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A survey study on task scheduling schemes for workflow executions in cloud computing environment: classification and challenges

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JOURNAL OF SUPERCOMPUTING 2024年第7期80卷 9384-9437页

作者： Shirvani, Mirsaeid Hosseini Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran

Several real-world scientific and industrial workflow applications adopt elastic and cost-efficient cloud services to fulfill their requirement. There are two stakeholders in the system, namely the user and the provider each of which tries to maximize its profit and at the same time minimize their possible overall costs. Since task scheduling algorithms for workflow executions determine which task should be possibly executed on what resources, they have a drastic impact on both the user's quality of experiences and on the underlying resource utilization. Indeed, an efficient task scheduling algorithm can meet service-level agreement (SLA) for both sides. For the sake of the importance of the issue, this survey presents a subjective taxonomy on the task scheduling schemes in the literature for workflow executions in cloud computing environments to be a guideline for future improvement. It classifies the literature based on the proposed algorithms in the literature, objectives, stakeholders' requirements, and evaluation metrics. This survey highlights research trends, challenges, research gaps, potential solutions, and future direction. It can also pave the way for further processing, improvement and strengthening of existing approaches or devising novel ones for interested researchers in the field of task scheduling problems.

关键词： Cloud computing Workflow application task scheduling algorithm Directed acyclic graph (DAG) List schedulers Meta-heuristic algorithms

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Performance Analysis of different task scheduling algorithms in Cloud Computing under Dynamic Environment 2019

Performance Analysis of different Task Scheduling Algorithms...

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Proceedings of the 2019 International Communication Engineering and Cloud Computing Conference

作者： Neeraj Arora Rohitash Kumar Banyal School of Science and Technology V.M. Open University Kota Rajasthan India Computer Science and Engineering Rajasthan Technical University Kota Rajasthan India

ISBN: (纸本)9781450376396

Cloud computing is one of the emerging technology in the field of computer science in which services are provided through the internet on-demand. It has various features like simple to use, minimum power consumption, and minimum cost. Cloud environment contains various virtualized computing resources, various development platforms, and users jobs. task scheduling is one of the main essential parts of the cloud environment which is mainly concerned with minimizing energy consumption and maximizing resource *** this paper, we analyze seven popular job scheduling algorithms i.e. FCFS, SJF, Round Robin, Min-Min, Max-Min, Genetic algorithm, and Ant colony optimization. The comparative analysis of the algorithms is based on response time and makespan. The analysis results show that the Genetic algorithm outperforms in terms of response time and Ant Colony Optimization is performed better in terms of makespan.

关键词： Cloud Computing task scheduling algorithm Ant Colony Optimization Genetic algorithm

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Integration process simulator: A tool for performance evaluation of task scheduling of integration processes

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JOURNAL OF SIMULATION 2022年第6期16卷 604-623页

作者： Freire, Daniela L. Frantz, Rafael Z. Roos-Frantz, Fabricia Basto-Fernandes, Vitor Univ Inst Lisbon ISCTE IUL ISTAR IUL Dept Informat Sci & Technol Lisbon Portugal Unijui Univ Dept Exact Sci & Engn Ijui Brazil

Due to large volumes of data from Cloud Computing and from the Internet of Things, the companies' software ecosystem requires an efficient integration of applications and services. Performance improvement from integration platforms' runtime systems is directly related to task scheduling strategies from integration processes. It is still a challenge to find the proper heuristic for a given integration process subject to high inbound data rates. This article proposes a simulation tool for the field of Enterprise Application Integration, which implements different scheduling heuristics and allows the extraction of performance metrics. Three task scheduling heuristics were simulated during the integration process, and the results were evaluated through statistical tests.

