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AI-based & heuristic workflow scheduling in cloud and fog computing: a systematic review

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第8期27卷 10265-10298页

作者： Khaledian, Navid Voelp, Marcus Azizi, Sadoon Shirvani, Mirsaeid Hosseini Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT Esch Sur Alzette Luxembourg Univ Kurdistan Dept Comp Engn & IT Sanandaj Iran Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran

Fog and cloud computing are emerging paradigms that enable distributed and scalable data processing and analysis. However, these paradigms also pose significant challenges for workflow scheduling and assigning related tasks or jobs to available resources. Resources in fog and cloud environments are heterogeneous, dynamic, and uncertain, requiring efficient scheduling algorithms to optimize costs and latency and to handle faults for better performance. This paper aims to comprehensively survey existing workflow scheduling techniques for fog and cloud environments and their essential challenges. We analyzed 82 related papers published recently in reputable journals. We propose a subjective taxonomy that categorizes the critical difficulties in existing work to achieve this goal. Then, we present a systematic overview of existing workflow scheduling techniques for fog and cloud environments, along with their benefits and drawbacks. We also analyze different workflow scheduling techniques for various criteria, such as performance, costs, reliability, scalability, and security. The outcomes reveal that 25% of the scheduling algorithms use heuristic-based mechanisms, and 75% use different Artificial Intelligence (AI) based and parametric modelling methods. Makespan is the most significant parameter addressed in most articles. This survey article highlights potentials and limitations that can pave the way for further processing or enhancing existing techniques for interested researchers.

关键词： Fog computing Cloud computing workflow scheduling Artificial intelligence Systematic survey

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A novel cost-efficient approach for deadline-constrained workflow scheduling by dynamic provisioning of resources

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 79卷 95-110页

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

workflow scheduling is a crucial aspect of cloud computing that should be performed in an efficient manner for optimal utilization of resources. The development of a cost-efficient algorithm has always been an important topic of research in this regard. In this paper, we propose a novel workflow scheduling algorithm, which is cost-efficient and deadline-constrained. The proposed algorithm is consolidated by dynamic provisioning of the resources, using k-means clustering technique and a variant of the Subset Sum problem. In the algorithm, we consider level based scheduling using the concept of Bag of Tasks (bots) and develop a new technique for associating deadlines with each bot. Through extensive simulation runs, we show that the proposed algorithm outperforms the existing algorithms like Dynamic Provisioning Dynamic scheduling (DPDS) and Infrastructure as a Service (IaaS) Cloud-Partial Critical Path (IC-PCP). The effectiveness of our algorithm over these two algorithms is also illustrated through the popular statistical test ANOVA and its subsequent post-hoc analysis. (c) 2017 Elsevier B.V. All rights reserved.

关键词： workflow scheduling Dynamic provisioning Partition problem k-means clustering Cost Deadline

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

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第5期17卷 2095-2108页

作者： Nazemi, Shahin Khorsand, Reihaneh Islamic Azad Univ Dept Comp Engn Isfahan Khorasgan Branch Esfahan Iran

Today, fog computing as a complement to cloud computing has attracted much attention in research communities, because it has great potential to provide the processing resources and services nenoeded for applications at the edge of the network close to users. Nonetheless, inefficient scheduling of workflows in fog computing infrastructures leads to bandwidth wastage, resource wastage, and unfavorable quality of service (QoS). The main challenges in fog computing are finding suitable nodes to execute workflows and scheduling them in such a way as to improve the speed of convergence and avoid local optima. To overcome these problems, in this paper, we propose a multi-objective workflow scheduling algorithm using Chaotic Krill Herd optimization and improved Cuckoo Search called C-KHCS, while applying the chaotic map to improve the initial population production and escape from a local optimal solution. Moreover, the improved cuckoo search algorithm increases the global search space of the krill algorithm to reach a global optimal solution. The simulation results indicate that the proposed algorithm outperforms its competitors to minimize makespan and energy consumption.

关键词： Task analysis Internet of Things Processor scheduling scheduling Edge computing Heuristic algorithms Search problems workflow scheduling krill herd chaotic map cuckoo search Levy flight

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Hybrid evolutionary workflow scheduling algorithm for dynamic heterogeneous distributed computational environment

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JOURNAL OF APPLIED LOGIC 2017年 24卷 50-61页

作者： Nasonov, D. Visheratin, A. Butakov, N. Shindyapina, N. Melnik, M. Boukhanovsky, A. ITMO Univ St Petersburg Russia Netherlands Inst Adv Study Humanities & Social Sc Wassenaar Netherlands

The optimal workflow scheduling is one of the most important issues in heterogeneous distributed computational environments. Existing heuristic and evolutionary scheduling algorithms have their advantages and disadvantages. In this work we propose a hybrid algorithm based on heuristic methods and genetic algorithm (GA) that combines best characteristics of both approaches. We propose heuristic algorithm called Linewise Earliest Finish Time (LEFT) as an alternative for HEFT in initial population generation for GA. We also experimentally show efficiency of described hybrid schemas GAHEFT, GALEFT, GACH for traditional workflow scheduling as well as for variable workload in dynamically changing heterogeneous computational environment. (C) 2016 Elsevier B.V. All rights reserved.

