A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments

Summary Large‐scale scientific problems are often modeled as workflows. The ever‐growing data and compute requirements of these applications has led to extensive research on how to efficiently schedule and deploy them in distributed environments. The emergence of the latest distributed systems parad...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Concurrency and computation Ročník 29; číslo 8; s. np - n/a
Hlavní autori: Rodriguez, Maria Alejandra, Buyya, Rajkumar
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Chichester, UK John Wiley & Sons, Ltd 25.04.2017
Wiley Subscription Services, Inc
Predmet:
Algorithms
Cloud computing
Computer networks
Concurrency
IaaS cloud, resource provisioning, scientific workflow, scheduling, survey, taxonomy
Pools
Schedules
Scheduling
Taxonomy
Workflow
ISSN:1532-0626, 1532-0634
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Summary Large‐scale scientific problems are often modeled as workflows. The ever‐growing data and compute requirements of these applications has led to extensive research on how to efficiently schedule and deploy them in distributed environments. The emergence of the latest distributed systems paradigm, cloud computing, brings with it tremendous opportunities to run scientific workflows at low costs without the need of owning any infrastructure. It provides a virtually infinite pool of resources that can be acquired, configured, and used as needed and are charged on a pay‐per‐use basis. However, along with these benefits come numerous challenges that need to be addressed to generate efficient schedules. This work identifies these challenges and studies existing algorithms from the perspective of the scheduling models they adopt as well as the resource and application model they consider. A detailed taxonomy that focuses on features particular to clouds is presented, and the surveyed algorithms are classified according to it. In this way, we aim to provide a comprehensive understanding of existing literature and aid researchers by providing an insight into future directions and open issues.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.4041