Optimized Deployment Plans for Platform As a Service Clouds

Benjamin Byholm; Ivan Porres Paltor

doi:10.1145/3147234.3148112

Optimized Deployment Plans for Platform As a Service Clouds

Benjamin Byholm, Ivan Porres Paltor

Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review

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Abstract

We approximately solve the problem of computing deployment plans of multiple cloud services with soft real-time constraints. We recognize that this is a generalized bin packing problem with fragmentable items. We formalize the problem domain and develop an autonomous deployment planning system with soft real-time constraints. The system incorporates a genetic algorithm with quadratic worst-case time complexity for approximately solving the packing problem, providing a service deployment plan with an optimal number of servers and an approximately optimal number of service instances.

Original language	Undefined/Unknown
Title of host publication	UCC '17 Companion : Proceedings of the10th International Conference on Utility and Cloud Computing
Editors	Geoffrey Fox, Yong Chen
Publisher	ACM
Pages	41–46
ISBN (Print)	978-1-4503-5195-9
DOIs	https://doi.org/10.1145/3147234.3148112
Publication status	Published - 2017
MoE publication type	A4 Article in a conference publication
Event	IEEE/ACM International Conference on Utility and Cloud Computing (UCC) - 10th International Conference on Utility and Cloud Computing (UCC '17) Duration: 5 Dec 2017 → 8 Dec 2017

Conference

Conference	IEEE/ACM International Conference on Utility and Cloud Computing (UCC)
Period	05/12/17 → 08/12/17

Keywords

cloud computing

Access to Document

10.1145/3147234.3148112

inpByPo17a.full.pdf

http://urn.fi/URN:NBN:fi-fe2020102788461

Cite this

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title = "Optimized Deployment Plans for Platform As a Service Clouds",

abstract = "We approximately solve the problem of computing deployment plans of multiple cloud services with soft real-time constraints. We recognize that this is a generalized bin packing problem with fragmentable items. We formalize the problem domain and develop an autonomous deployment planning system with soft real-time constraints. The system incorporates a genetic algorithm with quadratic worst-case time complexity for approximately solving the packing problem, providing a service deployment plan with an optimal number of servers and an approximately optimal number of service instances.",

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AU - Porres Paltor, Ivan

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AB - We approximately solve the problem of computing deployment plans of multiple cloud services with soft real-time constraints. We recognize that this is a generalized bin packing problem with fragmentable items. We formalize the problem domain and develop an autonomous deployment planning system with soft real-time constraints. The system incorporates a genetic algorithm with quadratic worst-case time complexity for approximately solving the packing problem, providing a service deployment plan with an optimal number of servers and an approximately optimal number of service instances.

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DO - 10.1145/3147234.3148112

M3 - Konferensbidrag

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BT - UCC '17 Companion : Proceedings of the10th International Conference on Utility and Cloud Computing

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