Service Selection Algorithm Research Articles

A Simulation‐Based Approach of QoS‐Aware Service Selection in Mobile Edge Computing

Edge computing is an emerging computational model that enables efficient offloading of service requests to edge servers. By leveraging the well‐developed technologies of cloud computing, the computing capabilities of mobile devices can be significantly enhanced in edge computing paradigm. However, upon the arrival of user requests, whether to dispatch them to the edge servers or cloud servers in order to guarantee the quality of service (QoS), i.e., the QoS‐aware service selection problem, still remains an open problem. Due to the dynamic mobility of users and the variation of task arrivals and service processes, it is extremely costly to obtain the global optimal solution by both mathematical approaches and simulation‐based schemes. To attack this challenge, this paper proposes a simulation‐based approach of QoS‐aware dynamic service selection for mobile edge computing systems. Stochastic system models are presented and mathematical analyses are provided. Based on the analytical results, the QoS‐aware service selection problem is formulated by a dynamic optimization problem. Goal softening is applied to the original problem, and service selection algorithms are designed using ordinal optimization techniques. Simulation experiments are conducted to validate the efficacy of the approach presented in this paper.

Normal Cloud Model-Based Algorithm for Multi-Attribute Trusted Cloud Service Selection

With the wide deployment of cloud computing, many security challenges have arisen, such as data and storage integrity and virtualization security. The crisis of trust caused by these security issues has become one of the important factors restricting the wide applications of cloud service. Especially for security-sensitive users, it is challenging to quickly select a cloud service which has the high level of trust and can meet both the user preferences and specific functional demands. This paper explores the multi-granularity selection standard of trust level, the users’ preference calculation model, and the cloud service selection algorithm. First, the trust evaluation mechanisms among different entities in the human society are fitted, and the multi-granularity selection standard of trust levels based on Gaussian cloud transformation is constructed. Then, the calculation model of user preferences based on the cloud analytic hierarchy process is developed. Finally, the trusted cloud service selection algorithm based on two-step fuzzy comprehensive evaluation is proposed and experimentally validated.

Dynamic Service Provisioning and Selection for Satisfying Cloud Applications and Cloud Providers in Hybrid Cloud

The paper presents a hybrid cloud service provisioning and selection optimization scheme, and proposes a hybrid cloud model which consists of hybrid cloud users, private cloud and public cloud. This scheme aims to effectively provide cloud service and allocate cloud resources, such that the system utility can be maximized subject to public cloud resource constraints and hybrid cloud users constraints. The paper makes use of a utility-driven approach to solve interaction among private cloud user, hybrid cloud service provider and public cloud provider in hybrid cloud environment. The paper presents hybrid cloud service provisioning and selection algorithm in hybrid cloud. The hybrid cloud market consists of hybrid cloud user agent, hybrid cloud service agent and hybrid cloud agent, which represent the interests of different roles. The experiments are designed to compare the performance of proposed algorithm with the other related work.

Prioritizing the solution of cloud service selection using integrated MCDM methods under Fuzzy environment

Cloud service selection plays a crucial role in terms of on-demand service selection on a subscription basis. As a result of wide-range availability of cloud services with similar functionalities, it is very crucial to determine which service best addresses the user’s desires and objectives. This paper aims to design a new cloud service selection model under the fuzzy environment by utilizing the analytical hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS). The AHP method is enforced to configure the structure of cloud service selection problem and to impel the criteria weight using the pairwise comparisons, and the TOPSIS method utilizes the final ranking of the solution. In our proposed model, the non-functional quality of service requirements is taken into consideration for selecting appropriate service. Furthermore, the proposed model exploits a set of pre-defined linguistic variables, parameterized by triangular fuzzy numbers for evaluating each criteria weights. The experimental results obtained using the real-time cloud service domains prove the efficacy of our proposed model and demonstrate the effectiveness by inducing better performance, when compared against other available cloud service selection algorithms. Finally, the sensitivity analysis is persuaded to confirm the robustness of our proposed model.

Multi-level power consumption model and energy-aware server selection algorithm

Multi-level power consumption model and energy-aware server selection algorithm

Multi-level power consumption model and energy-aware server selection algorithm

Electric power consumed by servers has to be reduced in a cluster in order to realise eco society. We take a macro-level approach to reducing the total electric energy consumption of servers to perform application processes. In this paper, we discuss a Multi-Level Power Consumption (MLPC) model of a server with a multi-thread CPU and a Multi-Level Computation (MLC) model. Based on the MLPC and the MLC models, we newly propose an energy-aware (EA) algorithm to select a server for each process requested by a client in a cluster to reduce the total electric energy consumption of the cluster while satisfying deadline requirements of the processes. We evaluate the EA algorithm and show that not only the total electric energy consumption of servers but also the average execution time of each process are reduced in the EA algorithm compared with the round-robin (RR) and random (RD) algorithms.