关键词： Application integration task scheduling algorithm workflow scheduling simulation tool high volume data

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An Optimised task scheduling of Remote Sensing Data Processing for Smart Patient Health Monitoring

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 542-553页

作者： Ravikishan, Surapaneni Reddy, B. Eswar Rao, K. V. Sambasiva Department of Computer Science and Engineering JNTUA Anantapur 515002 India Department of Computer Science and Engineering NRI Institute of Technology Vijayawada 521212 India

In healthcare, remote sensing technologies are popular for smart patient health monitoring. Real-time health assessment and early intervention are possible using remote sensing data from wearable sensors and imaging equipment. However, processing and analysing huge remote sensing data are complex. task scheduling improves processing workflow for accurate and fast health monitoring. This research offered the optimised task scheduling of remote sensing data processing for smart patient health monitoring, to describe the processing activities such as data preparation, task scheduling algorithms, and decision-making involved in data analysis from remote sensing. We used datasets associated with Internet of Things (IoT) devices that patients wear as sensors on their wrists. We used min–max normalisation to standardise the data’s scale after preprocessing the data and choose the task scheduling algorithm methods to distribute work among resources effectively. We proposed the multi-objective dwarf mongoose optimised with Deep Q Network (MODMO-DQN), which aims to address the task scheduling issue for remote sensing data processing in the Internet of Things by monitoring vital sign data of faraway patients. Next, to optimise the e-health services, an optimisation module built on top of MODMO-DQN is developed. The experiment compares the variation in completion time in the remote sensing data process. The results showed that the proposed perform to compare the existing methods. This will make it possible to evaluate the suggested works in terms of throughput, latency, energy usage, and task scheduling efficiency. The performance analysis shows that the recommended method is successful and may be a feasible and efficient solution to track patient vital sign data in Internet of Things-based e-Health. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Cloud E-health Min–max normalisation Multi-objective dwarf mongoose optimised with Deep Q Network (MODMO-DQN) Remote sensing Smart healthcare task scheduling algorithm

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task scheduling Optimization on Enterprise Application Integration Platforms Based on the Meta-heuristic Particle Swarm Optimization 17

Task Scheduling Optimization on Enterprise Application Integ...

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31st Brazilian Symposium on Software Engineering (SBES)

作者： Sellaro, Daniela F. Frantz, Rafael Z. Hernandez, Inma Roos-Frantz, Fabricia Sawicki, Sandro Unijui Univ Ijui RS Brazil Univ Seville Seville Spain

ISBN: (纸本)9781450353267

Companies seek technological alternatives that provide competitiveness for their business processes. Among these alternatives, there are integration platforms that allow you to connect applications to your software ecosystems. These ecosystems are often composed of local applications and cloud computing services, such as SaaS and PaaS, and still, interact with social media. Integration platforms are specialized software that allows you to design, execute and monitor integration solutions, which connect functionality and data from different applications. Integration platforms typically provide a specific domain language, development toolkit, runtime engine, and monitoring tool. The efficiency of the engine in scheduling and performing integration tasks has a direct impact on the performance of a solution and this is one of the challenges faced by integration platforms. Our literature review has identified that integration engines adopt task scheduling algorithms based on the textit First-In-First-Out discipline, which may be inefficient. Therefore, it is appropriate to seek a task scheduling algorithm that optimizes engine performance, providing a positive impact on the performance of the integration solution in different scenarios. This article proposes an algorithm for task scheduling based on the meta-heuristic optimization technique, which assigns the tasks to the computational resources, considering the waiting time in the queue of ready tasks and the computational complexity of Each task in order to optimize the performance of the integration solution.

关键词： Integrating Enterprise Applications Optimization Cloud computing Runtime System task scheduling algorithm

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Selective task scheduling for Time-targeted Workflow Execution on Cloud 16

Selective Task Scheduling for Time-targeted Workflow Executi...

