About

Radhika Mittal is an Assistant Professor in Electrical and Computer Engineering at the University of Illinois Urbana-Champaign, affiliated with the Siebel School of Computing and Data Science. Her research areas include Systems and Networking, with recent courses taught in Distributed Systems and High-Speed/Programmable Networks. She has received recognition for her work, including an NSF CAREER award to improve 5G networks with 'slicing' and a Facebook Research Award. Her contributions focus on advancing computing technologies, particularly in the areas of network systems and high-speed networks, and she is actively involved in pioneering research to improve microservice-based applications and network performance.

Research topics

• Computer Science
• Computer network
• Distributed computing
• Operating system
• World Wide Web
• Embedded system
• Engineering
• Computer Security
• Artificial Intelligence
• Real-time computing
• Telecommunications

Selected publications

• Probabilistic Fair Ordering of Events

  ArXiv.org · 2026-02-09

  articleOpen access

  A growing class of applications depends on fair ordering, where events that occur earlier should be processed before later ones. Providing such guarantees is difficult in practice because clock synchronization is inherently imperfect: events generated at different clients within a short time window may carry timestamps that cannot be reliably ordered. Rather than attempting to eliminate synchronization error, we embrace it and establish a probabilistically fair sequencing process. Tommy is a sequencer that uses a statistical model of per-clock synchronization error to compare noisy timestamps probabilistically. Although this enables ordering of two events, the probabilistic comparator is intransitive, making global ordering non-trivial. We address this challenge by mapping the sequencing problem to a classical ranking problem from social choice theory, which offers principled mechanisms for reasoning with intransitive comparisons. Using this formulation, Tommy produces a partial order of events, achieving significantly better fairness than a Spanner TrueTime-based baseline approach.

  PublisherOA PDF

• Probabilistic Fair Ordering of Events

  Open MIND · 2026-02-09

  preprint

  A growing class of applications depends on fair ordering, where events that occur earlier should be processed before later ones. Providing such guarantees is difficult in practice because clock synchronization is inherently imperfect: events generated at different clients within a short time window may carry timestamps that cannot be reliably ordered. Rather than attempting to eliminate synchronization error, we embrace it and establish a probabilistically fair sequencing process. Tommy is a sequencer that uses a statistical model of per-clock synchronization error to compare noisy timestamps probabilistically. Although this enables ordering of two events, the probabilistic comparator is intransitive, making global ordering non-trivial. We address this challenge by mapping the sequencing problem to a classical ranking problem from social choice theory, which offers principled mechanisms for reasoning with intransitive comparisons. Using this formulation, Tommy produces a partial order of events, achieving significantly better fairness than a Spanner TrueTime-based baseline approach.

  DOI

• Performance Isolation for 5G RAN Slices Across Multiple Interfering Cells

  Leibniz-Zentrum für Informatik (Schloss Dagstuhl) · 2026-01-01

  articleOpen accessSenior author

  Radio Access Network (RAN) slicing, a key 5G feature, enables different slices (i.e. tenants or applications) to share the same physical network infrastructure while pursuing diverse objectives such as fairness, prioritization, or maximizing throughput. Each slice is allocated a share of radio resource blocks (RBs), which it further schedules among its users as per its own performance objective. In this paper, we identify the unique challenges that arise when performing RAN slicing in today’s multi-cell deployments that require a mechanism for managing interference among cells. We highlight how interference management decisions, that can be easily made in the absence of slicing (where all users share a common objective set by the network operator), become challenging with 5G slicing where we must respect the individual objectives of multiple slices, while retaining performance isolation across slices. We present a system, RadioNinja, that tackles this challenge through a unique decision-making framework that allows different slices to independently contribute towards interference management decisions. RadioNinja further employs a series of techniques to make such decisions within tight RAN scheduling budget of hundreds of microseconds. Trace-driven simulations with real-world channel measurements show that RadioNinja improves slice-level objectives (e.g., throughput, fairness, flow completion times) by 20–60% over state-of-the-art baselines, while consistently meeting sub-millisecond decision deadlines.

