Achieving Energy Efficiency on Networking Systems with Optimization Algorithms and Compressed Data Structures

To cope with the increasing quantity, capacity and energy consumption of transmission and routing equipment in the Internet, energy efficiency of communication networks has attracted more and more attention from researchers around the world. In this dissertation, we proposed three methodologies to achieve energy efficiency on networking devices: the NP-complete problems and heuristics, the compressed data structures, and the combination of the first two methods. We first consider the problem of achieving energy efficiency in Data Center Networks (DCN). We generalize the energy efficiency networking problem in data centers as optimal flow assignment problems, which is NP-complete, and then propose a heuristic called CARPO, a correlation-aware power optimization algorithm, that dynamically consolidate traffic flows onto a small set of links and switches in a DCN and then shut down unused network devices for power savings. We then achieve energy efficiency on Internet routers by using the compressive data structure. A novel data structure called the Probabilistic Bloom Filter (PBF), which extends the classical bloom filter into the probabilistic direction, so that it can effectively identify heavy hitters with a small memory foot print to reduce energy consumption of network measurement. To achieve energy efficiency on Wireless Sensor Networks (WSN), we developed one data collection protocol called EDAL, which stands for Energy-efficient Delay-aware Lifetime-balancing data collection. Based on the Open Vehicle Routing problem, EDAL exploits the topology requirements of Compressive Sensing (CS), then implement CS to save more energy on sensor nodes

Path Similarity Evaluation using Bloom Filters

The performance of several Internet applications often relies on the measurability of path similarity between different participants. In particular, the performance of content distribution networks mainly relies on the awareness of content sources topology information. It is commonly admitted nowadays that, in order to ensure either path redundancy or efficient content replication, topological similarities between sources is evaluated by exchanging raw traceroute data, and by a hop by hop comparison of the IP topology observed from the sources to the several hundred or thousands of destinations. In this paper, based on real data we collected, we advocate that path similarity comparisons between different Internet entities can be much simplified using lossy coding techniques, such as Bloom filters, to exchange compressed topology information. The technique we introduce to evaluate path similarity enforces both scalability and data confidentiality while maintaining a high level of accuracy. In addition, we demonstrate that our technique is scalable as it requires a small amount of active probing and is not targets dependent