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Journal articles on the topic 'Heterogeneous Earliest Finish Time (HEFT) algorithm'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Nasser, Thaar Habeb, Ekhlas Kadhum Hamza, and Ahmed Mudheher Hasan. "MOCAB/HEFT algorithm of multi radio wireless communication improved achievement assessment." Bulletin of Electrical Engineering and Informatics 12, no. 1 (2023): 224–31. http://dx.doi.org/10.11591/eei.v12i1.4078.

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Network-wide conveying is vital in remote associations, and the great part of these broadcasts are built on single-channel single-radio (SC-SR) network frameworks. The problem of the current work is divided into two parts. The first part shows that increasing broadcast and redundancy lead to an increase in time consumption. The second problem is solving complexity problems when tasks are scheduled in a heterogeneous manner in a computing system, where the processors in the network may not be identical and take different time periods to carry out the same task. The goals of this work are to red

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2

Chaudhuri, Pranay, and Jeffrey Elcock. "Process Scheduling in Heterogeneous Multiprocessor Systems Using Task Duplication." International Journal of Business Data Communications and Networking 6, no. 1 (2010): 58–69. http://dx.doi.org/10.4018/jbdcn.2010010104.

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Scheduling tasks in heterogeneous parallel and distributed computing environments continues to be a challenging problem. In this paper, the authors investigate the Heterogeneous Earliest Finish Time (HEFT) algorithm, along with alternative scheduling policies for task prioritising phases and the Critical Path on a Processor (CPOP) for scheduling tasks on a heterogeneous multiprocessor system. The authors show that by combining the HEFT algorithm selection policy with the task duplication strategy, it is possible to further reduce the schedule length produced by both HEFT and CPOP. The process

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3

Li, Yuzhong, Wenming Tang, and Guixiong Liu. "HPEFT for Hierarchical Heterogeneous Multi-DAG in a Multigroup Scan UPA System." Electronics 8, no. 5 (2019): 498. http://dx.doi.org/10.3390/electronics8050498.

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Multidirected acyclic graph (DAG) workflow scheduling is a key problem in the heterogeneous distributed environment in the distributed computing field. A hierarchical heterogeneous multi-DAG workflow problem (HHMDP) was proposed based on the different signal processing workflows produced by different grouping and scanning modes and their hierarchical processing in specific functional signal processing modules in a multigroup scan ultrasonic phased array (UPA) system. A heterogeneous predecessor earliest finish time (HPEFT) algorithm with predecessor pointer adjustment was proposed based on the

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4

Hilda, Jabanjalin, and Srimathi Chandrasekaran. "Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing." Future Internet 13, no. 10 (2021): 263. http://dx.doi.org/10.3390/fi13100263.

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A heterogeneous system can be portrayed as a variety of unlike resources that can be locally or geologically spread, which is exploited to implement data-intensive and computationally intensive applications. The competence of implementing the scientific workflow applications on heterogeneous systems is determined by the approaches utilized to allocate the tasks to the proper resources. Cost and time necessity are evolving as different vital concerns of cloud computing environments such as data centers. In the area of scientific workflows, the difficulties of increased cost and time are highly

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Hilda, Jabanjalin, and Srimathi Chandrasekaran. "Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing." Future Internet 13, no. 10 (2021): 263. http://dx.doi.org/10.3390/fi13100263.

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A heterogeneous system can be portrayed as a variety of unlike resources that can be locally or geologically spread, which is exploited to implement data-intensive and computationally intensive applications. The competence of implementing the scientific workflow applications on heterogeneous systems is determined by the approaches utilized to allocate the tasks to the proper resources. Cost and time necessity are evolving as different vital concerns of cloud computing environments such as data centers. In the area of scientific workflows, the difficulties of increased cost and time are highly

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6

Gupta, Sachi, Sailesh Iyer, Gaurav Agarwal, et al. "Efficient Prioritization and Processor Selection Schemes for HEFT Algorithm: A Makespan Optimizer for Task Scheduling in Cloud Environment." Electronics 11, no. 16 (2022): 2557. http://dx.doi.org/10.3390/electronics11162557.

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Cloud computing is one of the most commonly used infrastructures for carrying out activities using virtual machines known as processing units. One of the most fundamental issues with cloud computing is task scheduling. The optimal determination of scheduling criteria in cloud computing is a non-deterministic polynomial-time (NP)-complete optimization problem, and several procedures to manage this problem have been suggested by researchers in the past. Among these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm is recognized to produce optimal outcomes in a shorter time period

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D. Hanabaratti, Kavita, and Rudragoud Patil. "Efficient algorithm for replanning web service composition." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 1 (2023): 491. http://dx.doi.org/10.11591/ijeecs.v31.i1.pp491-500.

