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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/2227

Title: Optimality Analysis and Middleware Design for Heterogeneous Cloud HPC in Mobile Devices
Authors: Naod, Duga
Advisors: Prof. Dr. Heiko Schr ̈der
Keywords: service-oriented architecture
Android
GPGPU
parallel algorithms and programming
middleware
commodity supercomputing
energy-response time optimality
mobile cloud computing
hetero- geneous HPC
Copyright: Jul-2011
Date Added: 3-May-2012
Publisher: AAU
Abstract: Mobile and embedded devices such as smartphones, wireless sensor nodes, and other ubiquitous consumer and industrial electronic systems play increasingly sig- nificant roles in our lives. Those devices provision a range of functionalities despite their limitations in terms of size, processing capacity and power constraints. The advent of ubiquitous systems like those finds itself in par with another emerging trend – cloud computing, wherein computational resources like storage, processing hardware and software systems are provisioned to clients as services via the Inter- net. Mobile cloud computing and the cloud in general could allow for yet more use cases while saving the otherwise limited battery power and augmenting the comparatively small computational workhorse available in mobile devices. Such a scenario of cloud HPC in mobile devices needs supporting middleware for transparent end-to-end (i.e. mobile-to/from-cloud) service oriented communica- tion. In so doing the middleware abstracts and exposes heterogeneous cloud re- sources like general-purpose CPUs; GPU and FPGA clusters; raw and relational datastores; et cetera. In addition, such middleware should be able to provide run- time support and brokerage mechanism for the mobile devices in order to consume an abstract cloud HPC service. In this thesis, a number of novel and existing techniques are explored towards lightweight middleware architecture and an end-to-end mobile-cloud infrastruc- ture is introduced. A case study is done with Android powered mobile devices, a GPU-accelerated algorithm, a Wi-Fi network and said infrastructure towards an experimental evaluation and model driven optimality analysis of the envisioned system. Results showed that middleware-supported cloud HPC in mobile devices is indeed promising and results in significant energy savings, faster response times and paves way for more mobile computing use cases.
URI: http://hdl.handle.net/123456789/2227
Appears in:Thesis - Electrical Engineering

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