Personalized QoS Prediction of Cloud Services via Learning Neighborhood-Based Model

Wu, Hao; He, Jun; Li, Bo; Pei, Yijian

doi:10.1007/978-3-319-28910-6_10

Cited by 4 publications

(3 citation statements)

References 27 publications

(55 reference statements)

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“…Wu et al [36] put forward a learning neighbourhood-based prediction method. In this approach, the previous profile record is critical for the prediction of a service violation.…”

Section: Releated Studiesmentioning

confidence: 99%

Analysing Cloud QoS Prediction Approaches and Its Control Parameters: Considering Overall Accuracy and Freshness of a Dataset

Hussain

Sohaib

2019

IEEE Access

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service level agreement (SLA) management is one of the key issues in cloud computing. The primary goal of a service provider is to minimize the risk of service violations, as these results in penalties in terms of both money and a decrease in trustworthiness. To avoid SLA violations, the service provider needs to predict the likelihood of violation for each SLO and its measurable characteristics (QoS parameters) and take immediate action to avoid violations occurring. There are several approaches discussed in the literature to predict service violation; however, none of these explores how a change in control parameters and the freshness of data impact prediction accuracy and result in the effective management of an SLA of the cloud service provider. The contribution of this paper is two-fold. First, we analyzed the accuracy of six widely used prediction algorithms-simple exponential smoothing, simple moving average, weighted moving average, Holt-Winter double exponential smoothing, extrapolation, and the autoregressive integrated moving average-by varying their individual control parameters. Each of the approaches is compared to 10 different datasets at different time intervals between 5 min and 4 weeks. Second, we analyzed the prediction accuracy of the simple exponential smoothing method by considering the freshness of a data; i.e., how the accuracy varies in the initial time period of prediction compared to later ones. To achieve this, we divided the cloud QoS dataset into sets of input values that range from 100 to 500 intervals in sets of 1-100, 1-200, 1-300, 1-400, and 1-500. From the analysis, we observed that different prediction methods behave differently based on the control parameter and the nature of the dataset. The analysis helps service providers choose a suitable prediction method with optimal control parameters so that they can obtain accurate prediction results to manage SLA intelligently and avoid violation penalties.

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“…Wu et al [36] put forward a learning neighbourhood-based prediction method. In this approach, the previous profile record is critical for the prediction of a service violation.…”

Section: Releated Studiesmentioning

confidence: 99%

Analysing Cloud QoS Prediction Approaches and Its Control Parameters: Considering Overall Accuracy and Freshness of a Dataset

Hussain

Sohaib

2019

IEEE Access

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“…The effectiveness of the approach is validated using a probabilistic checking model, PRISM, and a Smart Grid case study. Wu et al [18] proposed a learning neighborhood-based prediction method to predict personalized QoS parameters. In their proposed model, machine-learning methods are used to build a neighborhood-based approach which gives optimal results compared to other collaborative filtering methods, such as a memory-based method.…”

Section: Qos Prediction In Cloud Service Managementmentioning

confidence: 99%

Comparing time series with machine learning-based prediction approaches for violation management in cloud SLAs

Hussain

Saberi

et al. 2018

Future Generation Computer Systems

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In cloud computing, service level agreements (SLAs) are legal agreements between a service provider and consumer that contain a list of obligations and commitments which need to be satisfied by both parties during the transaction. From a service provider's perspective, a violation of such a commitment leads to penalties in terms of money and reputation and thus has to be effectively managed. In the literature, this problem has been studied under the domain of cloud service management. One aspect required to manage cloud services after the formation of SLAs is to predict the future Quality of Service (QoS) of cloud parameters to ascertain if they lead to violations. Various approaches in the literature perform this task using different prediction approaches however none of them study the accuracy of each. However, it is important to do this as the results of each prediction approach vary according to the pattern of the input data and selecting an incorrect choice of a prediction algorithm could lead to service violation and penalties. In this paper, we test and report the accuracy of time series and machine learning-based prediction approaches. In each category, we test many different techniques and rank them according to their order of accuracy in predicting future QoS. Our analysis helps the cloud service provider to choose an appropriate prediction approach (whether time series or machine learning based) and further to utilize the best method depending on input data patterns to obtain an accurate prediction result and better manage their SLAs to avoid violation penalties.

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“…They concluded that ML based methods out-performed the TS based methods in predicting the service quality. A neighborhood-based approach is suggested in [15] for quality of service estimation. Comparetively, their proposed method produce optimal results.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Runtime Monitoring of Service Level Agreement Violations in Cloud Computing

Khan¹,

Nazir²,

Hanif³

et al. 2022

Computers, Materials &Amp; Continua

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The cloud service level agreement (SLA) manage the relationship between service providers and consumers in cloud computing. SLA is an integral and critical part of modern era IT vendors and communication contracts. Due to low cost and flexibility more and more consumers delegate their tasks to cloud providers, the SLA emerges as a key aspect between the consumers and providers. Continuous monitoring of Quality of Service (QoS) attributes is required to implement SLAs because of the complex nature of cloud communication. Many other factors, such as user reliability, satisfaction, and penalty on violations are also taken into account. Currently, there is no such policy of cloud SLA monitoring to minimize SLA violations. In this work, we have proposed a cloud SLA monitoring policy by dividing a monitoring session into two parts, for critical and non-critical parameters. The critical and non-critical parameters will be decided on the interest of the consumer during SLA negotiation. This will help to shape a new comprehensive SLA based Proactive Resource Allocation Approach (RPAA) which will monitor SLA at runtime, analyze the SLA parameters and try to find the possibility of SLA violations. We also have implemented an adaptive system for allocating cloud IT resources based on SLA violations and detection. We have defined two main components of SLA-PRAA i.e., (a) Handler and (b) Accounting and Billing Manager. We have also described the function of both components through algorithms. The experimental results validate the performance of our proposed method in comparison with state-of-the-art cloud SLA policies.

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Personalized QoS Prediction of Cloud Services via Learning Neighborhood-Based Model

Cited by 4 publications

References 27 publications

Analysing Cloud QoS Prediction Approaches and Its Control Parameters: Considering Overall Accuracy and Freshness of a Dataset

Analysing Cloud QoS Prediction Approaches and Its Control Parameters: Considering Overall Accuracy and Freshness of a Dataset

Comparing time series with machine learning-based prediction approaches for violation management in cloud SLAs

Adaptive Runtime Monitoring of Service Level Agreement Violations in Cloud Computing

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