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Web-service-composition (WSC) workload execution inside a hybrid cloud environment is challenging. A dynamic approach for allocating resources to various tasks, as well as associated sub-tasks having a satisfactory quality-ofservice (QoS) requirement, is necessary for the present real-time demand. As a result of focusing primarily on decreasing processing time as well as cost, current approaches improve latency as well as energy while executing a given workload. This study introduces an efficient re-planning (ERP) algorithm for running many scientific workloads inside a heterogeneous cloud env

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8

Kavita, D. Hanabaratti, and Patil Rudragoud. "Efficient algorithm for replanning web service composition." Efficient algorithm for replanning web service composition 31, no. 1 (2023): 491–500. https://doi.org/10.11591/ijeecs.v31.i1.pp491-500.

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Web-service-composition (WSC) workload execution inside a hybrid cloud environment is challenging. A dynamic approach for allocating resources to various tasks, as well as associated sub-tasks having a satisfactory quality-ofservice (QoS) requirement, is necessary for the present real-time demand. As a result of focusing primarily on decreasing processing time as well as cost, current approaches improve latency as well as energy while executing a given workload. This study introduces an efficient re-planning (ERP) algorithm for running many scientific workloads inside a heterogeneous cloud env

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9

Aziz, Maslina Abdul, and Izuan Hafez Ninggal. "Scalable workflow scheduling algorithm for minimizing makespan and failure probability." Bulletin of Electrical Engineering and Informatics 8, no. 1 (2019): 283–90. http://dx.doi.org/10.11591/eei.v8i1.1436.

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This paper presents an algorithm called Failure-Aware Workflow Scheduling (FAWS). The proposed algorithm discussed in this paper schedules parallel applications on homogeneous systems without sacrificing the two conflicting objectives: reliability and makespan. The proposed algorithm handles unexpected failure causes rescheduling of the failed task to available resources. In order to analyse the performance of the FAWS algorithm, it will be compared with the popular scheduling algorithm namely Heterogeneous Earliest Finish Time (or HEFT) and Critical Path (CP). A simulation-driven analysis bas

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10

Maslina, Abdul Aziz, and Hafez Ninggal Izuan. "Scalable workflow scheduling algorithm for minimizing makespan and failure probability." Bulletin of Electrical Engineering and Informatics 8, no. 1 (2019): 283–90. https://doi.org/10.11591/eei.v8i1.1436.

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This paper presents an algorithm called Failure-Aware Workflow Scheduling (FAWS). The proposed algorithm discussed in this paper schedules parallel applications on homogeneous systems without sacrificing the two conflicting objectives: reliability and makespan. The proposed algorithm handles unexpected failure causes rescheduling of the failed task to available resources. In order to analyse the performance of the FAWS algorithm, it will be compared with the popular scheduling algorithm namely Heterogeneous Earliest Finish Time (or HEFT) and Critical Path (CP). A simulation-driven analysis bas

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11

Liang, Jie, Kenli Li, Chubo Liu, and Keqin Li. "Are task mappings with the highest frequency of servers so good? A case study on Heterogeneous Earliest Finish Time (HEFT) algorithm." Journal of Systems Architecture 121 (December 2021): 102311. http://dx.doi.org/10.1016/j.sysarc.2021.102311.

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12

Sung, Tegg Taekyong, Jeongsoo Ha, Jeewoo Kim, Alex Yahja, Chae-Bong Sohn, and Bo Ryu. "DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip (SoC) Resource Scheduling." Electronics 9, no. 6 (2020): 936. http://dx.doi.org/10.3390/electronics9060936.

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In this paper, we present a novel scheduling solution for a class of System-on-Chip (SoC) systems where heterogeneous chip resources (DSP, FPGA, GPU, etc.) must be efficiently scheduled for continuously arriving hierarchical jobs with their tasks represented by a directed acyclic graph. Traditionally, heuristic algorithms have been widely used for many resource scheduling domains, and Heterogeneous Earliest Finish Time (HEFT) has been a dominating state-of-the-art technique across a broad range of heterogeneous resource scheduling domains over many years. Despite their long-standing popularity

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13

Sing, Ranumayee, Sourav Kumar Bhoi, Niranjan Panigrahi, Kshira Sagar Sahoo, Muhammad Bilal, and Sayed Chhattan Shah. "EMCS: An Energy-Efficient Makespan Cost-Aware Scheduling Algorithm Using Evolutionary Learning Approach for Cloud-Fog-Based IoT Applications." Sustainability 14, no. 22 (2022): 15096. http://dx.doi.org/10.3390/su142215096.

