Manifold learning in SLA violation detection and prediction for cloud-based system

5G network is an enhanced communication network, designed to converge the requirements of quality of service (QoS) parameters, data and capacity, by means of signals with high QoS and high-speed data rate. Several state-of-art technologies are involved to obtain the requirement of energy efficient communication system with increased number of users, devices, higher data rate with low latency. This paper presents a system which demonstrates the energy and spectral efficiency achieved for various number of nodes in a specific area. This study addresses the improvement in energy and spectral efficiency when the proposed algorithm is used. The proposed algorithm is a combination of swarm based artificial bee colony (ABC) algorithm with neural network. Experimental results have been carried out to observe the performance of QoS parameters such as bit error rate (BER), throughput, power consumption and mean square error (MSE). The maximum energy efficiency achieved is 34% and Spectral efficiency is 36%.

Service level agreement (SLA) is a formal contract between a service provider and a service consumer to guarantee the Quality of service (QoS) expectations, it is used in all areas of information technology, such as Cloud Computing, Internet of Things (IoT), networks and Web services. For IoT applications, the main challenges are: (1) how to describe the SLA terms, such as QoS properties, service levels, penalties in SLA violation, (2) how to monitor these terms, and (3) how to integrate an SLA into all the IoT application layers. Therefore, in this paper, we propose ML-SLA-IoT, a framework for SLA specification and monitoring. It covers the entire layers of an IoT application. It describes precisely QoS levels, provided services, and obligations. It differs from other SLA specification languages in the specification of user preferences, the use of microservices (for reusability, dynamism and ease of integration) and ML-SLA (multi-level metrics and QoS according to existing constraints and user preferences). Furthermore, ML-SLA-IoT monitors SLA terms in an automatic and decentralised manner using smart contracts and blockchain technologies without the intervention of the third-party. Finally, we present a comparative study and experiments with existing solutions regarding to some criteria for SLA specification and monitoring. Our results show that ML-SLA-IoT gives better performance in terms of dynamic pricing, obligation combination, and SLA monitoring.

With the widespread adoption of cloud computing, Service-Orientated Architecture (SOA) facilitates the deployment of large-scale online applications in many key areas where quality and reliability are critical. In order to ensure the performance of cloud applications, Quality of Service (QoS) is widely used as a key metric to enable QoS-driven service selection, composition, adaption, etc. Since QoS data observed by users is sparse due to technical constraints, previous studies have proposed prediction approaches to solve this problem. However, the dynamic nature of the cloud environment requires timely prediction of time-varying QoS values. In addition, unreliable QoS data from untrustworthy users may significantly affect the prediction accuracy. In this paper, we propose a credible online QoS prediction approach to address these challenges. We evaluate user credibility through a reputation mechanism and employ online learning techniques to provide QoS prediction results at runtime. The proposed approach is evaluated on a large-scale real-world QoS dataset, and the experimental results demonstrate its effectiveness and efficiency in unreliable cloud environment.

Virtual machine (VM) consolidation in Cloud computing environments provides a great opportunity for energy saving. However, the obligation of providing suitable quality of service to end users leads to the necessity in dealing with energy-performance tradeoff. In this paper, we propose a redesigned energy-aware heuristic framework for VM consolidation to achieve a better energy-performance tradeoff. The main contribution of us is a Service Level Agreement (SLA) violation decision algorithm, which is used to decide a host is overload with SLA violation or not. Finally, we have evaluated our framework through simulation on large-scale experiments driven by workload traces from more than a thousand VMs, and the results show that our framework outperforms previous work, which has 11.8%~27% decrease in energy consumption, 57.9%~78.4% decrease in SLA violation, 63.2%~84.1% decrease in energy-performance metric which is a product metric of the energy consumption and SLA violation.

A Service Level Agreement (SLA) is a contract between a service provider and a consumer which specifies in detail the level of service expected from the service provider, obligations, commitment and objectives. In the cloud computing environment, both the cloud provider and the cloud consumer want to know of a likely service violation before the actual violation occurs and to adjust the scaling of the cloud resources appropriately. A consumer's previous resource usage profile is a key element in determining the possibility of service violation in the cloud computing environment, which has not been an area of research focus so far. In this paper, we analyze and compare QoS prediction by considering the consumer's previous resource usage profile in various conditions. From comparative analysis, we observe that by combining a consumer's previous resource usage profile history along with the previous resource usage profile history of its nearest neighbors, we obtain an optimal result.