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16th IEEE Int Conf on High Performance Computing and Communications/11th IEEE Int Conf on Embedded Software and Systems\6th Int Symposium on Cyberspace Safety and Security

作者： Jung, In-Yong Jeong, Chang-Sung Korea Univ Dept Elect Engn Seoul South Korea

ISBN: (纸本)9781479961238

With increasing demand of data-intensive applications, a workflow with multiple distributed parallel applications becomes a useful way to build various large scale data processing applications on cloud environments. However, previous scheduling approaches for executing workflows are focused on estimating minimum make span of executions on pre-configured clusters. In this paper, we present two time-targeted selective task scheduling schemes which consider malleability and rigidity of each task, named SDER (Sequential Deployment with Expanded Resources) and DDMS (Distributed Deployment on Multiple Streams). These scheduling algorithms deploy all task of workflow in sequential and distributed ways each, and estimate optimized number of resources for completing workflow execution within given deadline. We show the experimental results which compares the performance of two scheduling schemes for various workflows.

关键词： workflow execution task scheduling algorithm cloud computing

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A smart energy and reliability aware scheduling algorithm for workflow execution in DVFS-enabled cloud environment

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 112卷 431-448页

作者： Hassan, Hadeer A. Salem, Sameh A. Saad, Elsayed M. Helwan Univ Fac Engn Helwan Egypt

The energy consumption is one of the major concerns addressed by recent researches in the green cloud environment. As a result, to decrease the enormous increase in energy consumption, one of the most promising scheduling techniques used nowadays is the Dynamic Voltage Frequency Scaling (DVFS) technique. DVFS reduces energy consumption by lowering the processors' frequency for virtual machines (VMs);this results in an increase in the occurrence of errors during the execution of the workflow, which decreases the reliability of the system. As a consequence, this paper addresses the DVFS problem by proposing a new Smart Energy and Reliability Aware scheduling algorithm (SERAS) for workflow execution in the cloud environment. The SERAS approach split the target deadline of workflow across tasks. Afterward, the proposed algorithm decreases the frequency of processors for VMs using the DVFS technique without missing the tasks' deadline. As a consequence, the SERAS algorithm allocates the tasks to the most appropriate VMs with suitable frequencies levels while guaranteeing both the reliability and the completion time requirement of green cloud systems. To vindicate the effectiveness of the SERAS algorithm in real-world applications, we carried out a series of experiments on four real workflows generated using a scientific toolkit. Also, we performed comprehensive experiments with recent researches. The results showed that the SERAS algorithm outperforms its competitors while keeping both the reliability and completion time requirements. Furthermore, the estimated time complexity and average execution time show the applicability of the SERAS algorithm compared with their competitors. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Energy efficient DVFS-enabled Green cloud computing task scheduling algorithm Reliability DAG workflow Heterogeneous system

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Grouped tasks scheduling algorithm based on QoS in cloud computing network

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EGYPTIAN INFORMATICS JOURNAL 2017年第1期18卷 11-19页

作者： Ali, Hend Gamal El Din Hassan Saroit, Imane Aly Kotb, Amira Mohamed Cairo Univ Fac Comp & Informat Dept Informat Technol Giza Egypt

This paper proposes the grouped tasks scheduling (GTS) algorithm that is used to schedule tasks in cloud computing network by applying quality of service to satisfy user's needs. The proposed algorithm distributes tasks into five categories;each category has tasks with similar attributes (user type, task type, task size, and task latency). After adding tasks into right category, it starts scheduling these tasks into available services. scheduling is done in two steps: first step is deciding which category will be scheduled first. This depends on the attributes of the tasks that belong to each category so the category that has tasks with high value of attributes will be scheduled first. Second step is deciding which task inside the chosen category will be scheduled first. This depends on the execution time of task so the task that has minimum execution time will be scheduled first. (C) 2016 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University. This is an open access article under the CC BY-NC-ND license.

