Greedy-Ant: Ant Colony System-Inspired Workflow Scheduling for Heterogeneous Computing

Xiang, Bin; Zhang, Bibo; Zhang, Lin

doi:10.1109/access.2017.2715279

Cited by 29 publications

(21 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work in [8] aligns with our opinion that not only jobs on the critical path but also other jobs should be accounted when we compute the scheduling priority.…”

Section: Related Worksupporting

confidence: 64%

“…The LDCP has better scheduling performance than HEFT, but with higher complexity. The work in [8] studies the problem of minimizing the execution time of a workflow in heterogeneous environments and designs an ant-colony based heuristic algorithm. The heuristic generates task sequences considering both the forward and backward (i.e., global) dependency of tasks, where the forward dependency is defined as the number of predecessors, and the backward dependency is defined as the number of successors, respectively.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Workflow Scheduling in the Cloud With Weighted Upward-Rank Priority Scheme Using Random Walk and Uniform Spare Budget Splitting

et al. 2019

View full text Add to dashboard Cite

We study a difficult problem of how to schedule complex workflows with precedence constraints under a limited budget in the cloud environment. We first formulate the scheduling problem as an integer programming problem, which can be optimized and used as the baseline of performance. We then consider the traditional approach of scheduling jobs in a prioritized order based on the upward-rank of each job. For those jobs with no precedence constraints among themselves, the plain upward-rank priority scheme assigns priorities in an arbitrary way. We propose a job prioritization scheme that uses the Markovian chain stationary probabilities as a measure of the importance of jobs. The scheme keeps the precedence order for the jobs that have precedence constraints between each other and assigns priorities according to the jobs' importance for the jobs without precedence constraints. We finally design a uniform spare budget-splitting strategy that splits the spare budget uniformly across all the jobs. We test our algorithms on a variety of workflows, including the Fast Fourier transform (FFT), the Gaussian elimination, typical scientific workflows, randomly generated workflows, and workflows from an in-production cluster of an online streaming service company. We compare our algorithms with state-of-the-art algorithms. The empirical results show that the uniform spare budget splitting scheme outperforms the splitting scheme in proportion to extra demand on average for most cases, and the Markovian-based prioritization further improves the workflow makespan. INDEX TERMS Workflow scheduling, heterogeneous clouds, budget constraints, precedence constraints, schedule length.

show abstract

“…The work in [8] aligns with our opinion that not only jobs on the critical path but also other jobs should be accounted when we compute the scheduling priority.…”

Section: Related Worksupporting

confidence: 64%

Section: Related Workmentioning

confidence: 99%

Workflow Scheduling in the Cloud With Weighted Upward-Rank Priority Scheme Using Random Walk and Uniform Spare Budget Splitting

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The hybrid heuristic-genetic algorithm with adaptive parameters for static task scheduling in a heterogeneous computing system is presented in [8] to further improve the performance of existing algorithms. In [9], a greedy ant algorithm is used for workflow scheduling for heterogeneous computing. This shows a novel workflow scheduling algorithm named Greedy-Ant to minimize the total execution time of an application in heterogeneous environments.…”

Section: Background and Related Workmentioning

confidence: 99%

A Heuristic Approach for Scheduling in Heterogeneous Distributed Embedded Systems

Prongnuch¹,

Sitjongsataporn²,

Wiangtong³

2020

IJIES

View full text Add to dashboard Cite

This paper presents a heuristic approach for workflow scheduling in heterogeneous distributed embedded system (HDES). A genetic algorithm (GA) and ant colony optimization (ACO) modified with the greedy algorithm introduced to the system contains multiple heterogeneous embedded machines (HEMs) working as a cluster. Users can remotely access and utilize their computational power. The communications on different types of buses are taken into account to find an optimal solution. New meta-heuristic information based on forwarding dependency is proposed to build probability for ACO to generate task priorities. Besides, a greedy algorithm for machine allocation is incorporated to complete task scheduling. Experiments based on random task graphs running in the HEM cluster demonstrate the effectiveness of the modified greedy ant colony optimization algorithm which outperforms the others by 33% more result quality.

show abstract

“…They may be divided into two classes. In the first class, the algorithms just minimize a total cost [12][13][14]. In the second class, the algorithms minimize a total cost under deadline constraint [15,16].…”

Section: Meta-heuristic Algorithmsmentioning

confidence: 99%

“…Recently, several algorithms are developed to solve the resource scheduling problem [6][7][8][9][10][11][12][13][14][15][16]. Nevertheless, the problem still exists with complex applications such as workflows.…”

mentioning

confidence: 99%

Cost-Effective Algorithm for Workflow Scheduling in Cloud Computing Under Deadline Constraint

Nasr¹,

El‐Bahnasawy

Attiya

et al. 2018

Arab J Sci Eng

View full text Add to dashboard Cite

Cloud computing is a popular model that allows users to store, access, process, and retrieve data remotely. It provides a high-performance computing with large scale of resources. However, this model requires an efficient scheduling strategy for resources management. Recently, several algorithms are presented to solve the resource scheduling problem. Nevertheless, still the problem exists with complex applications such as workflows, which need an efficient algorithm to be scheduled on the available resources. This paper presents a novel hybrid algorithm, called CR-AC, combining both the chemical reaction optimization (CRO) and ant colony optimization (ACO) algorithms to solve the workflow-scheduling problem. The proposed CR-AC algorithm is implemented in the CloudSim toolkit and evaluated by using real applications and Amazon EC2 pricing model. Moreover, the results are compared with the most recent algorithms: modified particle swarm optimization (PSO) and cost-effective genetic algorithm (CEGA). The experimental results indicate that the CR-AC algorithm achieves better results than the traditional CRO, the ACO, the modified PSO and CEGA algorithms, in terms of total cost, time complexity, and schedule length. IntroductionCloud computing is considered as one of the most famous computing models. It gained a lot of attention in both academia and industry fields as it provides many benefits for users and organizations. It enables users to store and process their complex applications on a large datacenter. It also allows users to access their data remotely from everywhere. Further, it enables organizations and companies to obtain high computing capacity without bearing the elevated price of building huge datacenters inside. It also enables organizations and companies to outsource infrastructures using cloud environment and thus transfer their data and applications to

show abstract

Greedy-Ant: Ant Colony System-Inspired Workflow Scheduling for Heterogeneous Computing

Cited by 29 publications

References 13 publications

Workflow Scheduling in the Cloud With Weighted Upward-Rank Priority Scheme Using Random Walk and Uniform Spare Budget Splitting

Workflow Scheduling in the Cloud With Weighted Upward-Rank Priority Scheme Using Random Walk and Uniform Spare Budget Splitting

A Heuristic Approach for Scheduling in Heterogeneous Distributed Embedded Systems

Cost-Effective Algorithm for Workflow Scheduling in Cloud Computing Under Deadline Constraint

Contact Info

Product

Resources

About