By Jih-Ching Chiu, Kai-Ming Yang, Chen-Ang Wong (auth.), Yang Xiang, Ivan Stojmenovic, Bernady O. Apduhan, Guojun Wang, Koji Nakano, Albert Zomaya (eds.)
The quantity set LNCS 7439 and 7440 contains the lawsuits of the twelfth overseas convention on Algorithms and Architectures for Parallel Processing, ICA3PP 2012, in addition to a few workshop papers of the CDCN 2012 workshop which used to be held at the side of this convention. The forty typical paper and 26 brief papers integrated in those lawsuits have been rigorously reviewed and chosen from 156 submissions. The CDCN workshop attracted a complete of nineteen unique submissions, eight of that are incorporated partly II of those court cases. The papers disguise many dimensions of parallel algorithms and architectures, encompassing primary theoretical ways, functional experimental effects, and advertisement parts and systems.
Read or Download Algorithms and Architectures for Parallel Processing: 12th International Conference, ICA3PP 2012, Fukuoka, Japan, September 4-7, 2012, Proceedings, Part II PDF
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Additional resources for Algorithms and Architectures for Parallel Processing: 12th International Conference, ICA3PP 2012, Fukuoka, Japan, September 4-7, 2012, Proceedings, Part II
Because the two-ray ground model does not consider the interference influence, our simulation model also does not consider the interference influence. , only the communication of one source/destination device pair is possible at a moment). Enhancing the Performance of a Distributed Mobile Computing Environment 6 27 Results Figure 2 shows the percentage of completed tasks using the SR, EMTE, KPE, and MET heuristics for different topology construction methods. A task is successful or completed when the final result is received by the task requester by the deadline.
All devices’ direction, duration, and the speed of mobility are determined using a random distribution. It is assumed that there is a central entity called the resource management system (RMS) that controls the whole system. The RMS decides where the requested tasks will be sent for execution, it is located at the center and does not move, and assumed to have unlimited energy. It is assumed that the RMS knows all of the devices’ information and the tasks’ execution times on those devices. The estimated execution times of each task on each device is assumed to be known based on user supplied information, experiential data, task profiling, and analytical benchmarking, or other techniques.
After the number of levels for each device is decided, the relative speed of each level is determined. The lowest speed level of a device is assumed to be one third of the maximum speed level. The rest of the levels are determined dividing the gap between the maximum and lowest speed level equally according to the number of levels in each device (linearly). In order to determine the power usage at each of the levels, using the simplifying relationship of power being proportional to voltage squared, a constant is calculated using the voltage and the active power in .
Algorithms and Architectures for Parallel Processing: 12th International Conference, ICA3PP 2012, Fukuoka, Japan, September 4-7, 2012, Proceedings, Part II by Jih-Ching Chiu, Kai-Ming Yang, Chen-Ang Wong (auth.), Yang Xiang, Ivan Stojmenovic, Bernady O. Apduhan, Guojun Wang, Koji Nakano, Albert Zomaya (eds.)