K. V. M. Naidu

Efficient gossip-based aggregate computation

Recently, there has been a growing interest in gossip-based protocols that employ randomized communication to ensure robust information dissemination. In this paper, we present a novel gossip-based scheme using which all the nodes in an n-node overlay network can compute the common aggregates of MIN, MAX, SUM, AVERAGE, and RANK of their values using O(n log log n) messages within O(log n log log n) rounds of communication. To the best of our knowledge, ours is the first result that shows how to compute these aggregates with high probability using only O(n log log n) messages. In contrast, the best known gossip-based algorithm for computing these aggregates requires O(nlog n) messages and O(log n) rounds. Thus, our algorithm allows system designers to trade off a small increase in round complexity with a significant reduction in message complexity. This can lead to dramatically lower network congestion and longer node lifetimes in wireless and sensor networks, where channel bandwidth and battery life are severely constrained.

Efficient Detection of Distributed Constraint Violations

In many distributed environments, the primary function of monitoring software is to detect anomalies, i.e., instances when system behavior deviates substantially from the norm. In this paper, we propose communication-efficient schemes for the anomaly detection problem, which we model as one of detecting the violation of global constraints defined over distributed system variables. Our approach eliminates the need to continuously track the global system state by decomposing global constraints into local constraints that can be checked efficiently at each site. Only in the occasional event that a local constraint is violated, do we resort to more expensive global constraint checking. We show that the problem of selecting the local constraints, based on frequency distribution of individual system variables, so as to minimize the communication cost is NP-hard. We propose approximation algorithms for computing provably near-optimal (in terms of the number of messages) local constraints. Experimental results with real-life network traffic data sets demonstrate that our technique can reduce message communication overhead by as much as 70% compared to existing data distribution-agnostic approaches.

Diagnosing Link-Level Anomalies Using Passive Probes

In this paper, we develop passive network tomography techniques for inferring link-level anomalies like excessive loss rates and delay from path-level measurements. Our approach involves placing a few passive monitoring devices on strategic links within the network, and then passively monitoring the performance of network paths that pass through those links. In order to keep the monitoring infrastructure and communication costs low, we focus on minimizing (1) the number of passive probe devices deployed, and (2) the set of monitored paths. For mesh topologies, we show that the above two minimization problems are NP-hard, and consequently, devise polynomial-time greedy algorithms that achieve a logarithmic approximation factor, which is the best possible for any algorithm. We also consider tree topologies typical of Enterprise networks, and show that while similar NP-hardness results hold, constant factor approximation algorithms are possible for such topologies.

Minimum Cost Topology Construction for Rural Wireless Mesh Networks

IEEE 802.11 WiFi equipment based wireless mesh networks have recently been proposed as an inexpensive approach to connect far-flung rural areas. Such networks are built using high-gain directional antennas that can establish long-distance wireless point-to-point links. Some nodes in the network (called gateway nodes) are directly connected to the wired internet, and the remaining nodes connect to the gateway(s) using one or more hops. The dominant cost of constructing such a mesh network is the cost of constructing antenna towers at nodes. The cost of a tower depends on its height, which in turn depends on the length of its links and the physical obstructions along those links. We investigate the problem of selecting which links should be established such that all nodes are connected, while the cost of constructing the antenna towers required to establish the selected links is minimized. We show that this problem is NP-hard and that a better than O(log n) approximation cannot be expected, where n is the number of vertices in the graph. We then present the first algorithm in the literature, for this problem, with provable performance bounds. More precisely, we present a greedy algorithm that is an O(log n) approximation algorithm for this problem. Finally, through simulations, we compare our approximation algorithm with both the optimal solution, and a naive heuristic.