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The tremendous expansion of the Internet of Things (IoTs) has generated an enormous volume of near and remote sensing data, which is increasing with the emergence of new solutions for sustainable environments. Cloud computing is typically used to help resource-constrained IoT sensing devices. However, the cloud servers are placed deep within the core network, a long way from the IoT, introducing immense data transactions. These transactions require heavy electricity consumption and release harmful CO2 to the environment. A distributed computing environment located at the edge of the network na

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14

Bano, Farheen, Faisal Ahmad, Mohammad Shahid, Mahfooz Alam, Faraz Hasan, and Mohammad Sajid. "A Levelized Multiple Workflow Heterogeneous Earliest Finish Time Allocation Model for Infrastructure as a Service (IaaS) Cloud Environment." Algorithms 18, no. 2 (2025): 99. https://doi.org/10.3390/a18020099.

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Cloud computing, a superset of heterogeneous distributed computing, allows sharing of geographically dispersed resources across multiple organizations on a rental basis using virtualization as per demand. In cloud computing, workflow allocation to achieve the optimum schedule has been reported to be NP-hard. This paper proposes a Levelized Multiple Workflow Heterogeneous Earliest Finish Time (LMHEFT) model to optimize makespan in the cloud computing environment. The model has two phases: task prioritization and task allocation. The task prioritization phase begins by dividing workflows into th

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15

Li, Tao, Dingyuan Cao, Ye Lu, et al. "DBEFT: A Dependency-Ratio Bundling Earliest Finish Time Algorithm for Heterogeneous Computing." IEEE Access 7 (2019): 173884–96. http://dx.doi.org/10.1109/access.2019.2956759.

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16

Zhai, Wenzheng, Yue-Li Hu, and Feng Ran. "CQPSO scheduling algorithm for heterogeneous multi-core DAG task model." Modern Physics Letters B 31, no. 19-21 (2017): 1740050. http://dx.doi.org/10.1142/s0217984917400504.

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Efficient task scheduling is critical to achieve high performance in a heterogeneous multi-core computing environment. The paper focuses on the heterogeneous multi-core directed acyclic graph (DAG) task model and proposes a novel task scheduling method based on an improved chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm. A task priority scheduling list was built. A processor with minimum cumulative earliest finish time (EFT) was acted as the object of the first task assignment. The task precedence relationships were satisfied and the total execution time of all tasks was

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17

Allahverdyan, A., A. Zhadan, I. Kondratov, et al. "Heterogeneous Computational Scheduling Using Adaptive Neural Hyper-Heuristic." Doklady Mathematics 110, S1 (2024): S151—S161. https://doi.org/10.1134/s106456242460221x.

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Abstract In heterogeneous computing environments, efficiently scheduling tasks, especially those forming Directed Acyclic Graphs (DAGs), is critical. This is particularly true for various Cloud and Edge computing tasks, as well as training Large Language Models (LLMs). This paper introduces a new scheduling approach using an Adaptive Neural Hyper-heuristic. By integrating a neural network trained with genetic algorithms, our method aims to minimize makespan. The approach uses a two-level algorithm: the first level prioritizes tasks using adaptive heuristic and the second level assigns resource

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18

Zhang, Yujian, Fei Tong, Chuanyou Li, and Yuwei Xu. "Bi-Objective Workflow Scheduling on Heterogeneous Computing Systems Using a Memetic Algorithm." Electronics 10, no. 2 (2021): 209. http://dx.doi.org/10.3390/electronics10020209.

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Due to the high power bills and the negative environmental impacts, workflow scheduling with energy consciousness has been an emerging need for modern heterogeneous computing systems. A number of approaches have been developed to find suboptimal schedules through heuristics by means of slack reclamation or trade-off functions. In this article, a memetic algorithm for energy-efficient workflow scheduling is proposed for a quality-guaranteed solution with high runtime efficiency. The basic idea is to retain the advantages of population-based, heuristic-based, and local search methods while avoid

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19

Al-Rahayfeh, Amer, Saleh Atiewi, Abdullah Abuhussein, and Muder Almiani. "Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms." Future Internet 11, no. 5 (2019): 109. http://dx.doi.org/10.3390/fi11050109.

