Service-level agreement

About: Service-level agreement is a research topic. Over the lifetime, 4358 publications have been published within this topic receiving 75333 citations. The topic is also known as: SLA.

On the flexibility of WS-agreement for job submission

Abstract: This paper considers extensions to the WS-Agreement specification, namely the Guarantee Terms of WS-Agreement [1]. Experiences and conclusions drawn are in the context of Agreement-based job management systems. A key idea of these extensions is the use of functions for the Guarantee Terms of the Agreement rather than constant values or ranges. Functions may contain variables defined in a particular agreement or be drawn from the known set of reference variables, such as wall-clock time, job start time, etc. We show that such an approach can potentially reduce the negotiation overheads associated with job renegotiation and/or reduce the number of failed agreements.

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

Abstract: 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.

General Sum Markov Games for Strategic Detection of Advanced Persistent Threats Using Moving Target Defense in Cloud Networks

Abstract: The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions. It has been shown that deploying all possible detection measures incur a cost on performance by using up valuable computing and networking resources, thereby resulting in Service Level Agreement (SLA) violations promised to the cloud-service users. Thus, there has been a recent interest in developing Moving Target Defense (MTD) mechanisms that helps to optimize the joint objective of maximizing security while ensuring that the impact on performance is minimized. Often, these techniques model the challenge of multi-stage attacks by stealthy adversaries as a single-step attack detection game and use graph connectivity measures as a heuristic to measure performance, thereby (1) losing out on valuable information that is inherently present in multi-stage models designed for large cloud networks, and (2) come up with strategies that have asymmetric impacts on performance, thereby heavily affecting the Quality of Service (QoS) for some cloud users. In this work, we use the attack graph of a cloud network to formulate a general-sum Markov Game and use the Common Vulnerability Scoring System (CVSS) to come up with meaningful utility values in each state of the game. We then show that, for the threat model in which an adversary has knowledge of a defender’s strategy, the use of Stackelberg equilibrium can provide an optimal strategy for placement of security resources. In cases where this assumption turns out to be too strong, we show that the Stackelberg equilibrium turns out to be a Nash equilibrium of the general-sum Markov Game. We compare the gains obtained using our method(s) to other baseline techniques used in cloud network security. Finally, we highlight how the method was used in a real-world small-scale cloud system.

Trends and Directions in Cloud Service Selection

Abstract: With the growing popularity of cloud computing the number of cloud service providers and services have significantly increased. Thus selecting the best cloud services becomes a challenging task for prospective cloud users. The process of selecting cloud services involves various factors such as characteristics and models of cloud services, user requirements and knowledge, and service level agreement (SLA), to name a few. This paper investigates into the cloud service selection tools, techniques and models by taking into account the distinguishing characteristics of cloud services. It also reviews and analyses academic research as well as commercial tools in order to identify their strengths and weaknesses in the cloud services selection process. It proposes a framework in order to improve the cloud service selection by taking into account services capabilities, quality attributes, level of user's knowledge and service level agreements. The paper also envisions various directions for future research.

Percentile Performance Estimation of Unreliable IaaS Clouds and Their Cost-Optimal Capacity Decision

Abstract: Through Internet, a cloud computing system provides shared resources, data, and information to users or tenant users in an on-demand and pay-as-you-go styles. It delivers large-scale utility computing services to a wide range of consumers. To ensure that their provisioned service is acceptable, cloud providers must exploit techniques and mechanisms that meet the service-level-agreement (SLA) performance commitment to their clients. Thus, performance issues of cloud infrastructures have been receiving considerable attention by both researchers and practitioners as a prominent activity for improving service quality. This paper presents an analytical approach to percentile-based performance analysis of unreliable infrastructure-as-a-service clouds. The proposed analytical model is capable of calculating percentiles of the request response time under variable load intensities, fault frequencies, multiplexing abilities, and instantiation processing time. A case study based on a real-world cloud is carried out to prove the correctness of the proposed theoretical model. To achieve optimal performance-cost tradeoff, we formulate the performance model into an optimal capacity decision problem for cost minimization subjected to the constraints of request rejection and SLA violation rates. We show that the optimization problem can be numerically solved through a simulated-annealing method.