An Autonomic SLA Monitoring Framework Managed by Trusted Third Party in the Cloud Computing

Abstract

This article describes how currently, service level agreements (SLAs) assurance forms one of the major challenges for cloud computing (CC) in order to guarantee quality of service (QoS) in real-time and control SLA violations. However, due to the highly dynamic nature of this open environment, it is important to have a binding agreement between all the service parties for ensuring trust while fulfilling the expected QoS. To properly operate and manage such complex situations, an effective and efficient monitoring is crucial. The participation of a trusted third party (TTP) is necessary in order to resolve conflicts between involved parties. This article proposes an autonomic SLA monitoring framework managed by TTP composed of two modules: the first one SLA establishment module, which aims at providing support for automated SLA generation and management. The second one, a service monitoring module to dynamically monitor QoS metrics by detecting SLA violations at runtime to verify compliances for the respective SLAs, and to propose a mechanism for an adaptive remedy rectification, as a contribution at the third maturity level of the autonomic computing paradigm as defined by IBM. The framework is validated with scenarios on response time and availability, the results obtained are promising. They confirm that this framework manages SLAs in an efficient way as it detects all violations to be communicated to concerned parties, and identifies particular penalty clauses that can be used to modify the reputation of a provider over time. The TTP framework equipped with such reputation module can provide real-time assessment for consumers informed decision making to continue using a service or to migrate to another service provider in the case of service degradation. This creates a fair competitiveness between providers and hence improves service performance and the reliability in the cloud.

An Adaptive Autonomic Framework for Optimizing Energy Consumption in the Cloud Data Centers

“…Autonomic Computing (AC) refers to the ability of a computing system or application to be selfmanaging, which means that the system can manage itself and can be adaptable to any changes and adjustments in its environment [24].…”

“…The managed resources can be software or hardware resources including operating systems, wired or wireless network, CPU, database, servers, switches, routers, application modules, Web services or VMs, etc. [24]. 2) The maturity levels of the Autonomic Computing paradigm: Implementing an autonomic framework which can incorporate the MAPE-K loop is additionally a complex task since you have to go through five Maturity Levels (MLs) [30] for example, Fig.…”