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

Summary One of the most critical issues in using service‐oriented technologies is the combination of services, which has become an important challenge in the present. There are some significant challenges in the service composition, most notable is the quality of service (QoS), which is more challenging due to changing circumstances in dynamic service environments. Also, trust value in the case of selection of more reliable services is another challenge in the service composition. Due to NP‐hard complexity of service composition, many metaheuristic algorithms have been used so far. Therefore, in this paper, the honeybee mating optimization algorithm as one of the powerful metaheuristic algorithms is used for achieving the desired goals. To improve the QoS, inspirations from the mating stages of the honeybee, the interactions between honeybees and queen bee mating and the selection of the new queen from the relevant optimization algorithm have been used. To address the trust challenge, a trust‐based clustering algorithm has also been used. The simulation results using C# language have shown that the proposed method in small scale problem acts better than particle swarm optimization algorithm, genetic algorithm, and discrete gbest‐guided artificial bee colony algorithm. With the clustering and reduction of the search space, the response time is improved; also, more trusted services are selected. The results of the simulation on a large‐scale problem have indicated that the proposed method is exhibited worse performance than the average results of previous works in computation time.

Section: Introductionmentioning

confidence: 99%

A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm

Zanbouri

Navimipour

2019

“…Conducted studies in web service parameter computation field, different algorithms are presented. A quality‐aware web service composition is a multiobjective optimization problem, because multiple properties of a service quality should be concurrently optimized . Majority of web service optimization methods lack necessary flexibility.…”

Section: Introductionmentioning

confidence: 99%

“…Especially, many methods have asked from consumers to specify their desired service quality preferences and even convert them into numerical measurements. Generally, it is almost impossible for consumers to determine suitable service quality limits because they might not understand web service markets well enough . In addition, most of the approaches are working in black box method that consumers of which present their service quality limits, and an optimization solution is automatically selected by the system .…”

Section: Introductionmentioning

confidence: 99%

A pessimistic method in web service composition by view on execution states

Alizadeh

2019

Summary The application of web services in implementing enterprise workflows has recently been prevalent. In this regard, one of the important challenges is to create web service compositions that satisfy quality properties. One of the properties is rollback cost that it must be paid for resolving effects of a web service that its execution is completed. In the previous methods, a web service was evaluated based on whether failure or successful execution state and other two states (in the middle of execution and waiting for execution) were neglected. By considering these two states, it is possible to prevent imposition in a web service rollback cost or reduce it if needed to finally provide the possibility to decrease a web service average rollback cost. Two mentioned states are related to execution time, and it is clear that the proposed solution should be related to execution time. According to implemented tests, the method not only reduces rollback cost but also causes the execution time to reduce. The reduction in execution time parameter is due to necessary waiting for a web service completion before providing the ability to rollback it in conventional methods, but in the method, it is possible to rollback web service before execution completion and waiting for completion is not mandatory.

“…The on‐demand use of distributed resources in the cloud systems requires the Quality‐of‐Services (QoS)‐aware service composition . In other words, QoS‐aware service composition is selecting an appropriate service from a collection of distributed services to meet the users' QoS constraints . Basically, cloud service composition is a combinatorial optimization issue with powerful NP‐hard complexity .…”

Section: Introductionmentioning

confidence: 99%

Nature inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments

Asghari

Navimipour

2018

Summary Many sorts of services in the cloud environments must be composited based on the user's requests to meet the requirements. Thus, the distributed services are joined to the cloud services through service composition. Also, it is known as NP‐hard problems and many researchers significantly are focused on this problem in recent years. Therefore, many different nature‐inspired meta‐heuristic techniques are proposed for solving this problem. The nature‐inspired meta‐heuristic techniques have an important role in solving the service composition problem in the cloud environments, but there is not a wide‐ranging and detailed paper about reviewing and studying the important mechanisms in this field. Therefore, this study presents a comprehensive analysis of the nature‐inspired meta‐heuristic techniques for the service composition issue in the cloud computing. The review also contains a classification of the important techniques. These classifications include Ant Colony Optimization, Bee Colony Optimization, Genetic Algorithm, Particle Swarm Optimization, Cuckoo Optimization Algorithm, Bat Algorithm, greedy algorithm, and hybrid algorithm. An important aim of this paper is to highlight the emphasis on the optimization algorithms, and the benefits to tackle the challenges are encountered in the cloud service composition. Also, this paper presents the advantages and disadvantages of the nature‐inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments. Moreover, this paper aims to provide more efficient service composition algorithms in the future. Finally, the obtained results have shown that the discussed algorithms have an important effect in solving the cloud service composition problem, and this effect has been increased in recent years.