Service workload patterns for QoS-driven cloud resource management

Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and availability of their lower-level platform infrastructure. While availability management is more mature, performance management is less reliable. In order to support a continuous approach that supports the initial static infrastructure configuration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We propose a prediction technique that combines a workload pattern mining approach with a traditional collaborative filtering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common infrastructure workloads from monitoring logs and act as a part of a first-stage high-performant configuration mechanism before more complex traditional methods are considered. This enhances current reactive rule-based scalability approaches and basic prediction techniques by a hybrid prediction solution. Uncertainty and noise are additional challenges that emerge in multi-layered, often federated cloud architectures. We specifically add log smoothing combined with a fuzzy logic approach to make the prediction solution more robust in the context of these challenges

Workload patterns for quality-driven dynamic cloud service configuration and auto-scaling

Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and availability of their lower-level platform infrastructure. While availability management is more mature, performance management is less reliable. In order to support an iterative approach that supports the initial static infrastructure configuration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We propose a prediction-based technique that combines a pattern matching approach with a traditional collaborative filtering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common infrastructure workloads from monitoring logs and act as a part of a first-stage high-performant configuration mechanism before more complex traditional methods are considered. This enhances current reactive rule-based scalability approaches and basic prediction techniques based on for example exponential smoothing

Gaussian-based Probabilistic Deep Supervision Network for Noise-Resistant QoS Prediction

Quality of Service (QoS) prediction is an essential task in recommendation systems, where accurately predicting unknown QoS values can improve user satisfaction. However, existing QoS prediction techniques may perform poorly in the presence of noise data, such as fake location information or virtual gateways. In this paper, we propose the Probabilistic Deep Supervision Network (PDS-Net), a novel framework for QoS prediction that addresses this issue. PDS-Net utilizes a Gaussian-based probabilistic space to supervise intermediate layers and learns probability spaces for both known features and true labels. Moreover, PDS-Net employs a condition-based multitasking loss function to identify objects with noise data and applies supervision directly to deep features sampled from the probability space by optimizing the Kullback-Leibler distance between the probability space of these objects and the real-label probability space. Thus, PDS-Net effectively reduces errors resulting from the propagation of corrupted data, leading to more accurate QoS predictions. Experimental evaluations on two real-world QoS datasets demonstrate that the proposed PDS-Net outperforms state-of-the-art baselines, validating the effectiveness of our approach

Towards an Effective QoS Prediction of Web Services using Context-Aware Dynamic Bayesian Network Model

The functionally equivalent web services (WSs) with different quality of service (QoS) leads to WS discovery models to identify the optimal WS. Due to the unpredictable network connections and user environment, the predicted values of the QoS are likely to fluctuate. The proposed Context-Aware Bayesian Network (CABN) system overcomes these limitations by incorporating the contextual factors in user, server, and environmental perspective. In this paper, three components are introduced for personalized QoS prediction. First, the CABN incorporates the pre-clustering model and reduces the searching space for QoS prediction. Second, the CABN confronts with the multi-constraint problem while considering the multi-dimensional QoS parameters of similar QoS data in WS discovery. Third, the CABN sends the normalized QoS value of records in similar as well as neighbor clusters as inputs to the Dynamic Bayesian Network and improves the prediction accuracy. The experimental results prove that the proposed CABN achieves better WS-Discovery than the existing work within a reasonable time

Assessing cloud QoS predictions using OWA in neural network methods

Quality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods—Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy

Value- and debt-aware selection and composition in cloud-based service-oriented architectures using real options

