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Ramakrishna Gummadi

Researcher at University of Southern California

Publications -  32
Citations -  7449

Ramakrishna Gummadi is an academic researcher from University of Southern California. The author has contributed to research in topics: Wireless sensor network & Wireless network. The author has an hindex of 22, co-authored 32 publications receiving 7367 citations. Previous affiliations of Ramakrishna Gummadi include University of California, Berkeley & University of Massachusetts Amherst.

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Journal ArticleDOI

OceanStore: an architecture for global-scale persistent storage

TL;DR: OceanStore monitoring of usage patterns allows adaptation to regional outages and denial of service attacks; monitoring also enhances performance through pro-active movement of data.

Adaptive RED: An Algorithm for Increasing the Robustness of RED's Active Queue Management

TL;DR: This revised version of Adaptive RED, which can be implemented as a simple extension within RED routers, removes the sensitivity to parameters that affect RED’s performance and can reliably achieve a specified target average queue length in a wide variety of traffic scenarios.
Proceedings ArticleDOI

The impact of DHT routing geometry on resilience and proximity

TL;DR: The basic finding is that, despite the initial preference for more complex geometries, the ring geometry allows the greatest flexibility, and hence achieves the best resilience and proximity performance.
Journal ArticleDOI

The Ninja architecture for robust Internet-scale systems and services373423

TL;DR: The Ninja project as mentioned in this paper is a workstation cluster environment with a software platform that simplifies scalable service construction, including base stations, units, services, and active proxies, which are transformational elements that are used for unit-or service-specific adaptation.
Journal ArticleDOI

Interference-aware fair rate control in wireless sensor networks

TL;DR: Interferenceaware fair rate control (IFRC) detects incipient congestion at a node by monitoring the average queue length, communicates congestion state to exactly the set of potential interferers using a novel low-overhead congestion sharing mechanism, and converges to a fair and efficient rate using an AIMD control law.