Metaheuristic Optimization for Long-term IaaS Service Composition

A flexible QoS-aware Web service composition method by multi-objective optimization in cloud manufacturing

In today's highly dynamic economy, businesses have to adapt quickly to market changes, be it customer, competition- or regulationdriven. Cloud computing promises to be a solution to the ever changing computing demand of businesses. Current SaaS, PaaS and IaaS services are often found to be too inflexible to meet the diverse customer requirements regarding service composition and Quality-of-Service. We therefore propose an ontology-based optimization framework allowing Cloud providers to find the best suiting resource composition based on an abstract request for a custom service. Our contribution is three-fold. First, we describe an OWL/SWRL based ontology framework for describing resources (hard- and software) along with their dependencies, interoperability constraints and meta information. Second, we provide an algorithm that makes use of some reasoning queries to derive a graph over all feasible resource compositions based on the abstract request. Third, we show how the graph can be transformed into an integer program, allowing to find the optimal solution from a profit maximizing perspective.

SUMMARYService selection in service‐oriented computing has emerged to be an increasingly important research area. From the client's point of view, in addition to the QoS of a service or a service composition, the trust level becomes an important part. As the complexity of invocation in service composition has been greatly increased, a comprehensive mechanism, which could evaluate both the subjective aspect as trust expression and the objective aspect as QoS, is needed. In this paper, we provide a formal service composition architecture for service selection. In addition, we propose a trust evaluation method for the service composition plan based on the subjective probability theory, based on them, our trust‐oriented genetic algorithm (TOGA) is proposed to find a near‐optimal service composition plan with QoS constraints. Experimental results have illustrated that our proposed approach can discover the near‐optimal solution efficiently. Copyright © 2013 John Wiley & Sons, Ltd.

In heterogeneous cellular networks, the deployment of low-powered picocells provides user offloading and capacity enhancement. The expansion of a picocell's coverage by adding a positive bias for cell association can maximize these effects. Under this circumstance, downlink cross-tier interference from a macro base station to pico mobile stations in the expanded picocell range deteriorates those pico mobile stations' performance significantly. In this paper, a coordinated scheduling and power control algorithm is proposed, whereby the macro base station reduces its transmission power for those victim pico mobile stations in the expanded picocell range only on a set of resource blocks to minimize performance degradation at the macro base station. First, the transmission power level is calculated based on the mobile stations' channel condition and QoS requirements. Then, a set of resource blocks is determined by solving a binary integer programming to minimize the sum of transmission power reduction subject to victim pico mobile stations' QoS constraints. To reduce computational complexity, we utilize a heuristic algorithm, i.e., max-min greedy method, to solve the problem. Through system level simulations, we show that average and 5%-ile throughputs of victim pico mobile stations are significantly improved.

Spectrum efficiency (SE) and QoS guarantee are two critical issues in wireless communication systems. The truncated ARQ and the adaptive modulation and coding (AMC) are often used to improve the SE and transmission reliability in wireless communications. In this paper, we study the SE and QoS performance of a system with a cross-layer combining of the truncated ARQ with the AMC. First, we analyze the performance of current combinations of the ARQ and the AMC. The analysis results indicate that the current combination is too “ineffectiveness” or “conservative.” Then, based on the analysis, we design a new rule for the AMC, which is used to adaptively choose transmission mode. Through analyzing the AMC and the combination algorithm using the proposed rule, both the SE and the packet dropped probability of the system are improved. Furthermore, the analysis result also point outs the suitable scenario of each algorithm. Simulation results verify the analysis result and demonstrate that the proposed algorithm can achieve higher SE while the QoS requirement is guaranteed. At last, to further improve the SE of the system, we propose an algorithm jointing power control and the combination of the ARQ with the AMC under the QoS constraint.

The advances in mobile technologies enable us to consume or even provide services through powerful mobile devices anytime and anywhere. Services running on mobile devices within limited range can be composed to coordinate together through wireless communication technologies and perform complex tasks. However, the mobility of users and devices in mobile environment imposes high risk on the execution of the tasks. This paper targets reducing this risk by constructing a dependable service composition after considering the mobility of both service requesters and providers. It first proposes a risk model and clarifies the risk of mobile service composition; and then proposes a service composition approach by modifying the simulated annealing algorithm. Our objective is to form a service composition by selecting mobile services under the mobility model and to ensure the service composition have the best quality of service and the lowest risk. The experimental results demonstrate that our approach can yield near-optimal solutions and has a nearly linear complexity with respect to a problem size.

Federated cloud emerges recently to overcome resource limitation of IaaS providers by federating different cloud resources which belong to separate organizations. The main objective of IaaS providers is to obtain maximal profits and guarantee QoS requirements of customers at the same time. In this paper, we propose a cooperative game algorithm that helps the resource allocation decision-making process with a flexible resource amount of the total federation. This algorithm also takes the QoS constraints into account and provides two different approaches for cost-sensitive or time-sensitive customers. Simulation results show that the proposed algorithm improves various QoS requirements and increases the satisfaction level of customer's requests.

One important step in dynamic web service composition is to bind concrete web services to the activities involved in the composition. Ideally, the component services are assigned such that the resulting composite web service meets the service level agreements and offers the best trade-off between the various QoS parameters. This implies the ability to express preferences related to the trade-offs. In this paper, we propose a binding-as-a-service (BaaS) approach based on a powerful, but at the same time simple and intuitive notation for specifying user preferences. The typical client of a BaaS provider is a module of a service composition framework. The service request describes the abstract composition model, the available component services and the QoS constraints and preferences. A prototype implementation of this binding-as-a-service approach validates our method.

