Big Data-Driven Service Composition Using Parallel Clustered Particle Swarm Optimization in Mobile Environment

Hossain, M. Shamim; Moniruzzaman, M.; Muhammad, Ghulam; Ghoneim, Ahmed; Alamri, Atif

doi:10.1109/tsc.2016.2598335

Cited by 80 publications

(21 citation statements)

References 26 publications

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“…Many services composition approaches are based on machine learning technique different from RL. For instance, Hossain et al [18], [23] proposed services selection approaches in the context of QoS-aware services composition. In [23], the services selection with global QoS constraints is first formulated as a set-based optimization problem, whereas the k-Means clustering method is then used to find the composite service by maximizing the QoS value and satisfying the global QoS constraints.…”

Section: Related Workmentioning

confidence: 99%

“…In [23], the services selection with global QoS constraints is first formulated as a set-based optimization problem, whereas the k-Means clustering method is then used to find the composite service by maximizing the QoS value and satisfying the global QoS constraints. On the other hand, Hossain et al [18] presented an algorithm, which first uses the k-means clustering to obtain clusters of candidates services, then, for each cluster it obtains the composite service in terms of QoS using a heuristic method.…”

Section: Related Workmentioning

confidence: 99%

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Reinforcement Learning for Interactive QoS-Aware Services Composition

Alizadeh

Osmani

Khanouche

et al. 2021

IEEE Systems Journal

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An important and challenging research problem in web of things is how to select an appropriate composition of concrete services in a dynamic and unpredictable environment. The main goal of this article is to select from all possible compositions the optimal one without knowing a priori the users' quality of service (QoS) preferences. From a theoretical point of view, we give bounds on the problem search space. As the QoS user's preferences are unknown, we propose a vector-valued MDP approach for finding the optimal QoS-aware services composition. The algorithm alternatively solves MDP with dynamic programming and learns the preferences via direct queries to the user. An important feature of the proposed algorithm is that it is able to get the optimal composition and, at the same time, limits the number of interactions with the user. Experiments on a real-world large size dataset with more than 3500 web services show that our algorithm finds the optimal composite services with around 50 interactions with the user. Index Terms-Quality of services (QoS), reinforcement learning (RL), services composition, web of things (WoT). I. INTRODUCTION T HE web of things (WoT) opens the way for the development of new intelligent applications that can be used to provide innovative and valuable services in several domains, such as smart cities, smart homes, ambient assisted living, and connected cars [11], [35]. One of the WoT challenges is to deal with services composition to ensure suitable services flexibility and customization. This is crucial to meet the evolving needs of users, which can be expressed through several parameters of quality of service (QoS) (such as response time, throughput, availability, price, popularity, etc.). Following the web service paradigm [4] services, composition problems are specified as a workflow involving abstract and concrete services.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning for Interactive QoS-Aware Services Composition

Alizadeh

Osmani

Khanouche

et al. 2021

IEEE Systems Journal

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“…The discrete particle swarm optimization algorithm is combined with the Hadoop platform to select the service composition. In [18], parallel -means algorithm and particle swarm algorithm are used to select the service composition on the Hadoop platform. Despite the full use of its computational advantage, Hadoop parallel computing platform features inefficiency in reading data.…”

Section: Related Workmentioning

confidence: 99%

Service Composition Optimization Method Based on Parallel Particle Swarm Algorithm on Spark

Guo¹,

Chen²,

Zhang³

et al. 2017

Security and Communication Networks

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Web service composition is one of the core technologies of realizing service-oriented computing. Web service composition satisfies the requirements of users to form new value-added services by composing existing services. As Cloud Computing develops, the emergence of Web services with different quality yet similar functionality has brought new challenges to service composition optimization problem. How to solve large-scale service composition in the Cloud Computing environment has become an urgent problem. To tackle this issue, this paper proposes a parallel optimization approach based on Spark distributed environment. Firstly, the parallel covering algorithm is used to cluster the Web services. Next, the multiple clustering centers obtained are used as the starting point of the particles to improve the diversity of the initial population. Then, according to the parallel data coding rules of resilient distributed dataset (RDD), the large-scale combination service is generated with the proposed algorithm named Spark Particle Swarm Optimization Algorithm (SPSO). Finally, the usage of particle elite selection strategy removes the inert particles to optimize the performance of the combination of service selection. This paper adopts real data set WS-Dream to prove the validity of the proposed method with a large number of experimental results.

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“…They prune the candidate services based on the QoS comparison between one service and other functionally-equivalent services, or guide the exploration towards the optimum. The second category transforms the optimization problem into several sub-problems [9]- [11]. Although these approaches solve the scalability problem to some extent, the required computational time is usually high.…”

Section: Introductionmentioning

confidence: 99%

Multi-Clusters Adaptive Brain Storm Optimization Algorithm for QoS-Aware Service Composition

Peng

Wang

2020

IEEE Access

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Service composition provides an effective means to fulfill users' personalized requirements and complex business applications. With the number of web services rapidly growing, finding the best combination among services based on quality of service (QoS) poses a critical computational challenge due to the exponential growth of alternative composite services. Some efforts are developed to find the near-optimal service combination within an acceptable time range. They fall into two categories of solutions: exploring partial combinations in the service space and downsizing the optimization problem in scale. Although they solve the scalability problem to some extent, the required computational time is usually high. A promising direction is to integrate these two categories of solutions. However, its practical application suffers from three challenges: no good search scheme, no consideration of the natural organization of sub-problems, and the lack of diverse combinations. In this work, we propose a novel approach, called multi-clusters adaptive brain storm optimization (MCaBSO) algorithm. The proposed method uses brain storm optimization (BSO) as the search scheme to combine the division of search space with the exploration of the reduced search space. MCaBSO uses the twin support vector machine (TWSVM) to effectively divide the search space according to the natural organization of sub-problems. MCaBSO provides an adaptive dual strategy that gives guidance for the generation of diverse combinations. MCaBSO enables the agile exploration of the reduced search space and generates more high-quality combinations. MCaBSO is evaluated on two datasets to show effectiveness and efficiency.

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Big Data-Driven Service Composition Using Parallel Clustered Particle Swarm Optimization in Mobile Environment

Cited by 80 publications

References 26 publications

Reinforcement Learning for Interactive QoS-Aware Services Composition

Reinforcement Learning for Interactive QoS-Aware Services Composition

Service Composition Optimization Method Based on Parallel Particle Swarm Algorithm on Spark

Multi-Clusters Adaptive Brain Storm Optimization Algorithm for QoS-Aware Service Composition

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