log in  |  register  |  feedback?  |  help  |  web accessibility
Logo
MAPLE : A Scalable Architecture for Maintaining Packet Latency Measurements
Wednesday, February 20, 2013, 2:00-3:00 pm Calendar
  • You are subscribed to this talk through .
  • You are watching this talk through .
  • You are subscribed to this talk. (unsubscribe, watch)
  • You are watching this talk. (unwatch, subscribe)
  • You are not subscribed to this talk. (watch, subscribe)
Abstract
Latency has become an important metric for network monitoring since the emergence of new latency-sensitive applications (e.g., algorithmic trading and high-performance com- puting). To satisfy the need, researchers have proposed new architectures such as LDA and RLI that can provide fine-grained latency measurements. However, these archi- tectures are fundamentally ossified in their design as they are designed to provide only a specific pre-configured aggre- gate measurement—either average latency across all packets (LDA) or per-flow latency measurements (RLI).Network op- erators, however, need latency measurements at both finer (e.g., packet) as well as flexible (e.g., flow subsets) levels of granularity. To bridge this gap, we propose an architecture called MAPLE that essentially stores packet-level latencies in routers and allows network operators to query the latency of arbitrary traffic sub-populations. MAPLE is built using scalable data structures with small storage needs (uses only 12.8 bits/packet), and uses a novel mechanism to reduce the query bandwidth significantly (by a factor of 17 compared to the naive method of sending packet queries individually).
Bio

Ramakrishna Padmanabhan is a second year PhD student working with Dr. Neil Spring. His interests like primarily in networking and distributed systems.

This talk is organized by Ramakrishna Padmanabhan