TaScaaS: A Multi-Tenant Serverless Task Scheduler and Load Balancer as a Service
A combination of distributed multi-tenant infrastructures, such as public Clouds and on-premises installations belonging to different organisations, are frequently used for scientific research because of the high computational requirements involved. Although resource sharing maximises their usage, i...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9528423/ |
| _version_ | 1818597198045118464 |
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| author | Vicent Gimenez-Alventosa German Molto J. Damian Segrelles |
| author_facet | Vicent Gimenez-Alventosa German Molto J. Damian Segrelles |
| author_sort | Vicent Gimenez-Alventosa |
| collection | DOAJ |
| description | A combination of distributed multi-tenant infrastructures, such as public Clouds and on-premises installations belonging to different organisations, are frequently used for scientific research because of the high computational requirements involved. Although resource sharing maximises their usage, it typically causes undesirable effects such as the <italic>noisy neighbour</italic>, producing unpredictable variations of the infrastructure computing capabilities. These fluctuations affect execution efficiency, even of loosely coupled applications, such as many Monte Carlo based simulation programs. This highlights the need of a service capable to handle workload distribution across multiple infrastructures to mitigate these unpredictable performance fluctuations. With this aim, this work introduces TaScaaS, a highly scalable and completely serverless service deployed on AWS to distribute loosely coupled jobs among several computing infrastructures, and load balance them using a completely asynchronous approach to cope with the performance fluctuations with minimum impact in the execution time. We demonstrate how TaScaaS is not only capable of handling these fluctuations efficiently, achieving reduction in execution times up to 45% in our experiments, but also split the jobs to be computed to meet the user-defined execution time. |
| first_indexed | 2024-12-16T11:43:59Z |
| format | Article |
| id | doaj.art-f9ced840bb4f4141a190554a8f42041c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T11:43:59Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f9ced840bb4f4141a190554a8f42041c2022-12-21T22:32:52ZengIEEEIEEE Access2169-35362021-01-01912521512522810.1109/ACCESS.2021.31099729528423TaScaaS: A Multi-Tenant Serverless Task Scheduler and Load Balancer as a ServiceVicent Gimenez-Alventosa0https://orcid.org/0000-0003-1646-6094German Molto1https://orcid.org/0000-0002-8049-253XJ. Damian Segrelles2https://orcid.org/0000-0001-5698-7965Instituto de Instrumentación para Imagen Molecular (I3M), Centro mixto CSIC - Universitat Politècnica de València, Camino de Vera s/n, Valencia, SpainInstituto de Instrumentación para Imagen Molecular (I3M), Centro mixto CSIC - Universitat Politècnica de València, Camino de Vera s/n, Valencia, SpainInstituto de Instrumentación para Imagen Molecular (I3M), Centro mixto CSIC - Universitat Politècnica de València, Camino de Vera s/n, Valencia, SpainA combination of distributed multi-tenant infrastructures, such as public Clouds and on-premises installations belonging to different organisations, are frequently used for scientific research because of the high computational requirements involved. Although resource sharing maximises their usage, it typically causes undesirable effects such as the <italic>noisy neighbour</italic>, producing unpredictable variations of the infrastructure computing capabilities. These fluctuations affect execution efficiency, even of loosely coupled applications, such as many Monte Carlo based simulation programs. This highlights the need of a service capable to handle workload distribution across multiple infrastructures to mitigate these unpredictable performance fluctuations. With this aim, this work introduces TaScaaS, a highly scalable and completely serverless service deployed on AWS to distribute loosely coupled jobs among several computing infrastructures, and load balance them using a completely asynchronous approach to cope with the performance fluctuations with minimum impact in the execution time. We demonstrate how TaScaaS is not only capable of handling these fluctuations efficiently, achieving reduction in execution times up to 45% in our experiments, but also split the jobs to be computed to meet the user-defined execution time.https://ieeexplore.ieee.org/document/9528423/Cloud computingheterogeneous computingload balanceserverless |
| spellingShingle | Vicent Gimenez-Alventosa German Molto J. Damian Segrelles TaScaaS: A Multi-Tenant Serverless Task Scheduler and Load Balancer as a Service IEEE Access Cloud computing heterogeneous computing load balance serverless |
| title | TaScaaS: A Multi-Tenant Serverless Task Scheduler and Load Balancer as a Service |
| title_full | TaScaaS: A Multi-Tenant Serverless Task Scheduler and Load Balancer as a Service |
| title_fullStr | TaScaaS: A Multi-Tenant Serverless Task Scheduler and Load Balancer as a Service |
| title_full_unstemmed | TaScaaS: A Multi-Tenant Serverless Task Scheduler and Load Balancer as a Service |
| title_short | TaScaaS: A Multi-Tenant Serverless Task Scheduler and Load Balancer as a Service |
| title_sort | tascaas a multi tenant serverless task scheduler and load balancer as a service |
| topic | Cloud computing heterogeneous computing load balance serverless |
| url | https://ieeexplore.ieee.org/document/9528423/ |
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