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Cloud computing (CC) is fast-growing and frequently adopted in information technology (IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the hot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load balancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, users’ tasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more clusters. After tas

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20

Zhou, Naqin, Xiaowen Liao, Fufang Li, Yuanyong Feng, and Liangchen Liu. "List Scheduling Algorithm Based on Virtual Scheduling Length Table in Heterogeneous Computing System." Wireless Communications and Mobile Computing 2021 (December 11, 2021): 1–16. http://dx.doi.org/10.1155/2021/9529022.

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Edge computing needs the close cooperation of cloud computing to better meet various needs. Therefore, ensuring the efficient implementation of applications in cloud computing is not only related to the development of cloud computing itself but also affects the promotion of edge computing. However, resource management and task scheduling strategy are important factors affecting the efficient implementation of applications. Therefore, aiming at the task scheduling problem in cloud computing environment, this paper proposes a new list scheduling algorithm, namely, based on a virtual scheduling l

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21

Lakhan, Abdullah, Mazin Abed Mohammed, Ahmed N. Rashid, et al. "Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System." Sensors 21, no. 12 (2021): 4093. http://dx.doi.org/10.3390/s21124093.

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The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it to a distributed hospital for further study. IoMT provides patients with a variety of paid programmes to help them keep track of their health problems. However, the current system services are expensive, and offloaded data in the healthcare network are insecure. The research develops a new, cost-effective and stable IoMT framework based on a blockchain-enabled fog cloud. The study aims to reduce the cost of healthcare app

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22

Keshanchi, Bahman, and Nima Jafari Navimipour. "Priority-Based Task Scheduling in the Cloud Systems Using a Memetic Algorithm." Journal of Circuits, Systems and Computers 25, no. 10 (2016): 1650119. http://dx.doi.org/10.1142/s021812661650119x.

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Task scheduling is one of the major issues to achieve high performance in distributed systems such as Grid, Peer-to-Peer and cloud environment. Generally, there are two phases in heuristics-based task scheduling algorithms in heterogeneous distributed computing systems (HeDCSs). These phases are task prioritization and processor assigning respectively. Heuristic-based task scheduling algorithms may use different policies to assign priority to subtasks which produce different makespans in a heterogeneous computing system. Thus, a suitable scheduling algorithm is one that can efficiently assign

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23

Bothra, Sandeep Kumar, Sunita Singhal, and Hemlata Goyal. "Cost effective hybrid genetic algorithm for workflow scheduling in cloud." System research and information technologies, no. 3 (October 30, 2022): 121–38. http://dx.doi.org/10.20535/srit.2308-8893.2022.3.08.

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Cloud computing plays a significant role in everyone’s lifestyle by snugly linking communities, information, and trades across the globe. Due to its NP-hard nature, recognizing the optimal solution for workflow scheduling in the cloud is a challenging area. We proposed a hybrid meta-heuristic cost-effective load-balanced approach to schedule workflow in a heterogeneous environment. Our model is based on a genetic algorithm integrated with predict earliest finish time (PEFT) to minimize makespan. Instead of assigning the task randomly to a virtual machine, we apply a greedy strategy that assign

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24

Kanwal, Samira, Zeshan Iqbal, Aun Irtaza, Muhammad Sajid, Sohaib Manzoor, and Nouman Ali. "Head Node Selection Algorithm in Cloud Computing Data Center." Mathematical Problems in Engineering 2021 (July 24, 2021): 1–12. http://dx.doi.org/10.1155/2021/3418483.

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Cloud computing provides multiple services such as computational services, data processing, and resource sharing through multiple nodes. These nodes collaborate for all prementioned services in the data center through the head/leader node. This head node is responsible for reliability, higher performance, latency, and deadlock handling and enables the user to access cost-effective computational services. However, the optimal head nodes’ selection is a challenging problem due to consideration of resources such as memory, CPU-MIPS, and bandwidth. The existing methods are monolithic, as they sele

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25

Thaar, Habeb Nasser, Kadhum Hamza Ekhlas, and Mudheher Hasan Ahmed. "MOCAB/HEFT algorithm of multi radio wireless communication improved achievement assessment." Bulletin of Electrical Engineering and Informatics 12, no. 1 (2022). https://doi.org/10.11591/eei.v12i1.4078.

