ASETS: A SDN Empowered Task Scheduling System for HPCaaS on the Cloud

Jamalian, Saba; Rajaei, Hassan

doi:10.1109/ic2e.2015.56

Cited by 10 publications

(9 citation statements)

References 22 publications

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“…Our experiments identified the networking, multi-tenancy and virtualization overhead of the cloud as the primary challenges of HPCaaS. In a recent publication [2], we proposed a dynamic configurable scheme for scheduling HPC tasks on the cloud that utilizes the capabilities of Software-Defined Networking. The scheme called ASETS (A SDN Empowered Task Scheduling System) aims to mitigate the multi-tenancy of the cloud for simultaneous HPC applications on the cloud.…”

Section: A Reconfigurable Task Scheduler On the Cloudmentioning

confidence: 99%

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Data-Intensive HPC Tasks Scheduling with SDN to Enable HPC-as-a-Service

Jamalian

Rajaei²

2015

2015 IEEE 8th International Conference on Cloud Computing

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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.

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Section: A Reconfigurable Task Scheduler On the Cloudmentioning

confidence: 99%

“…In a recent paper [2], we proposed a novel scheme (ASETS) on task scheduling for data intensive HPC tasks that utilize the capability of SDN to smooth the cloud networking bandwidth. A task scheduling algorithm (SETSA: SDN Empowered Task Scheduling Algorithm) is also proposed to run on top of ASETS.…”

Section: Introductionmentioning

confidence: 99%

Data-Intensive HPC Tasks Scheduling with SDN to Enable HPC-as-a-Service

Jamalian

Rajaei²

2015

2015 IEEE 8th International Conference on Cloud Computing

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“…By increasing the degree of the multi-tenancy in the cloud, the variability of the network bandwidths increases. Our empirical analysis of SETSA shows that the algorithm in such cases can enhance the performance of HPCaaS in term of turnaround time up to 75% by better utilizing the network bandwidth 5 . Furthermore, Analyzing the performance of SETSA on two implementations of ASETS on a private OpenStack cloud and Amazon public cloud indicates that SETSA is capable of improving the performance of HPCaaS significantly up to 67% 41 .…”

Section: Parallel Programmingmentioning

confidence: 98%

“…An SDN controller in the cloud network enables us to be aware of all the links' bandwidths in runtime. Based on this idea, we have proposed a SDN Empowered Task Scheduling System (ASETS) 5 . Figure 6 represents the architecture of the system.…”

Section: Parallel Programmingmentioning

confidence: 99%

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HPC as a Service in Education

Rajaei¹,

Jamalian²

2016 ASEE Annual Conference &Amp; Exposition Proceedings

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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.

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Deployment and Analysis of a Hybrid Shared/Distributed-Memory Parallel Visualization Tool for 3-D Oil Reservoir Grid on OpenStack Cloud Computing

et al. 2020

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The main goal of oil reservoir management is to provide more efficient, price-effective and environmentally more secure oil production. Oil production management includes an accurate characterization of the reservoir and strategies that involve interactions between reservoir data and human assessment. Hence, it is important to graphically visualize and handle massive data sets of oil and gas pressure / saturation levels to help decision makers in statistical analysis, history matching and recovery of hydrocarbons of the reservoir. In this article, we experimentally study the parallelization of intensive computation for a 3-D (three dimensional) oil reservoir data visualization tool. For this tool, we develop and implement a transformation and lighting model to visualize and react with the grid. Herein, we propose a hybrid (shared memory and distributed memory) parallelization technique to adapt with the data processing scalability. We tested these implementations on OpenStack Cloud Virtual Cluster. Our results indicate that although the virtual platform adds overhead for running parallel implementations, utilizing knowledge of the VM location on the compute host and network traffic among VMs to deploy the virtual environment can achieve significant performance enhancements. Hybrid parallel implementation using large data size can achieve 70× speedup over serial execution without owning a costly HPC infrastructure as the conventional parallel processing deployment model.INDEX TERMS Cloud computing, data visualization tool, HPC, hybrid (distributed/shared)-memoryparallel programming, MPI, multi-threading, OpenStack.

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ASETS: A SDN Empowered Task Scheduling System for HPCaaS on the Cloud

Cited by 10 publications

References 22 publications

Data-Intensive HPC Tasks Scheduling with SDN to Enable HPC-as-a-Service

Data-Intensive HPC Tasks Scheduling with SDN to Enable HPC-as-a-Service

HPC as a Service in Education

Deployment and Analysis of a Hybrid Shared/Distributed-Memory Parallel Visualization Tool for 3-D Oil Reservoir Grid on OpenStack Cloud Computing

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