An automated approach to forecasting QoS attributes based on linear and non-linear time series modeling

Amin, Ayman A.; Grunske, Lars; Colman, Alan

doi:10.1145/2351676.2351695

Cited by 55 publications

(28 citation statements)

References 27 publications

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“…Second, we plan to increase the order of the filter to further improve its ability of trading off reaction to changes versus robustness to outliers. Finally, we plan to investigate the combination of LAF with forecasting techniques [46,48] for proactive problem detection. (g) Boxplots of the relative error over 1000 random instances of the six change patterns and combinations thereof.…”

Section: Discussionmentioning

confidence: 99%

“…A threat to external validity is the use of predefined input data patterns for the comparison of the approaches and the ability to generalize these results to common traces of realistic software systems. We have selected these inputs inline with common theory, common practice in control theory to stress the response of dynamic systems (Step, Ramp), and of filters in particular (Noisy, Periodic, Outlier), and the related approaches [14,15,37] and could observe similar patterns also in QoS data sets of web services [46] and web systems (cp. next Section VII).…”

Section: Experimental Evaluationmentioning

confidence: 99%

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Lightweight Adaptive Filtering for Efficient Learning and Updating of Probabilistic Models

Filieri

Grunske

Leva

2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

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Abstract-Adaptive software systems are designed to cope with unpredictable and evolving usage behaviors and environmental conditions. For these systems reasoning mechanisms are needed to drive evolution, which are usually based on models capturing relevant aspects of the running software. The continuous update of these models in evolving environments requires efficient learning procedures, having low overhead and being robust to changes. Most of the available approaches achieve one of these goals at the price of the other. In this paper we propose a lightweight adaptive filter to accurately learn time-varying transition probabilities of discrete time Markov models, which provides robustness to noise and fast adaptation to changes with a very low overhead. A formal stability, unbiasedness and consistency assessment of the learning approach is provided, as well as an experimental comparison with state-of-the-art alternatives.

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Section: Discussionmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Lightweight Adaptive Filtering for Efficient Learning and Updating of Probabilistic Models

Filieri

Grunske

Leva

2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

Self Cite

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“…The analysis and investigation of collected real-world QoS data of WSs presented in [46] and [47] demonstrate that, even when calling the same WS, the actual values of certain QoS attributes (e.g., response time and availability) are not identical for different service consumers because of their disparate environments and conditions. Similarly, the real-world QoS data collected and used in [107], [108], [109], and [110] demonstrate that, even for the same WS invoked by the same service consumer, the actual QoS value will vary over different calling (service invocation) times. For these two factors, dedicated prediction approaches are already being developed for forecasting real QoS values by considering different factors, such as [111], [112], [113], and [114] for the first factor and [107], [108], [109], and [110] for the second.…”

Section: Fourth Issue: Qos Analysis and Predictionmentioning

confidence: 99%

An Overview and Classification of Service Description Approaches in Automated Service Composition Research

Fanjiang

Syu

et al. 2017

IEEE Trans. Serv. Comput.

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In recent years, automated service composition has been a fervid research area in service computing. Within this area, service description plays a crucial role in terms of the development of a diverse number of automation schemes. In this paper, we provide an investigation and classification of the service description approaches that have been used in a diverse collection of automated service composition studies. To position the service description approaches used in automated service composition throughout the service description world and to clearly classify them, we propose five two-value dimensions. Using the proposed dimensions, we first perform a categorization and provide a simple introduction to current representative industrial service description standards. Subsequently, because we discovered that most of the studied automated composition approaches follow a tuple-based service description paradigm, an exhaustive classification and discussion of this paradigm is made, with the automated composition approaches adopting this paradigm as an example. Finally, we discuss issues that are currently relevant to the service description field and possible solutions. With this study, the reader can obtain a complete understanding of service description approaches used in automated service composition research, including their common formulation and assumptions.

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“…Step 1: White Noise Checking: The time series forecasting method is constructed based on the assumptions of the time series, which are serial dependency, normality, stationarity, and invertibility [11]. In order to ensure the time series data can be effectively characterized by time series models, it must satisfy the serial dependency over the observed time period.…”

Section: Model Of Service Ranking Predictionmentioning

confidence: 99%

Time-Aware Service Ranking Prediction in the Internet of Things Environment

Huang

Cheng

et al. 2017

Sensors

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With the rapid development of the Internet of things (IoT), building IoT systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedures of building IoT systems, QoS-aware service selection is an important concern, which requires the ranking of a set of functionally similar services according to their QoS values. In reality, however, it is quite expensive and even impractical to evaluate all geographically-dispersed IoT services at a single client to obtain such a ranking. Nevertheless, distributed measurement and ranking aggregation have to deal with the high dynamics of QoS values and the inconsistency of partial rankings. To address these challenges, we propose a time-aware service ranking prediction approach named TSRPred for obtaining the global ranking from the collection of partial rankings. Specifically, a pairwise comparison model is constructed to describe the relationships between different services, where the partial rankings are obtained by time series forecasting on QoS values. The comparisons of IoT services are formulated by random walks, and thus, the global ranking can be obtained by sorting the steady-state probabilities of the underlying Markov chain. Finally, the efficacy of TSRPred is validated by simulation experiments based on large-scale real-world datasets.

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An automated approach to forecasting QoS attributes based on linear and non-linear time series modeling

Cited by 55 publications

References 27 publications

Lightweight Adaptive Filtering for Efficient Learning and Updating of Probabilistic Models

Lightweight Adaptive Filtering for Efficient Learning and Updating of Probabilistic Models

An Overview and Classification of Service Description Approaches in Automated Service Composition Research

Time-Aware Service Ranking Prediction in the Internet of Things Environment

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