关键词： Cloud computing Min-Min algorithm QoS task scheduling algorithm

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Metascheduling Using Discrete Particle Swarm Optimization for Fault Tolerance in Time-Triggered IoT-WSN

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IEEE INTERNET OF THINGS JOURNAL 2023年第14期10卷 12666-12675页

作者： Baniabdelghany, Haytham Obermaisser, Roman Khalifeh, Ala' Muoka, Pascal Univ Siegen Fac Sci & Technol D-57076 Siegen Germany German Jordanian Univ Commun Engn Dept Amman 11180 Jordan Univ Siegen Elect Engn & Comp Sci D-57076 Siegen Germany

In time-triggered (TT) systems, adaptation is performed by using metascheduling approaches to ensure temporal predictability. Wireless sensor networks (WSNs) are growing to be used as a reliable, an energy efficient, and a scalable network infrastructure for numerous Internet of Things (IoT) applications. Because of run-time changes because of failure events, a metascheduling system is required to address the changes by precomputing schedules for several context events at design time. Therefore, this article proposes a metascheduler that solves a new scheduling problem for each adaptation scenario in IoT-WSN using our offline algorithm named discrete particle swarm optimization for reliable task allocation (DPSO-TA), resulting in a multischedule graph (MSG) that combines the repeated schedules. In this work, a TT IoT-WSN metascheduler is proposed that takes into considerations the node or link failures that are common in WSN. Single- and two-failure events are considered, where a graph for all feasible schedules is formed at the network design time. Such a large number of schedules causes a state space explosion problem, which is managed using a reconvergence method. Different application model sizes and network topologies are used to assess the proposed metascheduler under various failure event scenarios. The results show a reduction in the size of the MSG by applying inputs with different deadline values and different number of tasks. Furthermore, the validity suggested by the proposed metascheduler, which is the ratio of valid to invalid schedules, is improved when compared to algorithms that schedule tasks to hosts with the shortest completion time or algorithms that select hosts for a set of sorted tasks based on energy consumed and task arrival time.

关键词： Metascheduler task scheduling algorithm wire-less sensor networks (WSNs)

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Reliable task Allocation for Time-Triggered IoT-WSN Using Discrete Particle Swarm Optimization

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IEEE INTERNET OF THINGS JOURNAL 2021年第14期9卷 11974-11992页

作者： Baniabdelghany, Haytham Obermaisser, Roman Khalifeh, Ala' Univ Siegen Fac Sci & Technol D-57076 Siegen Germany German Jordanian Univ Elect Engn Dept Amman 11180 Jordan

A wireless sensor network (WSN) constitutes of nodes that are used to sense a certain physical environment phenomena and sends these collected data to a remote node for further processing and decision making. With the proliferation of Internet of Things (IoT), WSN is evolving to be used as reliable, energy efficient, cost effective, and scalable network infrastructure for many IoT applications. To have a robust WSN with IoT (IoT-WSN), it is a challenge to allocate the tasks among different sensor nodes because the distribution of dependent tasks among wireless nodes must satisfy precedence and time constraints, minimize energy consumption, and prevent signal interference. The problem becomes more complex if a system requires high reliability in the event of link or node failures. This article presents an offline discrete particle swarm optimization algorithm for reliable task allocation (DPSO-TA) in IoT-WSN. DPSO-TA defines a utility function to optimize the task allocation problem by iteratively trying to improve the solution. The defined function is designed to satisfy goals, including saving energy, reducing task completion time, and minimizing the failure rates. A load balance mechanism is applied to make balancing of the tasks on several hosts. During the task allocation process, the frame replication and elimination for reliability (FRER) approach is used to support flow fault tolerance by replicating transmitted flows over redundant routes. DPSO-TA considers the periodicity of the time-trigged (TT) flows and applies a physical interference model to prevent signal interference on the transmitted flows through assigning them to conflict-free time slots. The analysis and simulation results show the optimization of DPSO-TA on timeliness, deadline missing ratio, failure rates, and energy efficiency comparing it with algorithms that allocate tasks to hosts that produce the minimum completion time or that use a traditional procedure to find the most suitable nodes d

关键词： task analysis Wireless sensor networks Resource management Reliability Internet of Things Sensors scheduling Physical interference model task scheduling algorithm WSN with IoT (IoT-WSN)

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