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Deadline-Constrained Cost Optimization Approaches for workflow scheduling in Clouds

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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An adaptive multi-objective evolutionary algorithm for constrained workflow scheduling in Clouds

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DISTRIBUTED AND PARALLEL DATABASES 2018年第2期36卷 339-368页

作者： Zhang, Miao Li, Huiqi Liu, Li Buyya, Rajkumar Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Univ Melbourne Parkville Vic Australia

The Cloud workflow scheduling is to find proper Cloud resources for the execution of workflow tasks to efficiently utilize resources and meet different user's quality of service requirements. Cloud workflow scheduling is a constrained and NP-complete problem and multi-objective evolutionary algorithms have shown their excellent ability to solve such problem. But most existing works simply use static penalty function to handle constraints which usually result in premature when the constraints become strict. On the other hand, with the search space being more tremendous and chaotic, how to balance the ability of exploring the entire search space and exploiting the important regions during the evolutionary process is increasingly important. In this paper, an adaptive individual-assessment scheme based on evolutionary states is proposed to handle the constraints in multi-objective optimization problems. In addition, the evolutionary parameters are also adjusted accordingly to balance the exploration and exploitation ability. These are distinguishable from most previous studies that directly incorporate multi-objective evolutionary algorithm to search excellent solutions for Cloud workflow scheduling. Experimental results demonstrate the proposed algorithm outperforms other state-of-the-art methods in convergence and diversity, and it also achieves better optimization ability when it is applied to solve Cloud workflow scheduling problem.

关键词： Cloud computing workflow scheduling Evolutionary algorithm Pareto entropy

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Deadline-constrained workflow scheduling in IaaS clouds with multi-resource packing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 101卷 880-893页

作者： Zhu, Zhaomeng Tang, Xueyan Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

workflow is a common model to represent large computations composed of dependent tasks. Most existing workflow scheduling algorithms use computing resources in a non-multiprogrammed way, by which only one task can run on a service (machine) at a time. In this paper, we study a new workflow scheduling model on heterogeneous Infrastructure-as-a-Service (IaaS) platforms, which allows multiple tasks to run concurrently on a virtual machine (VM) according to their multi-resource demands. First, we propose a list-scheduling framework for the new multiprogrammed cloud resource model. In the order of a priority list, this framework gradually appoints tasks the best placements found on both existing and new VMs on the platform. Different task prioritization and placement comparison methods can be employed for different scheduling objectives. To fully exploit the heterogeneity of IaaS platforms, the VMs can be scaled up during the scheduling process. Then, we propose a deadline-constrained workflow scheduling algorithm (called DyDL) based on this framework to optimize the cost of workflow execution. This algorithm prioritizes tasks by their latest start times and appoints tasks the placements which can meet their latest start times and incur the minimal cost increases. Experimental results show that DyDL can achieve significantly better schedules in most test cases compared to several existing deadline-constrained workflow scheduling algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Infrastructure as a Service Directed Acyclic Graph (DAG) workflow scheduling Multi-resource packing

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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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Reliability Based workflow scheduling on Cloud Computing with Deadline Constraint

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WIRELESS PERSONAL COMMUNICATIONS 2023年第2期130卷 1417-1434页

作者： Khurana, Savita Sharma, Gaurav Kumar, Manni Goyal, Nitin Sharma, Bhanu Seth Jai Parkash Mukand Lal Inst Engn & Technol Informat Technol Dept Radaur Haryana India Seth Jai Parkash Mukand Lal Inst Engn & Technol Comp Sci & Engn Dept Radaur Haryana India Chandigarh Univ Dept Comp Sci Engn SAS Nagar Mohali Punjab India Cent Univ Haryana Sch Engn & Technol Dept Comp Sci & Engn Mahendragarh Haryana India Chitkara Univ Inst Engn & Technol Immers & Interact Technol Lab Rajpura Punjab India

Distributed computing workflow is an effective paradigm to express a range of applications with cloud computing platforms for scientific research explorations. One of the most difficult application areas of cloud computing technology is task scheduling. In a cloud, heterogeneous context, job scheduling with minimal execution cost and time, as well as workflow reliability, are critical. While working in the heterogeneous cloud environment, tasks that are successfully executed are widely identified by considering the failure of the processor or any communication technologies link. It will also have an impact on the workflow's reliability as well as the user's service quality expectations. This research paper proposes a Critical Parent Reliability-based scheduling (CPRS) method that uses the reliability parameter to plan the task while taking into account the user-defined cost and deadline metrics. The effectiveness of the algorithm is compared to current algorithms utilizing scientific workflows as a benchmark, such as Cybershake, Sipht, and Montage. The simulation results supported the assertions by efficiently allocating resources to the cloudlets and stabilizing all of the aforementioned parameters using sufficient performance metrics growth.

