Skip to main content
Log in

A bio-inspired distributed algorithm to improve scheduling performance of multi-broker grids

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

The scheduling in grids is known to be a NP-hard problem. The distributed deployment of nodes, their heterogeneity and their fluctuations in terms of workload and availability make the design of an effective scheduling algorithm a very complex issue. The scientific literature has proposed several heuristics able to tackle this kind of optimization problem using techniques and strategies inspired by nature. The algorithms belonging to ant colony optimization (ACO) paradigm represent an example of these techniques: each one of these algorithms uses strategies inspired by the self-organization ability of real ants for building effective grid schedulers. In this paper, the authors propose an on line, non-clairvoyant, distributed scheduling solution for multi-broker grid based on the alienated ant algorithm (AAA), a new ACO inspired technique exploiting a “non natural” behavior of ants and an inverse interpretation of pheromone trails. The paper introduces the proposed algorithm, explains the differences with other classical ACO approaches, and compares AAA with two different algorithms. The results of simulations show that the AAA guarantees good performance in terms of makespan, average queue waiting time and load balancing capability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Notes

  1. Usually, if it is not differently specified in JDL (Pacini) files, each UI refers to a specific “default” RB.

  2. In terms of previous submitted jobs.

  3. Inversely proportional to the quantity of pheromone.

  4. In these systems several RBs can submit their jobs to the same CEs.

References

  • Aggarwal M, Kent RD, Ngom A (2005) Genetic algorithm based scheduler for computational grids. In: Proceedings of 19th international symposium on high performance computing systems and applications, Los Alamitos, pp 209–215

  • Andrieux A, Berry D, Garibaldi J, Jarvis S, MacLaren J, Ouelhadj D, Snelling D (2003) Open issues in grid scheduling UK e-Science. Technical report series ISSN 1751-5971

  • Bandieramonte M, Di Stefano A, Morana G (2008) An ACO inspired strategy to improve jobs scheduling in a grid environment. In: Proceedings of ICA3 PP. Springer, Berlin, pp 30–41

  • Bandieramonte M, Di Stefano A, Morana G (2010a) Grid jobs scheduling: the alienated ant algorithm solution. Multiagent Grid Syst 6(3):225–243

    MATH  Google Scholar 

  • Bandieramonte M, Di Stefano A, Morana G (2010b) Pheromone impact on ants-based algorithms pheromones: theories, types and uses. Nova Publisher, New York, pp 283–300

  • Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35(3):268–308

    Article  Google Scholar 

  • Blum C, Sampels M (2002) Ant colony optimization for fop shop scheduling: a case study on different pheromone representations. In: Proceedings of the 2002 congress on evolutionary computing, Honolulu, pp 1558–1563

  • Blum C, Sampels M (2004) An ant colony optimization algorithm for shop scheduling problems. J Math Model Algorithms 3:285– 308

    Google Scholar 

  • Braun TD, Siegel HJ, Beck N, Boloni LL, Maheswaran M, Reuther AI, Robertson JP, Theys MD, Yao B, Hensgen D, Freund RF (2006) A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J Parallel Distrib Comput 61(6):810–837

    Article  Google Scholar 

  • Cao J, Spooner DP, Jarvis SA, Nudd GR (2005) Grid load balancing using intelligent agents. Futur Gener Comput Syst 21:135–149

    Article  Google Scholar 

  • Chang R-S, Chang J-S, Lin P-S (2009) An ant algorithm for balanced job scheduling in grids. Futur Gener Comput Syst 25:20–27

    Article  Google Scholar 

  • Chiang C-W, Lee Y-C, Lee C-N, Chou T-Y (2006) Ant colony optimization for task matching and scheduling. IEE Proc 153:373–380

    Google Scholar 

  • den Besten ML, Stutzle T, Dorigo M (2001) Design of iterated local search algorithms: an example application to the single machine total weighted tardiness problem. In: Proceedings of EvoStim01, lecture notes in computer science, Springer, Berlin, pp 441–452,

  • Di Caro G, Dorigo M (1999) AntNet: distributed stigmergetic control for communications networks. J Artif Intell Res 9:317–365

