References
[1]. D. Saha, D. Menasce, and S. Porto, (1995).
“Static and
dynamic processor scheduling disciplines in
heterogeneous parallel architectures”, Journal of Parallel
and Distributed Computing, Vol.28(1), pp.1–18.
[2]. H. Yan, X. Qin, X. Li, and M.-H. Wu, (2005).
“An improved
ant algorithm for job scheduling in grid computing”,
Proceedings of 2005 International Conference on
Machine Learning and Cybernetics, Vol.5, pp.2957–2961.
[3]. M. Dorigo, and C. Blum, (2005). “Ant colony
optimization theory: A survey”, Theoretical Computer
Science, Vol.344(2–3), pp.243–278.
[4]. F. Dong, and S.K. Akl, (2006). “Scheduling
algorithms
for grid computing: State of the art and open problems”,
Technical Report No. 2006-504, School of Computing,
Queen's University, Kingston, Ontario, Canada.
[5]. Ruay-Shiung Chang, Jih-Sheng Chang, and Po-
Sheng Lin, (2009). “An ant algorithm for balanced job
scheduling in grids”, Elsevier/Future Generation
Computer Systems, Vol.25, pp.20–27.
[6]. K. Jairam Naik, (2012). “Scheduling Tasks on
Most
Suitable Fault tolerant Resource for Execution in Comp.
Grid”, IJGDC, Vol.5, No.3, pp.121–132.
[7]. K. Jairam Naik, (2013). “A Novel Fault-tolerant
Task
Scheduling Algorithm for Comp”, Grids, (15th ICACT- 978-1-4673-
2818-0/13 ©2013 IEEE).
[8]. D. Kondo, D.P. Anderson, and J. McLeod, (2007).
“Performance evaluation of scheduling policies for
volunteer computing”, Proc. IEEE International
Conference on e-Science and Grid Computing,
pp.415–422.
[9]. M. Dorigo, Ant Colony Optimization, Retrieved from
http://www.aco-metaheuristic.org.
[10]. BOINC website. Retrieved from http://boinc.
berkeley.edu/.
[11]. D. Paranhos, W. Cirne, and F. Brasileiro, (2003).
“Trading cycles for information: Using replication to
schedule bag-of-tasks applications on computational
grids”, International Conference on Parallel and
Distributed Computing (Euro-Par), Lecture Notes in
Computer Science, Vol.2790, pp.169–180.
[12]. M. Dorigo, and L.M. Gambardella, (1997). “Ant
colony system: A cooperative learning ap-proach to the
traveling salesman problem”, IEEE Transactions on
Evolutionary Computation, Vol.1(1), pp.53–66.
[13]. E.D. Taillard, and L.M. Gambardella, (1997).
“Adaptive memories for the quadratic assign-ment
problem”, Technical Report IDSIA-87-97, IDSIA, Lugano,
Switzerland.
[14]. T. Stutzle, (1997). MAX-MIN Ant System for
Quadratic
Assignment Problems, Technical Report AIDA-97-04,
Intellectics Group, Department of Compute Science,
Darmstadt University of Technology, Germany.
[15]. R. Buyya, and M. Murshed, (2012). “GridSim:
Practice and Experience”,14(2002)13–15 ,
Y.Haoetal./FGCS, Vol.28, pp.657–665 665.
[16]. F. Howell, (1998). “SimJava: a discrete Java
event
simulation package with appl. in comp. sys. modeling”,
1st Intnl Conf on Web-based Modelling and Simulation,
Society for Comp. Simulation, SanDiego, CA.
[17]. Huda M, Schmidt H, and Peake I., (2005). “An
agent
oriented proactive fault-tolerant framework for grid
computing”, Proceedings of International Conference
on E-Science and Grid Computing, Melbourne, Australia,
Vol.5–8, pp.304–11.
[18]. M. Dorigo, V. Maniezzo, and A. Colorni, (1996).
“The
ant system: Optimization by a colony of cooperating
agents”, IEEE Transactions on Systems, Man, and
Cybernetics, Part B, Vol.26(1), pp.29–41.
[19]. Jianfu Li, and Wei Zhang, (2006). “Solution
to multiobjective
optimization of flow shop problem based on
ACO algorithm”, Proceeding of 2006 International
Conference Computational Intelligence and Security,
Vol.1, pp.417–420.
[20]. E. Burke, G. Kendall, Silva Landa, R. O'Brien, and
E.
Soubeiga, (2005). “An ant algorithm hyperheuristic for the
project presentation scheduling problem”, IEEE Congress
on Evolutionary Computing, Vol.3, pp.2263–2270.
[21]. Xiaoxia Zhang, and Lixin Tang, (2005).
“CTACO—
hybridizing ant colony optimization with cycle
transfer search for the vehicle routing problem”, Congress
on Computational Intelligence Methods and
Applications, 15–17, pp.6.
[22]. E. Salari, K. Eshghi, (2005). “An ACO
algorithm for
graph coloring problem”, Congress on Computational
Intelligence Methods and Applications, Vol.15–17, pp.5.
[23]. J. Heinonen, and F. Pettersson, (2005).
“Hybrid ant
colony optimization and visibility studies applied to a jobshop
scheduling problem”, Applied Mathematics and
Computation, Vol.187(2), pp.989–998.
[24]. Kwang Mong Sim, and Weng Hong, Sun, (2005).
“Multiple ant-colony optimization for network routing”, in:
Proceedings of the First International Symposium on
Cyber Worlds, Vol.6–8, pp.277–281.
[25]. Robert G. Bland, Donald Goldfarb, and Michael J.
Todd, (1981). “Ellipsoid method, a survey”, Operations
Research, Vol.29(6), pp.1039–1091.
[26]. V. Sarkar, (1989). “Determining average
program
execution times and their variance”, Proceedings of the
ACM SIGPLAN Conference on Programming Language
Design and Implementation, pp.298–312.
[27]. C.Y. Park, (1993). “Predicting program
execution
times by analyzing static and dynamic program paths”,
Real-Time Systems, Vol.5(1), pp.31–62
[28]. J. Engblom, and A. Ermedahl, (2000).
“Modeling
complex flows for worst-case execution time analysis”,
Proceedings of the 21st IEEE Real-Time Systems
Symposium, pp.163–174.
[29]. F. Stappert, and P. Altenbernd, (2000).
“Complete
worst-case execution time analysis of straight-line hard
real-time programs”, Journal of Systems Architecture,
Vol.46(4), pp.339–355.
[30]. M. Maheswaran, S. Ali, H.J. Siegel, D. Hensgen, and
R. Freund, (1999). “Dynamic matching and scheduling of
a class of independent tasks onto heterogeneous
computing system”, Journal of Parallel and Distributed
Computing, Vol.59, pp.107–131.
[31]. B. Bullnheimer, R.F. Hartl, and C. Strauss, (1999).
“A
new rank-based version of the ant system: A
computational study”, Central European Journal for
Operations Research and Economics, Vol.7(1),
pp.25–38.
