Multi-objective Workflow Scheduling Research Articles

C-KHCS: Multi-Objective Workflow Scheduling using Chaotic Krill Herd Optimization and Improved Cuckoo Search in Fog Computing

C-KHCS: Multi-Objective Workflow Scheduling using Chaotic Krill Herd Optimization and Improved Cuckoo Search in Fog Computing

Multi-objective secure aware workflow scheduling algorithm in cloud computing based on hybrid optimization algorithm

Cloud computing provides the on-demand service of the user with the use of distributed physical machines, in which security has become a challenging factor while performing various tasks. Several methods were developed for the cloud computing workflow scheduling based on optimal resource allocation; still, the security consideration and efficient allocation of the workflow are challenging. Hence, this research introduces a hybrid optimization algorithm based on multi-objective workflow scheduling in the cloud computing environment. The Regressive Whale Water Tasmanian Devil Optimization (RWWTDO) is proposed for the optimal workflow scheduling based on the multi-objective fitness function with nine various factors, like Predicted energy, Quality of service (QoS), Resource utilization, Actual task running time, Bandwidth utilization, Memory capacity, Make span equivalent of the total cost, Task priority, and Trust. Besides, secure data transmission is employed using the triple data encryption standard (3DES) to acquire enhanced security for workflow scheduling. The method’s performance is evaluated using the resource utilization, predicted energy, task scheduling cost, and task scheduling time and acquired the values of 1.00000, 0.16587, 0.00041, and 0.00314, respectively.

Dynamic multi-objective workflow scheduling for combined resources in cloud

Cloud resource providers offer idle resources to users as spot instances. The price of the instances changes with market supply and demand, and the dynamic price can have a significant impact on workflow scheduling. In this work, we use a combination of spot and on-demand instances as the foundation cloud resource and characterize the dynamic workflow scheduling problem as a dynamic multi-objective optimization problem (DMOP), where the dynamics originate from the dynamic price of spot instances. The scheduling solution is found by considering three objectives: maximizing the reliability of the instances while minimizing the makespan and cost. In addition, we provide an enhanced MOEAD algorithm called MOEA/D-URDI that combines diversity introduction and uniform random sampling, where the uniform random sampling paradigm is used to generate the initial weight vector. The dynamic multi-objective optimization evolutionary algorithm DMOEA/D-URDI is then created by combining the method with a dynamic optimization framework. Our technique beats existing algorithms, according to experimental data based on dynamic benchmark sets and three well-known scientific procedures in terms of metrics on dynamic benchmark sets and better ensures reliability in scheduling scientific workflows while reducing makespan and cost.

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

Workflow scheduling in cloud platforms is a highly challenging issue because it faces multiple conflicting optimization objectives and large-scale decision variables. Most of the existing multi-objective workflow scheduling algorithms regard the focused problems as black boxes, and optimize large-scale decision variables as a whole. This leads to inefficiency in searching solution spaces that grow exponentially with the increase of decision variables. To compensate the above deficiency, this paper proposes a knowledge-driven adaptive evolutionary multi-objective scheduling algorithm, KAMSA for short, to optimize makespan and cost of workflow execution in cloud platforms. Specifically, we excavate the knowledge that adjustment of a task’s execution only affects its successor tasks to divide large-scale decision variables into a series of groups, so as to give play to the strengths of divide-and-conquer technology to improve the evolutionary search efficiency. Moreover, we develop an adaptive resource allocation scheme to reward more evolution opportunities for groups with high contributions to further improve the evolutionary search efficiency. We compare the proposed KAMSA with five state-of-the-art competitors in the context of 20 real-world workflows and the Amazon elastic compute cloud (EC2). The comparison results verify the KAMSA’s advantages by prevailing over the five competitors on 18 out of the 20 test cases with respect to the metric hypervolume.

