Generalized aggregate Quality of Service computation for composite services

Yang, Yong; Dumas, Marlon; García‐Bañuelos, Luciano; Polyvyanyy, Artem; Zhang, Liang

doi:10.1016/j.jss.2012.03.005

Cited by 25 publications

(5 citation statements)

References 30 publications

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“…The aggregation functions for QoS attributes response time, reputation, reliability and availability under the structures of sequence and parallel are shown in Table 1. More aggregation functions can be found in (Canfora et al, 2005;Yang et al, 2012). (1).…”

Section: Qos Modelmentioning

confidence: 99%

Broker-based SLA-aware composite service provisioning

Wu¹,

Zhu²,

Jian³

et al. 2014

Journal of Systems and Software

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Section: Qos Modelmentioning

confidence: 99%

Broker-based SLA-aware composite service provisioning

Wu¹,

Zhu²,

Jian³

et al. 2014

Journal of Systems and Software

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“…A direction for future work is to further investigate the factors affecting the performance of the proposed approaches in order to better understand their strength and weaknesses. Furthermore, we plan to extend the proposed approaches so that they would be able to deal with more complex models with overlapping loops, using process model structuring techniques such as the one proposed in a previous study …”

Section: Discussionmentioning

confidence: 99%

“…In case of structured process models, we can relate each fragment to one of the four described types and use the aforementioned equations to estimate the required performance measure. However, in case of an unstructured process model or if a model contains other modeling constructs besides AND and XOR gateways, the method for calculating performance measures becomes more complicated …”

Section: Introductionmentioning

confidence: 99%

Predicting process performance: A white‐box approach based on process models

Verenich

Dumas

Rosa

et al. 2019

J Software Evolu Process

Self Cite

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Predictive business process monitoring methods exploit historical process execution logs to provide predictions about running instances of a process. These predictions enable process workers and managers to preempt performance issues or compliance violations. A number of approaches have been proposed to predict quantitative process performance indicators for running instances of a process, including remaining cycle time, cost, or probability of deadline violation. However, these approaches adopt a black-box approach, insofar as they predict a single scalar value without decomposing this prediction into more elementary components. In this paper, we propose a white-box approach to predict performance indicators of running process instances. The key idea is to first predict the performance indicator at the level of activities and then to aggregate these predictions at the level of a process instance by means of flow analysis techniques. The paper develops this idea in the context of predicting the remaining cycle time of ongoing process instances. The proposed approach has been evaluated on real-life event logs and compared against several baselines. KEYWORDS explainable artificial intelligence, flow analysis, predictive process monitoring, process mining, transparent models INTRODUCTIONPredictive business process monitoring techniques seek to predict the future state or properties of ongoing executions of a process based on models extracted from historical event logs. A wide range of predictive business process monitoring techniques have been proposed to predict, for example, compliance violations, 1,2 the next activity or the remaining sequence of activities of a process instance, 3,4 or quantitative process performance indicators, such the remaining cycle time of a process instance. 5-7 These predictions can be used to alert process workers to problematic process instances or to support resource allocation decisions, eg, to allocate additional resources to instances that are at risk of a deadline violation.This article addresses the problem of predicting quantitative process performance indicators, with a specific focus on predicting the remaining cycle time of ongoing process instances. Existing approaches to this problem adopt a "black-box" approach by building stochastic models or regression models which, given a process instance, predict the remaining execution time as a single scalar value, without seeking to explain this prediction in terms of more elementary components. Yet, quantitative performance indicators such as cost or time are aggregations of corresponding performance indicators of the activities composing the process. For example, the cycle time of a process instance with sequentially performed activities consists of the sum of the cycle time of the activities performed in that process instance. In this respect, existing known as flow analysis. 8 The idea of flow analysis is to estimate a quantitative performance indicator at the level of a process by aggregating the estimated values of this perfo...

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“…Rationally, the QoS values can be specified directly by service providers in Service Level Agreements (SLAs). But in practical applications, there are some factors: (1) Time-varing: the varying network environment, such as the Internet [13], and the different network delays of dynamic service workloads, which generate a lot of QoS attribute values the fluctuate greatly over time. (2) Location-specific: users from different locations usually observe different QoS values on the same service.…”

Section: Qos Attributesmentioning

confidence: 99%

An expanded Matrix Factorization model for real-time Web service QoS prediction

2021

KSII TIIS

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Real-time prediction of Web service of quality (QoS) provides more convenience for web services in cloud environment, but real-time QoS prediction faces severe challenges, especially under the cold-start situation. Existing literatures of real-time QoS predicting ignore that the QoS of a user/service is related to the QoS of other users/services. For example, users/services belonging to the same group of category will have similar QoS values. All of the methods ignore the group relationship because of the complexity of the model. Based on this, we propose a real-time Matrix Factorization based Clustering model (MFC), which uses category information as a new regularization term of the loss function. Specifically, in order to meet the real-time characteristic of the real-time prediction model, and to minimize the complexity of the model, we first map the QoS values of a large number of users/services to a lower-dimensional space by the PCA method, and then use the K-means algorithm calculates user/service category information, and use the average result to obtain a stable final clustering result. Extensive experiments on real-word datasets demonstrate that MFC outperforms other state-of-the-art prediction algorithms.

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Generalized aggregate Quality of Service computation for composite services

Cited by 25 publications

References 30 publications

Broker-based SLA-aware composite service provisioning

Broker-based SLA-aware composite service provisioning

Predicting process performance: A white‐box approach based on process models

An expanded Matrix Factorization model for real-time Web service QoS prediction

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