A Secure Resource and Job scheduling model with Job Grouping strategy in Grid computing

Abstract

Grid computing is a group of clusters connected over high-speed networks that involves coordinating and sharing computational power, data storage and network resources operating across dynamic and geographically dispersed locations. However, scheduling in grid is confronted with many challenges, because resources are heterogeneous, geographically dispersed and dynamic in nature. In this paper, a secure scheduling model is presented, that performs job grouping activity at runtime and the simulation results shows significant improvement in the processing time of jobs and resource utilization as compared to others.

Online Job Scheduling with <i>K</i> Servers

“…This approach involves presenting data in a stream and incrementally updating the model parameters in real-time as new data is presented. Job scheduling framework arises in many application domains, including resource allocation in grid computing [1], big data processing over systems [2], energy and response time-saving in cloud computing [3], etc.…”

Scheduling of workflow jobs based on twostep clustering and lowest job weight

“…Moreover, to define a job weight in grid system, researchers used mainly two main methods including clustering models [31][32][33] and run-time prediction models. 11,12,[34][35][36][37] In clustering models, jobs are divided into groups by using different discrimination methods such as clustering algorithms, wherein the runtime prediction model is used to predict the job's run time using job variables.…”