Low Power Computing in Distributed Systems
Abstract
Three research topics have been investigated: (I) Energy aware programming techniques for embedded systems; (2) Dynamic power management of a sensor node with periodic incoming tasks; and (3) Resource management of a sensor network using a distributed Genetic Algorithm. The research shows that (I) Certain simple program transformations can significantly improve the performance and energy dissipation of the embedded processor, which is the core part of a sensor node or a mobile computing device; (2) The traditional task scheduling algorithm does not work well with power management in a sensor node. Two heuristic algorithms are proposed which give lower power consumption than the traditional algorithms. (3) The power management problem in a distributed system may be formulated as an optimization problem and solved using a distributed GA. The performance and energy of a distributed GA is determined by its configuration parameters such as: the sub-population size, the number of processors, the length of epoch, and the number of migrating individuals. Their relations with the convergence speed and the energy dissipation is analyzed in this report. This effort leveraged a distributed (parallel) Island Model Genetic Algorithm code developed in IFTO that was adapted to the 3rd problem and run on a cluster computer in FTC this summer. A follow-on grant will study a fuller version of the problems with more realistic scenarios.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 01, 2006
- Accession Number
- ADA450272
Entities
People
- Qinru Qiu
Organizations
- Binghamton University