关键词： Cloud computing Reliability workflow scheduling Quality of service

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Multi-Agent Deep Reinforcement Learning Framework for Renewable Energy-Aware workflow scheduling on Distributed Cloud Data Centers

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2024年第4期35卷 604-615页

作者： Jayanetti, Amanda Halgamuge, Saman Buyya, Rajkumar Univ Melbourne Sch Comp & Informat Syst Cloud Comp & Distributed Syst CLOUDS Lab Melbourne Vic 3010 Australia

The ever-increasing demand for the cloud computing paradigm has resulted in the widespread deployment of multiple datacenters, the operations of which consume very high levels of energy. The carbon footprint resulting from these operations threatens environmental sustainability while the increased energy costs have a direct impact on the profitability of cloud providers. Using renewable energy sources to satisfy the energy demands of datacenters has emerged as a viable approach to overcome the aforementioned issues. The problem of scheduling workflows across multi-cloud environments powered through a combination of brown and green energy sources includes multiple levels of complexities. First, the general case of workflow scheduling in a distributed system itself is NP-hard. The need to schedule workflows across geo-distributed cloud datacenters adds a further layer of complexity atop the general problem. The problem becomes further challenging when the datacenters are powered through renewable sources which are inherently intermittent in nature. Consequently, traditional workflow scheduling algorithms and single-agent reinforcement learning algorithms are incapable of efficiently meeting the decentralized and adaptive control required for addressing these challenges. To this end, we have leveraged the recent advancements in the paradigm of MARL (Multi-Agent Reinforcement Learning) for designing and developing a multi-agent RL framework for optimizing the green energy utilization of workflow executions across multi-cloud environments. The results of extensive simulations demonstrate that the proposed approach outperforms the comparison algorithms with respect to minimizing energy consumption of workflow executions by 47% while also keeping the makespan of workflows in par with comparison algorithms. Furthermore, with the proposed optimizations, the multi-agent technique learnt 5 times faster than a generic multi-agent algorithm.

关键词： Deep reinforcement learning cloud computing workflow scheduling green computing

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A Novel Reinforcement-Learning-Based Approach to workflow scheduling Upon Infrastructure-as-a-Service Clouds

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INTERNATIONAL JOURNAL OF WEB SERVICES RESEARCH 2021年第1期18卷 21-33页

作者： Chen, Peng Xia, Yunni Yu, Chun Xihua Univ Sch Comp & Software Engn Chengdu Peoples R China Chongqing Univ Chongqing Peoples R China

Recently, the cloud computing paradigm has become increasingly popular in large-scale and complex workflow applications. The workflow scheduling problem, which refers to finding the most suitable resource for each task of the workflow to meet user defined quality of service, attracts considerable research attention. Multi-objective optimization algorithms in workflow scheduling have many limitations (e.g., the encoding schemes in most existing heuristic-based scheduling algorithms require prior experts' knowledge), and thus, they can be ineffective when scheduling workflows upon dynamic cloud infrastructures with real time. A novel reinforcement-learning-based algorithm to multi-workflow scheduling over IaaS is proposed. It aims at optimizing make-span and dwell time and is to achieve a unique set of correlated equilibrium solution. The proposed algorithm is evaluated for famous workflow templates and real-world industrial IaaS by simulation and compared to the current state-of-the-art heuristic algorithms. The result shows that the algorithm outperforms compared algorithm.

关键词： Cloud workflows Cost Markov Game Model Multi-Agent Reinforcement Learning (MARL) Turnaround Time workflow scheduling

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An improved Caledonian crow learning algorithm based on ring topology for security-aware workflow scheduling in cloud computing

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2023年第6期16卷 2929-2984页

作者： Zade, B. Mohammad Hasani Javidi, M. M. Mansouri, N. Shahid Bahonar Univ Kerman Dept Comp Sci Box Kerman 76135133 Iran

