates solutions into two populations; the latency optimal population and QoS optimal population. It also uses non-dominated sorting to guide search towards near optimal Pareto set. Lastly, NFOA is a fruit fly optimization algorithm that looks for network positions of a composite service with optimal QoS. We compared the optimality and performance of the four algorithms against each other and against other previous algorithm such as linear integer programming (LIP), particle swarm algorithm and NSGA-II algorithm. The results proved that INSGA outperformed other algorithms in terms of optimality while NFOA, NMPSO and KNSGA had better balance between performance and optimality than other algorithms in a large scale cloud environment.
Chapter 5 investigated QoS-based web service composition in a dynamic cloud environment which entails an environment where web service QoS scores fluctuate constantly. The previous approaches in Chapter 4 were first tested in a dynamic environment. Preliminary results showed that they were incapable of sustaining search for near optimal solutions. This motivated the development of a technique called cellular automata-based NSGA-II algorithm (CellGA) to addresses the problem. The main idea behind CellGA is the development of cellular automata rules that decide which gene needs to be altered to guide the search towards the global Pareto set. The chapter then presented a comparison of CellGA against previous algorithms. Results of the evaluation demonstrated its superiority in maintaining search for near-optimal solutions despite QoS fluctuations.