This paper presents a game theoretic model and an iterative bidding framework for the service request prioritizing and scheduling problem in Software as a Service (SaaS) environments. We focus on a type of settings in which the service provider's existing capacity is over demanded and customers' service requests are constrained with completion times. To gain more profit, the provider needs to prioritize customers' requests based on their profitability and schedule as many profitable requests as possible. The prioritizing and scheduling problem is considered in a strategic setting in which customers' values on the service requests are their private information that is not known to the provider, which calls for economic based models. The key issue here is how to maximize economic social welfare across all customers given the presence of customers' self-interests. We propose an iterative bidding framework as a multilateral negotiation platform for the service provider and its customers to allocate compute capacity among service requests. The proposed bidding framework is evaluated by a computational study. The data from the experiments show that the proposed approach produces near optimal solutions with moderate information revelation. It also scales well to large size problem instances.

Service composition is a key enabling technology in service-oriented computing for developing versatile applications by integrating various existing interoperable services. Although actively studied, most existing works on service composition neglect the existence of complementarity among candidate services within a service class, so-called internal complementarity. In fact, complementary candidate services within a service class can be composed to form a composite candidate service which may yield better service utility than that provided by any existing candidate service within that service class. This work focuses on web service composition where internal complementarity is more likely to happen. Specifically, we aim at addressing the problem of QoS-aware web service selection with internal complementarity (WSS-IC). We first transform this problem into a multi-choice multi-dimensional knapsack problem (MMKP) and prove such a transformation per se has non-polynomial time complexity in the worse case.Then, we perform complexity analysis to demonstrate that existing approaches to MMKPs are not computationally feasible to resolve QoS-aware WSS-IC. This fact motivates us to propose an iteratively improving framework for deriving the solution iteration by iteration while taking into account both solution structure and QoS constraints. At each iteration, the current solution gets improved by solving a disjunctively constrained knapsack problem. To verify the effectiveness of the proposed framework, two heuristic approaches are implemented under this framework. Experimental results demonstrate that our approaches outperform the compared methods in terms of both solution quality and computation time.

In recent years, the Web service composition technology has developed rapidly, as a result, the energy consumption problem is also increasingly prominent. Faced with this situation, the paper firstly gives out the power model of single service; then derives the model of service composition according to its characteristics, and gives out the corresponding QoS constraints at the same time. After finishing the model, the paper talks about the allocation of feasible path, which is one of the problem in model calculation. The author introduces a graph-plan based searching algorithm, and uses the concept of hierarchical search to solve the problem. At last, the experimental results show that compared with traditional methods, the approach in this paper can not only satisfy the user requests with QoS restricted, but also reduce the energy consumption of service composition effectively.

Energy-aware service composition in multi-Cloud

Web Services are materialized as a major technology carried out for automated interaction between distributed and miscellaneous applications. It is defined as a software service that provides business solutions consumed by different service requester. It can be accessed by a standard web protocol. Service composition is the mechanism used for selecting, reusing and combining existing web services to build new web services. With immense increase in web services, quality assessment plays an essential role in the selection approach. QoS is defined as the ability to guarantee the requirements like latency, reliability, bandwidth, etc. in order to satisfy a service level agreement between an application provider and end-user. QoS based dynamic service composition leads to the upward growth of an organization that implements multiple services to provide its business solution. The proposed research work elaborates the research work on different methodologies of QoS prediction with its implications to provide a proper road map for future research on efficient service composition.

In the present work we address a problem of optimal resource partitioning in high-speed networks with heterogeneous traffic classes and differentiated QoS requirements. The problem arguments are bandwidth and network-edge buffer space allocations for end-to-end connections. The problem formulation can take into account sensitivity variation of application performance for both aggregated and single service requests. The proposed method is recursive and solves two problems on each iteration step. The first step defines optimal relations between network media, equipment performance parameters and selected routing scheme in accordance with generalized mean of links and network nodes loads. Mutual influence of flows in the network is considered in this stage. The second stage is intended to provide optimal bandwidth and buffer size allocation parameters under network resource and QoS constraints. At this stage, stochastic resource allocation problem formulation with piecewise linearization of nonlinear constraints is applied. We use connection request distribution, average connection duration time and quantity of information exchange as sensitive parameters in post optimal analysis to improve solution convergence to optimal tradeoff between resource allocation and acceptable application performance.

The run-time binding of web services has been recently put forward in order to support rapid and dynamic web service compositions. With the growing number of alternative web services that provide the same functionality but differ in quality parameters, the service composition becomes a decision problem on which component services should be selected such that user's end-to-end QoS requirements (e.g. availability, response time) and preferences (e.g. price) are satisfied. Although very efficient, local selection strategy fails short in handling global QoS requirements. Solutions based on global optimization, on the other hand, can handle global constraints, but their poor performance renders them inappropriate for applications with dynamic and real-time requirements. In this paper we address this problem and propose a solution that combines global optimization with local selection techniques to benefit from the advantages of both worlds. The proposed solution consists of two steps: first, we use mixed integer programming (MIP) to find the optimal decomposition of global QoS constraints into local constraints. Second, we use distributed local selection to find the best web services that satisfy these local constraints. The results of experimental evaluation indicate that our approach significantly outperforms existing solutions in terms of computation time while achieving close-to-optimal results.