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CPTF-a new heuristic based branch and bound algorithm for workflow scheduling in heterogeneous distributed computing systems

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CCF TRANSACTIONS ON HIGH PERFORMANCE COMPUTING 2024年第5期6卷 472-487页

作者： Sirisha, D. Prasad, S. Sambhu Nadimpalli Satyanarayana Raju Inst Technol Visakhapatnam Andhra Pradesh India

Computationally intensive applications embodied as workflows entail interdependent tasks that involve multifarious computation requirements and necessitate Heterogeneous Distributed Computing Systems (HDCS) to attain high performance. The scheduling of workflows on HDCS was demonstrated as an NP-Complete problem. In the current work, a new heuristic based Branch and Bound (BnB) technique namely Critical Path_finish Time First (CPTF) algorithm is proposed for workflow scheduling on HDCS to achieve the best solutions. The primary merits of CPTF algorithm are due to the bounding functions that are tight and of less complexity. The sharp bounding functions could precisely estimate the promise of each state and aid in pruning infeasible states. Thus, the search space size is reduced. The CPTF algorithm explores the most promising states in the search space and converges to the solution quickly. Therefore, high performance is achieved. The experimental results on random and scientific workflows reveal that CPTF algorithm could effectively exploit high potency of BnB technique in realizing better quality solutions against the widely referred heuristic scheduling algorithms. The results on the benchmark workflows show that CPTF algorithm has improved schedules for 89.36% of the cases.

关键词： workflow scheduling Task scheduling Heuristics Heterogeneous distributed computing systems Branch and bound technique Makespan

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Failure-aware workflow scheduling in cluster environments

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2010年第4期13卷 421-434页

作者： Yu, Zhifeng Wang, Chenjia Shi, Weisong Wayne State Univ Detroit MI 48202 USA

The goal of workflow application scheduling is to achieve minimal makespan for each workflow scheduling workflow applications in high performance cluster environments is an NP-Complete problem, and becomes more complicated when potential resource failures are considered While more research on failure prediction has been witnessed in recent years to improve system availability and reliability, very few of them attack the problem in the context of workflow application scheduling In this paper, we study how a workflow scheduler benefits from failure prediction and propose FLAW, a failure-aware workflow scheduling algorithm We propose two important definitions on accuracy, Application Oblivious Accuracy (AOA) and Application Aware Accuracy (AAA), from the perspectives of system and scheduling respectively, as we observe that the prediction accuracy defined conventionally Imposes different performance implications on different applications and fails to measure how that improves scheduling effectiveness The comprehensive evaluation results using real failure traces show that FLAW performs well with practically achievable prediction accuracy by reducing the average makespan, the loss time and the number of job rescheduling

关键词： Failure-aware workflow scheduling Cluster computing

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Deadline-Constrained Cost Optimization Approaches for workflow scheduling in Clouds

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2017年第12期28卷 3401-3412页

作者： Wu, Quanwang Ishikawa, Fuyuki Zhu, Qingsheng Xia, Yunni Wen, Junhao Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Natl Inst Informat Tokyo 1010003 Japan Chongqing Univ Coll Software Engn Chongqing 400044 Peoples R China

Nowadays it is becoming more and more attractive to execute workflow applications in the cloud because it enables workflow applications to use computing resources on demand. Meanwhile, it also challenges traditional workflow scheduling algorithms that only concentrate on optimizing the execution time. This paper investigates how to minimize execution cost of a workflow in clouds under a deadline constraint and proposes a metaheuristic algorithm L-ACO as well as a simple heuristic ProLiS. ProLiS distributes the deadline to each task, proportionally to a novel definition of probabilistic upward rank, and follows a two-step list scheduling methodology: rank tasks and sequentially allocates each task a service which meets the sub-deadline and minimizes the cost. L-ACO employs ant colony optimization to carry out deadline-constrained cost optimization: the ant constructs an ordered task list according to the pheromone trail and probabilistic upward rank, and uses the same deadline distribution and service selection methods as ProLiS to build solutions. Moreover, the deadline is relaxed to guide the search of L-ACO towards constrained optimization. Experimental results show that compared with traditional algorithms, the performance of ProLiS is very competitive and L-ACO performs the best in terms of execution costs and success ratios of meeting deadlines.