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Abstract:

Network-wide conveying is vital in remote associations, and the great part of these broadcasts are built on single-channel single-radio (SC-SR) network frameworks. The problem of the current work is divided into two parts. The first part shows that increasing broadcast and redundancy lead to an increase in time consumption. The second problem is solving complexity problems when tasks are scheduled in a heterogeneous manner in a computing system, where the processors in the network may not be identical and take different time periods to carry out the same task. The goals of this work are to red

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26

Zhang, Honglin, Yaohua Wu, and Zaixing Sun. "EHEFT-R: multi-objective task scheduling scheme in cloud computing." Complex & Intelligent Systems, July 31, 2021. http://dx.doi.org/10.1007/s40747-021-00479-7.

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AbstractIn cloud computing, task scheduling and resource allocation are the two core issues of the IaaS layer. Efficient task scheduling algorithm can improve the matching efficiency between tasks and resources. In this paper, an enhanced heterogeneous earliest finish time based on rule (EHEFT-R) task scheduling algorithm is proposed to optimize task execution efficiency, quality of service (QoS) and energy consumption. In EHEFT-R, ordering rules based on priority constraints are used to optimize the quality of the initial solution, and the enhanced heterogeneous earliest finish time (HEFT) al

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27

T., Vigneswari, and A. Maluk Mohamed M. "Optimal Grid Scheduling Using Improved Artificial Bee Colony Algorithm." January 1, 2015. https://doi.org/10.5281/zenodo.1099196.

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Job Scheduling plays an important role for efficient utilization of grid resources available across different domains and geographical zones. Scheduling of jobs is challenging and NPcomplete. Evolutionary / Swarm Intelligence algorithms have been extensively used to address the NP problem in grid scheduling. Artificial Bee Colony (ABC) has been proposed for optimization problems based on foraging behaviour of bees. This work proposes a modified ABC algorithm, Cluster Heterogeneous Earliest First Min- Min Artificial Bee Colony (CHMM-ABC), to optimally schedule jobs for the available resources.

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Zeedan, Maha, Gamal Attiya, and Nawal El-Fishawy. "Enhanced hybrid multi-objective workflow scheduling approach based artificial bee colony in cloud computing." Computing, October 1, 2022. http://dx.doi.org/10.1007/s00607-022-01116-y.

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AbstractThis 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

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D., Sumathi, and Poongodi P. "An Improved Scheduling Strategy in Cloud Using Trust Based Mechanism." September 2, 2015. https://doi.org/10.5281/zenodo.1109371.

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Cloud Computing refers to applications delivered as services over the internet, and the datacenters that provide those services with hardware and systems software. These were earlier referred to as Software as a Service (SaaS). Scheduling is justified by job components (called tasks), lack of information. In fact, in a large fraction of jobs from machine learning, bio-computing, and image processing domains, it is possible to estimate the maximum time required for a task in the job. This study focuses on Trust based scheduling to improve cloud security by modifying Heterogeneous Earliest Finis

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30

Hai, Tao, Jincheng Zhou, Dayang Jawawi, et al. "Task scheduling in cloud environment: optimization, security prioritization and processor selection schemes." Journal of Cloud Computing 12, no. 1 (2023). http://dx.doi.org/10.1186/s13677-022-00374-7.

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AbstractCloud computing is an extremely important infrastructure used to perform tasks over processing units. Despite its numerous benefits, a cloud platform has several challenges preventing it from carrying out an efficient workflow submission. One of these is linked to task scheduling. An optimization problem related to this is the maximal determination of cloud computing scheduling criteria. Existing methods have been unable to find the quality of service (QoS) limits of users- like meeting the economic restrictions and reduction of the makespan. Of all these methods, the Heterogeneous Ear

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31

Hosseini Shirvani, Mirsaeid, and Reza Noorian Talouki. "Bi-objective scheduling algorithm for scientific workflows on cloud computing platform with makespan and monetary cost minimization approach." Complex & Intelligent Systems, November 15, 2021. http://dx.doi.org/10.1007/s40747-021-00528-1.

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AbstractScheduling of scientific workflows on hybrid cloud architecture, which contains private and public clouds, is a challenging task because schedulers should be aware of task inter-dependencies, underlying heterogeneity, cost diversity, and virtual machine (VM) variable configurations during the scheduling process. On the one side, reaching a minimum total execution time or makespan is a favorable issue for users whereas the cost of utilizing quicker VMs may lead to conflict with their budget on the other side. Existing works in the literature scarcely consider VM’s monetary cost in the s

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32

AGARWAL, GAURAV, SACHI GUPTA, and SAURABH MUKHERJEE. "WEB GRAPH BASED SEARCH BY USING DENSITY OF KEYWORD AND AGE FACTOR." International Journal of Computer Science and Informatics, October 2013, 89–93. http://dx.doi.org/10.47893/ijcsi.2013.1124.

