Constrained Multi-objective Optimization Method for Practical Scientific Workflow Resource Selection

This paper presents and evaluates a constrained multi-objective optimization method for scientific workflow resource selection that uses equivalent transformation for constraint handling. Two different approaches are compared using a case study of optimal cloud resource configuration selection for a practical genomic analytics workflow. In the first approach, called the nondominated sorting equivalent transformation (NSET) method, feasible configurations are generated via equivalent transformation and the Pareto-optimal configurations are selected from among them via a process of nondominated sorting, reference points association, and niching/elitism. In the second approach, Pareto-optimal configurations are generated via the nondominated sorting genetic algorithms II/III (NSGA-II/III) and feasible configurations are generated via equivalent transformation. Then, the configurations that are common to both processes are considered to be both feasible and optimal. Preliminary results based on the Pareto-optimal configuration sets generated by NSGA-II/III indicate that NSET is feasible for constrained multi-objective optimization of practical scientific workflow resource selection problems.

[1]  Rajkumar Buyya,et al.  Optimizing the makespan and reliability for workflow applications with reputation and a look-ahead genetic algorithm , 2011, Future Gener. Comput. Syst..

[2]  Masaharu Munetomo,et al.  A Predicate Logic-Defined Specification Method for Systems Deployed by Intercloud Brokerages , 2016, 2016 IEEE International Conference on Cloud Engineering Workshop (IC2EW).

[3]  W. Gui,et al.  A multi-objective state transition algorithm for continuous optimization , 2017, 2017 36th Chinese Control Conference (CCC).

[4]  Qingfu Zhang,et al.  Push and Pull Search for Solving Constrained Multi-objective Optimization Problems , 2017, Swarm Evol. Comput..

[5]  GeunSik Jo,et al.  A genetic algorithm for multi-objective optimisation in workflow scheduling with hard constraints , 2013, Int. J. Metaheuristics.

[6]  Bernhard Sendhoff,et al.  A critical survey of performance indices for multi-objective optimisation , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[7]  Victor W. Marek.  Book review: The Art of Prolog Advanced Programming Techniques by L. Sterling and E. Shapiro (The MIT Press) , 1988, SGAR.

[8]  Yalan Zhou,et al.  Ensemble of many-objective evolutionary algorithms for many-objective problems , 2015, Soft Computing.

[9]  Kiyoshi Akama,et al.  Formalization of the Equivalent Transformation Computation Model , 2006, J. Adv. Comput. Intell. Intell. Informatics.

[10]  Qingfu Zhang,et al.  An Effective Ensemble Framework for Multiobjective Optimization , 2019, IEEE Transactions on Evolutionary Computation.

[11]  Hisao Ishibuchi,et al.  Benchmarking Multi- and Many-Objective Evolutionary Algorithms Under Two Optimization Scenarios , 2017, IEEE Access.

[12]  C. Powell.  A Formal Methodology for Concurrent Componentwise Development of Rich Internet Applications , 2011 .

[13]  A. E. Eiben,et al.  Evolutionary Algorithm Parameters and Methods to Tune Them , 2012, Autonomous Search.

[14]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[15]  Changhe Li,et al.  Many-objective optimization with dynamic constraint handling for constrained optimization problems , 2017, Soft Comput..

[16]  E. Barrett,et al.  Predicting the Available Bandwidth on Intra Cloud Network Links for Deadline Constrained Workflow Scheduling in Public Clouds , 2017, ICSOC.

[17]  Carlos A. Coello Coello,et al.  Constraint-handling in nature-inspired numerical optimization: Past, present and future , 2011, Swarm Evol. Comput..

[18]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[19]  Yannis E. Ioannidis,et al.  Multi-objective optimization of scheduling dataflows on heterogeneous cloud resources , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[20]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[21]  Wilhelm Hasselbring,et al.  Search-based genetic optimization for deployment and reconfiguration of software in the cloud , 2013, 2013 35th International Conference on Software Engineering (ICSE).

[22]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.