Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure.
Scientific computing involves the construction of mathematical models and numerical solution techniques to solve scientific, social scientific and engineering problems. These models often require a huge number of computing resources to perform large scale experiments or to cut down the computational complexity into a reasonable time frame. These needs have been initially addressed with dedicated high-performance computing (HPC) infrastructures such as clusters.
“Cloud computing refers to both the applications delivered as services over the Internet and the hardware and system software in the datacentres that provide those services”.
A more structured definition is given by Buyya et al. who define a Cloud as a “type of parallel and distributed system consisting of a collection of interconnected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources based on service-level agreement”..
A group of researchers led by Visiting Professor Takashi Yoshikawa developed the world’s first system for flexibly providing high-performance computation by cloud computing.
This group developed a job-resource integrated management system that can change system components according to the nature of a job as well as functions and performance necessary for running a computation job.
Infrastructure services (Infrastructure-as-a-service), provided by cloud vendors, allow any user to provision a large number of compute instances fairly easily. Whether leased from public clouds or allocated from private clouds, utilizing these virtual resources to perform data/compute intensive analyses requires employing different parallel runtimes to implement such applications. Among many parallelizable problems, most “pleasingly parallel” applications can be performed using MapReduce technologies such as Hadoop, CGL-MapReduce, and Dryad, in a fairly easy manner.The introduction of commercial cloud infrastructure services such as Amazon EC2/S3 andGoGridallow users to provision compute clusters fairly easily and quickly by paying a monetary value only for the duration of the usage of resources.
The availability of open source cloud infrastructure software such as Nimbus and Eucalyptus , and the open source virtualization software stacks such as Xen Hypervisor, allows organizations to build private clouds to improve the resource utilization of the available computation facilities.
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