Smart job scheduling for high-performance cloud computing services

Name: Smart job scheduling for high-performance cloud computing services
Author: Muhtaroğlu, Nitel, Arı, İsmail

İsim	Smart job scheduling for high-performance cloud computing services
Yazar	Muhtaroğlu, Nitel, Arı, İsmail
Basım Tarihi:	2011-01
Basım Yeri	- Civil-comp
Konu	Cloud computing, Finite element analysis, Paas, Structural mechanics, Calculix, Task scheduling, Multi-core, Parallel, MPI
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1759-3433
Kayıt Numarası	7de69f2c-8016-4b0e-af3a-fe6ad890d59b
Lokasyon	Computer Science
Tarih	2011-01
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	In this paper, we describe the challenges faced and lessons learned while establishing a large-scale high performance cloud computing service that enables online mechanical structural analysis and many other scientific applications using the finite element analysis (FEA) technique. The service is intended to process many independent and loosely-dependent (e.g. assembled system) tasks concurrently. Challenges faced include accurate job characterization, handling of many-task mixed jobs, sensitivity of task execution to multi-threading parameters, effective multi-core scheduling in a single node, and achieving seamless scale across multiple nodes. We find that significant performance gains in terms of both job completion latency and throughput are possible via dynamic or "smart" partitioning and resource-aware scheduling compared to shortest first and aggressive job scheduling techniques. We also discuss issues related to secure and private processing of sensitive models in the cloud.

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Smart job scheduling for high-performance cloud computing services

Yazar Muhtaroğlu, Nitel, Arı, İsmail

Basım Tarihi 2011-01

Basım Yeri - Civil-comp

Konu Cloud computing, Finite element analysis, Paas, Structural mechanics, Calculix, Task scheduling, Multi-core, Parallel, MPI

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1759-3433

Kayıt Numarası 7de69f2c-8016-4b0e-af3a-fe6ad890d59b

Lokasyon Computer Science

Tarih 2011-01

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin In this paper, we describe the challenges faced and lessons learned while establishing a large-scale high performance cloud computing service that enables online mechanical structural analysis and many other scientific applications using the finite element analysis (FEA) technique. The service is intended to process many independent and loosely-dependent (e.g. assembled system) tasks concurrently. Challenges faced include accurate job characterization, handling of many-task mixed jobs, sensitivity of task execution to multi-threading parameters, effective multi-core scheduling in a single node, and achieving seamless scale across multiple nodes. We find that significant performance gains in terms of both job completion latency and throughput are possible via dynamic or "smart" partitioning and resource-aware scheduling compared to shortest first and aggressive job scheduling techniques. We also discuss issues related to secure and private processing of sensitive models in the cloud.