Guided Search-Based Multi-Objective Evolutionary Algorithm for Grid Workflow Scheduling

Currently the research on the grid workflow scheduling algorithms commonly suffer by one or several of the following drawbacks: grid workflow model of directed acyclic graph (DAG), not enough considerations of multi-dimensional Quality of Service (QoS) parameters and single objective optimal scheduling. Based on abstract grid workflow language (AGWL) model, this paper presents an algorithm for multi-objective optimal grid workflow scheduling with QoS constraints, which make uses of an improved NSGA-II algorithm. Theoretical analysis and experimental results indicate the feasibility and efficiency of the algorithm.KeywordsQuality of service (QoS)grid workflow schedulingNSGA-II

Feature selection aims to select a small subset of relevant features while maintaining or even improving the classification performance over using all features. Feature selection can be considered as a multi-objective problem, i.e., minimizing the number of selected features and maximizing the classification accuracy (minimizing the classification error) simultaneously. Most evolutionary multi-objective algorithms encounter difficulties when handling a feature selection task due to the discrete search space, although they perform well on continuous/numeric optimization problems. This paper proposes a grid-dominance based multi-objective evolutionary algorithm to address feature selection. The aim is to explore the potential of the grid-dominance method to strengthen the selection pressure toward the optimal direction while maintaining an extensive distribution among the objective values of feature subsets. To increase the population diversity, a subset filtration mechanism is proposed. The performance of the proposed two algorithms is tested on fourteen datasets of varying difficulty. With the proposed methods, the performance metrics, hypervolume and inverted generational distance have been significantly improved compared with other commonly used multi-objective algorithms, and the population diversity has also been increased.

A Scheduler based on Resource Competition for Parameter Sweep Workflow

Hybrid flow shop scheduling (HFS) is an on sought problem modelling for production manufacturing. Due to its impact on productivity, researchers from different backgrounds have been attracted to solve its optimum solution. The HFS is a complex dilemma and provides ample solutions, thus inviting researchers to propose niche optimization methods for the problem. Recently, researchers have moved on to multi-objective solutions. In real-world situations, HFS is known for multi-objective problems, and consequently, the need for optimum solutions in multi-objective HFS is a necessity. Regarding sustainability topic, energy utilization is mainly considered as one of the objectives, including the common makespan criteria. This paper presents the existing multi-objective approach for solving energy utilization and makespan problems in HFS scheduling using Non-Dominated Sorting Genetic Algorithm-III (NSGA-III), and a comparison to other optimization models was subjected for analysis. The model was compared with the most sought algorithm and latest multi-objective algorithms, Strength Pareto Evolutionary Algorithm 2 (SPEA -II), Multi-Objective Algorithm Particle Swarm Optimization (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D). The research interest starts with problem modelling, followed by a computational experiment with an existing multi-objective approach conducted using twelve HFS benchmark problems. Then, a case study problem is presented to assess all models. The numerical results showed that the NSGA-III obtained 50% best overall for distribution performance metrics and 42% best in convergence performance metrics for HFS benchmark problems. In addition, the case study results show that NSGA-III obtained the best overall convergence and distribution performance metrics. The results show that NSGA-III can search for the best fitness solution without compromising makespan and total energy utilization. In the future, these multi-objective algorithms’ potential can be further investigated for hybrid flow shop scheduling problems.

The design of Renewable Energy Power Plants (REPPs) is crucial not only for the investments' performance and attractiveness measures, but also for the maximization of resource (source) usage (e.g. sun, water, and wind) and the minimization of raw materials (e.g. aluminum: Al, cadmium: Cd, iron: Fe, silicon: Si, and tellurium: Te) consumption. Hence, several appropriate and satisfactory Multi-Objective Problems (MOPs) are mandatory during the REPPs' design phases. MOPs related tasks can only be managed by very well organized knowledge acquisition on all REPPs' design equations and models. The proposed MOPs need to be solved with one or more multi-objective algorithm, such as Multi-Objective Evolutionary Algorithms (MOEAs). In this respect, the first aim of this research study is to start gathering knowledge on the REPPs' MOPs. The second aim of this study is to gather detailed information about all MOEAs and available free software tools for their development. The main contribution of this research is the initialization of a proposed multi-objective evolutionary algorithm knowledge acquisition system for renewable energy power plants (MOEAs-KAS-F-REPPs) (research and development loopwise process: develop, train, validate, improve, test, improve, operate, and improve). As a simple representative example of this knowledge acquisition system research with two selective and elective proposed standard objectives (as test objectives) and eight selective and elective proposed standard constraints (as test constraints) are generated and applied as a standardized MOP for a virtual small hydropower plant design and investment. The maximization of energy generation (MWh) and the minimization of initial investment cost (million €) are achieved by the Multi-Objective Genetic Algorithm (MOGA), the Niched Sharing Genetic Algorithm/Non-dominated Sorting Genetic Algorithm (NSGA-I), and the NSGA-II algorithms in the Scilab 6.0.0 as only three standardized MOEAs amongst all proposed standardized MOEAs on two desktop computer configurations (Windows 10 Home 1709 64 bits, Intel i5-7200 CPU @ 2.7 GHz, 8.00 GB RAM with internet connection and Windows 10 Pro, Intel(R) Core(TM) i5 CPU 650 @ 3.20 GHz, 6,00 GB RAM with internet connection). The algorithm run-times (computation time) of the current applications vary between 20.64 and 59.98 seconds.

