Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments Article Swipe
Kairong Duan
,
Simon Fong
,
Shirley W. I. Siu
,
Wei Song
,
Sheng-Uei Guan
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/sym10050168
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/sym10050168
Cloud computing is a new commercial model that enables customers to acquire large amounts of virtual resources on demand. Resources including hardware and software can be delivered as services and measured by specific usage of storage, processing, bandwidth, etc. In Cloud computing, task scheduling is a process of mapping cloud tasks to Virtual Machines (VMs). When binding the tasks to VMs, the scheduling strategy has an important influence on the efficiency of datacenter and related energy consumption. Although many traditional scheduling algorithms have been applied in various platforms, they may not work efficiently due to the large number of user requests, the variety of computation resources and complexity of Cloud environment. In this paper, we tackle the task scheduling problem which aims to minimize makespan by Genetic Algorithm (GA). We propose an incremental GA which has adaptive probabilities of crossover and mutation. The mutation and crossover rates change according to generations and also vary between individuals. Large numbers of tasks are randomly generated to simulate various scales of task scheduling problem in Cloud environment. Based on the instance types of Amazon EC2, we implemented virtual machines with different computing capacity on CloudSim. We compared the performance of the adaptive incremental GA with that of Standard GA, Min-Min, Max-Min , Simulated Annealing and Artificial Bee Colony Algorithm in finding the optimal scheme. Experimental results show that the proposed algorithm can achieve feasible solutions which have acceptable makespan with less computation time.
Related Topics
Concepts
Computer science
Cloud computing
Virtual machine
CloudSim
Crossover
Distributed computing
Job shop scheduling
Scheduling (production processes)
Genetic algorithm
Load balancing (electrical power)
Dynamic priority scheduling
Computation
Real-time computing
Algorithm
Mathematical optimization
Artificial intelligence
Embedded system
Operating system
Computer network
Quality of service
Machine learning
Mathematics
Geometry
Grid
Routing (electronic design automation)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/sym10050168
- https://www.mdpi.com/2073-8994/10/5/168/pdf?version=1526551220
- OA Status
- gold
- Cited By
- 42
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2803521530
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2803521530Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/sym10050168Digital Object Identifier
- Title
-
Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud EnvironmentsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-05-17Full publication date if available
- Authors
-
Kairong Duan, Simon Fong, Shirley W. I. Siu, Wei Song, Sheng-Uei GuanList of authors in order
- Landing page
-
https://doi.org/10.3390/sym10050168Publisher landing page
- PDF URL
-
https://www.mdpi.com/2073-8994/10/5/168/pdf?version=1526551220Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2073-8994/10/5/168/pdf?version=1526551220Direct OA link when available
- Concepts
-
Computer science, Cloud computing, Virtual machine, CloudSim, Crossover, Distributed computing, Job shop scheduling, Scheduling (production processes), Genetic algorithm, Load balancing (electrical power), Dynamic priority scheduling, Computation, Real-time computing, Algorithm, Mathematical optimization, Artificial intelligence, Embedded system, Operating system, Computer network, Quality of service, Machine learning, Mathematics, Geometry, Grid, Routing (electronic design automation)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
42Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 3, 2023: 8, 2022: 3, 2021: 6Per-year citation counts (last 5 years)
- References (count)
-
25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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