QoS and SLA Aware Web Service Composition in Cloud Environment

As a service-oriented paradigm, web service composition has obtained great attention from both academia and industry, especially in the area of cloud service. Nowadays more and more web services providing the same function but different in QoS are available in cloud, so an important mission of service composition strategy is to select the optimal composition solution according to QoS. Furthermore, the selected composition solution should satisfy the service level agreement (SLA) which defines users’ request for the performance of composite service, such as price and response time. A composite service is feasible only if its QoS satisfies user’s request. In order to obtain composite service with the optimal QoS and avoid SLA violations simultaneously, in this paper we first propose a QoS evaluation method which takes the SLA satisfaction into account. Then we design a service selection algorithm based on our QoS evaluation method. At last, we put forward a parallel running strategy for the proposed selection algorithm. The simulation results show that our approach outperforms existing approaches in terms of solutions’ optimality and feasibility. Through our running strategy, the computation time can be reduced to a large extent.

S-ABC: A paradigm of service domain-oriented artificial bee colony algorithms for service selection and composition

With the rapid development of Cloud Computing, Big Data, Social Networks, and the Internet of Things, typical service optimization problems (SOPs) such as service selection, service composition and service resource scheduling in the service computing field have become more and more complicated due to the constant enrichment and dynamic aggregation of large number of services, as well as the unceasing variation of user requirements. Meanwhile, with the long-term development and evolution of business in many application domains, some service domain features (such as priori, correlation and similarity) are usually formed, which have strong influences on solving SOPs. Unfortunately, the existing research efforts on SOPs primarily concentrate on designing general algorithms for specific problems without considering the service domain features. This often leads to undesirable results of SOPs. Therefore, how to design a paradigm of service domain-oriented optimization algorithms with service domain features becomes a challenge for providing optimization strategies and algorithms to solve SOPs effectively. By considering the influences of service domain features on solving SOPs, this paper proposes a set of service domain-oriented artificial bee colony algorithms (S-ABC) based on the optimization mechanism of Artificial Bee Colony (ABC) method. Furthermore, by configuring the items and parameters of the S-ABC paradigm in detail, optimization algorithms for particular SOPs (e.g., service selection and composition) could be derived. In this paper, the superiority of our proposed S-ABC is verified through solving concurrent service selection and service composition problem. By exploiting the artificial bee colony algorithms for the optimization problems in service domains, this work makes novel contributions for solving SOPs, as well as extends the theory of the swarm intelligence optimization.

Service Selection Algorithm of Two-layer QoS Model based on Functional Grouping

With the development and application of service computing technology,Quality of Service has become one of the important basis for the selection of web services in service composition. In order to avoid the influence of functional difference of services on the Qos of services in the service compositon, this paper proposes a two-layer QoS model service selection algorithm based on feature grouping. First of all, in the process of service composition, the services are grouped according to their function, then the services in the same group are further grouped by Qos, which includes common Qos and reputation degree QoS based on the subjective and objective factors. The reliability of service is defined to express the common Qos, while a kind of evaluation method of reputation degree is raised to express the reputation degree Qos. Experimental results show that this method can obtain web services with high reliability and high credit standing, meanwhile it can increase the success rate of web service composition.

Lightweight approach for multi‐objective web service composition

Service composition is an efficient way to implement a service of complex business process in heterogeneous environment. Existing service selection methods mainly utilise fitness function or constraint technique to convert multiple objectives service composition problems to single objective ones. These methods need to take effect with priori knowledge of problem's solution space. Besides, in each execution only one solution can be obtained, hence, users can hardly acquire evenly distributed solutions with acceptable computation cost. The authors also propose a lightweight particle swarm optimisation service selection algorithm for multi-objective service composition problems. Simulation results illustrate that the proposed algorithm surpasses the comparative algorithm in approximation, coverage and execution time.

A novel Bi-Ant colony optimization algorithm for solving multi-objective service selection problem

The multi-objective service selection problem is a basic problem in Service Computing and is NP-Hard. This paper proposes a novel Bi-Ant colony optimization multi-objective service selection algorithm for this problem. Two objective functions related with response time and cost attributes are considered. For each objective, a heuristic function and a pheromone updating policy are defined against the characteristics of this problem. Then, a combined state transition rule is designed based on them. The algorithm has been tested in nine cases and compared with the related MOACO algorithm for this problem. The result demonstrates that our approach is effective and better than MOACO.

