Person:
Zegura, Ellen W.

Permanent Link
https://hdl.handle.net/1853/71879
Associated Organization(s)
Organizational Unit
ORCID
ArchiveSpace Name Record

Filters

2001 - 2004 3
3
3
3
3
Reset filters

Settings
Author: Merugu, Shashidhar × End date: 2009 × Start date: 2000 × Type: Text ×

Publication Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Routing in Space and Time in Networks with Predictable Mobility
    (Georgia Institute of Technology, 2004) Ammar, Mostafa H. ; Zegura, Ellen W. ; Merugu, Shashidhar
    We consider the problem of routing in emerging wireless networks where nodes move around explicitly carrying messages to facilitate communication in an otherwise partitioned network. The absence of a path at any instant of time between a source and destination makes the traditional mobile ad hoc routing protocols unsuitable for these networks. However, the explicit node movements create paths over time that include the possibility of a node carrying a message before forwarding to another suitable node. Identifying such paths over space and time is a key challenge in these store, carry and forward networks. In most of these networks, the mobility of nodes is predictable either over a finite time horizon or indefinitely due to periodicity in node motion. We propose a new space-time routing framework for these networks leveraging the predictability in node motion. Specifically, we construct space-time routing tables where the next hop node is selected from the current as well as the future neighbors. Unlike traditional routing tables, our space-time routing tables use both the destination and the arrival time of message to determine the next hop node. We devise an algorithm to compute these space-time routing tables to minimize the end-to-end message delivery delay. Our routing algorithm is based on a space-time graph model derived from the mobility of nodes. We empirically evaluate our approach using simulations and observe improved performance as compared to other approaches based on heuristics.
  • Thumbnail Image
    Item
    Adding structure to unstructured peer-to-peer networks: the role of overlay topology
    (Georgia Institute of Technology, 2003) Merugu, Shashidhar ; Srinivasan, Sridhar ; Zegura, Ellen W.
    Our work examines the role of overlay topology on the performance of unstructured peer-to-peer systems. We focus on two metrics of performance: (a) search protocol performance, a local gain perceived directly by a user of the system and (b) utilization of the network, a global property that is of interest to network service providers. We present a class of overlay topologies based on distance between a node and its neighbors. We show, by simulation, that a particular topology instance of this class where every node has many close neighbors and few random neighbors exhibits better properties than other examined instances. In this overlay topology, the chances of locating files are high and the nodes where these files are found are, on average, close to the query source. This improvement in search protocol performance is achieved while decreasing the traffic load on the links in the underlying network. We propose a simple greedy algorithm to construct such topologies where each node operates independently and in a decentralized manner to select its neighbors.
  • Thumbnail Image
    Item
    Fast Packet Classification with a Varying Rule Set
    (Georgia Institute of Technology, 2001) Merugu, Shashidhar ; Gummalla, Ajay Chandra V. ; Sala, Dolors ; Zegura, Ellen W.
    Multi-dimensional packet classification is increasingly important for applications ranging from fire-walls to traffic accounting. Fast link speeds, the desire to classify with fine granularity, and the need for agility in a dynamic environment all pose significant challenges for packet classification. We propose an approach that is capable of handling a changing set of classification rules that span multiple fields. Our approach is based on extracting a relatively small set of bits that uniquely identify the packets satisfying each rule. Changes to the rule set are handled in-line via a fast update mode that adds to the rule table, while a background process performs reoptimization of the full rule table less frequently. The classification process can be efficiently implemented using pipelined hardware and supports high packet arrival rate.