  PublisherOA PDFDOI

• AeroResQ: Edge-accelerated UAV framework for scalable, resilient and collaborative escape route planning in wildfire scenarios

  Future Generation Computer Systems · 2026-05-07

  articleOpen access
  PublisherOA PDFDOI

• Centralized Traffic Engineering for Networked Farm Applications

  2025-12-03

  articleOpen accessSenior author

  Emerging farming techniques rely on smart devices such as multi-spectral cameras that collect fine-grained data, and tele-operated robots that perform tasks such as de-weeding, berry-picking, etc. These networked farm applications (requiring 10s of Mbps of throughput per device to the edge servers, with tens to hundreds of devices in a typical farm) must be supported on a wireless mesh network with limited capacity. In this work, we use these networked farm applications as a compelling case-study to design FarmNetes, a centralized traffic engineering (TE) system for wireless mesh networks. FarmNetes leverages explicit control over farm workloads to make centralized TE decisions (temporal flow schedules, sending rates, load-aware routes, and channel configurations) from an edge server, so as to best meet task requirements. FarmNetes' centralized TE decisions enable it to work with commodity devices and control how the network is shared across flows based on the desired policies (prioritization and fairness) irrespective of the underlying MAC layer link sharing mechanisms. This further enables MAC-agnostic reasoning of wireless network behavior when making TE decisions. Our evaluation, using testbeds in a farm and trace-driven simulations, shows how FarmNetes achieves 3 × higher end-end network throughput and better meets application demands, compared to status-quo wireless mesh strategies.

  PublisherOA PDFDOI

• Beyond Lamport, Towards Probabilistic Fair Ordering

  arXiv (Cornell University) · 2025-10-15

  preprintOpen access

  A growing class of applications demands \emph{fair ordering} of events, which ensures that events generated earlier are processed before later events. However, achieving such sequencing is challenging due to the inherent errors in clock synchronization: two events at two clients generated close together may have timestamps that cannot be compared confidently. We advocate for an approach that embraces, rather than eliminates, clock synchronization errors. Instead of attempting to remove the error from a timestamp, \systemname{}, our proposed system, leverages a statistical model to compare two noisy timestamps probabilistically by learning per-clock synchronization error distributions. Our preliminary statistical model computes the probability that one event precedes another by only relying on local clocks of clients. This serves as a foundation for a new relation: \emph{likely-happened-before} denoted by $\xrightarrow{p}$ where $p$ represents the probability that an event happened before another. The $\xrightarrow{p}$ relation provides a basis for ordering multiple events which are otherwise considered \emph{concurrent} by Lamport's \emph{happened-before} ($\rightarrow$) relation. We highlight various related challenges including the intransitivity of the $\xrightarrow{p}$ relation as opposed to the transitive $\rightarrow$ relation. We outline several research directions: online fair sequencing, stochastically fair total ordering, and handling byzantine clients.

  PublisherOA PDFDOI

• Beyond Lamport, Towards Probabilistic Fair Ordering

  2025-11-17

  article

  A growing class of applications demands fair ordering of events, which ensures that events generated earlier are processed before later events. However, achieving such sequencing is challenging due to the inherent errors in clock synchronization: two events at two clients generated close together may have timestamps that cannot be compared confidently. We advocate for an approach that embraces, rather than eliminates, clock synchronization errors. Instead of attempting to remove the error from a timestamp, Tommy, our proposed system, leverages a statistical model to compare two noisy timestamps probabilistically by learning per-clock synchronization error distributions. Our preliminary statistical model computes the probability that one event precedes another by only relying on local clocks of clients. This serves as a foundation for a new relation: likely-happened-before denoted by →p where p represents the probability that an event happened before another. The →p relation provides a basis for ordering multiple events which are otherwise considered concurrent by Lamport's happened-before (→) relation. We highlight various related challenges including the intransitivity of the →p relation as opposed to the transitive → relation. We outline several research directions: online fair sequencing, stochastically fair total ordering, and handling byzantine clients.

  PublisherDOI

• Network Support For Scalable And High Performance Cloud Exchanges

  2025-08-27 · 1 citations

  articleOpen access

  Financial exchanges are migrating to the public cloud, but the best-effort nature of the cloud fabric is at odds with the stringent networking requirements of the exchanges. We present Onyx, a system for meeting such requirements which uses many well-studied techniques in a new context as well as introduces new techniques that enable a scalable cloud financial exchange. An overlay multicast tree is used to disseminate data to 1000 participants with ≤ 1 μs difference in data reception time between any two participants, crucial for maintaining fair competition. Several techniques for mitigating latency variance are introduced. Onyx also presents a scheduling policy for trade orders that enhances an exchange's performance and gracefully services bursty traffic. Onyx achieves ≈50% lower latency than the AWS multicast service [1]. Onyx outperforms an existing system, CloudEx [2] in terms of supported number of participants, exchange's throughput and multicast latency. Onyx's techniques can be applied to other existing systems (e.g., DBO) to enhance their performance.

