Improving the Selection of Surrogates During the Cold-Start Phase of a Cyber Foraging Application to Increase Application Performance

• Main Author: Kowalczk, Brian
• Format: Others
• Published: NSUWorks 2014
• Subjects: Cyberforaging; Distributed Systems; Pervasive Computing; Computer Sciences
• Online Access: http://nsuworks.nova.edu/gscis_etd/5; http://nsuworks.nova.edu/cgi/viewcontent.cgi?article=1003&context=gscis_etd

Mobile devices are generally less powerful and more resource constrained than their desktop counterparts are, yet many of the applications that are of the most value to users of mobile devices are resource intensive and difficult to support on a mobile device. Applications such as games, video playback, image processing, voice recognition, and facial recognition are resource intensive and often exceed the limits of mobile devices. Cyber foraging is an approach that allows a mobile device to discover and utilize surrogate devices present in the local environment to augment the capabilities of the mobile device. Cyber foraging has been shown to be beneficial in augmenting the capabilities of mobile devices to conserve power, increase performance, and increase the fidelity of applications. The cyber foraging scheduler determines what operation to execute remotely and what surrogate to use to execute the operation. Virtually all cyber foraging schedulers in use today utilize historical data in the scheduling algorithm. If historical data about a surrogate is unavailable, execution history must be generated before the scheduler's algorithm can utilize the surrogate. The period between the arrival time of a surrogate and when historical data become available is called the cold-start state. The cold-start state delays the utilization of potentially beneficial surrogates and can degrade system performance. The major contribution of this research was the extension of a historical-based prediction algorithm into a low-overhead estimation-enhanced algorithm that eliminated the cold-start state. This new algorithm performed better than the historical and random scheduling algorithms in every operational scenario. The four operational scenarios simulated typical use-cases for a mobile device. The scenarios simulated an unconnected environment, an environment where every surrogate was available, an environment where all surrogates were initially unavailable and surrogates joined the system slowly over time, and an environment where surrogates randomly and quickly joined and departed the system. One future research possibility is to extend the heuristic to include storage system I/O performance. Additional extensions include accounting for architectural differences between CPUs and the utilization of Bayesian estimates to provide metrics based upon performance specifications rather than direct