An approach for selecting best available services through a new method of decomposing QoS constraints

Surianarayanan, Chellammal; Ganapathy, Gopinath; Ramasamy, Manikandan Sethunarayanan

doi:10.1007/s11761-014-0154-x

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…A SOA solution is generally an integrated product set that can be a collection of legacy applications, third-party components, or custom-developed components. Testers are expected to assess not only the individual products and their functionality but also the interaction of components within an overall integrated solution [50,51]. A strategy such as ATLBO can be useful in assessing the component interaction, which is in line with the continuous growth of SOA with its new features and additional components.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites

Zamli

Din

Baharom

et al. 2017

Engineering Applications of Artificial Intelligence

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The teaching learning-based optimization (TLBO) algorithm has shown competitive performance in solving numerous real-world optimization problems. Nevertheless, this algorithm requires better control for exploitation and exploration to prevent premature convergence (i.e., trapped in local optima), as well as enhance solution diversity. Thus, this paper proposes a new TLBO variant based on Mamdani fuzzy inference system, called ATLBO, to permit adaptive selection of its global and local search operations. In order to assess its performances, we adopt ATLBO for the mixed strength t-way test generation problem. Experimental results reveal that ATLBO exhibits competitive performances against the original TLBO and other meta-heuristic counterparts.The teaching learning-based optimization algorithm (TLBO) [6,7] adopts a simplistic approach of disregarding the control parameters (i.e., parameter free). TLBO specifically performs both global and local search sequentially per iteration to balance exploration and exploitation. Given that exploration and exploitation are dynamic in nature depending on the current search space region, any preset division between the two can be counter-productive and may lead to poor quality solutions. This paper addresses these issues through a new TLBO variant, adaptive TLBO (ATLBO) integrated with the Mamdani-type fuzzy inference system [8,9]. ATLBO adaptively selects its local and global search operations. In order to assess its performances, we adopt ATLBO for the mixed strength t-way test generation problem.Our contributions are summarized as follows:The novel ATLBO strategy based on the Mamdani-type fuzzy inference system is presented for exploration (i.e., global search) and exploitation (i.e., local search) selection. ATLBO is the first TLBO-variant strategy that addresses generation for both uniform and mixedstrength t-way test suite.This study is organized as follows. Section 2 presents the theoretical framework that covers the generation problem of t-way test and its mathematical notation. Section 3 describes the related work. Section 4 highlights the original TLBO algorithm and its variants, along with its applications. Section 5 outlines the novel ATLBO. Benchmark experiments are presented in Section 6. Section 7 and 8 discusses the experimental observations and validity threats, respectively. Finally, Section 9 concludes this study and presents the scope for future work. Covering Array (CA) and the Generation Problem of Mixed-Strength t-way TestThe generation problem of t-way test is often associated with CA notation, where t represents the desired interaction strength. A CA (N; t, p, v), which is also expressed as CA (N; t, v p ), is a combinatorial structure constructed as an array of N rows and p columns (i.e., parameters) on v values, such that every N × t sub-array contains all ordered subsets from the v values of size t at least once [10]. When the number of component values varies, this condition can be handled by a mixed CA (MCA) (N; t, p, (v 1 , v 2 , …v i )) or MCA (N;...

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Section: Discussionmentioning

confidence: 99%

Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites

Zamli

Din

Baharom

et al. 2017

Engineering Applications of Artificial Intelligence

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“…Sun and Zhao [18] decomposed global QoS constraints into local QoS constraints through a mixedinteger programming method, thus transforming the service composition problem belonging to the global optimization problem into the local optimal service selection problem and reducing the complexity of the problem. Surianarayanan et al [19] proposed a service composition method of constraint decomposition method to calculate the utility of composite services through the utility of component services and the utility constraints of component services of composite service constraints. e literature [20] decomposed service composition into two stages: constraint decomposition and service selection.…”

