Papers by Courtney Powell
Evaluation of Three Steady-State NSGA-III Offspring Selection Schemes for Many-Objective Optimization
2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS), 2016
In this study, we implemented three steady-state versions of NSGA-III that differ by the manner i... more In this study, we implemented three steady-state versions of NSGA-III that differ by the manner in which the offspring combined with the parent population is selected. These three schemes were then evaluated on the standard problem sets DTLZ1–4 in terms of four popular criteria: inverse generational distance (IGD), hypervolume (HV), convergence, and diversity. The results obtained suggest that utilizing a selection scheme in which the offspring is selected from the first non-dominated rank results in better solutions than other steady-state offspring selection schemes.
Towards a small diverse pareto-optimal solutions set generator for multiobjective optimization problems
Proceedings of the Genetic and Evolutionary Computation Conference Companion, Jul 6, 2018
Multiobjective evolutionary algorithms (MOEAs) try to produce enough and sufficiently diverse Par... more Multiobjective evolutionary algorithms (MOEAs) try to produce enough and sufficiently diverse Pareto-optimal tradeoff solutions to cover the entire Pareto surface. However, in practical scenarios, presenting numerous solutions to stakeholders may result in confusion and indecision. This paper proposes a method for generating a small (user-specified) number of well-distributed Pareto-optimal feasible solutions for multiobjective problems. The proposed method can be applied to a set of aggregate solutions produced by (1) one MOEA over multiple runs, (2) several different MOEAs, or (3) a universal set of feasible solutions produced by one or more constraint solvers.
Research Square (Research Square), Jan 24, 2022
This paper proposes a framework for time-efficiently finding multiple answers that meet multiple ... more This paper proposes a framework for time-efficiently finding multiple answers that meet multiple logical constraints and are Pareto-optimal for multiple objective functions. The proposed framework determines a set of optimal answers by iteratively replacing a set of definite clauses. Clause replacement is performed using a SAT solver consisting of equivalent transformation rules (ETRs). An ETR replaces a definite clause with one or more clauses while preserving the declarative meaning of the union of the original clause and problem clauses. To efficiently find optimal answers, in this paper, we define a new class of ETRs that are generated based on the evaluation results of a multi-objective genetic algorithm (MOGA), and propose a method for generating ETRs that belong to the new class. ETRs belonging to the new class help to replace definite clauses according to user objectives such as cost-benefit performance, reliability, and financial constraints. Thus, a SAT solver that uses the new ETR class in addition to extant ETR classes can preferentially replace definite clauses that produce the optimal answer for user objectives. Experimental results indicate that the proposed framework can significantly reduce the computation time and memory usage necessary to determine a set of optimal answers for user objectives.
Optimal and Feasible Cloud Resource Configurations Generation Method for Genomic Analytics Applications
This paper proposes a new method that efficiently generates optimal and feasible cloud resource c... more This paper proposes a new method that efficiently generates optimal and feasible cloud resource configurations for deploying genomic analytics applications. The proposed method generates optimal and feasible configurations with respect to system requirements by employing two different problem-solving techniques: an equivalent transformation algorithm and a multi-objective genetic algorithm. The equivalent transformation algorithm first generates feasible configurations through computation based on (1) state replacement of clause sets representing various resource configurations and (2) evaluation of their equivalence in terms of declarative meaning to the given requirements. Subsequently, the multi-objective genetic algorithm identifies the optimal configurations with respect to estimated financial cost and computational performance. The input to the proposed method is a logical formula describing the system requirements, whereas the output is a set of unit clauses representing Pareto-optimal and feasible cloud resource configurations for deploying a given genomic analytics application. The results of experiments conducted using a sample genomic analytics workflow and Amazon EC2 instances verify the efficacy of the proposed method.
Constrained Multi-objective Optimization Method for Practical Scientific Workflow Resource Selection
Lecture Notes in Computer Science, 2019
This paper presents and evaluates a constrained multi-objective optimization method for scientifi... more 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.
Constrained Multi-objective Optimization Method for Practical Scientific Workflow Resource Selection
Lecture Notes in Computer Science, 2019
This paper presents and evaluates a constrained multi-objective optimization method for scientifi... more 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.
A Level-Wise Load Balanced Scientific Workflow Execution Optimization Using NSGA-II
2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 2017
Over the past decade, cloud computing has grown in popularity for the processing of scientific ap... more Over the past decade, cloud computing has grown in popularity for the processing of scientific applications as a result of the scalability of the cloud and the ready availability of on-demand computing and storage resources. It is also a cost-effective alternative for scientific workflow executions with a pay-per-use paradigm. However, providing services with optimal performance at the lowest financial resource deployment cost is still challenging. Several fine-grained tasks are included in scientific workflow applications, and efficient execution of these tasks according to their processing dependency to minimize the overall makespan during workflow execution is an important research area. In this paper, a system for level-wise workflow makespan optimization and virtual machine deployment cost minimization for overall workflow optimization in cloud infrastructure is proposed. Further, balanced task clustering, to ensure load balancing in different virtual machine instances at each workflow level during workflow execution, is also considered. The system retrieves the necessary workflow information from a directed acyclic graph and uses the non-dominated sorting genetic algorithm II (NSGA-II) to carry out multiobjective optimization. Pareto front solutions obtained for makespan time and instance resource deployment cost for several scientific workflow applications verify the efficacy of our system.
