Christina Delimitrou

About

Christina Delimitrou is a Professor in Communications and Technology and an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT. Her research areas include computer architecture, theory of computation, and artificial intelligence and decision-making, focusing on developing systems that interact with the external world through perception, communication, and action, while also learning, making decisions, and adapting to changing environments. She is involved in designing systems that sense, process, and transmit energy and information, leveraging computational, theoretical, and experimental tools to create groundbreaking sensors, energy transducers, and physical substrates for computation. Her work addresses shared challenges facing humanity through innovative system development.

Research topics

• Computer Science
• Engineering
• Engineering management

Selected publications

• Sinan: ML-based and QoS-aware resource management for cloud microservices

  2021-04-11 · 193 citations

  articleSenior author

  Cloud applications are increasingly shifting from large monolithic services, to large numbers of loosely-coupled, specialized microservices. Despite their advantages in terms of facilitating development, deployment, modularity, and isolation, microservices complicate resource management, as dependencies between them introduce backpressure effects and cascading QoS violations.

  PublisherDOI

• Sage: practical and scalable ML-driven performance debugging in microservices

  2021-04-11 · 153 citations

  articleSenior author

  Cloud applications are increasingly shifting from large monolithic services to complex graphs of loosely-coupled microservices. Despite the advantages of modularity and elasticity microservices offer, they also complicate cluster management and performance debugging, as dependencies between tiers introduce backpressure and cascading QoS violations. Prior work on performance debugging for cloud services either relies on empirical techniques, or uses supervised learning to diagnose the root causes…

  PublisherDOI

• Faster and Cheaper Serverless Computing on Harvested Resources

  2021-10-19 · 132 citations

  article

  Serverless computing is becoming increasingly popular due to its ease of programming, fast elasticity, and fine-grained billing. However, the serverless provider still needs to provision, manage, and pay the IaaS provider for the virtual machines (VMs) hosting its platform. This ties the cost of the serverless platform to the cost of the underlying VMs. One way to significantly reduce cost is to use spare resources, which cloud providers rent at a massive discount. Harvest VMs offer such cheap r…

  PublisherDOI

• AQUATOPE: QoS-and-Uncertainty-Aware Resource Management for Multi-stage Serverless Workflows

  2022-12-19 · 64 citations

  articleOpen accessSenior author

  Multi-stage serverless applications, i.e., workflows with many computation and I/O stages, are becoming increasingly representative of FaaS platforms. Despite their advantages in terms of fine-grained scalability and modular development, these applications are subject to suboptimal performance, resource inefficiency, and high costs to a larger degree than previous simple serverless functions.

  PublisherOA PDFDOI

• Resilient Baseband Processing in Virtualized RANs with Slingshot

  2023-09-01 · 15 citations

  articleOpen access

  In cellular networks, there is a growing adoption of virtualized radio access networks (vRANs), where operators are replacing the traditional specialized hardware for RAN processing with software running on commodity servers. Today's vRAN deployments lack resilience, since there is no support for vRAN failover or upgrades without long service interruptions. Enabling these features for vRANs is challenging because of their strict real-time latency requirements and black-box nature. Slingshot is a…

  PublisherOA PDFDOI

Recent grants

• CAREER: Learning-Based Hardware and Software Techniques for Quality-of-Service-Aware Cloud Microservices

  NSF · $402k · 2019–2023

Frequent coauthors

• Christos Kozyrakis

  46 shared

• Yu Gan

  Google (United States)

  19 shared

• Yanqi Zhang

  Beijing Institute of Petrochemical Technology

  16 shared

• N.V. Lazarev

  11 shared

• Zhuangzhuang Zhou

  9 shared

• Dailun Cheng

  Cornell University

  9 shared

• Zhiru Zhang

  9 shared

• Meghna Pancholi

  Columbia University

  9 shared

Labs

• EECS Communication LabPI

Awards & honors

• 2025-26 EECS Faculty Award Roundup
• Eleven MIT faculty receive Presidential Early Career Awards
• 2024-25 EECS Faculty Award Roundup

Similar researchers at Massachusetts Institute of Technology

• Manolis Kellish-164
• Antonio Torralbah-133
• Daniela Rush-129