  PublisherOA PDFDOI

• TraceWeaver: Distributed Request Tracing for Microservices Without Application Modification

  2024-07-31 · 4 citations

  articleOpen access

  Monitoring and debugging modern cloud-based applications is challenging since even a single API call can involve many interdependent distributed microservices. To provide observability for such complex systems, distributed tracing frameworks track request flow across the microservice call tree. However, such solutions require instrumenting every component of the distributed application to add and propagate tracing headers, which has slowed adoption. This paper explores whether we can trace requests without any application instrumentation, which we refer to as request trace reconstruction. To that end, we develop TraceWeaver, a system that incorporates readily available information from production settings (e.g., timestamps) and test environments (e.g., call graphs) to reconstruct request traces with usefully high accuracy. At the heart of TraceWeaver is a reconstruction algorithm that uses request-response timestamps to effectively prune the search space for mapping requests and applies statistical timing analysis techniques to reconstruct traces. Evaluation with (1) benchmark microservice applications and (2) a production microservice dataset demonstrates that TraceWeaver can achieve a high accuracy of ~90% and can be meaningfully applied towards multiple use cases (e.g., finding slow services and A/B testing).

  PublisherDOI

• Adaptive Heuristics for Scheduling DNN Inferencing on Edge and Cloud for Personalized UAV Fleets

  arXiv (Cornell University) · 2024-12-30

  preprintOpen access

  Drone fleets with onboard cameras coupled with computer vision and DNN inferencing models can support diverse applications. One such novel domain is for one or more buddy drones to assist Visually Impaired People (VIPs) lead an active lifestyle. Video inferencing tasks from such drones can help both navigate the drone and provide situation awareness to the VIP, and hence have strict execution deadlines. We propose a deadline-driven heuristic, DEMS-A, to schedule diverse DNN tasks generated continuously to perform inferencing over video segments generated by multiple drones linked to an edge, with the option to execute on the cloud. We use strategies like task dropping, work stealing and migration, and dynamic adaptation to cloud variability, to guarantee a Quality of Service (QoS), i.e. maximize the utility and the number of tasks completed. We also introduce an additional Quality of Experience (QoE) metric useful to the assistive drone domain, which values the frequency of success for task types to ensure the responsiveness and reliability of the VIP application. We extend our DEMS solution to GEMS to solve this. We evaluate these strategies, using (i) an emulated setup of a fleet of over 80 drones supporting over 25 VIPs, with real DNN models executing on pre-recorded drone video streams, using Jetson Nano edges and AWS Lambda cloud functions, and (ii) a real-world setup of a Tello drone and a Jetson Orin Nano edge generating drone commands to follow a VIP in real-time. Our strategies present a task completion rate of up to 88%, up to 2.7x higher QoS utility compared to the baselines, a further 16% higher QoS utility while adapting to network variability, and up to 75% higher QoE utility. Our practical validation exhibits task completion of up to 87% for GEMS and 33% higher total utility of GEMS compared to edge-only.

  PublisherOA PDFDOI

Frequent coauthors

• Scott Shenker

  University of California, Berkeley

  43 shared

• Sylvia Ratnasamy

  Google (United States)

  37 shared

• Justine Sherry

  Carnegie Mellon University

  11 shared

• Prateesh Goyal

  7 shared

• P. Brighten Godfrey

  University of Illinois Urbana-Champaign

  7 shared

• Hari Balakrishnan

  IIT@MIT

  6 shared

• Arvind Krishnamurthy

  Stanford University

  5 shared

• Aurojit Panda

  New York University

  5 shared

Labs

• Siebel School of Computing and Data SciencePI

Education

• Ph.D., Computer Science

  University of Illinois at Urbana-Champaign

  2006

• M.S., Computer Science

  University of Illinois at Urbana-Champaign

  2001

• B.S., Computer Science

  University of Illinois at Urbana-Champaign

  1999

Awards & honors

• NSF CAREER award to improve 5G networks with 'slicing'
• Facebook Research Award