The security of workflow scheduling is a significant concern and even is one of the most important metrics of QoS (Quality of Service). This paper presents two approaches to provide a secure connection between users and servers and handle large and medium task size problems. Firstly, a multi-objective scheduling (MO-Ring-IC-NCCLA) algorithm for scientific workflow in the cloud environment is proposed. It tries to minimize workflow makespan and cost as well as increase the cost of attack from an invader. The proposed multi-objective is based on the New Caledonian Crow Learning Algorithm (NCCLA). However, this algorithm has a few drawbacks, including poor exploration activity and inability to balance exploration and exploitation. The social and asocial learning part of standard NCCLA has been modified to tackle these limitations, then a concept of ring topology is used to better Pareto optimal can be found. Secondly, the structure of virtual machines is modified so that the cost of attack from invaders increases. Experimental results based on various real-world workflows indicate the performance improvement of MO-Ring-IC-NCCLA over SBDE, NSGA-II, and MOHFHB algorithms in terms of FS-metric. According to the delta metric (i.e., diversity measures), the proposed algorithm is superior to 85% of the compared metaheuristics. In terms of Inverted Generational Distance (IGD) metric, it outperforms NSGAII and Multi-Objective Artificial Hummingbird Algorithm (MOAHA) for 95% and 80% of the cases, respectively. Based on experiments, makespan and cost improved by 23.12% and 18.43% over existing workflow algorithms. Compared to Multi-Objective Hybrid Fuzzy Hitchcock Bird (MOHFHB), Simulated-annealing Based Differential Evolution (SBDE), and non-dominated sorting genetic algorithm (NSGAII), it improves the FS-metric by 23.35% on average.

关键词： Cloud computing workflow scheduling Security Meta-heuristic Ring topology

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An Energy Efficient Algorithm for workflow scheduling in IaaS Cloud

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JOURNAL OF GRID COMPUTING 2020年第3期18卷 357-376页

作者： Singh, Vishakha Gupta, Indrajeet Jana, Prasanta K. Indian Inst Technol ISM Dept Comp Sci & Engn Dhanbad 826004 Bihar India Bennett Univ Dept Comp Sci Engn Greater Noida 201310 India

Energy efficient workflow scheduling is the demand of the present time's computing platforms such as an infrastructure-as-a-service (IaaS) cloud. An appreciable amount of energy can be saved if a dynamic voltage scaling (DVS) enabled environment is considered. But it is important to decrease makespan of a schedule as well, so that it may not extend beyond the deadline specified by the cloud user. In this paper, we propose a workflow scheduling algorithm which is inspired from hybrid chemical reaction optimization (HCRO) algorithm. The proposed scheme is shown to be energy efficient. Apart from this, it is also shown to minimize makespan. We refer the proposed approach as energy efficient workflow scheduling (EEWS) algorithm. The EEWS is introduced with a novel measure to determine the amount of energy which can be conserved by considering a DVS-enabled environment. Through simulations on a variety of scientific workflow applications, we demonstrate that the proposed scheme performs better than the existing algorithms such as HCRO and multiple priority queues genetic algorithm (MPQGA) in terms of various performance metrics including makespan and the amount of energy conserved. The significance of the proposed algorithm is also judged through the analysis of variance (ANOVA) test and its subsequent LSD analysis.

关键词： workflow scheduling Energy conservation Chemical reaction optimization Makespan Cloud

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A divide and conquer approach to deadline constrained cost-optimization workflow scheduling for the cloud

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2021年第3期24卷 1711-1733页

作者： Khojasteh Toussi, Ghazaleh Naghibzadeh, Mahmoud Ferdowsi Univ Mashhad Dept Comp Engn Mashhad Razavi Khorasan Iran

The modeling of complex computational applications as giant computational workflows has been a critically effective means of better understanding the intricacies of applications and of determining the best approach to their realization. It is a challenging assignment to schedule such workflows in the cloud while also considering users' different quality of service requirements. The present paper introduces a new direction based on a divide-and-conquer approach to scheduling these workflows. The proposed Divide-and-conquer workflow scheduling algorithm (DQWS) is designed with the objective of minimizing the cost of workflow execution while respecting its deadline. The critical path concept is the inspiration behind the divide-and-conquer process. DQWS finds the critical path, schedules it, removes the critical path from the workflow, and effectively divides the leftover into some mini workflows. The process continues until only chain structured workflows, called linear graphs, remain. scheduling linear graphs is performed in the final phase of the algorithm. Experiments show that DQWS outperforms its competitors, both in terms of meeting deadlines and minimizing the monetary costs of executing scheduled workflows.

关键词： workflow scheduling Cloud computing Critical path Divide-and-conquer

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