Adaptive group scheduling mechanism using mobile agents in peer-to-peer grid computing environment

Abstract

Peer-to-peer grid computing is an attractive computing paradigm for high throughput applications. However, both volatility due to the autonomy of volunteers (i.e., resource providers) and the heterogeneous properties of volunteers are challenging problems in the scheduling procedure. Therefore, it is necessary to develop a scheduling mechanism that adapts to a dynamic peer-to-peer grid computing environment. In this paper, we propose a Mobile Agent based Adaptive Group Scheduling Mechanism (MAAGSM). The MAAGSM classifies and constructs volunteer groups to perform a scheduling mechanism according to the properties of volunteers such as volunteer autonomy failures, volunteer availability, and volunteering service time. In addition, the MAAGSM exploits a mobile agent technology to adaptively conduct various scheduling, fault tolerance, and replication algorithms suitable for each volunteer group. Furthermore,

Scheduling in Desktop Grid Systems: Theoretical Evaluation of Policies & Frameworks

“…Scheduling mechanism that performs most of below mentioned points is taken as better mechanism. Though none of these factors are considered collectively in the literature but few of them can be found in [2,3,5,6,7,14,15,22,25,26].…”

“…Any imbalance in the amount of work fetched would either result in wasted CPU cycles and other resources (RAM, disk) caused by missed deadlines or less than optimal utilization of the already scarce shared resources. BOINC Client uses two work fetch policies buffer none and buffer multiple tasks, also a number of other variations have been suggested in [7,9]. As stated earlier, work fetch policies addresses the issues of when to ask for more work, which project to ask work for and how much work to ask for.…”

“…Buffer None [7] The policy does not buffer any tasks. It only downloads a task after returning the result of the previous task.…”

“…Buffer Multiple Tasks [7] The policy buffers task for number of days. The amount of tasks is limited to a number that can possibly be completed before the tasks' deadlines.…”

A Novel-Based Multi-agent Brokering Approach for Job Scheduling in a Cloud Environment

“…It has a capability to create multiple instances as fast as possible, at the same time clusters can form base virtual machine. However, it is very much necessary to apply an approach which guarantees the distribution or proper allocation of workloads across thousands of users' queries per second, e.g., the job scheduling approach [14]. In a cloud environment, commodity servers also perform the job scheduling process.…”