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Profiling-based task scheduling for factory-worker applications in Infrastructure-as-a-Service clouds


Zabolotnyi, Rostyslav; Leitner, Philipp; Dustdar, Schahram (2014). Profiling-based task scheduling for factory-worker applications in Infrastructure-as-a-Service clouds. In: 40th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA), Verona, Italy, 27 August 2014 - 29 August 2014.

Abstract

With the recent advances of cloud computing, effec- tive resource usage (e.g., CPU, memory or network) becomes an important question as application developers have to continuously pay for rented resources, even if they are not used effectively. In order to maintain required performance levels, it is currently common to reserve resources for peak resource usage or possible resource usage overlaps, if more than one task is executed on a host. While this is a reasonable approach for long-running applications or web servers, for some applications with disperse resource usage over time, this strategy causes significant over- provisioning and thus resource wastage and financial loss. In this paper we present a profiling-based task scheduling approach for factory-worker applications that schedules tasks within the defined resource limitations (e.g., existing machine memory size or network quota) and distributes the tasks in the cloud environment in order to use resources effectively. The evaluation of our approach approved the efficiency of the proposed algorithm and minimal performance overhead. In case of evaluated application, presented scheduling process leads up to 33% resource saving with only 1% of performance loss.

Abstract

With the recent advances of cloud computing, effec- tive resource usage (e.g., CPU, memory or network) becomes an important question as application developers have to continuously pay for rented resources, even if they are not used effectively. In order to maintain required performance levels, it is currently common to reserve resources for peak resource usage or possible resource usage overlaps, if more than one task is executed on a host. While this is a reasonable approach for long-running applications or web servers, for some applications with disperse resource usage over time, this strategy causes significant over- provisioning and thus resource wastage and financial loss. In this paper we present a profiling-based task scheduling approach for factory-worker applications that schedules tasks within the defined resource limitations (e.g., existing machine memory size or network quota) and distributes the tasks in the cloud environment in order to use resources effectively. The evaluation of our approach approved the efficiency of the proposed algorithm and minimal performance overhead. In case of evaluated application, presented scheduling process leads up to 33% resource saving with only 1% of performance loss.

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2 citations in Scopus®
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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:29 August 2014
Deposited On:28 May 2014 06:59
Last Modified:24 Aug 2017 03:05
Publisher:Euromicro
Publisher DOI:https://doi.org/10.1109/SEAA.2014.42
Related URLs:http://esd.scienze.univr.it/dsd-seaa-2014/
Other Identification Number:merlin-id:9513

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