关键词： Ant colony optimization cloud computing constrained optimization deadline workflow scheduling

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Makespan-Cost-Reliability-Optimized workflow scheduling Using Evolutionary Techniques in Clouds

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2020年第10期29卷

作者： Zhou, Xiumin Zhang, Gongxuan Wang, Tian Zhang, Mingyue Wang, Xiji Zhang, Wei Nanjing Univ Sci & Technol Sch Comp Sci & Engn 200 Xiaolingwei St Nanjing 210094 Jiangsu Peoples R China

Most popular scientific workflow systems can now support the deployment of tasks to the cloud. The execution of workflow on cloud has become a multi-objective scheduling in order to meet the needs of users in many aspects. Cost and makespan are considered to be the two most important objects. In addition to these, there are some other Quality-of-Service (QoS) parameters including system reliability, energy consumption and so on. Here, we focus on three objectives: cost, makespan and system reliability. In this paper, we propose a Multi-objective Evolutionary Algorithm on the Cloud (MEAC). In the algorithm, we design some novel schemes including problem-specific encoding and also evolutionary operations, such as crossover and mutation. Simulations on real-world and random workflows are conducted and the results show that MEAC can get on average about 5% higher hypervolume value than some other workflow scheduling algorithms.

关键词： workflow scheduling cloud computing multi-objective optimization evolutionary algorithm

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Modified firefly algorithm for workflow scheduling in cloud-edge environment

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NEURAL COMPUTING & APPLICATIONS 2022年第11期34卷 9043-9068页

作者： Bacanin, Nebojsa Zivkovic, Miodrag Bezdan, Timea Venkatachalam, K. Abouhawwash, Mohamed Singidunum Univ Danijelova 32 Belgrade 11000 Serbia Univ Hradec Kralove Fac Sci Dept Appl Cybernet Hradec Kralove 50003 Czech Republic Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt Michigan State Univ Dept Computat Math Sci & Engn CMSE E Lansing MI 48824 USA

Edge computing is a novel technology, which is closely related to the concept of Internet of Things. This technology brings computing resources closer to the location where they are consumed by end-users-to the edge of the cloud. In this way, response time is shortened and lower network bandwidth is utilized. workflow scheduling must be addressed to accomplish these goals. In this paper, we propose an enhanced firefly algorithm adapted for tackling workflow scheduling challenges in a cloud-edge environment. Our proposed approach overcomes observed deficiencies of original firefly metaheuristics by incorporating genetic operators and quasi-reflection-based learning procedure. First, we have validated the proposed improved algorithm on 10 modern standard benchmark instances and compared its performance with original and other improved state-of-the-art metaheuristics. Secondly, we have performed simulations for a workflow scheduling problem with two objectives-cost and makespan. We performed comparative analysis with other state-of-the-art approaches that were tested under the same experimental conditions. Algorithm proposed in this paper exhibits significant enhancements over the original firefly algorithm and other outstanding metaheuristics in terms of convergence speed and results' quality. Based on the output of conducted simulations, the proposed improved firefly algorithm obtains prominent results and managed to establish improvement in solving workflow scheduling in cloud-edge by reducing makespan and cost compared to other approaches.

关键词： Edge computing Swarm intelligence workflow scheduling Firefly algorithm Genetic operator Quasi-reflection-based learning

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An assignment mechanism for workflow scheduling in Function as a Service edge environment

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 157卷 543-557页

作者： Mahdizadeh, Samaneh Hajy Abrishami, Saeid Ferdowsi Univ Mashhad Dept Comp Engn Mashhad Iran

Serverless computing has revolutionized cloud -based software development for software developers, addressing many of the associated challenges. With resource management and infrastructure provisioning handled by the provider, developers can focus on deploying services at the application level, which has gained significant popularity. Edge computing, with its proximity to end -users and ability to offer low-cost, high-speed services, has emerged as a promising solution for latency -sensitive applications. By providing Function -as -a -Service (FaaS) capabilities at the network edge, it becomes easier to leverage the available edge capacity. However, edge servers have limited capacity to provide FaaS services, and user access is typically restricted to their geographic region. This presents a challenge in achieving optimal resource allocation in a multi-user environment, given the dynamic and unpredictable nature of edge computing. To address these challenges, we have proposed a dynamic resource provisioning and allocation approach for Edge FaaS. Our approach models the application structure as a directed acyclic graph, representing a workflow of computational functions. Drawing inspiration from the many -to -many assignment mechanism used in the course allocation problem, we have developed a mechanism to assign multi-user workflows to the limited Edge FaaS resources. This mechanism considers the priority of users' ready tasks, determined by upward rank, with the goal of reducing workflow execution time. Additionally, we have proposed two methods for on -demand provisioning: the Highest Bid First Mechanism (HBFM) and the Warm Function First Mechanism (WFFM). Experimental results have shown that the proposed resource allocation methods effectively allocate limited resources to users, leading to reduced workflow completion time.

关键词： Edge function-as-a-service Resource allocation and provisioning Mechanism design workflow scheduling Course allocation

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

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第12期12卷 10789-10808页

作者： Kumar, Madhu Sudan Tomar, Abhinav Jana, Prasanta K. Indian Inst Technol ISM Dept Comp Sci & Engn Dhanbad 826004 Bihar India

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.