关键词： workflow scheduling Dynamic environment Evolutionary algorithms LEFT GA GAHEFT

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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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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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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 Intelligent workflow scheduling Scheme for Complex Network Robustness in Fuzzy Edge-Cloud Environments

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第1期11卷 1106-1123页

作者： Chen, Xing Lin, Chaowei Lin, Bing Fuzhou Univ Coll Math & Comp Sci Fuzhou 350118 Peoples R China Fujian Prov Key Lab Network Comp & Intelligent Inf Fuzhou 350118 Peoples R China Fujian Normal Univ Coll Phys & Energy Fujian Prov Key Lab Quantum Manipulat & New Energy Fuzhou 350117 Peoples R China Fujian Prov Collaborat Innovat Ctr Adv High Field Superconducting Mat & Engn Fuzhou 350117 Peoples R China Peking Univ Sch Comp Sci Beijing 100871 Peoples R China

Due to the complicated data dependencies between the tasks in a workflow application and the heterogeneous resources in edge-cloud environments, it is difficult to select an optimal tasks-servers solution for scheduling workflow applications in the complex environments. Current research on workflow applications scheduling is mainly concentrated on certain conditions, ignoring the fact that the scheduling environments usually fluctuate. In this article, we deal with reducing the execution cost of multiple workflow applications within the corresponding deadline constraints and improving the network robustness in fuzzy edge-cloud environments. Triangular Fuzzy Numbers (TFNs) are employed to describe the computing capacity of servers and the bandwidth between them in uncertain environments. Specially, a novel scheduling Strategy based on Particle Swarm Optimization algorithm employing the Quadratic Penalty Function (SSPSO_QPF) is proposed for scheduling multiple workflow applications. Compared with other classic scheduling strategies, simulation results demonstrate that the proposed strategy can generate feasible scheduling schemes even with the strict deadline constraints, and significantly reduce the fuzzy execution cost of multiple workflow applications.

关键词： Processor scheduling Task analysis Cloud computing scheduling Costs Servers Data communication workflow scheduling Complex network robustness Edge-cloud environments Multi-constraint combinatorial optimization

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HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2014年第1期17卷 129-137页

作者： Delavar, Arash Ghorbannia Aryan, Yalda Payame Noor Univ Dept Comp Tehran Iran

In heterogeneous distributed computing systems like cloud computing, the problem of mapping tasks to resources is a major issue which can have much impact on system performance. For some reasons such as heterogeneous and dynamic features and the dependencies among requests, task scheduling is known to be a NP-complete problem. In this paper, we proposed a hybrid heuristic method (HSGA) to find a suitable scheduling for workflow graph, based on genetic algorithm in order to obtain the response quickly moreover optimizes makespan, load balancing on resources and speedup ratio. At first, the HSGA algorithm makes tasks prioritization in complex graph considering their impact on others, based on graph topology. This technique is efficient to reduction of completion time of application. Then, it merges Best-Fit and Round Robin methods to make an optimal initial population to obtain a good solution quickly, and apply some suitable operations such as mutation to control and lead the algorithm to optimized solution. This algorithm evaluates the solutions by considering efficient parameters in cloud environment. Finally, the proposed algorithm presents the better results with increasing number of tasks in application graph in contrast with other studied algorithms.

关键词： Heterogeneous distributed computing systems Cloud computing workflow scheduling Heuristic Genetic Algorithm

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Energy-makespan optimization of workflow scheduling in fog-cloud computing

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COMPUTING 2021年第9期103卷 2033-2059页

作者： Ijaz, Samia Munir, Ehsan Ullah Ahmad, Saima Gulzar Rafique, M. Mustafa Rana, Omer F. COMSATS Univ Islamabad Wah Campus Islamabad Pakistan Rochester Inst Technol Dept Comp Sci Rochester NY 14623 USA Cardiff Univ Sch Comp Sci & Informat Cardiff Wales

The rapid evolution of smart services and Internet of Things devices accessing cloud data centers can lead to network congestion and increased latency. Fog computing, focusing on ubiquitously connected heterogeneous devices, addresses latency and privacy requirements of workflows executing at the network edge. However, allocating resources in this paradigm is challenging due to the complex and strict Quality of Service constraints. Moreover, simultaneously optimizing conflicting objectives, e.g., energy consumption and workflow makespan increases the complexity of the scheduling process. We investigate workflow scheduling in fog-cloud environments to provide an energy-efficient task schedule within acceptable application completion times. We introduce a scheduling algorithm, Energy Makespan Multi-Objective Optimization, that works in two phases. First, it models the problem as a multi-objective optimization problem and computes a tradeoff between conflicting objectives while allocating fog and cloud resources, and schedules latency-sensitive tasks (with lower computational requirements) to fog resources and computationally complex tasks (with low latency requirements) on cloud resources. We adapt the Deadline-Aware stepwise Frequency Scaling approach to further reduce energy consumption by utilizing unused time slots between two already scheduled tasks on a single node. Our evaluation using synthesized and real-world applications shows that our approach reduces energy consumption, up to 50%, as compared to existing approaches with minimal impact on completion times.

关键词： Cloud Computing Fog computing workflow scheduling Makespan Energy consumption DVFS

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