Cloud Service Selection System Approach based on QoS Model: A Systematic Review

The Internet of Things (IoT) has received a lot of interest from researchers recently. IoT is seen as a component of the Internet of Things, which will include billions of intelligent, talkative "things" in the coming decades. IoT is a diverse, multi-layer, wide-area network composed of a number of network links. The detection of services and on-demand supply are difficult in such networks, which are comprised of a variety of resource-limited devices. The growth of service computing-related fields will be aided by the development of new IoT services. Therefore, Cloud service composition provides significant services by integrating the single services. Because of the fast spread of cloud services and their different Quality of Service (QoS), identifying necessary tasks and putting together a service model that includes specific performance assurances has become a major technological problem that has caused widespread concern. Various strategies are used in the composition of services i.e., Clustering, Fuzzy, Deep Learning, Particle Swarm Optimization, Cuckoo Search Algorithm and so on. Researchers have made significant efforts in this field, and computational intelligence approaches are thought to be useful in tackling such challenges. Even though, no systematic research on this topic has been done with specific attention to computational intelligence. Therefore, this publication provides a thorough overview of QoS-aware web service composition, with QoS models and approaches to finding future aspects

An adaptive multi-population differential artificial bee colony algorithm for many-objective service composition in cloud manufacturing

Several conflicting criteria must be optimized simultaneously during the service composition and optimal selection (SCOS) in cloud manufacturing, among which tradeoff optimization regarding the quality of the composite services is a key issue in successful implementation of manufacturing tasks. This study improves the artificial bee colony (ABC) algorithm by introducing a synergetic mechanism for food source perturbation, a new diversity maintenance strategy, and a novel computing resources allocation scheme to handle complicated many-objective SCOS problems. Specifically, differential evolution (DE) operators with distinct search behaviors are integrated into the ABC updating equation to enhance the level of information exchange between the foraging bees, and the control parameters for reproduction operators are adapted independently. Meanwhile, a scalarization based approach with active diversity promotion is used to enhance the selection pressure. In this proposal, multiple size adjustable subpopulations evolve with distinct reproduction operators according to the utility of the generating offspring so that more computational resources will be allocated to the better performing reproduction operators. Experiments for addressing benchmark test instances and SCOS problems indicate that the proposed algorithm has a competitive performance and scalability behavior compared with contesting algorithms

Survey on Additive Manufacturing, Cloud 3D Printing and Services

Cloud Manufacturing (CM) is the concept of using manufacturing resources in a service oriented way over the Internet. Recent developments in Additive Manufacturing (AM) are making it possible to utilise resources ad-hoc as replacement for traditional manufacturing resources in case of spontaneous problems in the established manufacturing processes. In order to be of use in these scenarios the AM resources must adhere to a strict principle of transparency and service composition in adherence to the Cloud Computing (CC) paradigm. With this review we provide an overview over CM, AM and relevant domains as well as present the historical development of scientific research in these fields, starting from 2002. Part of this work is also a meta-review on the domain to further detail its development and structure

An Approach of QoS Evaluation for Web Services Design With Optimized Avoidance of SLA Violations

Quality of service (QoS) is an official agreement that governs the contractual commitments between service providers and consumers in respect to various nonfunctional requirements, such as performance, dependability, and security. While more Web services are available for the construction of software systems based upon service-oriented architecture (SOA), QoS has become a decisive factor for service consumers to choose from service providers who provide similar services. QoS is usually documented on a service-level agreement (SLA) to ensure the functionality and quality of services and to define monetary penalties in case of any violation of the written agreement. Consequently, service providers have a strong interest in keeping their commitments to avoid and reduce the situations that may cause SLA violations.However, there is a noticeable shortage of tools that can be used by service providers to either quantitively evaluate QoS of their services for the predication of SLA violations or actively adjust their design for the avoidance of SLA violations with optimized service reconfigurations. Developed in this dissertation research is an innovative framework that tackles the problem of SLA violations in three separated yet connected phases. For a given SOA system under examination, the framework employs sensitivity analysis in the first phase to identify factors that are influential to system performance, and the impact of influential factors on QoS is then quantitatively measured with a metamodel-based analysis in the second phase. The results of analyses are then used in the third phase to search both globally and locally for optimal solutions via a controlled number of experiments. In addition to technical details, this dissertation includes experiment results to demonstrate that this new approach can help service providers not only predicting SLA violations but also avoiding the unnecessary increase of the operational cost during service optimization

A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems

Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts