An enhanced genetic algorithm with new operators for task scheduling in heterogeneous computing systems

One of the important problems in heterogeneous computing systems is task scheduling. The task scheduling problem intends to assigns tasks to a number of processors in a manner that will optimize the overall performance of the system, i.e. minimizing execution time or maximizing parallelization in assigning the tasks to the processors. The task scheduling problem is an NP-complete and this is why the algorithms applied to this problem are heuristic or meta-heuristic by which we could reach a relatively optimal solution. This paper presents a genetic-based algorithm as a meta-heuristic method to address static task scheduling for processors in heterogeneous computing systems. The algorithm improves the performance of genetic algorithm through significant changes in its genetic functions and introduction of new operators that guarantee sample variety and consistent coverage of the whole space. Moreover, the random initial population has been replaced with some initial populations with relatively optimized solutions to lower repetitions in the genetic algorithm. The results of running this algorithm on the graphs of real-world applications and random graphs in heterogeneous computing systems with a wide range of characteristics, indicated significant improvements of efficiency of the proposed algorithm compared with other task scheduling algorithms.

[1]  Ehsan Ullah Munir,et al.  Efficient scheduling strategy for task graphs in heterogeneous computing environment , 2013, Int. Arab J. Inf. Technol..

[2]  Parag C. Pendharkar.  An ant colony optimization heuristic for constrained task allocation problem , 2015, J. Comput. Sci..

[3]  Pao-Ann Hsiung,et al.  Multi-objective exploitation of pipeline parallelism using clustering, replication and duplication in embedded multi-core systems , 2013, J. Syst. Archit..

[4]  Matteo Mario Savino,et al.  Simulation Approach to Optimize Production Costs Through Value Stream Mapping , 2009 .

[5]  Nikos S. Voros,et al.  Scheduling independent tasks on heterogeneous processors using heuristics and Column Pricing , 2016, Future Gener. Comput. Syst..

[6]  Yueh-Min Huang,et al.  Multiprocessor system scheduling with precedence and resource constraints using an enhanced ant colony system , 2008, Expert Syst. Appl..

[7]  Ming Luo,et al.  Dynamic batch scheduling in a continuous cycle-constrained production system , 2010 .

[8]  El-Sayed M. El-Horbaty,et al.  Intelligent cloud algorithms for load balancing problems: A survey , 2015, 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS).

[9]  Pier Luca Lanzi,et al.  Ant Colony Heuristic for Mapping and Scheduling Tasks and Communications on Heterogeneous Embedded Systems , 2010, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[10]  Prospero C. Naval,et al.  An effective use of crowding distance in multiobjective particle swarm optimization , 2005, GECCO '05.

[11]  Jian Jun Zhang,et al.  A Heuristic Greedy Algorithm for Scheduling Out-Tree Task Graphs , 2014 .

[12]  James C. Browne,et al.  General approach to mapping of parallel computations upon multiprocessor architectures , 1988 .

[13]  Zhihua Cui,et al.  Swarm Intelligence and Bio-Inspired Computation: Theory and Applications , 2013 .

[14]  Leandro Soares Indrusiak,et al.  A survey of scheduling metrics and an improved ordering policy for list schedulers operating on workloads with dependencies and a wide variation in execution times , 2013, Future Gener. Comput. Syst..

[15]  Hui Liu,et al.  HSIP: A Novel Task Scheduling Algorithm for Heterogeneous Computing , 2016, Sci. Program..

[16]  Nima Jafari Navimipour,et al.  Task Scheduling in Cloud Computing Based on The Cuckoo Search Algorithm , 2015, Iraqi Journal of Computer, Communication, Control and System Engineering.

[17]  Gurvinder Singh,et al.  Improved Task Scheduling on Parallel System using Genetic Algorithm , 2012 .

[18]  Jing Liu,et al.  A chaotic non-dominated sorting genetic algorithm for the multi-objective automatic test task scheduling problem , 2013, Appl. Soft Comput..

[19]  Pramod Kumar Mishra,et al.  Benchmarking the clustering algorithms for multiprocessor environments using dynamic priority of modules , 2012 .

[20]  Shigen Shen,et al.  Task Scheduling Optimization in Cloud Computing Based on Heuristic Algorithm , 2012, J. Networks.

[21]  Matteo Mario Savino,et al.  Kanban-driven parts feeding within a semi-automated O-shaped assembly line: a case study in the automotive industry , 2015 .

[22]  Hyunjin Kim,et al.  Communication-aware task scheduling and voltage selection for total energy minimization in a multiprocessor system using Ant Colony Optimization , 2011, Inf. Sci..

[23]  Kenli Li,et al.  A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues , 2014, Inf. Sci..

[24]  Sadiq M. Sait,et al.  A simulated evolution approach to task matching and scheduling in heterogeneous computing environments , 2002 .

[25]  Asif Ekbal,et al.  A new semi-supervised clustering technique using multi-objective optimization , 2015, Applied Intelligence.

[26]  Matteo Mario Savino,et al.  Agent-based flow-shop modelling in dynamic environment , 2014 .

[27]  Sarbjeet Singh,et al.  A review of metaheuristic scheduling techniques in cloud computing , 2015 .

[28]  P. Chitra,et al.  Modified genetic algorithm for multiobjective task scheduling on heterogeneous computing system , 2011, Int. J. Inf. Technol. Commun. Convergence.

[29]  Samee Ullah Khan,et al.  Multi-level hierarchic genetic-based scheduling of independent jobs in dynamic heterogeneous grid environment , 2012, Inf. Sci..

[30]  Dr. Vinay Kumar,et al.  A Scheduling Approach with Processor and Network Heterogeneity for Grid Environment , 2014 .

[31]  Kuldip Singh,et al.  An Improved Duplication Strategy for Scheduling Precedence Constrained Graphs in Multiprocessor Systems , 2003, IEEE Trans. Parallel Distributed Syst..

[32]  Deo Prakash Vidyarthi,et al.  A novel hybrid PSO–GA meta-heuristic for scheduling of DAG with communication on multiprocessor systems , 2015, Engineering with Computers.

[33]  Yuehui Chen,et al.  A Task Scheduling Algorithm Based on PSO for Grid Computing , 2008 .

[34]  Kenli Li,et al.  Maximizing reliability with energy conservation for parallel task scheduling in a heterogeneous cluster , 2015, Inf. Sci..

[35]  Parag C. Pendharkar,et al.  A multi-agent memetic algorithm approach for distributed object allocation , 2011, J. Comput. Sci..

[36]  Alessandro Brun,et al.  Dynamic workforce allocation in a constrained flow shop with multi-agent system , 2014, Comput. Ind..

[37]  Mitsuo Gen,et al.  A Performance Evaluation of Multiprocessor Scheduling with Genetic Algorithm , 2006 .

[38]  Jeffrey D. Ullman,et al.  NP-Complete Scheduling Problems , 1975, J. Comput. Syst. Sci..

[39]  Ishfaq Ahmad,et al.  Dynamic Critical-Path Scheduling: An Effective Technique for Allocating Task Graphs to Multiprocessors , 1996, IEEE Trans. Parallel Distributed Syst..

[40]  Amir Masoud Rahmani,et al.  A novel task scheduling in multiprocessor systems with genetic algorithm by using elitism stepping method , 2008 .

[41]  Tj,et al.  Comparative Study of Static Task Scheduling Algorithms for Heterogeneous Systems , 2013 .

[42]  Albert Y. Zomaya,et al.  Genetic Scheduling for Parallel Processor Systems: Comparative Studies and Performance Issues , 1999, IEEE Trans. Parallel Distributed Syst..

[43]  Salim Hariri,et al.  Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing , 2002, IEEE Trans. Parallel Distributed Syst..

[44]  Vikas Kumar,et al.  Task Scheduling in Multiprocessor System Using Genetic Algorithm , 2010, 2010 Second International Conference on Machine Learning and Computing.

[45]  Nawwaf N. Kharma,et al.  A hybrid heuristic-genetic algorithm for task scheduling in heterogeneous processor networks , 2011, J. Parallel Distributed Comput..

[46]  Yanyan Dai,et al.  A Synthesized Heuristic Task Scheduling Algorithm , 2014, TheScientificWorldJournal.

[47]  Ujjwal Maulik,et al.  A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA , 2008, IEEE Transactions on Evolutionary Computation.

[48]  Fatma A. Omara,et al.  Genetic algorithms for task scheduling problem , 2010, J. Parallel Distributed Comput..

[49]  Chee Sun Liew,et al.  A hybrid genetic algorithm for optimization of scheduling workflow applications in heterogeneous computing systems , 2016, J. Parallel Distributed Comput..

[50]  Purushothaman Damodaran,et al.  A simulated annealing algorithm to minimize makespan of parallel batch processing machines with unequal job ready times , 2012, Expert Syst. Appl..

[51]  Guiling Wu,et al.  Multi-objective optimization based on ant colony optimization in grid over optical burst switching networks , 2010, Expert Syst. Appl..

[52]  Jianqin Wang,et al.  A new algorithm for grid independent task schedule: Genetic simulated annealing , 2010, 2010 World Automation Congress.

[53]  Hui Lin,et al.  Hybrid Ant Colony Algorithm Clonal Selection in the Application of the Cloud's Resource Scheduling , 2014, ArXiv.

[54]  P. Dhavachelvan,et al.  Hybrid Algorithm using the advantage of ACO and Cuckoo Search for Job Scheduling , 2012 .

[55]  Jan Karel Lenstra,et al.  Job Shop Scheduling by Simulated Annealing , 1992, Oper. Res..

[56]  Hassan Rashidi,et al.  A multi-objectives scheduling algorithm based on cuckoo optimization for task allocation problem at compile time in heterogeneous systems , 2016, Expert Syst. Appl..

[57]  Wei Tan,et al.  Self-Adaptive Learning PSO-Based Deadline Constrained Task Scheduling for Hybrid IaaS Cloud , 2014, IEEE Transactions on Automation Science and Engineering.