A Study on Cloud Computing with Heterogeneous Computing and Communication Models
Date Issued
2011
Date
2011
Author(s)
Sun, Hou-Chiang
Abstract
In this thesis, we focus on the ad-hoc computing cloud, which consists of a client program running on a set of heterogeneous computers connected through heterogeneous networks.
The main goal is to utilize resources from heterogeneous computers efficiently to facilitate cloud computing in the target scenario.
We first set up a testbed and discover that the traditional Amdahl''s law of speedup does not fit the scenario with heterogeneous computers and networks. Therefore, we introduce heterogeneity score for evaluating heterogeneous machines linked by heterogeneous networks.
We then further propose a heterogeneity evaluation (HE) algorithm to distribute appropriate workload based on different abilities of individual machines.
However, because the accuracy of measuring the heterogeneous score is limited by bandwidth contention, we further propose the second algorithm, multi-round transmission (MRT) algorithm, to avoid bandwidth contention by transmitting the data in multiple rounds. Unlike HE, MRT does not need to measure variables in advance and hence it can eliminate estimation error in various applications.
To evaluate the performance of the proposed algorithms, we implement these two solutions in Message Passing Interface (MPI) framework. We find that both of these algorithms can save considerable completion time. In the target scenario, the traditional approach can achieve a speedup of only 2.78. The proposed HE algorithm, on the other hand, can lift the speedup to 4.72, whereas the MRT algorithm can lift the speedup to 5.35. However, the performance of the MRT algorithm drops 16.5\% due to the unnecessary communication overhead under high latency network, while the HE algorithm shows better resistance to high latency. The two algorithms thus can be appropriately used for different environments and applications under consideration.
Subjects
Cloud Computing
Heterogeneity
Parallel Computing
Type
thesis
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