Soft Computing, 2022
This paper proposes a framework for time-efficiently finding multiple answers that meet multiple ... more This paper proposes a framework for time-efficiently finding multiple answers that meet multiple logical constraints and are Pareto-optimal for multiple objective functions. The proposed framework determines a set of optimal answers by iteratively replacing a set of definite clauses. Clause replacement is performed using a SAT solver consisting of equivalent transformation rules (ETRs). An ETR replaces a definite clause with one or more clauses while preserving the declarative meaning of the union of the original clause and problem clauses. To efficiently find optimal answers, in this paper, we define a new class of ETRs that are generated based on the evaluation results of a multi-objective genetic algorithm (MOGA), and propose a method for generating ETRs that belong to the new class. ETRs belonging to the new class help to replace definite clauses according to user objectives such as cost-benefit performance, reliability, and financial constraints. Thus, a SAT solver that uses the new ETR class in addition to extant ETR classes can preferentially replace definite clauses that produce the optimal answer for user objectives. Experimental results indicate that the proposed framework can significantly reduce the computation time and memory usage necessary to determine a set of optimal answers for user objectives.
Optimal and Feasible Cloud Resource Configurations Generation Method for Genomic Analytics Applications
2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), 2018
This paper proposes a new method that efficiently generates optimal and feasible cloud resource c... more This paper proposes a new method that efficiently generates optimal and feasible cloud resource configurations for deploying genomic analytics applications. The proposed method generates optimal and feasible configurations with respect to system requirements by employing two different problem-solving techniques: an equivalent transformation algorithm and a multi-objective genetic algorithm. The equivalent transformation algorithm first generates feasible configurations through computation based on (1) state replacement of clause sets representing various resource configurations and (2) evaluation of their equivalence in terms of declarative meaning to the given requirements. Subsequently, the multi-objective genetic algorithm identifies the optimal configurations with respect to estimated financial cost and computational performance. The input to the proposed method is a logical formula describing the system requirements, whereas the output is a set of unit clauses representing Pareto-optimal and feasible cloud resource configurations for deploying a given genomic analytics application. The results of experiments conducted using a sample genomic analytics workflow and Amazon EC2 instances verify the efficacy of the proposed method.
Optimal Cloud Resource Selection Method Considering Hard and Soft Constraints and Multiple Conflicting Objectives
2018 IEEE 11th International Conference on Cloud Computing (CLOUD), 2018
This paper proposes a method for selecting optimal cloud resource configurations that satisfy har... more This paper proposes a method for selecting optimal cloud resource configurations that satisfy hard and soft user constraints and multiple conflicting objectives. In the proposed method, feasible configurations generation and optimal configurations selection are carried out as two separate and independent processes that execute either sequentially or concurrently depending on the level of complexity of the optimization problem and the size of its solution space. The feasible configurations generation process utilizes an equivalent transformation-based constraint satisfaction method to generate the universe of feasible resource configurations that satisfy user requirements and constraints for a given cloud resource selection problem. In the optimal resource configurations selection process, nondominated sorting, reference points association, and niching/elitism are performed to produce a user-specified number of diverse Pareto-optimal configurations from the feasible resource configurations universe. The results of application of the proposed method to (1) a three-tier web application and (2) a cloud-based workflow resource allocation scenario verify its efficacy.
Towards a small diverse pareto-optimal solutions set generator for multiobjective optimization problems
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018
Multiobjective evolutionary algorithms (MOEAs) try to produce enough and sufficiently diverse Par... more Multiobjective evolutionary algorithms (MOEAs) try to produce enough and sufficiently diverse Pareto-optimal tradeoff solutions to cover the entire Pareto surface. However, in practical scenarios, presenting numerous solutions to stakeholders may result in confusion and indecision. This paper proposes a method for generating a small (user-specified) number of well-distributed Pareto-optimal feasible solutions for multiobjective problems. The proposed method can be applied to a set of aggregate solutions produced by (1) one MOEA over multiple runs, (2) several different MOEAs, or (3) a universal set of feasible solutions produced by one or more constraint solvers.
