A Multi-Objective Task Scheduling Method based on ACO in Cloud Environment

Gao, Song; Liu, Ying; Huang, Hejiao

doi:10.1109/iccc51575.2020.9345040

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Meta-heuristic techniques are trendy in solving the issues related to cloud computing. Ant colony optimization(ACO) reduces job completion time, as shown in the study by Song et al [28]. The authors proved the effectiveness of ACO.…”

Section: Adhikari and Koleymentioning

confidence: 92%

Multi-Faceted Job Scheduling Optimization Using Q-learning With ABC In Cloud Environment

Sharma,

Kumar Pandey

2024

IJCDS

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Resource allocation is the utmost challenging and common problem, particularly in the cloud service model in Infrastructure as a Service (IaaS). The issue of load balancing is so harmful that irregular load balancing may result in a structure smash. Adopting a suitable access plan and allowing the system to spread work among all existing resources leads to utilizing Virtual Machines (VMs) appropriately. To get enhanced results from the Artificial Bee Colony Algorithm (ABC), a reinforcement learning technique Q-learning is combined using multifaceted job scheduling optimization based on ABC(QMFOABC) has been proposed. The proposed approach improves resource utilization and scheduling created on resource, cost, and makespan. The efficiency of the suggested strategy was evaluated in Datasets Synthetic Workload, Google Cloud Jobs (GoCJ), and Random by using CloudSim to the remaining scheduling strategies for load matching like Max-min, Multifaceted Cuckoo Search (MFCS), Multifaceted Particle Swarm Optimization (MFPSO), Q-learning, Heuristic job scheduling with Artificial Bee Colony approach with Largest Job First algorithm (HABC LJF), First come first serve (FCFS). According to the findings of the experiments, the algorithm that employed the QMFOABC method has better results in resource utilization, throughput, cost, and makespan. Compared to Max-Min (82.31%), MOPSO (35.62%), HABC LJF (21.65%), Q-Learning (11.72%), VTO-QABC FCFS(5.87%), and VTO-ABC LJF (5.86%) shorter time than MOCS, a considerable improvement is found.

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Section: Adhikari and Koleymentioning

confidence: 92%

Multi-Faceted Job Scheduling Optimization Using Q-learning With ABC In Cloud Environment

Sharma,

Kumar Pandey

2024

IJCDS

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“…al. [23] also considered the problem of scheduling independent tasks in cloud environment. They proposed improved multi-objective ant colony optimization (IMOACO) scheduling method to minimize the overall makespan and user cost at the same time, while guaranteeing the utilization of system.…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

PCP-ACO: a deadline-constrained workflow scheduling algorithm for cloud environment

Shobeiri

Rastaghi

Abrishami

et al. 2022

Preprint

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A cloud computing environment is the most popular choice for workflow execution, as it gives customers on-demand access to computing resources. However, in cloud workflow scheduling, cloud-native requirements regarding QoS requirements such as monetary cost and execution time should be taken into account. This paper proposes PCP-ACO, a list scheduling algorithm for minimizing the execution cost of a workflow, while meeting its user-defined deadline in cloud environments. In PCP-ACO, first a topological sort of the workflow tasks is computed to assign a priority to each task. Then, Ant Colony Optimization (ACO) meta-heuristic is used to assign a proper resource to each task of the workflow, in order of their priorities. The Partial Critical Path (PCP) concept is also used as a heuristic to guide ACO algorithm. Several experiments are conducted using real scientific workflows, and the cost saving is compared with PSO and IC-PCP algorithms. The experimental results show that the proposed algorithm outperforms other compared algorithms in terms of cost saving.

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A Multi-Objective Task Scheduling Method based on ACO in Cloud Environment

Cited by 2 publications

References 13 publications

Multi-Faceted Job Scheduling Optimization Using Q-learning With ABC In Cloud Environment

Multi-Faceted Job Scheduling Optimization Using Q-learning With ABC In Cloud Environment

PCP-ACO: a deadline-constrained workflow scheduling algorithm for cloud environment

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