Balanced Ant Colony Optimization BACO in Grid Computing Abstract

Introduction to Balanced Ant Colony Optimization BACO in Grid Computing:

A huge computing power and technique is required to solve complex and difficult scientific doubts and problems. The space required for storing data is also pretty huge as the solution takes up a lot of memory. Grid computing is an innovative computation technique through which we can manage a large number of files through interactive workloads distribution system. It mainly focuses on unused processing cycles and harnesses them to solve these problems. 

 Two types of grids:

  • Computing grid
  • Data grid

It would consume a lot of time to process, solving and storing a large amount of data and grid computing helps us to do the same with less storage space and time. Status conditions of resource and networks are closely monitored and if it is found to be unstable, the proposed job would be a failure and will result in a large computation time. In order to bring in more effectiveness to the job, a scheduling algorithm is proposed to schedule these jobs in the most effective manner.

This scheduling algorithm is extremely important as hundreds of computers are used as resources and the task is impossible to do manually.  Balanced Ant Colony Optimization or BACO is such a scheduling algorithm used in the grid environment to schedule jobs effectively. Although there are other scheduling algorithms such as FCFS and SJF, BACO excels in the dynamic grid environment. Local search are made extremely quick and efficient and the strategy used for scheduling will be dependent on the job types and present environment.

Some of the problems solved with the help of BACO

  • Traveling salesman problem
  • Vehicle routing problem
  • Graph coloring problem

BACO algorithm is of different types:

  • Ant colony system
  • Max min ant system
  • Fast ant system
  • Elitist ant system
  • Rank Based ant system

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