Robust Distributed System Nucleus (rDSN) is an open framework for quickly building and managing high performance and robust distributed systems. The core idea is a coherent and principled design that distributed systems, tools, and frameworks can be developed independently and later on integrated (almost) transparently.
Graph Engine, previously known as Trinity, is a distributed, in-memory, large graph processing engine.
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 to sensing devices, and must implement all sensing & inference logic, even as devices move or are temporarily disconnected. Our goal is to break down restrictive device-application silos and simplify app development.
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 to best leverage such new memory technologies inside systems of all sizes and shapes: from mobile to data center scale.
This is a project looking into design and evaluation of efficient and deployable algorithms for assignment of complex workloads to resources in modern cloud service platforms.
Catapult is a Microsoft project investigating the use of field-programmable gate arrays (FPGAs) to improve performance, reduce power, and provide new capabilities in the datacenter.
Parasail is a novel approach to parallelizing a large class of seemingly sequential applications wherein dependencies are, at runtime, treated as symbolic values. The efficiency of parallelization, then, depends on the efficiency of the symbolic computation, an active area of research in static analysis, verification, and partial evaluation. This is exciting as advances in these fields can translate to novel parallel algorithms for sequential computation.
An Ironclad App lets a user securely transmit her data to a remote machine with the guarantee that every instruction executed on that machine adheres to a formal abstract specification of the app's behavior. This does more than eliminate implementation vulnerabilities such as buffer overflows, parsing errors, or data leaks; it tells the user exactly how the app will behave at all times.
an overhead-constraint logging system
Data compression is essential to large-scale data centers to save both storage and network bandwidth. Current software based method suffers from high computational cost with limited performance. In this project, we are migrating the fundamental workload of the computer system to FPGA accelerator, aiming high throughput performance and high energy efficiency, as well as freeing some CPU resources.
Software-defined radios (SDR) have a potential to bring major innovation in wireless networking design. However, their impact so far has been limited due to complex programming tools. Ziria addresses this problem. It consists of a novel programming language and an optimizing compiler. It is able to synthesize a very efficient SDR code from a high-level PHY description written in Ziria language.
This project targets on using automatic techniques to reduce MTTR of large-scale online service systems.
MODIST is a practical software model checker for unmodified concurrent, distributed and cloud systems. MODIST explores different execution paths systematically as well as simulating a variety of environment faults to discover subtle corner-case defects. We have applied MODIST in Oracle Berkely DB, MPS(Paxos implementation), SQL Azure, Windows Azure Storage and other real systems, and found many new bugs.
This is the website of the rack-scale computing research project at MSRC
This project re-imagines and re-engineers wide area networks, to more than double their efficiency and allow flexible sharing of resources.
Face In The Crowd examines the social impact of crowdsourcing platforms—cloud-based computational systems that allow the outsourcing of work through open requests—and how they might shape the future of work.
Labs: New England
Scalable and Practical App Digging Engine
Connected devices – sensors and actuators – have a growing impact on our society, environment and health. For example, there have been significant advances in gaining visibility into buildings' daily operations. The next step is to enable people to do more with the increasingly ubiquity of connected devices. To this end, Human-Building Analytics (HBA) data platform explores, for a wide spectrum of users, (1) more natural programmability for connected devices and (2) more personalized analytics.
-- Making it easy for app developers of all levels to test their apps under real-world contexts on the cloud or real devices --
Waypoint project is up and running in Building 99.
As PHY layer data rates increase, CSMA MAC overheads dominate. The 9 us slot width at 1Gbps data rate can result in MAC efficiency of under 10%. WiFi-Nano proposes a novel speculative transmission based technique that leverages self-interference cancelation and allows for using 800ns slots -- reducing CSMA overheads by an order of magnitude.
The quest for higher data rates in WiFi is leading to the development of standards that make use of wide channels (e.g., 40MHz in 802.11n and 80MHz in 802.11ac). We argue against this trend of using wider channels, and instead advocate that radios should communicate concurrently over multiple narrow channels for efficient and fair spectrum utilization. We propose WiFi-NC, a novel PHY-MAC design that allows radios to use WiFi over multiple narrow channels simultaneously.
Dhwani enables information theoretically secure Near Field Communication (NFC) on existing mobile phones without requiring any special hardware or PKI infrastructure. It uses existing microphones and speakers on phones to perform acoustic NFC.
The LKW project is aimed at designing low-power algorithms and systems for admission control to speech systems: i.e., detecting foreground speech, recognizing leading keywords and verifying speakers on a continuously-on wearable device. Our goal is to consume under 10 mW average on generic embedded hardware available today and under 100uW on custom hardware.
In data centers, the IO path to storage is long and complex. It comprises many layers or “stages” with opaque interfaces between them. This makes it hard to enforce end-to-end policies that dictate a storage IO flow’s performance (e.g., guarantee a tenant’s IO bandwidth) and routing (e.g., route an untrusted VM’s traffic through a sanitization middlebox). We are researching architectures that decouple control from data flow to enable such policies.