Scientific Programming, 2017
Cloud computing in the field of scientific applications such as scientific big data processing an... more Cloud computing in the field of scientific applications such as scientific big data processing and big data analytics has become popular because of its service oriented model that provides a pool of abstracted, virtualized, dynamically scalable computing resources and services on demand over the Internet. However, resource selection to make the right choice of instances for a certain application of interest is a challenging problem for researchers. In addition, providing services with optimal performance at the lowest financial resource deployment cost based on users’ resource selection is quite challenging for cloud service providers. Consequently, it is necessary to develop an optimization system that can provide benefits to both users and service providers. In this paper, we conduct scientific workflow optimization on three perspectives: makespan minimization, virtual machine deployment cost minimization, and virtual machine failure minimization in the cloud infrastructure in a l...
Intercloud brokerages based on PLS method for deploying infrastructures for big data analytics
2016 IEEE International Conference on Big Data (Big Data), 2016
This paper proposes an intercloud brokerage method for system infrastructure deployments of genom... more This paper proposes an intercloud brokerage method for system infrastructure deployments of genomic big data analytics workflows. The proposed method utilizes a conjunction of universally quantified atomic formula to describe requirements given by users, and selects combinations of cloud services based on logical reasoning by the replacement of definite clause sets created from conjunction of the atomic formulas, while preserving the declarative meaning of the system infrastructures' constraint conditions. We also define algorithms for the replacement of definite clause sets, and present an example of the use of the proposed intercloud brokerage method.
Springer eBooks, 2013
A new wearable computing era featuring devices such as Google Glass, smartwatches, and digital co... more A new wearable computing era featuring devices such as Google Glass, smartwatches, and digital contact lenses is almost upon us, bringing with it usability issues that conventional human computer interaction (HCI) modalities cannot resolve. Brain computer interface (BCI) technology is also rapidly advancing and is now at a point where noninvasive BCIs are being used in games and in healthcare. Thought control of wearable devices is an intriguing vision and would facilitate more intuitive HCI; however, to achieve even a modicum of control BCI currently requires massive processing power that is not available on mobile devices. Cloud computing is a maturing paradigm in which elastic computing power is provided on demand over networks. In this paper, we review the three technologies and take a look at possible ways cloud computing can be harnessed to provide the computational power needed to facilitate practical thought control of next-generation wearable computing devices.
Formal Methods has been proven to improve software quality and provide a method of systematic sof... more Formal Methods has been proven to improve software quality and provide a method of systematic software construction. However, it has yet to gain widespread acceptance in the software industry. In this paper we look at the benefits to be derived from the use of Formal Methods and examine the obstacles to widespread acceptance. We also introduce the Equivalent Transformation Language (ETL)-A formal specification language that we feel can help to overcome some of these obstacles. We explain how this can be done by first examining the mathematical foundation of the ETL and then demonstrate the capabilities of ETL by systematically constructing an ETL specification for the cell automaton "Life". Finally, we discuss the benefits of using ETL as a formal specification language.
2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS)
Conceptualizing, visualizing, reasoning about and implementing Dynamic Interactive Systems (DISs)... more Conceptualizing, visualizing, reasoning about and implementing Dynamic Interactive Systems (DISs) are difficult and error-prone activities. To conceptualize and reason about the sorts of properties expected of any DIS, a framework that most naturally models DISs is essential. The declarative paradigm is closer than any other to the abstract behavior of DISs. In this paper we propose and explain why the Equivalent Transformation Framework (with its declarative roots) is an ideal framework for conceptually modeling DIS. The benefits to be derived from using this framework include guaranteed system correctness, high level abstraction, clarity, granular modularity, and an integrated framework for reasoning about, manipulating, and optimizing the various aspects of DISs.
Componentwise Modelling and Synthesis of Dynamic Interactive Systems using the Equivalent Transformation Framework
International Journal of Innovative Computing Information Control Ijicic, Jul 1, 2011
Abstract. The development and maintenance of Concurrent Systems, Reactive Systems and Dynamic Sys... more Abstract. The development and maintenance of Concurrent Systems, Reactive Systems and Dynamic Systems, remain fraught with challenges attributable to factors such as: 1) the tendency of concurrently executing processes to interact in unforeseen ways; 2) the ...
A Formal Methodology for Behavioral Modelling and Synthesis of Data-driven Rich Internet Applications
Rich Internet Applications (RIAs) seek to combine the best of traditional desktop applications wi... more Rich Internet Applications (RIAs) seek to combine the best of traditional desktop applications with the best of the Web. However, due to the complexity of these applications, traditional Web Application methodologies and techniques are proving inadequate to correctly model and implement them in a systematic way. In this paper we outline a stepwise incremental equivalent transformation-based methodology for systematically constructing behavioral models for RIAs and also for synthesizing implementation-level code from these models. We also introduce a composite RIA behavioral model and demonstrate its efficacy by using it to model a Bank Teller RIA; and extending it to model a GIS application.
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Papers by Courtney Powell