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Ticketing system is an example of a Service System (SS) which is responsible for handling huge volumes of tickets generated by large enterprise IT (Information Technology) infrastructure components, and ensuring smooth operation. An issue is captured as summary on the ticket and once a ticket is resolved, the solution is also noted down on the ticket as resolution. Further the system maintains the...
Prediction of Quality of Service (QoS) values plays an important role in service selection, discovery, and recommendation. Previous works show that the QoS values would be influenced by the location information. However, these researches do not consider the fact that some users may provide untrustworthy QoS values even though they are in the same location region. QoS values from these unreliable users...
The REliable CApacity Provisioning and enhanced remediation for distributed cloud applications (RECAP) project aims to advance cloud and edge computing technology, to develop mechanisms for reliable capacity provisioning, and to make application placement, infrastructure management, and capacity provisioning autonomous, predictable and optimized. This paper presents the RECAP vision for an integrated...
In this paper, we propose a Skyline service selection approach based on QoS prediction. We first consider the QoS history records as time series and predict the QoS values by using Autoregressive Integrated Moving Average (ARIMA) model to provide more accurate QoS attributes values. And then we calculate the uncertainty of the prediction result by adopting an improved Coefficient of Variation. In...
Hadoop framework has recently been adapted for use by the video analytics community for intensive, distributed video processing, storage. However, the challenge is to estimate the required amount of resources to be used in such an environment to fulfil the requirements of a user with requirements constraints. Therefore, it is important to understand how to model the performance of a Hadoop based implementation...
Traditionally resource utilization on physical servers in cloud data center is uncertain. On one hand, resources will be wasted if the assignment of tasks are not enough. On the other hand it will cause overload if the assignment of tasks are too much. This is especially obvious when the applications are the same type. To solve this issue and considering CPU intensive application is one of the most...
Failure instances in distributed computing systems (DCSs) have exhibited temporal and spatial correlations, where a single failure instance can trigger a set of failure instances simultaneously or successively within a short time interval. We investigate an effective approach to predict correlated failures of computing elements (CEs) in DCSs. Correlated-failure patterns are modeled using the concept...
Automatic Web-service selection is an important research topic in the domain of service computing. During this process, reliable predictions for quality of service (QoS) based on historical service invocations are vital to users. This work aims at making highly accurate predictions for missing QoS data via building an ensemble of nonnegative latent factor (NLF) models. Its motivations are: 1) the...
Web Services offered by service provider are expected to maintain a specific level of Quality of Service (QoS) as per the Service Level Agreement (SLA) made between the provider and consumer. The response time is considered as one of the important Quality parameter of a web service. Any breach in SLA in terms of service response time, will lead to a huge penalty in terms of cost and reputation for...
The phenomenal growth of Cloud Computing as an IT service across various organizational domains has resulted in the critical challenge of their performance evaluation. One of the key problems which is being faced by the cloud service providers and the cloud customers is the ability of assessing the QoS and QoE performance of cloud services under various service delivery scenarios. This has created...
Predicting the performance of cloud services is intrinsically hard. In this work, we pursue an approach based upon statistical learning, whereby the behaviour of a system is learned from observations. Specifically, our testbed implementation collects device statistics from a server cluster and uses a regression method that accurately predicts, in real-time, client-side service metrics for a video...
Latent-factor-based Quality-of-Service predictors can achieve high prediction accuracy and good scalability. However, most of them are based on first-order models that cannot well deal with their target problem that is inherently non-convex. Since second-order approaches have proven to be effective to such problems, this work proposes to implement a second-order predictor with an aim to achieve the...
Data centers have time-varying traffic and a wide range of demands in performance-power for the workloads. Understanding the trade-offs between performance and power for these varying types of demands given the time-variations, helps administrators to control the total power consumption and also facilitate for enhancing Quality of Service (QoS) levels. In this paper, we provide a methodology for administrators...
Recently, Web service has become an important issue in the research community. Especially, predicting the Quality of Service (QoS) for users has been a hot topic which needs researching and applicating. In the other hand, with the rapid growth of the number of service providers and users, it results a large number of datasets. It significantly effects on the QoS as management and supervision to describe...
Parallel execution and cloud technologies are the keys to speed-up service invocation when processing large-scale data. In SOA, service providers normally employ policies to limit parallel execution of the services based on arbitrary decisions. In order to attain optimal performance improvement, service users need to adapt to parallel execution policies of the services. A composite service is a combination...
Nowadays, more and more service consumers pay great attention to QoS (Quality of Service) when they find and select appropriate Web services. For most of the approaches to QoS-aware Web service recommendation, the list of Web services recommended to target users is generally obtained based on rating-oriented predictions, aiming at predicting the potential ratings that a target user may assign to the...
Quality of Service (QoS) has been widely used for personalized Web service recommendation. Since QoS information usually cannot be predetermined, how to make personalized QoS prediction precisely becomes a challenge of Web service recommendation. Time series forecasting and collaborative filtering are two mainstream technologies for QoS prediction. However, on one hand, existing time series forecasting...
Cloud computing is an attractive platform which offers on-demand resources as services. When many cloud services are available, some may have similar or same functionalities. So cloud service recommendation, which can help users to select the services based on their preferences, become an important technique for cloud services. In this paper, we review the relevant technologies that can perform cloud...
While real-time service assurance is critical for emerging telecom cloud services, understanding and predicting performance metrics for such services is hard. In this paper, we pursue an approach based upon statistical learning whereby the behavior of the target system is learned from observations. We use methods that learn from device statistics and predict metrics for services running on these devices...
With software systems increasingly being employed in more complex and critical contexts, service-oriented system of systems has been paid more and more attention as a novel software system structure, which considers System as a Service. Under the loosely coupled SoS's dynamic and uncertain running environment, self-healing process, as the important safeguard mechanism of system running, pose a great...
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