Portrait of Amar Phanishayee

Amar Phanishayee

Researcher

About

Hi! I am a Researcher at Microsoft Research in Redmond.

The goal of my research is to enable the creation of high-performance and efficient systems for large-scale data-intensive computing. To this end, my work follows two broad approaches:

1. Radically rethinking cluster architecture

  • Energy-efficient clusters for data intensive computing – FAWN
  • Freespace optics for datacenter networks – ProjecToR

2. Build robust clustered systems and protocols

  • First to analyse and solve TCP throughput collapse and latency insensitivity for clustered systems – TCP Incast
  • Distributed KV systems – FAWN-KV, Flex-KV (and the underlying Ouroborous protocol)
  • Reliable and scalable multicast – Ricochet, Plato
  • Compositional reasoning for systematically testing distributed systems [Ongoing work]
  • Efficient distributed deep learning [Ongoing work]

I have also occasionally looked at addressing issues in geo-distributed systems:

  • High-perf multi-hop wireless transfers using opportunistic caching – Ditto
  • Scalable one-hop overlay routing – NuRON
  • Systems/abstractions for connected devices – Bolt (storage) and Beam(programming framework and runtime)

As a graduate student at Carnegie Mellon University I worked with Dave Andersen and some stellar collaborators on FAWN, Incast, and Ditto.

Projects

ProjecToR: Agile Reconfigurable Data Center Interconnect

ProjecToR is a novel, free-space optics based approach for building data center interconnects. It uses a digital micromirror device (DMD) and mirror assembly combination as a transmitter and a photodetector on top of the rack as a receiver. Our approach…

Bam!

Established: April 17, 2015

The proliferation of connected devices can in theory enable a range of applications that make rich inferences about users and their environment. But in practice developing such applications today is arduous because they are constructed as monolithic silos, tightly coupled…

Kamino

Established: April 17, 2015

The Kamino project explores ways in which systems should adopt new memory technologies including SSDs (NAND-Flash), battery-backed DRAM and emerging non-volatile memory technologies (phase change memory, memristors, spin-torque transfer memory, etc.) for increased performance and efficiency. The project explores how…

Publications

2017

2016

2015

2014

2013

2012

2011

2009

2008

2007

2006

Other

Research Awards

  • 2012 CMU’s Allen Newell Award for Research Excellence for our work on “Energy-efficient Data Intensive Computing”
  • Best Paper Award, SOSP 2009 for FAWN
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