Current Projects

The projects below have been generously supported by the National Science Foundation, the state of Massachusetts, VMware, Intel, Google, and Amazon. Most projects are collaborations with other UMass Amherst faculty, undergraduate and graduate students, and other external collaborators from industry and academia.

The objective for the Computing for an Equitable Energy Transition (CEET) REU site is to expose undergraduate students to the important and significant role that computing will play in the transition to sustainable energy, both as an increasingly significant energy consumer and in optimizing society's energy- and carbon-efficiency. CEET's activities will focus on three distinct computing sub-disciplines that are important for enabling an equitable energy transition, including i) designing energy-efficient, reliable, and low-cost sensors, i.e., sensing, ii) designing energy- and carbon-efficient cloud platforms and applications, i.e., computing, and iii) analyzing collected data to identify and exploit opportunities for improving society's sustainability that are equitable, i.e., analysis. This project is funded by the National Science Foundation under grant CNS-2243853. 
While the cloud’s energy demand grew more slowly than expected over the past decade due to aggressive energy-efficiency optimizations, there are few remaining optimization opportunities using traditional methods. As a result, continuing the cloud's exponential growth will translate into exponentially rising energy demand, which will position it as one of the primary contributors to global carbon emissions. To address the problem, this project elevates carbon to a first-class metric in designing a sustainable and reliable cloud-edge software infrastructure that can enable continued exponential growth. This project is funded by the National Science Foundation and VMware under grant CNS-2105494. 
The rapid transition to an energy system based on renewable electricity sources instead of fossil fuels is one of the great challenges facing humanity. It also offers a unique opportunity to reshape our society to be more equitable and more sustainable. To address this challenge, ELEVATE draws upon expertise in a wide range of research fields to create an innovative graduate STEM program focused on resilience and equity in the energy transition. ELEVATE is funded by the National Science Foundation NSF Research Traineeship (NRT) program grant DGE-2021693 and Growing Convergence Research (GCR) program grant SES-2020888.

Optimizing Distributed Machine Learning for Transient Servers

Large-scale distributed machine learning (ML) is expensive to run on cloud platforms. To reduce cost, cloud platforms now offer cheap transient servers, which they may revoke at any time. This project is re-designing traditional distributed ML algorithms to use looser forms of synchrony, and designing adaptive policies for selecting transient servers based on their performance, cost, and volatility. This re-design will lower the cost of running distributed ML by enabling them to use cheap transient servers. This project is funded by the National Science Foundation under grant CNS-1908536.

Managing Resources at the Edge

Despite the significant network latency advantage of edge servers, edge computing remains vulnerable to numerous performance pitfalls that can lead to reduced performance. This counter-intuitive behavior primarily occurs when edge resource constraints or workload bursts cause high queuing delays and response times that significantly increase latency. To address this problem, this project is developing rigorous analytical models of edge and cloud performance to gain a fundamental understanding of when and why edge performance problems occur in practice. The project is applying these models to design novel, but practical, resource management policies that can enable edge computing to provide low latency for a wide range of real-world applications.This project is funded by the National Science Foundation  under grant CNS-2211888. 

Building a Testbed for Carbon-Efficient Applications

Cloud platforms are well-positioned to reduce their carbon emissions by transitioning to cleaner energy sources because cloud applications often have significant spatial, temporal, and performance flexibility, enabling them to shift the location, time, and intensity of their execution to better align with the availability of carbon-free renewable energy or low-carbon grid energy. Unfortunately, researchers cannot leverage this unique combination of advantages to experiment with and optimize cloud applications' carbon-efficiency because current cloud platforms do not expose energy's carbon characteristics to them. To address the problem, this project will design and implement a shared community testbed for experimenting with the design of carbon-efficient cloud applications. This project is funded by the National Science Foundation under grant CNS-2213636. 
The NSF “Open Cloud Testbed” (OCT) project is building and supporting a testbed for research and experimentation into new cloud platforms – the underlying software which provides cloud services to applications. Testbeds such as OCT are critical for enabling research into new cloud technologies – research that requires experiments which potentially change the operation of the cloud itself. This project is funded by the National Science Foundation under grant CNS-1925464.

