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.

A QoS Control Mechanism to Provide Service Differentiation and Overload Protection to Internet Scalable Servers

Abstract: Nowadays, enterprises providing services through Internet often require online services supplied by other enterprises. This entails the cooperation of enterprise servers using Web services technology. The service exchange between enterprises must be carried out with a determined level of quality, which is usually established in a service level agreement (SLA). However, the fulfilment of SLAs is not an easy task and requires equipping the servers with special control mechanisms which control the quality of the services supplied. The first contribution of this research work is the analysis and definition of the main requirements that these control mechanisms must fulfil. The second contribution is the design of a control mechanism which fulfils these requirements and overcomes numerous deficiencies posed by previous mechanisms. The designed mechanism provides differentiation between distinct categories of service consumers as well as protection against server overloads. Furthermore, it scales in a cluster and does not require any modification to the system software of the host server, or to its application logic.

A performance study of distributed architectures for the quality of Web services

Abstract: The second generation of Web sites provides more complex services than those related to Web publishing. Many users already rely on the Web for up-to-date personal and business information and transactions. This success motivates the need to design and implement Web architectures being able to guarantee the service level agreement that will rule the relationship between users and Web service providers. As many components of the Web infrastructure are beyond the control of Web system administrators, they should augment satisfaction percentage of the assessed service levels by relying on two mechanisms that can be integrated: differentiated classes of services/users, Web systems with multi-node architectures. The focus of this paper is on this latter approach. It reviews systems where replicated Web services are provided by locally and geographically distributed Web architectures. It considers different categories of Web applications, and evaluates how static dynamic and secure requests affect performance and quality of service of distributed Web sites.

Developing User Perceived Value Based Pricing Models for Cloud Markets

Abstract: With the rapid deployment of cloud computing infrastructures, understanding the economics of cloud computing has become a pressing issue for cloud service providers. However, existing pricing models rarely consider the dynamic interactions between user requests and the cloud service provider. Thus, the law of supply and demand in marketing is not fully explored in these pricing models. In this paper, we propose a dynamic pricing model based on the concept of user perceived value that accurately captures the real supply and demand relationship in the cloud service market. Subsequently, a profit maximization scheme is designed based on the dynamic pricing model that optimizes profit of the cloud service provider without violating service-level agreement. Finally, a dynamic closed loop control scheme is developed to adjust the cloud service price and multiserver configurations according to the dynamics of the cloud computing environment such as fluctuating electricity and rental fees. Extensive simulations using the data extracted from real-world applications validate the effectiveness of the proposed user perceived value-based pricing model and the dynamic profit maximization scheme. Our algorithm can achieve up to 31.32 percent profit improvement compared to a state-of-the-art approach.

GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

Abstract: Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the action-value distribution driven by minimizing the discrepancy between the estimated action-value distribution and the target action-value distribution. We put forward a reward-clipping mechanism to stabilize GAN-DDQN training against the effects of widely-spanning utility values. Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function. Finally, we verify the performance of the proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive simulations.