Multiobjective Prioritized Workflow Scheduling in Cloud Computing Using Cuckoo Search Algorithm

Effective workflow scheduling in cloud computing is still a challenging problem as incoming workflows to cloud console having variable task processing capacities and dependencies as they will arise from various heterogeneous resources. Ineffective scheduling of workflows to virtual resources in cloud environment leads to violations in service level agreements and high energy consumption, which impacts the quality of service of cloud provider. Many existing authors developed workflow scheduling algorithms addressing operational costs and makespan, but still, there is a provision to improve the scheduling process in cloud paradigm as it is an nondeterministic polynomial-hard problem. Therefore, in this research, a task-prioritized multiobjective workflow scheduling algorithm was developed by using cuckoo search algorithm to precisely map incoming workflows onto corresponding virtual resources. Extensive simulations were carried out on workflowsim using randomly generated workflows from simulator. For evaluating the efficacy of our proposed approach, we compared our proposed scheduling algorithm with existing approaches, i.e., Max–Min, first come first serve, minimum completion time, Min–Min, resource allocation security with efficient task scheduling in cloud computing-hybrid machine learning, and Round Robin. Our proposed approach is outperformed by minimizing energy consumption by 15% and reducing service level agreement violations by 22%.

A Cluster-Based Cooperative Co-Evolutionary Algorithm for Multiobjective Workflow Scheduling in a Cloud Environment

A Cluster-Based Cooperative Co-Evolutionary Algorithm for Multiobjective Workflow Scheduling in a Cloud Environment

MOHBA:multi-objective workflow scheduling in cloud computing using hybrid BAT algorithm

MOHBA:multi-objective workflow scheduling in cloud computing using hybrid BAT algorithm

Reliability-Aware Multi-Objective Memetic Algorithm for Workflow Scheduling Problem in Multi-Cloud System

With the development of cloud computing, multi-cloud systems have become common platforms for hosting and executing workflow applications in recent years. However, the complexity of workflow scheduling increases exponentially because of the diversified billing mechanisms, heterogeneous virtual machines, and reliability of multi-cloud systems. This article focuses on a multi-objective workflow scheduling problem in multi-cloud systems (MOWSP-MCS). The makespan, cost, and reliability are considered the optimization objectives from the perspective of users. Compared with the classical multi-objective workflow scheduling in the cloud environment, MOWSP-MCS allows users to apply the backup technique to improve reliability. To solve the MOWSP-MCS, this article proposes a reliability-aware multi-objective memetic algorithm (RA-MOMA) containing a diversification strategy and intensification strategy. In the diversification strategy, several problem-specific genetic operators are introduced to construct the diversified offspring individuals. In the intensification strategy, four problem-specific neighborhood operators are designed based on the critical path and resource utilization rate to improve the quality of the individuals in the archive set. A comprehensive numerical experiment is conducted to evaluate the effectiveness of RA-MOMA. The comparisons with several related algorithms demonstrate the superiority of RA-MOMA for solving the MOWSP-MCS.

A dynamic multipopulation genetic algorithm for multiobjective workflow scheduling based on the longest common sequence

A dynamic multipopulation genetic algorithm for multiobjective workflow scheduling based on the longest common sequence

Enhanced hybrid multi-objective workflow scheduling approach based artificial bee colony in cloud computing

This paper presents a hybrid approach based Binary Artificial Bee Colony (BABC) and Pareto Dominance strategy for scheduling workflow applications considering different Quality of Services (QoS) requirements in cloud computing. The main purpose is to schedule a given application onto the available machines in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The proposed approach is called Enhanced Binary Artificial Bee Colony based Pareto Front (EBABC-PF). Our proposed approach starts by listing the tasks according to priority defined by Heterogeneous Earliest Finish Time (HEFT) algorithm, then gets an initial solution by applying Greedy Randomized Adaptive Search Procedure (GRASP) and finally schedules tasks onto machines by applying Enhanced Binary Artificial Bee Colony (BABC). Further, several modifications are considered with BABC to improve the local searching process by applying circular shift operator then mutation operator on the food sources of the population considering the improvement rate. The proposed approach is simulated and implemented in the WorkflowSim which extends the existing CloudSim tool. The performance of the proposed approach is compared with Heterogeneous Earliest Finish Time (HEFT) algorithm, Deadline Heterogeneous Earliest Finish Time (DHEFT), Non-dominated Sort Genetic Algorithm (NSGA-II) and standard Binary Artificial Bee Colony (BABC) algorithm using different sizes of tasks and various benchmark workflows. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization.

