Enhancement of efficiency of military cloud computing using lanchester model

Title

Enhancement of efficiency of military cloud computing using lanchester model

Creator

Hyeon C.; Aurelia S.

Description

Cloud computing is a technology that uses centrally processed computing resources over the Internet by a large number of users. Because many requests are concentrated on cloud servers, they must be properly distributed to avoid degradation of quality. Load balancing categorizes requests from users according to established algorithms and assigns appropriate virtual machines. Because load balancing algorithms are developed according to the cloud's usage environment, various algorithms are being utilized. Recently, government agencies are also interested in introducing cloud technologies beyond private sectors. Many militaries have selected Cloud as its basic task to apply new technologies such as AI to military operations. However, there is no precedent for military cloud development, and the lack of doud technology research considering the operational environment has delayed the progress of cloud adoption. The algorithm presented by this paper makes the combat power, which varies according to the importance of the operation, an important variable. This variable makes each user's access to computing resources different. Although similar to other dynamic algorithms, the impact of priorities is so big that the degree of imbalance between tasks was higher. 2020 IEEE.

Source

Proceedings of the 4th International Conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC 2020, pp. 960-964.

Date

2020-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Cloud computing; Dynamic load balancing; Honey bee behavior; Lanchester model; Military; Weighted round robin

Coverage

Hyeon C., Christ(Deemed to Be University), Department of Computer Science, Bangalore, India; Aurelia S., Christ(Deemed to Be University), Department of Computer Science, Bangalore, India

Rights

Restricted Access

Relation

ISBN: 978-172815464-0

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/I-SMAC49090.2020.9243515

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