Energy-Centered and QoS-Aware Services Selection for Internet of Things

An important challenge to be addressed in the domain of Internet of Things (IoT) is the development of efficient services selection algorithms for an optimal management of both energy and Quality of Service (QoS) in the context of IoT services composition. This issue becomes crucial in the case of large-scale IoT environments composed of thousands of distributed entities. In this paper, an energy-centered and QoS-aware services selection algorithm (EQSA) is proposed for IoT services composition. The proposed selection approach consists of preselecting the services offering the QoS level required for user's satisfaction using a lexicographic optimization strategy and QoS constraints relaxation technique. In order to reduce the energy consumption of a composite service without affecting the user's satisfaction, the most suitable services among the preselected ones are then selected using the concept of relative dominance of services in the sense of Pareto. The relative dominance of a candidate service depends on its energy profile and QoS attributes, and user's preferences. The proposed algorithm has been evaluated through several simulation scenarios. The obtained results show clearly the good performances of the EQSA algorithm in terms of selection time, energy efficiency, composition lifetime, and optimality and its added value in comparison with algorithms dealing separately with QoS and energy consumption.

Strategy-Proof Pricing for Cloud Service Composition

The on-demand provisions of cloud services create a service market, where users can dynamically select services based on such attractive criteria as price and quality. An intuitive model of a service market is a reverse auction. In the first price auction, however, a service that is cheaper and provides better quality is not necessarily selected. This causes undesirable outcomes both for users and service providers. A possible solution is the Vickrey-Clarke-Groves (VCG) mechanism, where the dominant strategy for a service provider is to report the true cost of his service. In spite of this desirable property, implementing the VCG mechanism for service composition suffers from computational cost. The calculation of payments to service providers based on the VCG mechanism requires iterative service selection, even though each service selection can be NP-hard. Approximation algorithms cannot be applied because approximate solutions do not assure the desirable property of the VCG mechanism. Thus, we model VCG payments for service markets and propose a dynamic programming (DP)-based algorithm for service selection and VCG payment calculation. Our proposed algorithm solves service selection in quasi-polynomial time and gives an exact solution. Moreover, we extend it and focus on the iterative service selection process for VCG payment calculation to improve its performance. Our series of experiments show that our proposed algorithm solves practical scale service composition.

An efficient algorithm for partially matched services in internet of services

Internet of Things (IoT) connects billions of devices in an Internet-like structure. Each device encapsulated as a real-world service which provides functionality and exchanges information with other devices. This large-scale information exchange results in new interactions between things and people. Unlike traditional web services, internet of services is highly dynamic and continuously changing due to constant degrade, vanish and possibly reappear of the devices, this opens a new challenge in the process of resource discovery and selection. In response to increasing numbers of services in the discovery and selection process, there is a corresponding increase in number of service consumers and consequent diversity of quality of service (QoS) available. Increase in both sides' leads to the diversity in the demand and supply of services, which would result in the partial match of the requirements and offers. This paper proposed an IoT service ranking and selection algorithm by considering multiple QoS requirements and allowing partially matched services to be counted as a candidate for the selection process. One of the applications of IoT sensory data that attracts many researchers is transportation especially emergency and accident services which is used as a case study in this paper. Experimental results from real-world services showed that the proposed method achieved significant improvement in the accuracy and performance in the selection process.

Efficient service selection approach for mobile devices in mobile cloud

The paper proposes optimal efficient cloud service selection scheme for mobile device through local and public cloud in hybrid cloud. The proposed model consists of non-dedicated public cloud resources and local cloud resources. At different locations, local cloud service providers maintain finite dedicated resources that can be used by mobile device users. In the proposed model, the system middleware first examines if any of the local cloud servers have sufficient idle capacity to satisfy the desired requirement of mobile application. If the mobile users' requirement cannot be handled locally, the proposed architecture must choose mobile users' jobs to burst to the remote public cloud. The decision of when to trigger a cloud burst involves monitoring the system context and workload. A cloud-based service selection optimization is proposed, which can optimize the resource utilization, cloud supplier's benefit and mobile user's QoS. The proposed optimization problem is partitioned by local cloud service selection and public cloud resource scheduling, which is implemented by the mobile device, mobile cloud service broker and cloud supplier. A cloud service selection algorithm for mobile device is proposed, which is composed of two sub-algorithms: cloud service selection optimization and cloud resource scheduling optimization. The efficiency of the cloud service selection algorithm for mobile device is tested by experiments.

