## Nature's Heuristics for Scheduling Jobs on Computational Grids (2000)

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Venue: | IEEE INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING AND COMMUNICATIONS |

Citations: | 65 - 19 self |

### BibTeX

@INPROCEEDINGS{Abraham00nature'sheuristics,

author = {Ajith Abraham and Rajkumar Buyya and Baikunth Nath},

title = {Nature's Heuristics for Scheduling Jobs on Computational Grids},

booktitle = {IEEE INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING AND COMMUNICATIONS},

year = {2000},

pages = {45--52},

publisher = {}

}

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### Abstract

Computational Grid (Grid Computing) is a new paradigm that will drive the computing arena in the new millennium Unification of globally remote and diverse resources, coupled with the increasing computational needs for Grand Challenge Applications (GCA) and accelerated growth of the Internet and communication technology will further fuel the development of global computational power grids. In this paper, we attempt to address the scheduling of jobs to the geographically distributed computing resources. Conventional wisdom in the field of scheduling is that scheduling problems exhibit such richness and variety that no single scheduling method is sufficient. Heuristics derived from the nature has demonstrated a surprising degree of effectiveness and generality for handling combinatorial optimization problems. This paper begins with an introduction of computational grids followed by a brief description of the three nature's heuristics namely Genetic Algorithm (GA), Simulated Annealing (SA) and Tabu Search (TS). Experimental results using GA are included. We further demonstrate the hybridized usage of the above algorithms that can be applied in a computational grid environment for job scheduling.

### Citations

1534 | The Grid: Blueprint for a New Computing Infrastructure - Foster, Kesselman - 1999 |

222 | Efficient and Accurate Parallel Genetic Algorithms
- Cantu-Paz
- 2000
(Show Context)
Citation Context ...they may communicate by exchanging individuals. Communication involves extra costs and additional decision on topologies, on how many individuals are exchanged, and on the frequency of communications =-=[14]-=-. Currently a large number of research projects worldwide are exploring different approaches to the development of grid technologies and global scheduling systems [16]. Even though significant progres... |

112 |
Genetic algorithm in search, optimization and machine learning
- DE
- 1989
(Show Context)
Citation Context ...olutions to real world problems, if they have been suitably encoded. GA search is constrained neither by the continuity of the function under investigation, nor the existence of a derivative function =-=[7]-=-. Figure 2 illustrates the functional block diagram of a GA. It is assumed that a potential solution to a problem may be represented as a set of parameters. These parameters (known as genes) are joine... |

111 | An economy driven resource management architecture for global computational power grids
- Abramson, Giddy, et al.
(Show Context)
Citation Context ...ach in resource selection and often completely ignore the user requirements. In an economic-based approach an optimal schedule often relies on a trade off between cost and the user specified deadline =-=[4]-=-. Our approach is to dynamically generate an optimal schedule so as to complete the tasks in a minimum period of time as well as utilizing the resources in an efficient way. In recent years, several a... |

34 | An evolutionary tabu search algorithm and the NHL scheduling problem
- Costa
- 1995
(Show Context)
Citation Context ...ution s′ located in the neighborhood N(s) of s. These moves are performed with the aim of efficiently reaching optimal solution by the evaluation of some objective function f(s) to be minimized [8][=-=9][13]-=-. In the sequel we sketch the basic ingredients of TS. Let us define the notion of neighborhood N(s) for each solution s in X. By definition N(s) is a set of solutions in X reachable from s via a slig... |

6 |
Parallel tabu search techniques for the job shop scheduling problem
- Taillard
- 1994
(Show Context)
Citation Context ...er solution s′ located in the neighborhood N(s) of s. These moves are performed with the aim of efficiently reaching optimal solution by the evaluation of some objective function f(s) to be minimize=-=d [8]-=-[9][13]. In the sequel we sketch the basic ingredients of TS. Let us define the notion of neighborhood N(s) for each solution s in X. By definition N(s) is a set of solutions in X reachable from s via... |

5 |
Taillard E, de Werra D: A user’s guide to tabu search
- Glover
- 1993
(Show Context)
Citation Context ...solution s′ located in the neighborhood N(s) of s. These moves are performed with the aim of efficiently reaching optimal solution by the evaluation of some objective function f(s) to be minimized [=-=8][9]-=-[13]. In the sequel we sketch the basic ingredients of TS. Let us define the notion of neighborhood N(s) for each solution s in X. By definition N(s) is a set of solutions in X reachable from s via a ... |

