In recent years scientific computing has evolved into a massive usage of cloud computing, due to its flexibility in managing computing resources. In this paper, we focus on genomic data processing, which is rapidly gaining momentum in research and medical activities. The main characteristics of these data sets is that not only the number of available genome files is becoming extremely large, but also each individual data set is significantly large, in the order of tens of GB. Hence, a wide diffusion of cloud-based genomic data processing will have a significant impact on network resources, since each processing request will require the transfer of tens of GBs into computing nodes. To face this issue, in this paper we propose a resource discovery framework which provides decision agents with the needed information for selecting the most suitable computing nodes. We have implemented this resource discovery function in a distributed fashion, and extensively tested it in a lab testbed consisting of about 70 nodes. We found that the overhead of the proposed solution is negligible in comparison with the amount of transferred data.
A Resource Discovery Framework for Cloud-based Genomics Computing
FEMMINELLA, Mauro;REALI, Gianluca;VALOCCHI, DARIO;NUNZI, Emilia
2014
Abstract
In recent years scientific computing has evolved into a massive usage of cloud computing, due to its flexibility in managing computing resources. In this paper, we focus on genomic data processing, which is rapidly gaining momentum in research and medical activities. The main characteristics of these data sets is that not only the number of available genome files is becoming extremely large, but also each individual data set is significantly large, in the order of tens of GB. Hence, a wide diffusion of cloud-based genomic data processing will have a significant impact on network resources, since each processing request will require the transfer of tens of GBs into computing nodes. To face this issue, in this paper we propose a resource discovery framework which provides decision agents with the needed information for selecting the most suitable computing nodes. We have implemented this resource discovery function in a distributed fashion, and extensively tested it in a lab testbed consisting of about 70 nodes. We found that the overhead of the proposed solution is negligible in comparison with the amount of transferred data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