Resource performance in the computational grid is not only heterogeneous, but also changing dynamically. However scheduling algorithms designed for traditional parallel and distributed systems, such as clusters, only consider the heterogeneity of the resources. In this paper, a workflow scheduling algorithm, called PFAS, is proposed and tested in the grid environment. PFAS considers dynamic resource performance fluctuation in the grid, and conducts the scheduling according to its knowledge of the fluctuation. This new algorithm works in an offline way which allows it to be easily set up and run with less cost. Simulations show that our approach can achieve better schedules than the HEFT algorithm.

A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics.

Although technology changes quick still more sophisticated computational techniques are needed to preserve them. The majority of the computational grids work-load-logs show that node or job failure is the major challenging task to deal with. Since very robust scheduling algorithms are used to handle varied resource allocation in computational grids. However there is a need in previous studies to remedy the failures and delay of executing jobs with respect to resource availability, which can handle both scheduling and efficient failure handling in any large scale high performance computational applications. Consequently the major issues concerned here is fault-tolerance to tolerate failures with regard to job scheduling and efficient failure handling mechanism. So synchronization is needed to embed both techniques. Recurrently using techniques for fault tolerance in the widely held computational applications are periodic job checkpointing and replication. Hence most of the job checkpointing techniques are not merely based on scheduling algorithm. This work presents an automated checkpointing strategy in computational grid based on different scheduling algorithms. Experimental results have shown that the proposed automated checkpointing of jobs based on fault tolerant scheduling strategy has got considerable improvement over conventional adaptive checkpointing algorithms.

Knowledge-driven adaptive evolutionary multi-objective scheduling algorithm for cloud workflows

Workflow technology has been adopted in scientific domains to orchestrate and automate scientific processes in order to facilitate experimentation. Such scientific workflows often involve large data sets and intensive computation that necessitate the use of the Grid, which offers supercomputing power through shared distributed resources. To execute a scientific workflow in the Grid, tasks within the workflow that represent steps in the scientific process are assigned to Grid resources for execution. To ensure efficient execution of the workflow, Grid workflow scheduling is required to manage the allocation of Grid resources. Although many Grid workflow scheduling techniques exist, they are mainly designed for the execution of a single workflow. This is not the case with parameter sweep workflows which focus on parametric study and parameter optimisation. A parameter sweep workflow is executed numerous times with different input parameters in order to determine the effect of each parameter combination on the experiment. While executing multiple instances of a parameter sweep workflow in parallel can reduce the time required for the overall execution, this execution introduces new challenges to Grid workflow scheduling. Not only is a scheduling algorithm that is able to manage multiple workflow instances required, but this algorithm also needs the ability to schedule tasks across multiple workflow instances judiciously, as tasks may require the same set of Grid resources. Without appropriate resource allocation, resource competition problem could arise. In the thesis, we propose a new Grid workflow scheduling technique for parameter sweep workflow called the Besom scheduling algorithm. The scheduling decision of our algorithm is based on the resource dependencies of tasks in the workflow, as well as conventional Grid resource-performance metrics. In addition, the proposed technique is extended to handle loop structures in scientific workflows without using existing loop-unrolling techniques. We evaluate the Besom algorithm under a variety of conditions. A comparison between the simulation results of the Besom algorithm and of the three existing Grid workflow scheduling algorithms shows that the Besom algorithm is able to perform better than the existing algorithms for workflows that have complex structures and that involve overlapping resource dependencies of tasks. The Besom scheduling algorithm advances the ability to schedule parallel execution of parameter sweep workflows in the Grid. The outcomes of this thesis justify continuing research in this area to increase our understanding of scheduling multiple Grid workflow instances and to provide support to those involved in parametric study and scientific workflow management.