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Today, web servers, are the key repositories of the information & internet is the source of getting this information. There is a mammoth data on the Internet. It becomes a difficult job to search out the accordant data. Search Engine plays a vital role in searching the accordant data. A search engine follows these steps: Web crawling by crawler, Indexing by Indexer and Searching by Searcher. Web crawler retrieves information of the web pages by following every link on the site. Which is stored by web search engine then the content of the web page is indexed by the indexer. The main role of

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Sivanandam, Lokesh, Sakthivel Periyasamy, and Uma Maheswari Oorkavalan. "Time Optimization in Cloud Computing with the Heterogeneous Earliest Finish Time Algorithm." Dynamic Systems and Applications 30, no. 10 (2021). http://dx.doi.org/10.46719/dsa202130.10.08.

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Sivanandam, Lokesh, Sakthivel Periyasamy, and Uma Maheswari Oorkavalan. "Time Optimization in Cloud Computing with the Heterogeneous Earliest Finish Time Algorithm." Dynamic Systems and Applications 30, no. 10 (2021). http://dx.doi.org/10.46719/dsa202130.10.08.

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35

Jiang, Junqiang, Chenyan Zhu, Hailin Cai, et al. "Time Optimization for Workflow Scheduling by Incorporating Level Strategy into Out-Degree." Journal of Circuits, Systems and Computers, January 7, 2021, 2150163. http://dx.doi.org/10.1142/s0218126621501632.

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Efficient workflow scheduling plays a critical role in achieving high performance in heterogeneous distributed computing systems. Given its key importance, workflow scheduling has been extensively studied, and various algorithms have been proposed in the literature mainly for systems with homogeneous or heterogeneous processors. Most of the algorithms leverage the average computation cost to prioritize tasks, and few focus on the combination of the level and out-degree of tasks, which both have a considerable impact on scheduling. A new list scheduling algorithm called level and out-degree ear

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36

Ashwitha, A., Yadati Vijaya Suresh, S. Reshma, and Harika Vanam. "Task scheduling using glowworm-based optimal heterogeneous earliest finish time algorithm for mobile grid." International Journal of Information Technology, April 27, 2024. http://dx.doi.org/10.1007/s41870-024-01847-5.

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37

Kaur, Avinash, Parminder Singh, Ranbir Singh Batth, and Chee Peng Lim. "Deep‐Q learning‐based heterogeneous earliest finish time scheduling algorithm for scientific workflows in cloud." Software: Practice and Experience, February 14, 2020. http://dx.doi.org/10.1002/spe.2802.

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Pu, Juhua, Qiaolan Meng, Yexuan Chen, and Hao Sheng. "MPEFT: A novel task scheduling method for workflows." Frontiers in Environmental Science 10 (January 4, 2023). http://dx.doi.org/10.3389/fenvs.2022.996483.

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Optimizing the scheduling algorithm is a key problem to improving the service efficiency of urban heterogeneous computing platforms. In this paper, we propose a novel list-based scheduling algorithm called Modified Predict Earliest Finish Time (MPEFT) for heterogeneous computing systems with the aim to minimize the total execution time. The algorithm consists of two stages: task prioritization and processor selection. In the task prioritization phase, the priority of tasks is calculated by time cost of all paths from a task to the exit task. Compared with the prior works, more accurate task pr

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Oikonomou, Panagiotis, Kostas Kolomvatsos, and Christos Anagnostopoulos. "A proactive and uncertainty driven management mechanism for workflows of processing tasks." Computing 107, no. 5 (2025). https://doi.org/10.1007/s00607-025-01468-1.

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Abstract Efficient resource selection and scheduling in large-scale computing systems like the Cloud pose significant challenges, especially for workflows with unreliable or preemptible resources. These workflows depict a set of (sequential or parallel) processing activities that should be executed upon the available resources. In this paper, we focus on the efficient management of resources paying attention to unreliable executions and propose a novel checkpointing mechanism integrated into an uncertainty-aware Heterogeneous Earliest Finish Time (uHEFT) online algorithm. This algorithm determ

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