This work presents a novel scheme for Service Level Agreements (SLAs) in a service-oriented architecture (SOA). An SLA is a contract that guarantees quality of service (QoS) between service providers and consumers. An SLA can be split into several parts for an SOA system which is consisting of different services. A split SLA is used to guarantee quality of services for one of services in an SOA system. However, these contract rules do not regulate follow-up process in services. SLA violations may occur consequently. For instance, interactions between services are not encrypted because the encryption parameter isn't included in the split SLA contract. Confidential data would be transmitted through unsafe channels. There would be a security issue of SLA Management for follow-up processes in an SOA system. The novel scheme in this work addresses these problems. Using SLAs management proposed in this work can identify the services that consumer needs and split the contracts for follow-up services. SLAs are not only guaranteed between service providers and consumers but also assured in follow-up processes. The proposed method improves the security and completeness of SLA using in an SOA system.

Services as part of our daily life represent an important means to deliver value to their consumers and have a great economic impact for organizations. The service consumption and their exponential proliferation show the importance and acceptance by their customers. In this sense, it is possible to predict that the infrastructure of future cities will be supported by different kind of services, such as smart city services, open data services, as well as common services (e.g., e-mail services), etc. Nowadays a large percentage of services are provided on the web and are commonly called web services (WSs). This kind of services has become one of the most used technologies in software systems. Among the challenges when integrating web services in a given system, requirements-driven selection occupies a prominent place. A comprehensive selection process needs to check compliance of Non-Functional Requirements (NFRs) which can be assessed by analyzing the Quality of Service (QoS). In this paper, we describe a framework called WeSSQoS that aims at ranking available WSs based on the comparison of their QoS and the stated NFRs. The framework is designed as an open Service Oriented Architecture (SOA) that hosts a configurable portfolio of normalization procedures and ranking algorithms which can be selected by users when starting a selection process. The QoS data from WSs can be obtained either from a static, WSDL-like description or dynamically through monitoring techniques. WeSSQoS is designed to work over multiple WS repositories and QoS sources. The impact of having a portfolio of different normalization and ranking algorithms is illustrated with an example.

Constraint-based Quality of Service (QoS) prediction is a method for predicting violations of Service Level Agreements (SLAs) in an executing instance of a service orchestration. It uses assumptions about the ranges of QoS values for component services in the orchestration. Experiments suggest that the method, when given correct component QoS assumptions, produces highly accurate predictions according to a series of quality-of-prediction metrics, and that it does so well ahead of the time when the prediction is to happen. We study the behavior of this method when the component QoS assumptions become incorrect or too vague. We conclude that the effect is a graceful deterioration in prediction quality, unless gross (order-of-magnitude) imprecisions are introduced. However, the method is very sensitive to the loss of information on the lower bounds for component QoS values, since the knowledge of the upper bounds is not sufficient for failure prediction.

By leveraging the Internet, cloud computing allows users to have on-demand access to large pools of configurable computing resources. PaaS (Platform as a Service), IaaS (Infrastructure as a Service), and SaaS (Software as a Service) are three basic categories for the services provided by cloud the computing environments. Quality of service (QoS) metrics like reliability, availability, performance, and cost determine which resources and services are available in a cloud computing scenario. Provider and the user-specified performance characteristics, such as, rejection rate, throughput, response time, financial cost, and energy consumption, form the basis for QoS. To fulfil the needs of its customers, cloud computing must ensure that its services are given with the appropriate quality of service QoS. A “A legally enforceable agreement known as a “Service Level Agreement” (SLA) between a service provider and a customer that outlines service objectives, quality of service requirements, and any associated financial penalties for falling short. We, therefore, presented “A Proactive Resource Supply based Run-time Monitoring of SLA in Cloud Computing”, which allows for the proactive management of SLAs during run-time via the provisioning of cloud services and resources. Within the framework of the proposed work, SLAs are negotiated between cloud users and providers at run-time utilizing SLA Manager. Resources are proactively allocated via the Resource Manager to cut down on SLA violations and misdetection costs. As metrics of performance, we looked at the frequency with which SLAs were broken and the money lost due to false positives. We compared the proposed PRP-RM-SLA model’s simulated performance to the popular existing SLA-based allocation strategy SCOOTER. According to simulation data, the suggested PRP-RM-SLA model is 25% more effective than the current work SCOOTER at reducing SLA breaches and the cost of misdetection.

Service Level Agreements (SLA) represents the principal means to control the Quality of Service (QoS). In cloud computing, various advanced SLA strategies are used; some techniques among others try to avoid the SLA violations. Taking into account the cloud consumer contextual parameters is a promising way to predict and avoid costly SLA violations. Therefore, in this paper, we propose a context-aware system for the SLA in order to guarantee the QoS. We create a contextual ontology to introduce the semantic meaning of the cloud consumer contextual parameters. In addition, using reasoning techniques, we (1) predict SLA violations and we (2) choose and apply the corrective actions in autonomous manner. This allows getting a proactive adjustment of the cloud service execution and respecting the SLA parameters.