3 |
Hybrid Heuristics for Optimal Design
- Abraham, Nath
- 2000
(Show Context)
Citation Context ... analogies from the natural and social systems have been widely accepted to form powerful heuristics, which have proven to be highly successful in solving several NP hard global optimization problems =-=[5]-=-[12][18]. Some of the common characteristics of nature's heuristics are the close resemblance of a phenomenon existing in nature, nondeterministic; present implicitly a parallel structure and adaptabi... |

2 |
A Genetic Algorithm For Scheduling Independent Jobs On Uniform
- Nath, Lim, et al.
- 1998
(Show Context)
Citation Context ...tion time the last job j finishes processing. Define Cmax = max {ΣCj /M, j=1,…,N}, the makespan and ΣCj , as the flowtime. An optimal schedule will be the one that optimizes the flowtime and makes=-=pan [6].-=- The conceptually obvious rule to minimize ΣCj is to schedule Figure 1. General Framework of a Computational Grid 2 Shortest Job on the Fastest Resource (SJFR). The simplest rule to minimize Cmax is ... |

2 |
A New Simulated Annealing Algorithm
- unknown authors
- 1995
(Show Context)
Citation Context ... " and T are control parameters. Several SAs have been developed with annealing schedule inversely linear in time (Fast SA), exponential function of time (Very Fast SA) etc. We explain a SA algor=-=ithm [10]-=-, which is exponentially faster than Very Fast SA whose annealing schedule is given by T T ( k) = 0 , where T k 0 is the initial temperature, T (k) exp( e ) is the temperature we wish to approach to z... |

2 |
Heuristics from Nature for Hard
- Colorni, Dorigo, et al.
- 1996
(Show Context)
Citation Context ...alogies from the natural and social systems have been widely accepted to form powerful heuristics, which have proven to be highly successful in solving several NP hard global optimization problems [5]=-=[12]-=-[18]. Some of the common characteristics of nature's heuristics are the close resemblance of a phenomenon existing in nature, nondeterministic; present implicitly a parallel structure and adaptability... |

1 | Buyya R, Laforenza D, The Grid: International Efforts - Baker - 2000 |

1 |
Genetic Annealing, Dr
- KV
- 1994
(Show Context)
Citation Context ...tion 4). Annealing results from repeated cycles of collecting energy from successful mutants and then redistributing nearly all of it by raising the threshold energy of each population member equally =-=[11]-=-. The GA-SA algorithm 6 for job scheduling on computational grid can be formulated as follows: 1 and 2 are the same as in section 6.1 3. Generate an initial population of P schedule vectors and for i ... |

1 |
A Hybrid GA-SA Algorithm for Flowshop Scheduling Problems
- Nath
- 1997
(Show Context)
Citation Context ...ion is set to the inverse of the flow time (ΣCj) from the generated schedule. The sequences of the jobs could be coded in a sequence of integer arrays. In our scheduling experimentations using GAs [6=-=][15]-=-, we have achieved good results using 2-point crossover operator and mutation operators with a selection probability of 1.0. 6.2 Hybrid GA-SA Approach for Job Scheduling on the Grid GA-SA is a hybrid ... |

1 |
Parallel Machine Scheduling using Genetic Algorithms
- Nath
- 1999
(Show Context)
Citation Context ... long time, whereas minimizing Cmax, asks that no job takes too long, at the expense of most jobs taking a long time. In summary, minimization of Cmax will result in maximization of ΣCj or vice versa=-= [17]-=-. 3 Genetic Algorithm (GA) GAs are adaptive methods that can be used to solve optimization problems, based on the genetic process of biological organisms. Over many generations, natural populations ev... |

1 |
A Genetic Algorithm for Resource Constrained Scheduling
- Wall
- 1996
(Show Context)
Citation Context ...ies from the natural and social systems have been widely accepted to form powerful heuristics, which have proven to be highly successful in solving several NP hard global optimization problems [5][12]=-=[18]-=-. Some of the common characteristics of nature's heuristics are the close resemblance of a phenomenon existing in nature, nondeterministic; present implicitly a parallel structure and adaptability. In... |