Section: Related Workmentioning

confidence: 99%

Trusted QoS Assurance Approach for Composite Service

Cai

Cui

et al. 2021

Security and Communication Networks

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The characteristics of service computing environment, such as service loose coupling, resource heterogeneity, and protocol independence, propose higher demand for the trustworthiness of service computing systems. Trust provisioning for composite services has become a hot research spot worldwide. In this paper, quality of service (QoS) planning techniques are introduced into service composition-oriented QoS provisioning architecture. By QoS planning, the overall QoS requirement of the composite service is decomposed into separate QoS requirement for every constituent atom service, the QoS level of which can subsequently be satisfied through well-designed service entity selection policies. For any single service entity, its QoS level is variable when the deployment environment or the load of service node changes. To mitigate the uncertainty, we put forward QoS preprocessing algorithms to estimate the future QoS levels of service entities with their history execution data. Then, based on the modeling of composite service and QoS planning, we design three algorithms, which include the time preference algorithm, cost/availability (C/A) preference algorithm, and Euclidean distance preference algorithm, to select suitable atomic services meeting the user’s requirements. Finally, by combining genetic algorithm and local-search algorithm, we propose memetic algorithm to meet the QoS requirements of composite service. The effectiveness of the proposed methods by which the QoS requirements can be satisfied up to 90% is verified through experiments.

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“…If the freedom increases, the task can select the higher accuracy service; on the contrary, if the freedom decreases, the task can only select the smaller accuracy service. Taking {5, 10} as the number of tasks to form two workflow structures, for each task p i in the structure corresponding to a service pool S i , its number is a value of the interval [2,4], and the service has attribute pt pk . Based on time T min which the strategy based on minimum time costs, increasing 10%, 20%, 30%, 40% as the deadline respectively, and calculate the time T max and the accuracy that the strategy based on maximum accuracy cost.…”

Section: Size Of the Deadline Cmentioning

confidence: 99%

“…With the increasing number of tasks, it will influence the completion accuracy and the optimization performance. Taking {5, 10, 15, 20} as the number of tasks to form four workflow structures, for each task p i in the structure corresponding to a service pool S i , its number is a value of the interval [2,4], and the service has attribute pt pk . Based on time T min , that is, the strategy based on minimum completion time, increasing 20% as the deadline, through the comparison of the strategy based on minimum time and the serial reduction algorithm, Table 2 shows the influence of the tasks' number on the algorithm performance.…”

Section: Number Of Tasksmentioning

confidence: 99%

“…3 The project process as a whole is divided into several steps, usually since the problems exist in one or several steps, such as pursuing the efficiency but neglecting the quality of service or pursuing the quality of service but delaying the completion time, which make the completion time and the completion quality of the entire project imbalanced. 4,5 Therefore, pursuing efficiency unilaterally will result in low accuracy rate; on the contrary, pursuing service accuracy unilaterally will affect the completion time of complex products. To solve this problem, this article provides a high-quality algorithm that can be applied to ensure the workflow of complex products in time as well as the highest quality, which implements serial reduction optimization of completion path quality.…”

Section: Introductionmentioning

confidence: 99%

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Serial reduction optimization research of complex product workflow’s accuracy under the time constraint

Luo

Wang

You

et al. 2016

Advances in Mechanical Engineering

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Model complex production business contains the attributes of completion time, accuracy, and so on; these factors influence each other; and pursuing the balance of these factors is the core idea of the article. Due to the problems of low accuracy or more completion time which may be caused by the traditional algorithm, the research group proposed the serial reduction algorithm which can achieve higher accuracy under deadline for the business. The algorithm sets the active portion for each task and uses the inverse iterative way to obtain a path that can balance both the completion time and the accuracy. By the case analysis, compared with the traditional unidirectional objective algorithm, serial reduction algorithm can optimize the accuracy under the deadline. Finally, the article analyses the parameters that can affect the algorithm performance; the experiment shows that serial reduction algorithm applied to the workflow is feasible and effective, which has the simple advantage.

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An approach for selecting best available services through a new method of decomposing QoS constraints

Cited by 8 publications

References 23 publications

Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites

Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites

Trusted QoS Assurance Approach for Composite Service

Serial reduction optimization research of complex product workflow’s accuracy under the time constraint

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