Soft Constraints for Quality Aspects in Service Oriented Architectures

Bistarelli, Stefano; Santini, Francesco

doi:10.4204/eptcs.2.5

Cited by 6 publications

(12 citation statements)

References 26 publications

(61 reference statements)

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“…al [5] and Bistarelli et al [22]. Instead of using fixed hard bound values for parameters such as response time, the authors proposed a soft contract monitoring approach to model the QoS measurement.…”

Section: Evaluating the Pairwise Sampling Techniquementioning

confidence: 99%

Variability Modeling and QoS Analysis of Web Services Orchestrations

Kattepur

Sen

Baudry

et al. 2010

2010 IEEE International Conference on Web Services

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Abstract-The ever-growing choice in diverse services is making service orchestration variability an essential aspect of a composite web service. Influence of this variation on the Quality of Service (QoS) of a composite service is critical and the focus of our work. In this paper, we present a methodology to first model orchestration variability using a feature diagram (FD). The FD specifies a product line of orchestrations represented as configurations of invoked/rejected atomic services. Second, due to the potentially large set of configurations we employ combinatorial testing techniques to automatically generate configurations covering all valid pairwise interactions between services. Third, we analyze QoS variation for each configuration using probabilistic models of QoS. Using a crisis management system case study we experimentally show that pairwise generation covers all QoS outliers and eliminates analysis of > 75% of all possible configurations. The QoS analysis of the pairwise configurations reveals unsafe/ineffective configurations, helps determine realistic Service Level Agreements (SLAs), and provides valuable feedback to help remodel an orchestration.

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Section: Evaluating the Pairwise Sampling Techniquementioning

confidence: 99%

Variability Modeling and QoS Analysis of Web Services Orchestrations

Kattepur

Sen

Baudry

et al. 2010

2010 IEEE International Conference on Web Services

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“…The use of a soft constraint framework permits to manage SOAs in a declarative fashion by considering together both the requirements/interfaces of each service and their QoS estimation [2]. C-semirings can represent several QoS attributes, while soft constraints represent the specification of each service to integrate: they link these measures to the resources spent in providing it, for instance, "the reliability is equal to 80% plus 5% for each other processor used to execute the service".…”

Section: Managing Qos Measures With Soft Concurrent Constraintmentioning

confidence: 99%

“…However these metrics do not reflect the subjective criteria of individual users or groups. In this article, we consider fairness as a new parameter to be adopted in a QoS measure and we propose to use Soft Constraint to represent the QoS scores as suggested in [2]. When a user requests a service, he evaluates it with a QoS measure; we think that fairness is an important parameter to consider, as it directly affects the QoS value; therefore the service provider aims to guarantee a fair service in order to obtain a high QoS score.…”

Section: Introductionmentioning

confidence: 99%

Fairness as a QoS Measure for Web Services

Bistarelli¹,

Campli²

2009

Electron. Proc. Theor. Comput. Sci.

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Service Oriented Architectures (SOAs) are component-based architectures, characterized by reusability, modularization and composition, usually offered by HTTP (web services) and often equipped with a Quality of Services (QoS) measure. In order to guarantee the fairness property to each client requesting a service, we propose a fair version of the (Soft) Concurrent Constraint language to deal with the negotiation phases of the Service Level Agreement (SLA) protocol

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“…al [19] and Bistarelli et al [1]. Instead of using fixed hard bound values for parameters such as response time, the authors proposed a soft contract monitoring approach to model the QoS measurement.…”

Section: Related Workmentioning

confidence: 99%

“…In particular, we perform pairwise testing of composite services. The methodology consists of three main phases: (1) Modeling variability in a composite service (2) Generation of composite service configurations satisfying pairwise interactions (3) Analyzing these composite service configurations to test composite service QoS. In our approach, we model the variability of a composite service as a feature diagram where each feature represents an atomic service.…”

Section: Introductionmentioning

confidence: 99%

Pairwise testing of dynamic composite services

Kattepur

Sen

Baudry

et al. 2011

Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

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Online services encapsulate enterprises, people, software systems and often operate in poorly understood environments. Using such services in tandem to predictably orchestrate a complex task is one of the principal challenges of serviceoriented computing. A composite service orchestration soliciting multiple atomic services is plagued by a number of sources of variation. For instance, availability of an atomic service and its response time are two important sources of variation. Moreover, the number of possible variations in a composite service increases exponentially with increase in the number of atomic services. Testing such a composite service presents a crucial challenge as its often very expensive to exhaustively examine the variation space. Can we effectively test the dynamic behavior of a composite service using only a subset of these variations? This is the question that intrigues us. In this paper, we first model composite service variability as a feature diagram (FD) that captures all valid configurations of its orchestration. Second, we apply pairwise testing to sample the set of all possible configurations to obtain a concise subset. Finally, we test the composite service for selected pairwise configurations for a variety of QoS metrics such as response time, data quality, and availability. Using two case studies, Car crash crisis management and eHealth management, we demonstrate that pairwise generation effectively samples the full range of QoS variations in a dynamic orchestration. The pairwise sampling technique eliminates over 99% redundancy in configurations, while still calling all atomic services at least once. We rigorously evaluate pairwise testing for the criteria such as: a) ability to sample the extreme QoS metrics of the service b) stable behavior of the extracted configurations c) compact set of configurations that can help evaluate QoS tradeoffs and d) comparison with random sampling.

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Soft Constraints for Quality Aspects in Service Oriented Architectures

Cited by 6 publications

References 26 publications

Variability Modeling and QoS Analysis of Web Services Orchestrations

Variability Modeling and QoS Analysis of Web Services Orchestrations

Fairness as a QoS Measure for Web Services

Pairwise testing of dynamic composite services

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