This thesis presents a novel model for service selection and composition in Cloud-based Service-Oriented Architectures (CB-SOA), which is called CloudMTD, using real options, Dependency Structure Matrix (DSM) and propagation-cost metrics. CB-SOA architectures are composed of web services, which are leased or bought off the cloud marketplace. CB-SOA can improve its utility and add value to its composition by substituting its constituent services. The substitution decisions may introduce technical debt, which needs to be managed. The thesis defines the concept of technical debt for CB-SOA and reports on the available technical debt definitions and approaches in the literature. The formulation of service substitution problem and its technical debt valuation is based on options, which exploits Binomial Options Analysis. This thesis looks at different option types under uncertainty. This thesis is concerned with some scenarios that may lead to technical debt, which are related to web service selection and composition that has been driven by either a technical or a business objective. In each scenario, we are interested in three decisions (1) keep, (2) substitute or (3) abandon the current service. Each scenario takes into consideration either one or more QoS attribute dimension (e.g. Availability). We address these scenarios from an option-based perspective. Each scenario is linked to a suitable option type. A specific option type depends on the nature of the application, problem to be investigated, and the decision to be taken. In addition, we use Dependency Structure Matrix (DSM) in order to represent dependencies among web services in CB-SOA. We introduce time and complexity sensitive propagation-cost metrics to DSM to solve the problem. In addition, CloudMTD model informs the time-value of the decisions under uncertainty based on behavioral and structural aspects of CB-SOA

Quality assessment of service providers in a conformance-centric Service Oriented Architecture

In a Service Oriented Architecture (SOA), the goal of consumers is to discover and use services which lead to them experiencing the highest quality, such that their expectations and needs are satisfied. In supporting this discovery, quality assessment tools are required to establish the degree to which these expectations will be met by specific services. Traditional approaches to quality assessment in SOA assume that providers and consumers of services will adopt a performance-centric view of quality, which assumes that consumers will be most satisfied when they receive the highest absolute performance. However, adopting this approach does not consider the subjective nature of quality and will not necessarily lead to consumers receiving services that meet their individual needs. By using existing approaches to quality assessment that assume a consumer's primary goal as being optimisation of performance, consumers in SOA are currently unable to effectively identify and engage with providers who deliver services that will best meet their needs. Developing approaches to assessment that adopt a more conformance-centric view of quality (where it is assumed that consumers are most satisfied when a service meets, but not necessarily exceeds, their individual expectations) is a challenge that must be addressed if consumers are to effectively adopt SOA as a means of accessing services. In addressing the above challenge, this thesis develops a conformance-centric model of an SOA in which conformance is taken to be the primary goal of consumers. This model is holistic, in that it considers consumers, providers and assessment services and their relationship; and novel in that it proposes a set of rational provider behaviours that would be adopted in using a conformance-centric view of quality. Adopting such conformance-centric behaviour leads to observable and predictable patterns in the performance of the services offered by providers, due to the relationship that exists between the level of service delivered by the service and the expectation of the consumer. In order to support consumers in the discovery of high quality services, quality assessment tools must be able to effectively assess past performance information about services, and use this as a prediction of future performance. In supporting consumers within a conformance-centric SOA, this thesis proposes and evaluates a new set of approaches to quality assessment which make use of the patterns in provider behaviour described above. The approaches developed are non-trivial – using a selection of adapted pattern classification and other statistical techniques to infer the behaviour of individual services at run-time and calculating a numerical measure of confidence for each result that can be used by consumers to combine assessment information with other evidence. The quality assessment approaches are evaluated within a software implementation of a conformance-centric SOA, whereby they are shown to lead to consumers experiencing higher quality than with existing performance-centric approaches. By introducing conformance-centric principles into existing real-world SOA, consumers will be able to evaluate and engage with providers that offer services that have been differentiated based on consumer expectation. The benefits of such capability over the current state-of-the-art in SOA are twofold. Firstly, individual consumers will receive higher quality services, and therefore will increase the likelihood of their needs being effectively satisfied. Secondly, the availability of assessment tools which acknowledge the conformance-centric nature of consumers will encourage providers to offer a range of services for consumers with varying expectation, rather than simply offering a single service that aims to delivery maximum performance. This recognition will allow providers to use their resources more efficiently, leading to reduced costs and increased profitability. Such benefits can only be realised by adopting a conformance-centric view of quality across the SOA and by providing assessment services that operate effectively in such environments. This thesis proposes, develops and evaluates models and approaches that enable the achievement of this goal