Managing Electrical and Thermal Energy in Sustainable Computer Systems

Renewable-powered systems may be deployed in many different climates that subject them to a wide range of temperatures that significantly alter the efficiency and correctness of their underlying components, including their processors, batteries, solar cells, and clocks. Current systems are either designed for a narrow and ideal temperature range, and thus are often unreliable under even slight temperature variations, or must consume significant additional energy to maintain an ideal temperature within a narrow window, which significantly reduces their energy-efficiency. To address the problem, this project proposes fundamental research on the design of sustainable renewable-powered systems that are ectothermic in that they jointly manage and adapt to variations in the electrical and thermal energy available in their environment to optimize their energy-efficiency, performance, and reliability. This project is funded by the National Science Foundation under grant CNS-2230143.  

Past Projects

Software-defined Solar-powered Systems

This project focuses on the design of "smart" software-defined solar (SDS) systems that self-regulate the power they let "flow" into the grid. Similar to data transmission in the Internet, SDS systems dynamically regulate the solar energy that flows into the grid to maximize its available solar capacity, maintain a balanced supply and demand, and fairly share the available capacity among connected users. The project is funded by the National Science Foundation under grant CNS-1645952.

Managing Risk in Cloud Platforms

Cloud computing has become the foundation of our information-based economy, providing the computing power necessary for advances in all sectors.  However, cloud platforms expose users to a number of hidden risks.  This project develops techniques for efficiently managing risk in cloud platforms by adapting and extending concepts from economics.  A key goal of the project is to elevate risk management to a first-class systems design principle. The project is funded by the National Science Foundation under grant CNS-1802523, Google Faculty Research Award, and Amazon in the form of cloud credits.
This project developed a programmable data-driven testbed to enable a range of research and education activities focused on sustainable smart buildings. Our testbed is comprised of two components: a data collection component based on a large number of real instrumented buildings of various types to gather anonymous fine-grained energy usage and operational data at scale; and a programmable smart building component that uses programmable power sources and electrical loads to enable the evaluation of a variety of energy management mechanism. This project was funded by the National Science Foundation under grant CNS-1405826. 

Utility-driven Smart Energy Services

This project developed utility-driven smart energy services to improve grid energy efficiency, encourage energy conservation, and promote local renewable energy sources, such as rooftop solar. The research focused on developing new energy analytic techniques for smart meter data, combining these in novel ways to create specific applications that improve energy efficiency, packaging those applications as cloud-based web services that are accessible to end users, and evaluating their impact on user behavior and energy efficiency. This project was funded by the National Science Foundation under grant CNS-19254646 and a grant from the Massachusetts Department of Energy Resources (DOER).

System Support for Transient Cloud Computing

In many emerging scenarios, clouds and data centers can only provide transient server availability. This project was the first to develop system support for transience that treated it as a first-class design principle. Since traditional fault-tolerance techniques are too expensive for handling transient server availability, the project developed new transience-aware mechanisms that balanced cost, performance, and availability.  This project was funded by the National Science Foundation under grant CNS-1422245, a 2016 Google Faculty Research Award, and Amazon (in the form of cloud credits).

Enhancing Privacy in Smart Buildings

The design of smart buildings that automatically optimize energy usage is important for improving energy-efficiency. Unfortunately, a key barrier to the broad adoption of energy-efficiency optimizations is that Internet-connected sensors often leak private information about user behavior. This project designed low-cost, non-intrusive, privacy-enhancing techniques that reduced the private information leaked through smart devices, while still permitting the sophisticated analytics, control, and verification necessary to enable energy optimizations for smart buildings. This project was funded by the National Science Foundation and Intel under grant CNS-1505422.
This project developed BenchLab, an open, flexible community infrastructure comprising applications, workloads and tools to enable realistic performance evaluation and benchmarking by systems researchers. BenchLab is an open framework where source code and workload datasets are freely available for modification and use by researchers for their specific experiments. The framework consists of a suite of server-side benchmark applications and workloads that represent cloud, mobile web, and green computing environments. This project was funded by the National Science Foundation under grant OAC-1339839.
The project developed a model-based paradigm to design automated load discovery, monitoring, and scheduling techniques that are low in cost, highly transparent, and privacy preserving. The project bootstrapped an online data repository to enable new techniques for modeling loads based on their energy usage. The project leveraged these models to automatically discover the set of loads a building operates, and monitor their electricity usage using only aggregate electricity recorded by a building smart meter. This project was funded by the National Science Foundation under a NSF CAREER grant CNS-1253063.