With increasing demands for High Performance Computing (HPC), new ideas and methods are emerged to utilize computing resources more efficiently. Cloud Computing appears to provide benefits such as resource pooling, broad network access and cost efficiency for the HPC applications. However, moving the HPC applications to the cloud can face several key challenges, primarily, the virtualization overhead, multi-tenancy and network latency. Software-Defined Networking (SDN) as an emerging technology appears to pave the road and provide dynamic manipulation of cloud networking such as topology, routing, and bandwidth allocation. This paper presents a new scheme called ASETS which targets dynamic configuration and monitoring of cloud networking using SDN to improve the performance of HPC applications and in particular task scheduling for HPC as a service on the cloud (HPCaaS). Further, SETSA, (SDN-Empowered Task Scheduler Algorithm) is proposed as a novel task scheduling algorithm for the offered ASETS architecture. SETSA monitors the network bandwidth to take advantage of its changes when submitting tasks to the virtual machines. Empirical analysis of the algorithm in different case scenarios show that SETSA has significant potentials to improve the performance of HPCaaS platforms by increasing the bandwidth efficiency and decreasing task turnaround time. In addition, SETSAW, (SETSA Window) is proposed as an improvement of the SETSA algorithm.

Advances in Cloud Computing attracted scientists to deploy their HPC applications to the cloud to benefit from the flexibility of the platform such as scalability and on-demand services. Nevertheless, HPC applications can face serious challenges on the cloud that could undermine the gained benefit, if care is not taken. This paper starts first comparing the performance of several HPC benchmarks on a commodity cluster and Amazon public cloud to illustrate the confronted challenges. To mitigate the problem, we have introduced a novel approach called ASETS, "A SDN Empowered Task Scheduling System", to schedule data-intensive High Performance Computing (HPC) tasks on a Cloud environment. In this paper, we focus on the implementation and performance analysis of ASETS and its first algorithm called SETSA, (SDN Empowered Task Scheduling Algorithm). ASETS uses the "bandwidth awareness" capability of SDN to better utilize network bandwidths when assigning data intensive tasks to virtual machines (workers) in the cloud. This novel approach aims to improve the performance of HPC applications on the cloud in order the platform could provide efficient HPC-as-a-Service (HPCaaS). The paper briefly describes the ASETS architecture and its SETSA algorithm, and then focuses on the details of the implementation and performance analysis of ASETS and SETSA. Preliminary results indicate that ASETS provides substantial performance improvement for HPCaaS as the degree of multi-tenancy in the cloud increases. This result is significant since it indicate both the users and the cloud service providers can benefit from ASETS.

Advances in Cloud Computing have opened many chapters in Information Technology. Numerous service platforms offer clients of the cloud ease of use and flexibility of using the provided services. Education with billions of potential users worldwide is a major target. An emerging service called HPC-as-a-Service (HPCaaS) targets Science, Technology, Engineering, and Math (STEM) users. In this paper we discuss the use of HPCaaS platform in STEM education. We argue that such a service can significantly alleviate a major obstacle in teaching parallel programming for the STEM students. Cloud computing provides unique benefits such as resource pooling, cost efficiency, availability, and large computational power. These features have attracted scientists, engineers, scholars, and the High Performance Computing (HPC) users like a magnet towards the Cloud. However, HPC programs often consume large number of collaborating processors to reduce the execution time, where synchronization between these processors and the communication overhead among them can become real challenge even on dedicated and special hardware, but worse on shared and virtualized platform like cloud. As a result, moving HPC applications to the cloud can adversely impact the abovenamed difficulties with additional issues, primarily virtualization, multitenancy, and network latency. One solution is a new cloud service known as HPC-as-a-Service. In this paper, we present an HPCaaS platform called ASETS which uses Software Defined Networking (SDN) technologies to smooth the execution of parallel tasks in the cloud. Further, we provide application examples that could be used in a typical introductory parallel programing course. We argue that HPCaaS platform like ASETS can significantly benefit the users of HPC in the cloud as if their program is running on a dedicated hardware in their own laboratory. This is especially advantageous for students and educators who need not to deal with the underlying complexities of the cloud.