关键词： Cloud computing workflow scheduling MCDM TOPSIS EWM

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Integrating Cognition Cost With Reliability QoS for Dynamic workflow scheduling Using Reinforcement Learning

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2023年第4期16卷 2713-2726页

作者： Yu, Xiaoming Wu, Wenjun Wang, Yangzhou Beihang Univ State Key Lab Software Dev Beijing 100191 Peoples R China

The rapid rise of microservice architecture poses severe challenges to workflow scheduling, resource allocation, and goal optimization. However, faults and failures usually happen during workflow running. To ensure the workflow's successful execution during scheduling microservices, this article proposes a dynamic workflow scheduling algorithm by integrating cognition cost and reliability QoS for using reinforcement learning (WS-CCR). First, we explore the 'restart policy' of containers in the Kubernetes architecture, which lays the foundation for our work that adopts the redundancy strategy to ensure workflow operation. Then we consider the cognitive cost based on the fact that users have a cognitive process for different microservices in selecting microservices. Additionally, another optimization goal is the reliability of workflows. On this basis, we design a reasonable reward function in reinforcement learning to generate dynamic strategies. Furthermore, following some generated strategies, our engine will schedule candidate microservices for tasks to execute step by step. A series of experiments on Alibaba and business areas workflows have proven the superior performance of our algorithm. Our WS-CCR can generate better Pareto solution sets than other baselines in terms of improving the reliability of running workflows. Finally, we give a case study to prove the practicability of our method.

关键词： Reliability Costs Task analysis Microservice architectures Dynamic scheduling Reinforcement learning Optimization Cognitive cost microservice QoS reinforcement learning reliability workflow scheduling

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

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2021年第4期24卷 3607-3627页

作者： Doostali, Saeed Babamir, Seyed Morteza Eini, Maryam Univ Kashan Dept Software Engn Kashan Iran

When each task of the longest path in a task-dependent scientific workflow must meet a deadline, the path is called critical. Tasks in a critical path have priority over tasks in non-critical paths. Considering this fact that less methods have already dealt with the critical path problem for workflow scheduling in cloud, this study aims to present a critical-path based method to consider the problem based on our previous optimal workflow scheduling method, GWO-based (Grey Wolf Optimization). We applied our study to balance and imbalance scientific workflows. Our results show that considering the critical path improves the completion time of workflows while maintaining a proper level of resource cost and resource utilization. Moreover, to show the effectiveness of the current study, we compared the performance of the proposed method with non-critical-path aware algorithms, using three different indicators. The simulation demonstrates that compared to PGWO as the base method, the proposed approach achieves (1) approximately 68% improvement for makespan, (2) more accuracy in population sampling for about 70% of workflows, and (3) avoidance of the cost increases in more than 50% of workflows. Moreover, the proposed method decreases makespan approximately 3 times compared to the constrained-based approaches.

关键词： Critical path workflow scheduling Multi-objective optimization Grey Wolf optimization Cloud computing

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Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 102卷 307-322页

作者： Ismayilov, Goshgar Topcuoglu, Haluk Rahmi Marmara Univ Comp Engn Dept TR-34722 Istanbul Turkey

workflow scheduling is a largely studied research topic in cloud computing, which targets to utilize cloud resources for workflow tasks by considering the objectives specified in QoS. In this paper, we model dynamic workflow scheduling problem as a dynamic multi-objective optimization problem (DMOP) where the source of dynamism is based on both resource failures and the number of objectives which may change over time. Software faults and/or hardware faults may cause the first type of dynamism. On the other hand, confronting real-life scenarios in cloud computing may change number of objectives at runtime during the execution of a workflow. In this study, we propose a prediction based dynamic multi-objective evolutionary algorithm, called NN-DNSGA-II algorithm, by incorporating artificial neural network with the NSGA-II algorithm. Additionally, five leading non-prediction based dynamic algorithms from the literature are adapted for the dynamic workflow scheduling problem. scheduling solutions are found by the consideration of six objectives: minimization of makespan, cost, energy and degree of imbalance;and maximization of reliability and utilization. The empirical study based on real-world applications from Pegasus workflow management system reveals that our NN-DNSGA-II algorithm significantly outperforms the other alternatives in most cases with respect to metrics used for DMOPs with unknown true Pareto-optimal front, including the number of non-dominated solutions, Schott's spacing and Hypervolume indicator. (C) 2019 Elsevier B.V. All rights reserved.

关键词： workflow scheduling Resource failures Changing number of objectives Dynamic multi-objective evolutionary algorithms Neural networks

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