Business Process Adaptability Metrics for QoS-Based Service Compositions

Mirandola, Raffaela; Pérez-Palacín, Diego; Scandurra, Patrizia; Brignoli, Michele; Zonca, Andrea

doi:10.1007/978-3-319-24072-5_8

Cited by 2 publications

(5 citation statements)

References 11 publications

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“…Yan et al [14] proposed a method to combine the system search algorithm with the planning algorithm. Mirandola et al [15] proposed a set of software metrics that quantify the adaptability of serviceoriented applications.…”

Section: Classic Algorithms Many Classic Algorithms Have Beenmentioning

confidence: 99%

PPDRL: A Pretraining-and-Policy-Based Deep Reinforcement Learning Approach for QoS-Aware Service Composition

Yi¹,

Jin

Wang³

et al. 2022

Security and Communication Networks

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Service composition is a mainstream paradigm for rapidly constructing large-scale distributed applications. QoS-aware service composition, i.e., selection of the optimal execution plan that maximizes the composition’s end-to-end QoS properties, is an active area of research and development endeavors in service composition. In this paper, we propose PPDRL, a pretraining-and-policy-based deep reinforcement learning approach, to solve the QoS-aware service composition problem. Its significant feature is to incorporate a maximum likelihood estimate and a policy scoring mechanism into a deep reinforcement learning framework. As a result, our approach can balance the exploitation and exploration efforts adaptively and can search for the solution space in a robust and efficient manner. We have executed our approach to solve 6 randomly generated QoS-aware service composition problems with different sizes and structures based on QWS data set including 2,507 real Web services classified into 233 categories. The results indicate that our approach can find near-optimal solutions within moderate numbers of iteration and has performance superiority in comparison with five state-of-the-art algorithms.

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Section: Classic Algorithms Many Classic Algorithms Have Beenmentioning

confidence: 99%

PPDRL: A Pretraining-and-Policy-Based Deep Reinforcement Learning Approach for QoS-Aware Service Composition

Yi¹,

Jin

Wang³

et al. 2022

Security and Communication Networks

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“…On measuring adaptability of a process, the authors in [11] extend the metrics in [14] for BPEL processes. They compute the overall adaptability of a process by aggregating the derived adaptability indexes of its elements (i.e.…”

Section: Related Workmentioning

confidence: 99%

“…The aggregation should consider the behaviour of each BPEL structured activity construct that integrates the activities [11], [5], [6]. Thus, we define variability degree (VD) computations for the following structured activities and consider each formulation in the aggregation towards finding the VD of a process.…”

Section: Vd = Vv / R Where Vv Is the Variability Valuementioning

confidence: 99%

“…To measure process adaptability, we tag each join point activity with an adaptability value [16], [14], [11], [8]. This value depends on the number of specified variability (i.e.…”

Section: Measuring Adaptabilitymentioning

confidence: 99%

“…With our aim to evaluate the adaptability of aspect-oriented BPEL processes, we examine the implementation of AO4BPEL to formulate our approach. There are approaches introduced to measure software and process adaptability, such as [14] and [11], which can compute adaptability values based on the predefined composition logic and the number of comprising components. These approaches quantify adaptability of static process structures.…”

Section: Introductionmentioning

confidence: 99%

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Towards measuring the adaptability of an AO4BPEL process

Botangen

Sheng

2017

Proceedings of the Australasian Computer Science Week Multiconference

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Adaptability is a significant property which enables software systems to continuously provide the required functionality and achieve optimal performance. The recognised importance of adaptability makes its evaluation an essential task. However, the various adaptability dimensions and implementation mechanisms make adaptive strategies difficult to evaluate. In service oriented computing, several frameworks that extend the WS-BPEL, the de facto standard in composing distributed business applications, focus on enabling the adaptability of processes. We aim to evaluate the adaptability of processes specified from the extended-BPEL frameworks. In this paper, we propose metrics to measure the adaptability of an AO4BPEL process. The metrics is grounded in the perspective that a process is capable of dynamically adapting to changes in business requirements. This opens potential future work on evaluating the adaptability of processes specified from various aspect-oriented WS-BPEL frameworks. CCS Concepts• General and reference~Metrics• Software and its engineering~Software design engineering

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Business Process Adaptability Metrics for QoS-Based Service Compositions

Cited by 2 publications

References 11 publications

PPDRL: A Pretraining-and-Policy-Based Deep Reinforcement Learning Approach for QoS-Aware Service Composition

PPDRL: A Pretraining-and-Policy-Based Deep Reinforcement Learning Approach for QoS-Aware Service Composition

Towards measuring the adaptability of an AO4BPEL process

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