1,236 research outputs found
An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks
QoS identification for untrustworthy Web services is critical in QoS
management in the service computing since the performance of untrustworthy Web
services may result in QoS downgrade. The key issue is to intelligently learn
the characteristics of trustworthy Web services from different QoS levels, then
to identify the untrustworthy ones according to the characteristics of QoS
metrics. As one of the intelligent identification approaches, deep neural
network has emerged as a powerful technique in recent years. In this paper, we
propose a novel two-phase neural network model to identify the untrustworthy
Web services. In the first phase, Web services are collected from the published
QoS dataset. Then, we design a feedforward neural network model to build the
classifier for Web services with different QoS levels. In the second phase, we
employ a probabilistic neural network (PNN) model to identify the untrustworthy
Web services from each classification. The experimental results show the
proposed approach has 90.5% identification ratio far higher than other
competing approaches.Comment: 8 pages, 5 figure
Auto-Scaling Network Resources using Machine Learning to Improve QoS and Reduce Cost
Virtualization of network functions (as virtual routers, virtual firewalls,
etc.) enables network owners to efficiently respond to the increasing
dynamicity of network services. Virtual Network Functions (VNFs) are easy to
deploy, update, monitor, and manage. The number of VNF instances, similar to
generic computing resources in cloud, can be easily scaled based on load.
Hence, auto-scaling (of resources without human intervention) has been
receiving attention. Prior studies on auto-scaling use measured network traffic
load to dynamically react to traffic changes. In this study, we propose a
proactive Machine Learning (ML) based approach to perform auto-scaling of VNFs
in response to dynamic traffic changes. Our proposed ML classifier learns from
past VNF scaling decisions and seasonal/spatial behavior of network traffic
load to generate scaling decisions ahead of time. Compared to existing
approaches for ML-based auto-scaling, our study explores how the properties
(e.g., start-up time) of underlying virtualization technology impacts Quality
of Service (QoS) and cost savings. We consider four different virtualization
technologies: Xen and KVM, based on hypervisor virtualization, and Docker and
LXC, based on container virtualization. Our results show promising accuracy of
the ML classifier using real data collected from a private ISP. We report
in-depth analysis of the learning process (learning-curve analysis), feature
ranking (feature selection, Principal Component Analysis (PCA), etc.), impact
of different sets of features, training time, and testing time. Our results
show how the proposed methods improve QoS and reduce operational cost for
network owners. We also demonstrate a practical use-case example
(Software-Defined Wide Area Network (SD-WAN) with VNFs and backbone network) to
show that our ML methods save significant cost for network service leasers
Formulating and managing viable SLAs in cloud computing from a small to medium service provider's viewpoint: A state-of-the-art review
© 2017 Elsevier Ltd In today's competitive world, service providers need to be customer-focused and proactive in their marketing strategies to create consumer awareness of their services. Cloud computing provides an open and ubiquitous computing feature in which a large random number of consumers can interact with providers and request services. In such an environment, there is a need for intelligent and efficient methods that increase confidence in the successful achievement of business requirements. One such method is the Service Level Agreement (SLA), which is comprised of service objectives, business terms, service relations, obligations and the possible action to be taken in the case of SLA violation. Most of the emphasis in the literature has, until now, been on the formation of meaningful SLAs by service consumers, through which their requirements will be met. However, in an increasingly competitive market based on the cloud environment, service providers too need a framework that will form a viable SLA, predict possible SLA violations before they occur, and generate early warning alarms that flag a potential lack of resources. This is because when a provider and a consumer commit to an SLA, the service provider is bound to reserve the agreed amount of resources for the entire period of that agreement – whether the consumer uses them or not. It is therefore very important for cloud providers to accurately predict the likely resource usage for a particular consumer and to formulate an appropriate SLA before finalizing an agreement. This problem is more important for a small to medium cloud service provider which has limited resources that must be utilized in the best possible way to generate maximum revenue. A viable SLA in cloud computing is one that intelligently helps the service provider to determine the amount of resources to offer to a requesting consumer, and there are number of studies on SLA management in the literature. The aim of this paper is two-fold. First, it presents a comprehensive overview of existing state-of-the-art SLA management approaches in cloud computing, and their features and shortcomings in creating viable SLAs from the service provider's viewpoint. From a thorough analysis, we observe that the lack of a viable SLA management framework renders a service provider unable to make wise decisions in forming an SLA, which could lead to service violations and violation penalties. To fill this gap, our second contribution is the proposal of the Optimized Personalized Viable SLA (OPV-SLA) framework which assists a service provider to form a viable SLA and start managing SLA violation before an SLA is formed and executed. The framework also assists a service provider to make an optimal decision in service formation and allocate the appropriate amount of marginal resources. We demonstrate the applicability of our framework in forming viable SLAs through experiments. From the evaluative results, we observe that our framework helps a service provider to form viable SLAs and later to manage them to effectively minimize possible service violation and penalties
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