QoS-driven metaheuristic service composition schemes: a comprehensive overview

Màsdàrí, Mohàmmàd; Bonab, Mehdi Nozad; Özdemir, Suat

doi:10.1007/s10462-020-09940-4

Cited by 17 publications

(9 citation statements)

References 132 publications

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“…Many swarm-based algorithms are proposed to deal with WSC problems, achieving good performances. In the literature, many review studies introduced extensive reviews of swarm-based and other optimization algorithms applied to WSC, such as [11,12]. In this section, a review of recent web service composition ABC and CS research will be presented, and the research's contribution in improving the metaheuristic searching mechanisms (exploration and exploitation) of the ABC variants will be highlighted.…”

Section: Related Workmentioning

confidence: 99%

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

Dahan¹,

Alwabel²

2023

Intelligent Automation &Amp; Soft Computing

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In recent years, cloud computing has provided a Software As A Service (SaaS) platform where the software can be reused and applied to fulfill complicated user demands according to specific Quality of Services (QoS) constraints. The user requirements are formulated as a workflow consisting of a set of tasks. However, many services may satisfy the functionality of each task; thus, searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem. This work will introduce a hybrid Artificial Bee Colony (ABC) with a Cuckoo Search (CS) algorithm to untangle service composition problem. The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance. However, the ABC suffers from a slow convergence problem. Therefore, the CS is used to overcome the ABC's limitations by allowing the abandoned bees to enhance their search and override the local optimum. The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms (ABC and CS) and three state-of-the-art swarm-based composition algorithms. In addition, extensive parameter study experiments were conducted to set up the proposed algorithm's parameters. The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria (best fitness value, average fitness value, and average execution time) overall datasets in 30 different runs. Furthermore, the proposed algorithm also exhibits better performance than the state-of-the-art algorithms in the three comparison criteria over 30 different runs.

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Section: Related Workmentioning

confidence: 99%

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

Dahan¹,

Alwabel²

2023

Intelligent Automation &Amp; Soft Computing

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“…Metaheuristics are stochastic solutions whose output's optimality is classified into two categories deterministic and nondeterministic [124]. In current literature, an overwhelming majority of the solution has been nondeterministic, where a near-optimal solution has returned due to a stochastic search process [24], [31] by applying metaheuristics that were devised originally for continuous optimization problems. However, the search space for service composition problems is discrete.…”

Section: ) Algorithm Efficiency and Search Spacementioning

confidence: 99%

Hybrid Metaheuristics Using Rough Sets for QoS-Aware Service Composition

et al. 2022

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Quality of Service (QoS)-aware service composition plays an increasingly important role in various computational paradigms and delivery models, predominantly cloud computing. The proliferation of services with expanding quality attributes navigates this problem towards big service compositions, which fall under the umbrella of NP-hard. Within the realm of big services, performing composition also became a computationally expensive and challenging task. Since service composition is an NP-hard problem, numerous research aimed to determine optimal or near-optimal solutions within a reasonable budget. A large body of evidence suggests that metaheuristics could realize this goal to some extent. However, the proliferation of services with expanding quality attributes (search dimensions) may fail the most efficient techniques. In order to deal with the problem of big service composition, one trending approach has been devising hybrid metaheuristics methods by incorporating clustering techniques to minimize search space. This paper proposes a hybrid metaheuristic incorporated with a maximal discernibility heuristic based on rough set theory to perform composition in the subset of search space. Moreover, it introduces a parallel architecture and monitoring mechanism to provide immunity against premature convergence when the search space is minimized. The experiment was conducted for 25 datasets generated incrementally from a real-world QWS dataset, where the proposed hybrid solution effectively improves solution quality and reduces execution time with a statistical significance of 99 % confidence interval across diverse metaheuristics and datasets.

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“…The user needs are represented using a business process where these services deal with these processes and may be combined and interacted with other services to deal with the business process [2]. However, the users' needs and the services created and published by various organizations are overgrowing.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood Search Based Improved Bat Algorithm for Web Service Composition

Dahan¹

2023

Computer Systems Science and Engineering

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Web services are provided as reusable software components in the services-oriented architecture. More complicated composite services can be combined from these components to satisfy the user requirements represented as a workflow with specified Quality of Service (QoS) limitations. The workflow consists of tasks where many services can be considered for each task. Searching for optimal services combination and optimizing the overall QoS limitations is a Non-deterministic Polynomial (NP)-hard problem. This work focuses on the Web Service Composition (WSC) problem and proposes a new service composition algorithm based on the micro-bats behavior while hunting the prey. The proposed algorithm determines the optimal combination of the web services to satisfy the complex user needs. It also addresses the Bat Algorithm (BA) shortcomings, such as the tradeoff among exploration and exploitation searching mechanisms, local optima, and convergence rate. The proposed enhancement includes a developed cooperative and adaptive population initialization mechanism. An elitist mechanism is utilized to address the BA convergence rate. The tradeoff between exploration and exploitation is handled through a neighborhood search mechanism. Several benchmark datasets are selected to evaluate the proposed bat algorithm's performance. The simulation results are estimated using the average fitness value, the standard deviation of the fitness value, and an average of the execution time and compared with four bat-inspired algorithms. It is observed from the simulation results that introduced enhancement obtains significant results.

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QoS-driven metaheuristic service composition schemes: a comprehensive overview

Cited by 17 publications

References 132 publications

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

Hybrid Metaheuristics Using Rough Sets for QoS-Aware Service Composition

Neighborhood Search Based Improved Bat Algorithm for Web Service Composition

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