Cloud computing has recently emerged as an important service to manage applications efficiently over the Internet. Various cloud providers offer pay per use cloud services that requires Quality of Service (QoS) management to efficiently monitor and measure the delivered services through Internet of Things (IoT) and thus needs to follow Service Level Agreements (SLAs). However, providing dedicated cloud services that ensure user's dynamic QoS requirements by avoiding SLA violations is a big challenge in cloud computing. As dynamism, heterogeneity and complexity of cloud environment is increasing rapidly, it makes cloud systems insecure and unmanageable. To overcome these problems, cloud systems require self-management of services. Therefore, there is a need to develop a resource management technique that automatically manages QoS requirements of cloud users thus helping the cloud providers in achieving the SLAs and avoiding SLA violations. In this paper, we present SLA-aware autonomic resource management technique called STAR which mainly focuses on reducing SLA violation rate for the efficient delivery of cloud services. The performance of the proposed technique has been evaluated through cloud environment. The experimental results demonstrate that STAR is efficient in reducing SLA violation rate and in optimizing other QoS parameters which effect efficient cloud service delivery.

Online service is used to be as Pay-Per-Use in Cloud computing. Service user need not be in a long time contract with cloud service providers. Service level agreements (SLAs) are understandings marked between a cloud service providers and others, for example, a service user, intermediary operator, or observing operators. Since cloud computing is an ongoing technology giving numerous services to basic business applications and adaptable systems to manage online agreements are significant. SLA maintains the quality-of-service to the cloud user. If service provider fails to maintain the required service SLA is considered to be SLA violated. The main aim is to minimize the SLA violations for maintain the QoS of their cloud users. In this research article, a toolbox is proposed to help the procedure of exchanging of a SLA with the service providers that will enable the cloud client in indicating service quality demands and an algorithm as well as Negotiation model is also proposed to negotiate the request with the service providers to produce a better agreement between service provider and cloud service consumer. Subsequently, the discussed framework can reduce SLA violations as well as negotiation disappointments and have expanded cost-adequacy. Moreover, the suggested SLA toolkit is additionally productive to clients so clients can secure a sensible value repayment for diminished QoS or conceding time. This research shows the assurance level in the cloud service providers can be kept up by as yet conveying the services with no interruption from the client's perspective

Cloud computing is popular among industries, academia, and government to supply reliable and scalable computational power. High-speed networks in cloud data centers connect Virtual machines with Physical Machines. Virtualization assists the cloud service providers to manage resources effectively but unoptimized and inefficient services degrade the performance of the system. The scheduling architecture of cloud computing includes Physical Machines (PMs), Virtual Machines (VMs) and the allocation and migration policy of the VMs over the PMs. The overutilized PMs get few migrations and this paper introduces novel behavior of VM selection from overutilized PMs using Swarm intelligence. The evaluation of the proposed algorithm architecture is compared with another state-of-the-art optimization algorithm from the same series. The evaluation has been done on the base of Quality of Service (QoS) parameters, such as Service Level Agreement (SLA) violation, and energy consumption against various load variation scenarios to support elasticity. The proposed algorithm has outperformed other techniques by considerable margin in terms of QoS, and the details are presented in the results section. The simulation results demonstrate that the proposed technique exhibits 6.3% and 6.7% enhancement in terms of reduced energy consumption compared to both Cuckoo Search (CS) and general Dragonfly (DF) techniques, and 3% decrease in SLA violations in comparison to current methods. Additionally, the results reveal an 11% enhancement in VM migration compared to existing approaches.

Evaluation of Quality of Healthcare: To What Extent Can We Rely on Patient Expectations and Preferences

A Service Level Agreement (SLA) is a legal contract between parties to ensure the Quality of Service (QoS). It specifies one or more service level objectives (SLO), to ensure that the QoS delivered has met customer expectations. However, It becomes hard to guarantee QoS levels and detect SLA violations. Therefore, we propose to use MDE (Model Driven Engineering) to express the SLA contract requirements. This latter, created with a specific modeling language (DSML), will be used harmonically with a Multi-agent systems (MASs) in order to monitor SLA violations in real-time. Indeed, MASs are suitable tools for self-detection of failures and self-monitoring of cloud operations and services, QoS negotiation and SLA management. They are designed to operate in a dynamically changing environment. Our main motivation is firstly to use MDE technology for the creation of the SLA contract and then to integrate MASs in order to control the quality of service contract and guarantee transparency and symmetry with respect to the SLA contract between prospective signatories.