An Improved Fruit Fly Optimization Algorithm for QoS Aware Cloud Service Composition

Savarala, Bharath; Chella, Pradeep

doi:10.22266/ijies2017.1031.12

Cited by 6 publications

(7 citation statements)

References 20 publications

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“…As opposed to that, economic frameworks are described as "theoretical constructs that characterize economic processes by a collection of variables and a set of logical and quantitative linkages between them". The composition of cloud services challenge is seen as an optimization issue with multiple objectives to boost reaction times, cut costs, and increase throughput [12].…”

Section: Methodsmentioning

confidence: 99%

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Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

Mohammed

Soliman²,

Omara³

2023

IJECE

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<span>The expanding propensity of organization users to utilize cloud services urges to deliver services in a service pool with a variety of functional and non-functional attributes from online service providers. brokers of cloud services must intense rivalry competing with one another to provide quality of service (QoS) enhancements. Such rivalry prompts a troublesome and muddled providing composite services on the cloud using a simple service selection and composition approach. Therefore, cloud composition is considered a non-deterministic polynomial (NP-hard) and economically motivated problem. Hence, developing a reliable economic model for composition is of tremendous interest and to have importance for the cloud consumer. This paper provides “A location-aware deep learning framework for improving the QoS-based service composition for cloud consumers”. The proposed framework is firstly reducing the dimensions of data. Secondly, it applies a combination of the deep learning long short-term memory network and particle swarm optimization algorithm additionally to considering the location parameter to correctly forecast the QoS provisioned values. Finally, it composes the ideal services need to reduce the customer cost function. The suggested framework's performance has been demonstrated using a real dataset, proving that it superior the current models in terms of prediction and composition accuracy.</span>

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Section: Methodsmentioning

confidence: 99%

“…There are more research concerns about solving the problem of selecting the most appropriate services for the QoS-based cloud service composition [12], [13]. Notwithstanding, a large portion of this research focuses on static QoS values observed during composition time.…”

Section: Introductionmentioning

confidence: 99%

Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

Mohammed

Soliman²,

Omara³

2023

IJECE

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“…Podili et al [177] developed a hybrid BAT with a differential optimization technique. In addition, a series of work suggested the application of traditional metaheuristic, including PSO in a Mutant Beetle Swarm [178], the discrete immune algorithm in a fruit fly optimization [179], and gravi-tational attraction search embodiment in traditional imperialist competitive algorithm [180] in order to exploit advantages of both methods in a unified hybrid solution. Furthermore, Chifu et al [181] proposed a honey-bees mating optimization algorithm with a fusion of components from the genetics algorithm, tabu search, and reinforcement learning.…”

Section: Classification Of Hybrid Metaheuristicmentioning

confidence: 99%

Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

et al. 2022

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With the advent of Service-Oriented Architecture (SOA), services can be registered, invoked, and combined by their identical Quality of Services (QoS) attributes to create a new value-added application that fulfils user requirements. Efficient QoS-aware service composition has been a challenging task in cloud computing. This challenge becomes more formidable in emerging resource-constrained computing paradigms such as the Internet of Things and Fog. Service composition has regarded as a multi-objective combinatorial optimization problem that falls in the category of NP-hard. Historically, the proliferation of services added to problem complexity and navigated solutions from exact (none-heuristics) approaches to near-optimal heuristics and metaheuristics. Although metaheuristics have fulfilled some expectations, the quest for finding a high-quality, near-optimal solution has led researchers to devise hybrid methods. As a result, research on service composition shifts towards the hybridization of metaheuristics. Hybrid metaheuristics have been promising efforts to transcend the boundaries of metaheuristics by leveraging the strength of complementary methods to overcome base algorithm shortcomings. Despite the significance and frontier position of hybrid metaheuristics, to the best of our knowledge, there is no systematic research and survey in this field with a particular focus on strategies to hybridize traditional metaheuristics. This study's core contribution is to infer a framework for hybridization strategies by conducting a mapping study that analyses 71 papers between 2008 and 2020. Moreover, it provides a panoramic view of hybrid methods and their experiment setting in respect to the problem domain as the main outcome of this mapping study. Finally, research trends, directions and challenges are discussed to benefit future endeavours.

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“…In this paper, the focus is on QoS-aware WSC approaches based on GA only. Hence other optimization approaches such as fruit fly optimization [13] are not taken into consideration in this section. Also approaches that cover WSC in geodistributed cloud environment [14] are not taken into consideration in this section too.…”

Section: Related Workmentioning

confidence: 99%

PGAQK: An Adaptive QoS-aware Web Service Composition Approach

Elsayed¹,

Nasr²,

Ghazali³

et al. 2018

IJIES

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Web Service Composition (WSC) has attracted considerable attention and research to support Service Oriented Architecture (SOA). WSC constructs complex applications by combing atomic Web services together to achieve users' requirements. The selection of the best Web service that fulfils the Functional Requirements (FRs) and optimizes the Quality of Service (QoS) requirements, such as response time, cost, reliability, etc., is a critical part of WSC, especially in a dynamic environment. Since requirements frequently change, this demands that WSC must be adapted to provide the most suitable composite services and fulfil the users' requirements that emerge. This paper presents a new hybrid approach for dynamic optimization of WSC using Parallel Genetic Algorithm (PGA) based on Q-learning, which we integrate with K-means clustering. We call it PGAQK. Q-learning is utilized to generate an initial population to enhance the effectiveness of PGA. K-means clustering is used to cluster Web services based on a fitness function. It is applied in the mutation operator, and used for Web service substitution to prune the Web services in the search space to find the best Web services for the environment changes that might occur at runtime. To the best of our knowledge such a hybrid approach has not been used for WSC before. We implemented PGAQK over the .NET Framework using C# programming language. A series of comparable experiments were carried out showed that PGAQK outperforms traditional PGA and Q-learning approaches in terms of fitness values. PGAQK allows WSC to modify itself dynamically and achieve a better fitness value to fit the changing environment, where the characteristics of composite web services continue to change.

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An Improved Fruit Fly Optimization Algorithm for QoS Aware Cloud Service Composition

Cited by 6 publications

References 20 publications

Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

PGAQK: An Adaptive QoS-aware Web Service Composition Approach

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