Orthogonal simulated annealing for multiobjective optimization

This paper addresses a multiobjective scheduling problem in the permutation flow shop. The objectives are to minimize makespan and total flow time. The proposed approach is based on the framework of memetic algorithm, which is known as a hybrid of genetic algorithm and local search. The local search procedure is an iterative process repeating neighbor generation, neighbor evaluation, and neighbor selection. We take a problem-specific heuristic for neighbor generation and propose several strategies for neighbor evaluation and neighbor selection. Archive injection (adding non-dominated solutions to the population) is another issue under investigation. We examine the effects of the proposed strategies through experiments using forty widely used problem instances with different scales. We also evaluate the proposed approach by comparing it with other twenty-six ones in terms of three performance metrics. Our approach outperforms all benchmarks and updates a large portion of the sets of best known non-dominated solutions for large-scale instances.

In this work, a new multiobjective optimization algorithm called multiobjective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of transferring students from high school to college. The proposed technique produces a set of non-dominated solutions. To judge the ability and efficacy of the proposed multiobjective algorithm, it is evaluated against a group of benchmarks and five real-world engineering optimization problems. Additionally, to evaluate the proposed technique quantitatively, several most widely used metrics are applied. Moreover, the results are confirmed statistically. The proposed work is then compared with three multiobjective algorithms, which are MOWCA, NSGA-II, and MODA. Similar to the proposed technique, the other algorithms in the literature were run against the benchmarks, and the real-world engineering problems utilized in the paper. The algorithms are compared with each other employing descriptive, tabular, and graphical demonstrations. The results proved the ability of the proposed work in providing a set of non-dominated solutions, and that the algorithm outperformed the other participated algorithms in most of the cases.

Cloud record facilities provide flexible and scalable surroundings for executing loads of workloads. However, efficient useful resource allocation is crucial for the effective transport of cloud offerings, making sure they're fee-efficient and keep Quality of Service (QoS). Multi-Objective Scheduling Algorithms (MOSAs) have emerged as a promising solution to balance performance, value, and QoS metrics in cloud environments. This study investigates the effectiveness of numerous MOSAs for optimizing useful resource allocation in cloud workloads. We behavior a comparative has a look at of several algorithms, assessing their overall performance, fee implications, and QoS metrics across various cloud situations. Our method includes a quantitative analysis of execution time and fee, along with a qualitative assessment of QoS attributes inclusive of availability, reliability, and response time. Additionally, we discover how wonderful workload traits along with batch processing, real-time processing, and fact-intensive analytics impact the performance of those algorithms. The findings will provide valuable insights into the strengths and weaknesses of various scheduling algorithms and inform the future layout of greater effective cloud scheduling structures

Simultaneous optimization of two or more objectives is an instance of multi-objective optimization (MOO). However, for most of the Multi-Objective Problems (MOPs), no single solution can optimize all the objective functions simultaneously. Evolutionary algorithms are a type of optimization algorithm used for constructing a well-distributed optimal front more quickly than a much more efficient approach. One of the most commonly used algorithms in this regard is differential evolution (DE). To solve the problem of non-dominated sorting for Multi-Objective DE (MODE), an approach is proposed that reduces the time complexity. DE is commonly recognized as one of the best methods for solving the MOPs. Several versions of DEs have been proposed for MOPs. This article proposes a novel mutation technique, named Environmental Parameter-based Multi-Objective Differential Evolution (EP-MODE) that acquires two additional environmental parameters to preserve diversity and accelerate the convergence. The proposed approach also reduces the time complexity of non-dominated sorting for Multi-Objective DE (MODE). The proposed mutation's performance has been evaluated on DTLZ series and ZDT series benchmark test functions on the Pareto-optimal front and compared with existing multi-objective algorithms (MODE and MODE-RMO). The solution comparatively verifies that the proposed EP-MODE outperforms most of the MODEs for lower dimension functions and higher functions. HIGHLIGHTS New environmental parameters-based mutation has been introduced in a differential evolution algorithm to solve multi-objective optimization problems (EP-mode) The addition of two additional environmental parameters to EP-mode has been found to be appropriate for maintaining diversity while also accelerating the convergence rate EP-MODE evaluated on bi-objective (DTLZ series) and tri-objective (ZDT series) benchmark test functions on the Pareto-optimal front and found EP-MODE's efficiency is higher than that of the MODE algorithm and the MODE algorithm with a ranking-based mutation operator GRAPHICAL ABSTRACT