A Comprehensive Web Service Selection Algorithm on Just-in-Time Scheduling

This paper proposes a comprehensive service selection algorithm (CSA) for improving performance and real-time arrangement in service selection. The algorithm integrates the service provider’s elicited preference and requestor’s planned time. This innovative CSA autonomously aggregates multiple requestors’ preferences over multicriteria from multiple providers, selecting the most suitable service provider on the basis of voting. The sequencing portion of the algorithm also runs based on time scheduling, where the extra cost of the system is minimal due to its small time window. The service arrangement process functions by identifying and utilizing the best solution for the 0-1 knapsack problem; the improved genetic algorithm finds solutions via coding and evolution. Experiment results show that the proposed CSA effectively simplifies the preference convergence of service requestors and optimizes the time scheduling of the service provider. The algorithm also enhances selection efficiency and reduces selection time, and it can be applied feasibly to multi-provider and multi-requestor service selection.

Dynamics of service selection and provider pricing game in heterogeneous cloud market

This paper studies price competition in a heterogeneous cloud market formed by public providers and a cloud broker, all of which are also known as cloud service providers (CSPs). We formulate the price competition between CSPs as a two-stage noncooperative game. In stage I, in which CSPs set their service prices to maximize their revenues, we model the pricing game using the noncooperative static game. We provide sufficient conditions for the existence and uniqueness of Nash equilibrium prices, which can be obtained using an iterative algorithm. The convergence properties of the iterative algorithm are characterized using the contract mapping theorem. In stage II, given the prices set by CSPs, cloud users can select the services that provide them the best payoff in terms of performance (i.e., delay) and price. We apply an evolutionary game to study the evolution and dynamic behavior of cloud users. Furthermore, we use the Wardrop equilibrium and replicator dynamics to determine the equilibrium and its convergence properties of the service selection game. To attract users to the equilibrium, we implement the service selection algorithms using population evolution and reinforcement learning approaches. Numerical results illustrate that our game models can provide comprehensive understanding of the heterogeneous CSPs market and service selection in cloud computing.

A Multi-Agent Approach for Trust-based Service Discovery and Selection in Social Networks

Service discovery and selection approaches are often done using a centralized registry-based technique, which only captures common Quality of Service criteria. With more and more services offered via social networks, these approaches are not able to evaluate trust in service providers and often fail to comply with new requester's expectations. This is because theses approaches are not able (i) to take into consideration the social dimension and (ii) to capitalize on information resulting from previous experiences. To address these challenges, we propose the use of multi-agent systems as they have demonstrated the capability to use previous interactions, knowledge representation and distributed reasoning, as well as social metaphors like trust. More precisely, in this paper, we enhance service discovery and selection processes by integrating the societal view in trust modeling. Based on relationships between agents, their previous experiences and extracted information from social network, we define a trust model built upon social, expert and recommender-based components. The social-based component judges whether the provider is worthwhile pursuing before using his services (viz. trust in sociability). The expert-based component estimates whether the service behaves well and as expected (viz. trust in expertise). The recommender-based component assesses for an agent whether one's can rely on its recommendations (viz. trust in recommendation). However, when searching for a service in a social network, agents (service requester and service providers) may have no direct interactions or previous experiences. This requires a method to infer trust between them. Based on a probabilistic model, we estimate trust between non adjacent agents while taking into account roles (recommender or provider) of intermediate agents. Moreover, we propose a distributed algorithm for trustworthy service discovery and selection using referral systems in social networks. Experiments demonstrate that our approach is effective and outperforms existing ones, and can deliver more trustworthy results.

QoS-Aware Mobile Service Selection Algorithm

For the problem of mobile service selection, this paper gives a context-aware service selection algorithm based on Genetic Algorithm. In this algorithm, a tree encoding method, a fitness function, and a fitness-better strategy were proposed. The tree encoding mode made Genetic Algorithm support selection of various types of service combinations, for example, sequence composition, concurrence composition, probability composition, and loop composition. According to the encoding method, a fitness function was designed specially. The fitness-better strategy gives the direction of population evolution and avoids the degradation of population fitness. Some experiments analyses show that the provided service selection algorithm can get better service composition.

Distributed Approach for QoS Service Selection in Web of Objects

In the Internet of Things (IoT), web objects and services can be seen as distributed and cooperative agents that need to collaborate in order to reach advanced functionalities and also to optimize the overall quality offered to the end users. For this purpose, we propose in this paper a distributed and optimal QoS services selection approach based on Multi-Agents paradigm and Distributed Constraints Optimization Formalism (DCOP). Hence, we revisited a DCoP technique for developing a new complete algorithm for service selection: the’Synch4QoS’ implemented under a multi-agent architecture using a real-time Web protocol for communi- cation. The proposed algorithm takes into accounts the specificities of the service composition context and the satisfaction of the global user's constraints.