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

Forskningsoutput: Kapitel i bok/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

32 Nedladdningar (Pure)

Sammanfattning

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.

Originalspråk	Odefinierat/okänt
Titel på värdpublikation	UCC '17 Companion : Proceedings of the10th International Conference on Utility and Cloud Computing
Redaktörer	Geoffrey Fox, Yong Chen
Förlag	ACM
Sidor	41–46
ISBN (tryckt)	978-1-4503-5195-9
DOI	https://doi.org/10.1145/3147234.3148112
Status	Publicerad - 2017
MoE-publikationstyp	A4 Artikel i en konferenspublikation
Evenemang	IEEE/ACM International Conference on Utility and Cloud Computing (UCC) - 10th International Conference on Utility and Cloud Computing (UCC '17) Varaktighet: 5 dec. 2017 → 8 dec. 2017

Konferens

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

Nyckelord

cloud computing

Åtkomst av dokument

10.1145/3147234.3148112

inpByPo17a.full.pdf

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

Citera det här

@inproceedings{63fa24af27ab481ba24f1fd0dc204065,

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.",

keywords = "cloud computing, cloud computing, cloud computing",

author = "Benjamin Byholm and {Porres Paltor}, Ivan",

year = "2017",

doi = "10.1145/3147234.3148112",

language = "Odefinierat/ok{\"a}nt",

isbn = "978-1-4503-5195-9",

pages = "41–46",

editor = "Geoffrey Fox and Yong Chen",

booktitle = "UCC '17 Companion : Proceedings of the10th International Conference on Utility and Cloud Computing",

publisher = "ACM",

note = "IEEE/ACM International Conference on Utility and Cloud Computing (UCC) ; Conference date: 05-12-2017 Through 08-12-2017",

}

TY - GEN

T1 - Optimized Deployment Plans for Platform As a Service Clouds

AU - Byholm, Benjamin

AU - Porres Paltor, Ivan

PY - 2017

Y1 - 2017

N2 - 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.

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.

KW - cloud computing

U2 - 10.1145/3147234.3148112

DO - 10.1145/3147234.3148112

M3 - Konferensbidrag

SN - 978-1-4503-5195-9

SP - 41

EP - 46

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

A2 - Fox, Geoffrey

A2 - Chen, Yong

PB - ACM

T2 - IEEE/ACM International Conference on Utility and Cloud Computing (UCC)

Y2 - 5 December 2017 through 8 December 2017

ER -