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Dr. Abdullah Mohamed Ahmed Saad :: Theses :

Title Cloud Network Performance Model
Type MSc
Supervisors Ahmed El-Mahdy; Yasutaka Wada
Year 2014
Abstract Executing high performance computing (HPC) applications on the cloud is an emerging approach that can potentially provide a significantly cheaper alternative to supercomputers. However, clouds are largely oriented towards multiprogramming workloads with no signif- icant intercommunications. The placement of tightly coupled HPC virtual machines is thus not guaranteed to be physically affine, resulting in unpredictable communication times. This work proposes a new cloud analytical model that describes the physical placement of virtual machines in the communication hierarchy. The model is constructed through a set of automated experiments that measure virtual machines point-to-point communication speed parameters; the parameters are then clustered, and the topology of the cloud network seen by the virtual machines is identified. As a case study, we apply the model to the Ama- zon Cloud; the obtained hierarchical model is used to select a fast communicating subset of instances and discarding the other instances. For a message-passing all-to-all communication operation such selection resulted in 4.1 to 5.5 speedup enhancement in performance when randomly executing on a similarly sized subset. We further validate the proposed methodology on E-JUST private cloud. It correct- ly revealed the actual network topology for various configurations of virtual machine in- stances.
Keywords Cloud Computing; Networking; Modeling; Clustering
University Egypt-Japan University of Science and Technology
Country Egypt
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Title An Adaptable Performance Model Based Predictor for Parallel Applications on the Cloud
Type PhD
Supervisors Ahmed El-Mahdy; Amr El-Masry; Yasutaka Wada
Year 2020
Abstract With the continual increase in the high performance computing (HPC) market share, the need for a cheaper and widely available system rather than the expensive typical HPC systems increases. A promising alternative to HPC typical systems is the cloud computing environment which is characterised by being cheap, flexible, scalable and available. However, the cloud is based on virtualization which increases the latency to access the processing and network resources due to resource sharing. Also, in contrary to the traditional HPC systems that run on homogeneous, high-cost servers with fast networking providing for predictable performance; the cloud’s underlying hardware is heterogeneous, with slower network connection. This makes the cloud an unpredictable environment to long run time programs such as HPC applications and reduces the performance of communication intensive parallel applications on the cloud. Hence, modelling and understanding performance is essential for exploiting such environment. In this thesis we introduce an analytical performance model of the execution of such long run time and communication intensive applications on the cloud. The model accounts for both the communication intensive parallel workloads as well as the cloud’s processing and network resources. The model does that through considering the cloud resources as a queueing network, and the parallel applications as jobs con- testing for the shared resources. Based on the proposed model, we also introduce a predictor for the execution time of the message passing interface (MPI) based appli- cations on the cloud, as they are a major class of HPC applications. The prediction process considers different configurations of workloads and processing resources. The prediction based on the proposed model is measured on both a cluster of bare- metal servers and on a group of virtual machines. The overall accuracy of this pre- diction is 88% for 10 benchmarks, 5 benchmarks from SPEC-MPI and 5 benchmarks from The NASA Advanced Supercomputing (NAS) parallel benchmark suite (NPB). Moreover, a thorough analysis is conducted to the experiments’ results.
Keywords Modeling; Parallel Computing; MPI; Cloud Computing; HPC; Queuing Networks
University Egypt-Japan University of Science and Technology
Country Egypt
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