Graduation Year


Document Type




Degree Granting Department

Computer Science and Engineering

Major Professor

Kenneth Christensen, Ph.D.

Committee Member

Rafael Perez, Ph.D.

Committee Member

Miguel Labrador, Ph.D.

Committee Member

Stephen Suen, Ph.D.

Committee Member

Rahul Tripathi, Ph.D.


Data structures, energy efficiency, performance evaluation, proxying, network applications


One of the most urgent challenges of the 21st century is to investigate new technologies that can enable a transition towards a society with a reduced CO2 footprint. Information Technology generates about 2% of the global CO2, which is comparable to the aviation industry. Being connected to the Internet requires active participation in responding to protocol messages. Billions of dollars worth of electricity every year are used to keep network hosts fully powered-on at all times only for the purpose of maintaining network presence. Most network hosts are idle most of the time, thus presenting a huge opportunity for energy savings and reduced CO2 emissions.

Proxying has been previously explored as a means for allowing idle hosts to sleep yet still maintain network presence. This dissertation develops general requirements for proxying and is the first exploration of application-level proxying. Proxying for TCP connections, SIP, and Gnutella P2P was investigated. The TCP proxy keeps TCP connections open (when a host is sleeping) and buffers and/or discards packets as appropriate. The SIP proxy handles all communication with the SIP server and wakes up a sleeping SIP phone on an incoming call. The P2P proxy enables a Gnutella leaf node to sleep when not actively uploading or downloading files by handling all query messages and keyword lookups in a list of shared files. All proxies were prototyped and experimentally evaluated.

Proxying for P2P lead to the exploration of space and time efficient data structures to reduce the computational requirements of keyword search in the proxy. The use of pre-computation and hierarchical structures for reducing the false positive rate of a Bloom filter was explored. A Best-of-N Bloom filter was developed, which was shown to have a lower false positive rate than a standard Bloom filter and the Power-of-2 Bloom filter. An analysis of the Best-of-N Bloom Filter was completed using Order Statistics to predict the false positive rate.

Potential energy savings are shown to be in the hundreds of millions of dollars per year assuming a modest adoption rate of the methods investigated in this dissertation. Future directions could lead to greater savings.