A Scalable Cloud Based on Commodity Hardware

Network Protocols and Algorithms

Dharma Agrawal¹  Saibal Ghosh¹ 

[1]University of Cincinnati

关键词: Cloud Computing;    Internet of Things(IoT);    Machine to Machine Communications (M2M);    Resource Allocation;    Fine grained Task Parallelism;    Virtual Machines;    Geographic localization;   

DOI  :  10.5296/npa.v8i4.10291

学科分类：计算机应用

来源: Macrothink Institute, Inc.

【 摘 要 】

The recent explosion in the speed and connectivity of the Internet has opened up the possibility of millions and possibly billions of devices connected together. Combined with the development of small, low power devices, new paradigms in the field of computing have opened up. Traditional passive electronic devices now have rudimentary computing capabilities. The resulting Internet of Things (IoT), comprised of smart interconnected devices is improving our ability to gather ambient information and make informed decisions that directly benefit humanity. However, the ubiquity of these devices also presents an interesting scenario wherein the devices can perform limited general-purpose computations when they are not performing their primary functions. A computational task divided into a large number of smaller, micro tasks, each of which take only a few CPU cycles to complete. By distributing these tasks over a large number of devices, we can achieve a substantial amount of computation with seemingly modest devices. In this work, we explore a mechanism to enable such massively parallel computations in low powered commodity hardware devices through fine-grained task parallelism.

【 授权许可】

Unknown