VillageNet: A low-cost, 802.11-based mesh network for rural regions

VillageNet is a wireless mesh network that aims to provide low-cost broadband Internet access for rural regions. The cost of building the network is kept low by using off-the-shelf IEEE 802.11 equipment and optimizing the network topology to minimize cost. In this paper we describe the over-all operation of VillageNet and discuss two fundamental problems in building such a network. Nodes in VillageNet communicate using long-distance point-to-point wireless links that are established using high-gain directional antenna. VillageNet uses the 2P MAC protocol [?], that is suited for the interference pattern within such a network. However, the 2P protocol requires the underlying mesh graph (for each 802.11 channel) to be bi-partite. Thus, if K channels are available, then an important consideration is how to select K bi-partite subgraphs to activate, such that the demands of the nodes are best met. We formally pose this problem and present some initial results. Second, we observe that the dominant cost of constructing such a mesh network is the cost of constructing antenna towers at nodes. The cost of a tower depends on its height, which in turn depends on the length of its links, and the physical obstructions along those links. Thus to minimize cost, we pose the problem of deciding which links should be established, such that all villages are connected and the cost of constructing antenna towers to establish the selected links is minimized.

Bulk content delivery using co-operating end-nodes with upload/download limits

We study the problem of optimizing the cost of content delivery in a cooperative network of caches at end-nodes. The caches could be, for example, within the computers of users downloading videos from websites (such as Netflix, Blockbuster etc.), DVRs (such as TiVo, or cable boxes) used as part of video on demand services or public hot-spots (e.g. Wi-Fi access points with a cache) deployed over a city to serve content to mobile users. Each cache serves user requests locally over a medium that incurs no additional costs (i.e. WiFi, home LAN); if a request is not cached, it must be fetched from another cache or a central server. In our model, each cache has a tiered back-haul internet connection, with a usage cap (and fixed per-byte costs thereafter). Redirecting requests intended for the central server to other caches with unused back-haul capacity can bring down the network costs. Our goal is to develop a mechanism to optimally 1) place data into the caches and 2) route requests to caches to reduce the overall cost of content delivery. We develop a multi-criteria approximation based on a LP rounding procedure that with a small (constant factor) blow-up in storage and upload limits of each cache, gives a data placement that is within constant factor of the optimum. Further, to speed up the solution, we propose a technique to cluster caches into groups, solve the data placement problem within a group, and combine the results in the rounding phase to get the global solution. Based on extensive simulations, we show that our schemes perform very well in practice, giving costs within 5-15% to the optimal, and reducing the network load at a central server by as much as 55% with only a marginal blow up in the limits. Also we demonstrate that our approach out-performs a non-cooperative caching mechanism by about 20%.

Efficient Aggregate Computation over Data Streams

Ciscos NetFlow collector (NFC) is a powerful example of a real-world product that supports multiple aggregate queries over a continuous stream of IP flow records. NFC enables a plethora of network management tasks like traffic demands estimation, application traffic profiling, etc. In this paper, we investigate two computation sharing techniques for enabling streaming applications such as NFC to scale to hundreds of queries. Our first technique instantiates certain intermediate aggregates which are then used to generate the final answers for input queries. Our second technique coalesces the filter conditions of similar queries and uses the coalesced filter to pre-filter stream data input to these queries. Using these techniques, we propose a heuristic to compute a good query plan and perform extensive simulations to show that our heuristic delivers a factor of over 3 performance improvement compared to a naive approach.

Gossip-Based Aggregate Computation with Low Communication Overhead

Recently, there has been a growing interest in gossip-based protocols that employ randomized communication to ensure robust information dissemination. In this paper, we present a novel gossip-based scheme using which all the nodes in an n-node overlay network can compute the common aggregates of MIN, MAX, SUM, AVERAGE, and RANK of their values using O(n log log n) messages within O(log n log log n) rounds of communication. To the best of our knowledge, ours is the first result that shows how to compute these aggregates with high probability using only O(n log log n) messages. In contrast, the best known gossip-based algorithm for computing these aggregates requires O(n log n) messages and O(log n) rounds. Thus, our algorithm allows system designers to trade off a small increase in round complexity with a significant reduction in message complexity. This can lead to dramatically lower network congestion and longer node lifetimes in wireless and sensor networks, where channel bandwidth and battery life are severely constrained