Multi-objective workflow scheduling based on genetic algorithm in cloud environment

In recent years, cloud computing plays a crucial role in many real applications. Thus, how to solve workflow scheduling problems, i.e., allocating and scheduling different resources, under the cloud computing environment becomes more important. Although some evolutionary algorithms (EAs) can solve workflow scheduling problems with a small scale, they show some disadvantages on larger scale workflow applications. In this paper, a multi-objective genetic algorithm (MOGA) is applied to optimize workflow scheduling problems. To enhance the search efficiency, this study proposes an initialization scheduling sequence scheme, in which each task’s data size is considered when initializing its virtual machine (VM) instance. Relying on the initial scheduling sequence, a proper trade-off between the makespan and the energy consumption, which are two optimization objectives in this study, can be achieved. In the early evolution stage, traditional crossover and mutate operators are performed to keep the population’s exploration. On the contrary, the longest common subsequence (LCS) of multiple elite individuals, which can be regarded as a favorable gene block, is saved during the later evolution stage. Based on the LCS, the probability of some favorable gene blocks being destroyed will be reduced when performing the crossover operator and the mutate operator. Hence, the integration of the LCS in GA can satisfy different requirements in different evolution stages, and then to attain a balance between the exploration and the exploitation. Extensive experimental results verify that the proposed GA combined with LCS, named as GALCS in this paper, can find a better Pareto front than the ordinary GA as well as other state-of-the-art algorithms. Furthermore, effectivenesses of the new proposed strategies are also verified by a set of experiments.

An Improved Multi-Objective Workflow Scheduling Using F-NSPSO with Fuzzy Rules

An Improved Multi-Objective Workflow Scheduling Using F-NSPSO with Fuzzy Rules

RVEA-based multi-objective workflow scheduling in cloud environments

RVEA-based multi-objective workflow scheduling in cloud environments

RVEA-based multi-objective workflow scheduling in cloud environments

RVEA-based multi-objective workflow scheduling in cloud environments

Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost

Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost

CP-PGWO: multi-objective workflow scheduling for cloud computing using critical path

CP-PGWO: multi-objective workflow scheduling for cloud computing using critical path

IFFO: An Improved Fruit Fly Optimization Algorithm for Multiple Workflow Scheduling Minimizing Cost and Makespan in Cloud Computing Environments

Cloud computing platforms have been extensively using scientific workflows to execute large-scale applications. However, multiobjective workflow scheduling with scientific standards to optimize QoS parameters is a challenging task. Various metaheuristic scheduling techniques have been proposed to satisfy the QoS parameters like makespan, cost, and resource utilization. Still, traditional metaheuristic approaches are incompetent to maintain agreeable equilibrium between exploration and exploitation of the search space because of their limitations like getting trapped in local optimum value at later evolution stages and higher-dimensional nonlinear optimization problem. This paper proposes an improved Fruit Fly Optimization (IFFO) algorithm to minimize makespan and cost for scheduling multiple workflows in the cloud computing environment. The proposed algorithm is evaluated using CloudSim for scheduling multiple workflows. The comparative results depict that the proposed algorithm IFFO outperforms FFO, PSO, and GA.

TC3PoP: a time-cost compromised workflow scheduling heuristic customized for cloud environments

Recently, optimizing the monetary cost and performance metrics of executing workflow applications in cloud environments has become an important and interesting research subject. The most critical challenges with the existing methods are simplifying assumptions, which make them far from reality, offering a set of solutions that cannot cover the whole search space and are not real non-dominated solutions, and finally, their high time-complexity. To tackle the mentioned problems, in this paper, a multi-objective workflow scheduling algorithm named Time-Cost Compromised CPoP (TC3PoP) customized for cloud environments is proposed. We have modeled and formulated the workflow scheduling problem based on the cloud environment offers and characteristics. The proposed algorithm is light-weight and based on the offered resources by cloud providers, and produces a set of real Pareto optimal solutions so that a user can freely choose the best solution based on his/her budget and deadline. The results of the experiments show that in terms of HyperVolume indicator, the validity and diversity of TC3PoP solutions are 41.5% and 38.1% better than those of well-known and successful NSGA-II for two real scientific workflows, Montage and LIGO, respectively, and the proposed method is much faster than the compared method. Moreover, to evaluate the quality of Pareto fronts produced by the proposed algorithm, some statistical analysis was made to investigate the precision and distribution of the solutions.