    Google Scholar 

  • Dong F, Akl Selim G (2006) Scheduling algorithms for grid computing: state of the art and open problems. Technical report No. 2006-504

  • Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. J Theor Comput Sci 344(2–3):243–278

    Article  MathSciNet  MATH  Google Scholar 

  • Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1:53–66

    Article  Google Scholar 

  • Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B 26(1):29–41

    Article  Google Scholar 

  • Ducatelle F, Di Caro G, Gambardella LM (2010) Principles and applications of swarm intelligence for adaptive routing in telecommunications networks. Swarm Intell 4(3):173–198. doi:10.1007/s11721-010-0040-x

    Google Scholar 

  • Farooq M, Di Caro G (2008) Routing protocols for next-generation intelligent networks inspired by collective behaviors of insect societies. In: Blum C, Merkle D (eds) Swarm intelligence: introduction and applications, natural computing series. Springer, Berlin, pp 101–160

    Google Scholar 

  • Fernandez-Baca D (1989) Allocating modules to processors in a distributed system. IEEE Trans Softw Eng 15(11):1427–1436

    Article  Google Scholar 

  • Fidanova S (2006) Simulated annealing for grid scheduling problem. In: IEEE John Vincent Atanasoff 2006 international symposium on modern computing, Sofia, pp 41–45

  • Fidanova S, Durchova M (2006) Ant algorithm for grid scheduling problem. Large scale computing, Lecture notes in computer science No. 3743, Springer, pp 405–412

  • Foster I, Kesselman C (1999) The grid: blueprint for a new computing infrastructure. Morgan Kaufmann Publishers, San Francisco (ISBN:1-558660-475-8)

  • Foster I, Kesselman C, Nick J, Tuecke S (2002) The physiology of the grid: an open grid services architecture for distributed systems integration. http://www.globus.org/alliance/publications/papers/ogsa.pdf. Accessed 07 March 2012

  • Gagliardi F, Jones B, Grey F, Begin ME, Heikkurinen M (2005) Building an infrastructure for scientific grid computing: status and goals of the EGEE project. Phil Trans R Soc A 363(1833):1729–1742. doi:10.1098/rsta.2005.1603

    Article  Google Scholar 

  • Gao Y, Rong H, Huang JZ (2005) Adaptive grid job scheduling with genetic algorithms. Futur Gener Comput Syst 21:151–161

    Article  Google Scholar 

  • Gambardella LM, Dorigo M (2000) Ant colony system hybridized with a new local search for the sequential ordering problem. INFORMS J Comput 12:237–255

    Article  MathSciNet  MATH  Google Scholar 

  • http://www.italiangrid.org. Accessed 06 March 2012

  • http://simgrid.gforge.inria.fr/. Accessed 06 March 2012

  • Jian Y, Liu Y (2007) The state of the art in grid scheduling systems third international conference on natural computation. Haikou

  • Kesselman C, Foster I, Tuecke S (2001) The anatomy of the grid—enabling scalable virtual organizations. Int J High Perform Comput Appl 15(3):200–222

    Article  Google Scholar 

  • Kousalya K, Balasubramanie P (2007) Resource scheduling in computational grid using ANT algorithm. In: Proceedings of the international conference on computer control and communications, Karachi

  • Kousalya K, Balasubramanie P (2008a) Ant algorithm for grid scheduling powered by local search. Int J Open Problems Comput Math 1(3)

  • Kousalya K, Balasubramanie P (2008b) An enhanced ant algorithm for grid scheduling problem. Int J Comput Sci Netw Secur 8(4):262–271

    Google Scholar 

  • Merkle D, Middendorf M, Schmeck H (2003) Ant colony optimization for resource-constrained project scheduling. IEEE Trans Evol Comput 6(4):333–346

    Google Scholar 

  • Pacini F. Job Descripon Language (JDL) EGEE Document, https://edms.cern.ch/file/590869/1/WMS-JDL.pdf. Accessed 06 March 2012

