Search

As computing infrastructure expands, resource management in a large, heterogeneous, and distributed environment becomes a challenging task. In a cloud environment, with uncertainty and dispersion of resources, one encounters problems of allocation of resources, which is caused by things such as heterogeneity, dynamism, and failures. Unfortunately, existing resource management techniques, frameworks, and mechanisms are insufficient to handle these environments, applications, and resource behaviors. To provide efficient performance of workloads and applications, the aforementioned characteristics should be addressed effectively. This research depicts a broad methodical literature analysis of autonomic resource management in the area of the cloud in general and QoS (Quality of Service)-aware autonomic resource management specifically. The current status of autonomic resource management in cloud computing is distributed into various categories. Methodical analysis of autonomic resource management in cloud computing and its techniques are described as developed by various industry and academic groups. Further, taxonomy of autonomic resource management in the cloud has been presented. This research work will help researchers find the important characteristics of autonomic resource management and will also help to select the most suitable technique for autonomic resource management in a specific application along with significant future research directions. [ABSTRACT FROM AUTHOR]

In this paper, a fault tolerance based QoS (Quality of Services) scheduling using HTF (Hash Table Functionality) in Social Grid Computing (SGC) is introduced, where HTF is used as the underlying mechanism in SGC to logically manage the locations of the devices. Fault tolerance based QoS scheduling consists of four sub-scheduling algorithms: Unauthorized-user filtering, Grid service delivery, QoS provisioning, and Replication and load-balancing. Under the proposed scheduling, a device is used as a resource for providing Grid services, faults caused due to reason like user mobility are tolerated and user requirements for QoS are considered. Simulations include the scheduling by with and without HTF. The simulation results show that our proposed scheduling algorithm enhances Grid service execution time, finish time, reliability and reduces the Grid service error rate. [ABSTRACT FROM AUTHOR]

The increasing complexity of Grid services and systems demands larger human effort for system configuration and performance management. The physical management of these systems is not only tedious but also error-prone. Inherently, effective monitoring is essential for Grid management, which can further facilitate self-management of the Grid as advocated by Autonomic computing i.e. shifting the burden of managing systems from people to technologies. This paper proposes a pulse monitoring based Grid Monitoring System that passes the monitoring data to Fault Discovery and Healing System, which has been designed for facilitating self-healing in Grid. The Grid monitoring system demonstrates that through efficient monitoring, self-healing can be achieved in a Grid environment and Grid can work autonomously, thus minimizing the physical management and service-downtime. [ABSTRACT FROM AUTHOR]