Effective allocation of resources and task scheduling in heterogeneous parallel environment

Scientific studies on a large-scale are being performed in cooperation with teams located around the world. Each team exchanges data and conducts experiments using dispersed resources. Therefore, scientific data is duplicated and stored in geographically dispersed places. These data are incorporated in application processes which ease the operation and maintenance of applications in distributed/parallel computing systems. A workflow management system must take advantage of the presence of different data sources and distributed/parallel computing resources provided by platforms such as grids and clouds to effectively execute these procedures. This paper thus expands an existing workflow architecture and provides better planning algorithms for resources management. It begins with a comprehensive study of the planning techniques used in the past as the basis for parallel systems. The work offers a method that includes information management components and assesses its practical viability by utilising resources to operate several real-world applications.