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.

Method and apparatus for reactive and deliberative service level management (SLM)

Abstract: Method and apparatus for service level management, wherein business processes are composed of services. A state of the service is defined by one or more service parameters, and the service parameters depend upon performance of network components that support the service, e.g., component parameters. The state of the service may depend, for example, on a collection of service parameter values for availability, reliability, security, integrity and response time. A service level agreement is a contract between a supplier and a customer that identifies services supported by a network, service parameters for the services, and service levels (e.g., acceptable levels) for each service parameter.

System and method for providing throttling, prioritization and traffic shaping during request processing via a budget service

Abstract: A network gateway application is described that provides throttling, prioritization and traffic shaping for incoming requests from client applications. A request is received by a plug-in manager component of the gateway application. The plug-in manager can then invoke the budget service in order to determine a current available budget for the request. The budget can be computed according to a service level agreement for the service provider, application or network node. The requests can be of high or low priority. If the budget is greater than a specified priority threshold value, the low priority requests can be denied, while the high priority requests can be processed as long as there is some available budget left. If the budget for the request has reached the restricted level, the request can be denied and optionally enqueued to a traffic shaping queue to be processed at a later time.

Big media healthcare data processing in cloud: a collaborative resource management perspective

Abstract: Nowadays, big media healthcare data processing in cloud has become an effective solution for satisfying QoS demands of medical users. It can support various healthcare services such as pre-processing, storing, sharing, and analysis of monitored data as well as acquiring context-awareness. However, to support energy and cost savings, the union of cloud data centers termed as cloud confederation can be an promising approach, which helps a cloud provider to overcome the limitation of physical resources. However, the key challenge in it is to achieve multiple contradictory objectives, e.g., meeting the required level of services defined in service level agreement, maintaining medial users'application QoS, etc. while maximizing profit of a cloud provider. In this paper, for executing heterogeneous big healthcare data processing requests from users, we develop a local and global cloud confederation model, namely FnF, that makes an optimal selection decision for target cloud data center(s) by exploiting Fuzzy logic. The FnF trades off in between profit of cloud provider and user application QoS in selecting federated data center(s). In addition, FnF enhances its decision accuracy by precisely estimating the resource requirements for the big data processing tasks using multiple linear regression. The proposed FnF model is validated through numerical as well as experimental evaluations. Simulation results depict the effectiveness and efficiency of the FnF model compared to state-of-the-art approaches.

A Trust Evaluation Model for Cloud Computing Using Service Level Agreement

Abstract: To access cloud services the user needs to negotiate a service level agreement (SLA) with the service provider. There will be inadequate assurances to customers on whether the services are trustworthy to pick. Trust management plays a major role in guiding the users to access trustworthy services. Hence a trust mining model (TMM) is proposed to identify trusted cloud services while negotiating an SLA. The knowledge is discovered from a previously monitored dataset and a trust value is generated. The proposed trust model helps both the service provider and cloud user, where the user can make a decision on whether to continue or discontinue the service with the service provider. A Rough set and Bayesian inference are used together to generate the overall results. Using rough sets previously monitored data are mined and the indiscernibility in them is analyzed. Bayesian inference is applied to infer the overall trust degree. The accuracy of the results is compared with the previous models and the result shows that the TMM gives better accuracy. The model is simulated using CloudSim.