Scoring and Dynamic Hierarchy-Based NSGA-II for Multiobjective Workflow Scheduling in the Cloud

Cloud computing becomes a promising technology to reduce computation cost by providing users with elastic resources and application-deploying environments as a pay-per-use model. More scientific workflow applications have been moved or are being migrated to the cloud. Scheduling workflows turns to the main bottleneck for increasing resource utilization and quality of service (QoS) for users. This work formulates workflow scheduling as multiobjective optimization problems and proposes a Scoring and Dynamic Hierarchy-based NSGA-II (Nondominated Sorting Genetic Algorithm II), called SDHN for short, to minimize both makespan and cost of workflow execution. First, a scoring criterion is developed to calculate the total score for each individual during population updating, which is used as a quantitative index to evaluate the dominance degree of individuals among the whole population. Hence, SDHN can distinguish individuals within the same dominance level and target its search toward the directions of elite solutions as their different dominance degrees and accordingly improve search efficiency. Second, a population-based dynamic hierarchical structure (HS) and its evolutionary rules are presented to update HS by comparing each child with all parental individuals from bottom to up until finding a proper dominant level. Since traversing all HS levels is not needed in most cases, the number of individual comparisons is reduced and SDHN&#x2019;s updating efficiency is greatly improved, especially for large-scale and complex applications. Third, to guarantee its converging to the near-optimal solutions, adaptive adjustment strategies (AASs) are designed to prevent the search from falling into local optima or diverging by checking the number of individuals at the highest HS level and then modifying the relevant genetic operations to guide the evolutionary process to approach the global Pareto Front. Extensive experiments are conducted to verify SDHN, and the results show that it outperforms the existing algorithms in the quality and diversity of resulting solutions as well as convergence time. <i>Note to Practitioners</i>&#x2014;Most scientific applications are computation and/or data-intensive and need large-scale or high-performance resources for their execution. More and more scientists use workflows to manage their applications, but how to efficiently run them in the cloud is a big challenge due to their large scale as well as the dynamic characteristics of the elastic and heterogeneous cloud resources. In this article, we develop a novel multiobjective optimization technique for workflow scheduling such that the makespan and cost can be minimized simultaneously. A scoring criterion, dynamic hierarchical structure and its evolutionary rules, and adaptive adjustment strategies are designed to cooperate with each other and increase the search ability and efficiency of the original and widely used NSGA-II. Adequate experiments are conducted to verify the proposed method&#x2019;s performance, and the experimental results show that it can provide more near-optimal solutions than the existing methods. It can be readily applied for implementing more efficient and effective cloud data centers to execute large-scale scientific workflows.

Multi-objective workflow scheduling scheme: a multi-criteria decision making approach

Scheduling large workflows that are faced in many business as well as scientific domains such as economy, bioinformatics, astronomy and geophysics is an important area of research in the field of cloud computing. Many studies have been made to develop efficient algorithms for workflow scheduling that deal with multiple objectives. In the recent years, multi-criteria decision making (MCDM) methods have become popular for solving such multi-objective problems in various areas like risk management, climate change, renewable energy and so on. Particularly, the MCDM method called technique for order of preference by similarity to ideal solution (TOPSIS) has drawn extensive attention due to its easy understanding, fast and simple calculation. In this paper, we present a workflow scheduling algorithm in cloud environment based on TOPSIS that integrates entropy weight method (EWM). The proposed algorithm aims at minimizing makespan, cost, and energy consumption and maximizing the reliability. The algorithm is tested on various benchmark scientific workflows. The simulation results are compared with that of the related algorithms. The comparisons show that the proposed algorithm performs remarkably well in terms of cost and energy consumption while maintaining the other parameters within considerable limits.