A Review on Task Scheduling in Cloud Computing using parallel Genetic Algorithm

Nitesh Bharot; Shalini Shukla

doi:10.1109/ICCIT-144147971.2020.9213822

A Review on Task Scheduling in Cloud Computing using parallel Genetic Algorithm

Nitesh Bharot, Shalini Shukla

ShriRam College of Engineering Management

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

5 Citations (Scopus)

Abstract

This Cloud computing (CC) infrastructure has many issues including scheduling, budgeting load balancing (LB). Among them, the biggest challenge for load balancing is a cloud platform. In task scheduling environment generally, the occurrence of load imbalance tends to uncertainty and complexity. Cloud computing is growing Internet-based computing platform innovative that is emerging one of its biggest tasks. The goal is to use resources efficiently decrease resource consumption in the cloud environment. This can be achieved by increasing the LB rate when selecting the best resources for low work failure rates with low lead times. This paper discusses load balancing based on advanced genetic algorithms in the cloud computing platform.

Original language	English
Title of host publication	2020 International Conference on Computing and Information Technology, ICCIT 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728126807
DOIs	https://doi.org/10.1109/ICCIT-144147971.2020.9213822
Publication status	Published - 9 Sep 2020
Externally published	Yes
Event	2020 International Conference on Computing and Information Technology, ICCIT 2020 - Tabuk, Saudi Arabia Duration: 9 Sep 2020 → 10 Sep 2020

Publication series

Name	2020 International Conference on Computing and Information Technology, ICCIT 2020

Conference

Conference	2020 International Conference on Computing and Information Technology, ICCIT 2020
Country/Territory	Saudi Arabia
City	Tabuk
Period	9/09/20 → 10/09/20

Keywords

Cloud computing
Genetic algorithm
Load balancing
Max-Min algorithm
Min-Min algorithm
Task scheduling

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICCIT-144147971.2020.9213822

Cite this

Bharot, N., & Shukla, S. (2020). A Review on Task Scheduling in Cloud Computing using parallel Genetic Algorithm. In 2020 International Conference on Computing and Information Technology, ICCIT 2020 Article 9213822 (2020 International Conference on Computing and Information Technology, ICCIT 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCIT-144147971.2020.9213822

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title = "A Review on Task Scheduling in Cloud Computing using parallel Genetic Algorithm",

abstract = "This Cloud computing (CC) infrastructure has many issues including scheduling, budgeting load balancing (LB). Among them, the biggest challenge for load balancing is a cloud platform. In task scheduling environment generally, the occurrence of load imbalance tends to uncertainty and complexity. Cloud computing is growing Internet-based computing platform innovative that is emerging one of its biggest tasks. The goal is to use resources efficiently decrease resource consumption in the cloud environment. This can be achieved by increasing the LB rate when selecting the best resources for low work failure rates with low lead times. This paper discusses load balancing based on advanced genetic algorithms in the cloud computing platform.",

keywords = "Cloud computing, Genetic algorithm, Load balancing, Max-Min algorithm, Min-Min algorithm, Task scheduling",

author = "Nitesh Bharot and Shalini Shukla",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Computing and Information Technology, ICCIT 2020 ; Conference date: 09-09-2020 Through 10-09-2020",

year = "2020",

month = sep,

day = "9",

doi = "10.1109/ICCIT-144147971.2020.9213822",

language = "English",

series = "2020 International Conference on Computing and Information Technology, ICCIT 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 International Conference on Computing and Information Technology, ICCIT 2020",

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Bharot, N & Shukla, S 2020, A Review on Task Scheduling in Cloud Computing using parallel Genetic Algorithm. in 2020 International Conference on Computing and Information Technology, ICCIT 2020., 9213822, 2020 International Conference on Computing and Information Technology, ICCIT 2020, Institute of Electrical and Electronics Engineers Inc., 2020 International Conference on Computing and Information Technology, ICCIT 2020, Tabuk, Saudi Arabia, 9/09/20. https://doi.org/10.1109/ICCIT-144147971.2020.9213822

A Review on Task Scheduling in Cloud Computing using parallel Genetic Algorithm. / Bharot, Nitesh; Shukla, Shalini.
2020 International Conference on Computing and Information Technology, ICCIT 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9213822 (2020 International Conference on Computing and Information Technology, ICCIT 2020).

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

TY - GEN

T1 - A Review on Task Scheduling in Cloud Computing using parallel Genetic Algorithm

AU - Bharot, Nitesh

AU - Shukla, Shalini

PY - 2020/9/9

Y1 - 2020/9/9

N2 - This Cloud computing (CC) infrastructure has many issues including scheduling, budgeting load balancing (LB). Among them, the biggest challenge for load balancing is a cloud platform. In task scheduling environment generally, the occurrence of load imbalance tends to uncertainty and complexity. Cloud computing is growing Internet-based computing platform innovative that is emerging one of its biggest tasks. The goal is to use resources efficiently decrease resource consumption in the cloud environment. This can be achieved by increasing the LB rate when selecting the best resources for low work failure rates with low lead times. This paper discusses load balancing based on advanced genetic algorithms in the cloud computing platform.

AB - This Cloud computing (CC) infrastructure has many issues including scheduling, budgeting load balancing (LB). Among them, the biggest challenge for load balancing is a cloud platform. In task scheduling environment generally, the occurrence of load imbalance tends to uncertainty and complexity. Cloud computing is growing Internet-based computing platform innovative that is emerging one of its biggest tasks. The goal is to use resources efficiently decrease resource consumption in the cloud environment. This can be achieved by increasing the LB rate when selecting the best resources for low work failure rates with low lead times. This paper discusses load balancing based on advanced genetic algorithms in the cloud computing platform.

KW - Cloud computing

KW - Genetic algorithm

KW - Load balancing

KW - Max-Min algorithm

KW - Min-Min algorithm

KW - Task scheduling

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U2 - 10.1109/ICCIT-144147971.2020.9213822

DO - 10.1109/ICCIT-144147971.2020.9213822

M3 - Conference Publication

T3 - 2020 International Conference on Computing and Information Technology, ICCIT 2020

BT - 2020 International Conference on Computing and Information Technology, ICCIT 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 International Conference on Computing and Information Technology, ICCIT 2020

Y2 - 9 September 2020 through 10 September 2020

ER -

Bharot N, Shukla S. A Review on Task Scheduling in Cloud Computing using parallel Genetic Algorithm. In 2020 International Conference on Computing and Information Technology, ICCIT 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9213822. (2020 International Conference on Computing and Information Technology, ICCIT 2020). doi: 10.1109/ICCIT-144147971.2020.9213822

A Review on Task Scheduling in Cloud Computing using parallel Genetic Algorithm

Abstract

Publication series

Conference

Keywords

UN SDGs

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