  • Pavani GS, Waldman H (2006) Grid resource management by means of ant colony optimization. In: Proceedings of 3rd international conference on broadband communications, networks and systems. BROADNETS 2006. San José, Print ISBN:978-1-4244-0425-4

  • Ramírez-Alcaraz JM, Tchernykh A, Yahyapour R, Schwiegelshohn U, Quezada-Pina A, González-García JL, Hirales-Carbajal A (2011) Job allocation strategies with user run time estimates for online scheduling in hierarchical grids. J Grid Comput 9(1):95–116

    Article  Google Scholar 

  • Reimann M, Doerner K, Hartl RF (2004) D-ants: savings based ants divide and conquer the vehicle routing problems. Comput Oper Res 31:563–591

    Article  MATH  Google Scholar 

  • Salari E, Eshghi K (2005) An ACO algorithm for graph coloring problem, ICSC congress on computational intelligence methods and applications, doi:10.1109/CIMA.2005.1662331

  • Schoonderwoerd R, Holland O, Bruten J (1997) Ant-like agents for load balancing. In: telecommunications networks proceedings of the first international conference on autonomous agents. Marina del Rey

  • Shan H, Oliker L, Smith W, Biswas R (1998) High-performance schedulers chapter in the grid: blueprint for a future computing infrastructure. Morgan Kaufmann Publishers, San Francisco

    Google Scholar 

  • Sim KM, Sun WH (2003) Ant colony optimization for routing and load-balancing: survey and new directions systems. Man Cybern Part A 33:560–572

    Article  Google Scholar 

  • Spooner DP, Jarvis SA, Cao J, Saini S, Nudd 1221 GR (2003) Local grid scheduling techniques using performance prediction. IEE Proc E 150:87–96

  • Stutzle T (1998) An ant approach to the flow shop problem. In: Proceedings of the 6th European congress on intelligent techniques & soft computing, Orlando

  • Stützle T, Dorigo M (1999) ACO algorithms for the quadratic assignment problem, New ideas in optimization, ISBN:0-07-709506-5, McGraw-Hill Ltd., London, pp 33–50

  • Thomas S, Holger HH (2000) MAX–MIN ant system. Futur Gener Comput Syst 16(9):889–914

    Google Scholar 

  • Tsafrir D, Etsion Y, Feitelson DG (2006) Modeling user runtime estimates. In: Proceedings of 11th workshop on job scheduling strategies for parallel processing LNCS, vol 3834. Springer, Cambridge, pp 1–35

  • Tsafrir D, Etsion Y, Feitelson DG (2007) Backfilling using system-generated predictions rather than user run-time estimates. IEEE Trans Parallel Distrib Syst 18:789–803

    Google Scholar 

  • Volker H, Uwe S, Achim S, Ramin Y (2000) Evaluation of job-scheduling strategies for grid computing. In: Proceedings lecture notes in computer science, Berlin, pp 1611–3349 (ISSN 0302-9743)

  • Yan H, Shen X-Q, Li X, Wu M-H (2005) An improved ant algorithm for job scheduling. In: Grid computing proceedings of the fourth international conference on machine learning and cybernetics, Guangzhou

  • Yang L, Schopf JM, Foster I (2003) Conservative sched-1224 uling: using predicted variance to improve scheduling decisions in dynamic environments. In: Proceedings of the 2003 ACM/IEEE conference on supercomputing, Phoenix, pp 31–47

  • Zhang L, Chen Y, Sun R, Jing S, Yang B (2008) A task scheduling algorithm based on PSO for grid computing. Int J Comput Intell Res 4(1):37–43

    Google Scholar 

  • Zhong L, Long Z, Zhang J, Song H (2011) An efficient memetic algorithm for job scheduling in computing grid information and automation, communications in computer and information science, vol 86. Springer, Berlin, pp 650–656

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Giovanni Morana.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Di Stefano, A., Morana, G. A bio-inspired distributed algorithm to improve scheduling performance of multi-broker grids. Nat Comput 11, 687–700 (2012). https://doi.org/10.1007/s11047-012-9319-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-012-9319-8

Keywords

Navigation