Explanipedia

A House United Within Itself: SLO-Awareness for On-Premises Containerized ML Inference Clusters via Faro Open

Beomyeol Jeon, Chen Wang, Diana Arroyo, Alaa Youssef, Indranil Gupta · 2025

This paper tackles the challenge of running multiple ML inference jobs\n(models) under time-varying workloads, on a constrained on-premises production\ncluster. Our system Faro takes in latency Service Level Objectives (SLOs) for\neach job…

Counting How the Seconds Count: Understanding Algorithm-User Interplay in TikTok via ML-driven Analysis of Video Content Open

Maleeha Masood, Sampath Kannan, Zikun Liu, Deepak Vasisht, Indranil Gupta · 2025

Short video streaming systems such as TikTok, Youtube Shorts, Instagram Reels, etc have reached billions of active users. At the core of such systems is a (proprietary) recommendation algorithm which recommends a sequence of videos to each…

Topology and density control of satellite-defined photonic quantum networks Open

Xiaojuan Ma, Indranil Gupta, Yan Wang · 2025

Creating photonic quantum networks by distributing entangled photon pairs through low Earth orbit satellites is a promising technological advance. A recent work has studied a model of such networks and reported the presence of a heavy-tail…

There is More Control in Egalitarian Edge IoT Meshes Open

Anna Karanika, Rui Yang, Xiaojuan Ma, Jiangran Wang, Shalni Sundram , et al. · 2025

Transmitting, Fast and Slow: Scheduling Satellite Traffic through Space and Time Open

Yu Tao, Maleeha Masood, Indranil Gupta, Deepak Vasisht · 2023

Artifact for our Mobicom 2023 paper

Dirigo: Self-scaling Stateful Actors For Serverless Real-time Data Processing Open

Le Xu, Divyanshu Saxena, Neeraja J. Yadwadkar, Aditya Akella, Indranil Gupta · 2023

We propose Dirigo, a distributed stream processing service built atop virtual actors. Dirigo achieves both a high level of resource efficiency and performance isolation driven by user intent (SLO). To improve resource efficiency, Dirigo ad…

A User-Centric Evaluation of Smart Home Resolution Approaches for Conflicts Between Routines Open

Ali Zaidi, Rui Yang, Vinay Koshy, Camille Cobb, Indranil Gupta , et al. · 2023

With the increasing adoption of smart home devices, users rely on device automation to control their homes. This automation commonly comes in the form of smart home routines, an abstraction available via major vendors. Yet, questions remai…

CoMesh: Fully-Decentralized Control for Sense-Trigger-Actuate Routines in Edge Meshes Open

Anna Karanika, Rui Yang, Xiaojuan Ma, Jiangran Wang, Shalni Sundram , et al. · 2023

While mesh networking for edge settings (e.g., smart buildings, farms, battlefields, etc.) has received much attention, the layer of control over such meshes remains largely centralized and cloud-based. This paper focuses on applications w…

Transactional Panorama: A Conceptual Framework for User Perception in Analytical Visual Interfaces Open

Dixin Tang, Alan Fekete, Indranil Gupta, Aditya Parameswaran · 2023

Many tools empower analysts and data scientists to consume analysis results in a visual interface, such as a dashboard. When the underlying data changes, these results need to be updated, but this update can take a long time -- all while t…

Baechi: Fast Device Placement of Machine Learning Graphs Open

Beomyeol Jeon, Linda Cai, Chirag Shetty, Pallavi Srivastava, Jintao Jiang , et al. · 2023

Machine Learning graphs (or models) can be challenging or impossible to train when either devices have limited memory, or models are large. To split the model across devices, learning-based approaches are still popular. While these result …

Medley: A Membership Service for IoT Networks Open

Rui Yang, Jiangran Wang, J. Hu, Shichu Zhu, Yifei Li , et al. · 2022

Efficient and correct operation of an IoT network requires the presence of a failure detector and membership protocol amongst the IoT nodes. This paper presents a new failure detector for IoT settings wherein nodes are connected via a wire…

ZenoPS: A Distributed Learning System Integrating Communication Efficiency and Security Open

Cong Xie, Oluwasanmi Koyejo, Indranil Gupta · 2022

Distributed machine learning is primarily motivated by the promise of increased computation power for accelerating training and mitigating privacy concerns. Unlike machine learning on a single device, distributed machine learning requires …

Banyan: A Scoped Dataflow Engine for Graph Query Service Open

Su Li, Xiaoming Qin, Zichao Zhang, Rui Yang, Le Xu , et al. · 2022

Graph query services (GQS) are widely used today to interactively answer graph traversal queries on large-scale graph data. Existing graph query engines focus largely on optimizing the latency of a single query. This ignores significant ch…

Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo Open

Le Xu, Shivaram Venkataraman, Indranil Gupta, Luo Mai, Rahul Potharaju · 2020

Resource provisioning in multi-tenant stream processing systems faces the dual challenges of keeping resource utilization high (without over-provisioning), and ensuring performance isolation. In our common production use cases, where strea…

Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing\n with Cameo Open

Le Xu, Shivaram Venkataraman, Indranil Gupta, Luo Mai, Rahul Potharaju · 2020

Resource provisioning in multi-tenant stream processing systems faces the\ndual challenges of keeping resource utilization high (without\nover-provisioning), and ensuring performance isolation. In our common\nproduction use cases, where st…

CSER: Communication-efficient SGD with Error Reset Open

Cong Xie, Shuai Zheng, Oluwasanmi Koyejo, Indranil Gupta, Mu Li , et al. · 2020

The scalability of Distributed Stochastic Gradient Descent (SGD) is today limited by communication bottlenecks. We propose a novel SGD variant: Communication-efficient SGD with Error Reset, or CSER. The key idea in CSER is first a new tech…

Home, SafeHome: Smart Home Reliability with Visibility and Atomicity Open

Shegufta Bakht Ahsan, Rui Yang, Shadi A. Noghabi, Indranil Gupta · 2020

Smart environments (homes, factories, hospitals, buildings) contain an increasing number of IoT devices, making them complex to manage. Today, in smart homes where users or triggers initiate routines (i.e., a sequence of commands), concurr…

Medley Open

Rui Yang, Shichu Zhu, Yifei Li, Indranil Gupta · 2019

Efficient and correct operation of an IoT network requires the presence of a failure detector and membership protocol amongst the IoT nodes. This paper presents a new failure detector for IoT settings where nodes are connected via a wirele…

Local AdaAlter: Communication-Efficient Stochastic Gradient Descent with Adaptive Learning Rates Open

Cong Xie, Oluwasanmi Koyejo, Indranil Gupta, Haibin Lin · 2019

When scaling distributed training, the communication overhead is often the bottleneck. In this paper, we propose a novel SGD variant with reduced communication and adaptive learning rates. We prove the convergence of the proposed algorithm…

Read atomic transactions with prevention of lost updates: ROLA and its formal analysis Open

Si Liu, Peter Csaba Ölveczky, Qi Wang, Indranil Gupta, José Meseguer · 2019

Designers of distributed database systems face the choice between stronger consistency guarantees and better performance. A number of applications only require read atomicity (RA) (either all or none of a transaction’s updates are visible …

Zeno++: Robust Fully Asynchronous SGD Open

Cong Xie, Sanmi Koyejo, Indranil Gupta · 2019

We propose Zeno++, a new robust asynchronous Stochastic Gradient Descent~(SGD) procedure which tolerates Byzantine failures of the workers. In contrast to previous work, Zeno++ removes some unrealistic restrictions on worker-server communi…

SLSGD: Secure and Efficient Distributed On-device Machine Learning Open

Cong Xie, Sanmi Koyejo, Indranil Gupta · 2019

We consider distributed on-device learning with limited communication and security requirements. We propose a new robust distributed optimization algorithm with efficient communication and attack tolerance. The proposed algorithm has prova…

Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation Open

Cong Xie, Sanmi Koyejo, Indranil Gupta · 2019

Recently, new defense techniques have been developed to tolerate Byzantine failures for distributed machine learning. The Byzantine model captures workers that behave arbitrarily, including malicious and compromised workers. In this paper,…

Asynchronous Federated Optimization Open

Cong Xie, Oluwasanmi Koyejo, Indranil Gupta · 2019

Federated learning enables training on a massive number of edge devices. To improve flexibility and scalability, we propose a new asynchronous federated optimization algorithm. We prove that the proposed approach has near-linear convergenc…

Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance Open

Cong Xie, Oluwasanmi Koyejo, Indranil Gupta · 2018

We present Zeno, a technique to make distributed machine learning, particularly Stochastic Gradient Descent (SGD), tolerant to an arbitrary number of faulty workers. Zeno generalizes previous results that assumed a majority of non-faulty n…

Zeno: Distributed Stochastic Gradient Descent with Suspicion-based\n Fault-tolerance Open

Cong Xie, Oluwasanmi Koyejo, Indranil Gupta · 2018

We present Zeno, a technique to make distributed machine learning,\nparticularly Stochastic Gradient Descent (SGD), tolerant to an arbitrary number\nof faulty workers. Zeno generalizes previous results that assumed a majority of\nnon-fault…

Zeno: Byzantine-suspicious stochastic gradient descent. Open

Cong Xie, Oluwasanmi Koyejo, Indranil Gupta · 2018

We propose Zeno, a new robust aggregation rule, for distributed synchronous Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The key idea is to suspect the workers that are potentially malicious, and use a ranking…

Phocas: dimensional Byzantine-resilient stochastic gradient descent Open

Cong Xie, Oluwasanmi Koyejo, Indranil Gupta · 2018

We propose a novel robust aggregation rule for distributed synchronous Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The attackers can arbitrarily manipulate the data transferred between the servers and the wor…

Popular is cheaper Open

Mainak Ghosh, Ashwini Raina, Le Xu, Xiaoyao Qian, Indranil Gupta , et al. · 2018

This paper targets the growing area of interactive data analytics engines. We present a system called Getafix that intelligently decides replication levels and replica placement for data segments, in a way that is responsive to changing po…

Service fabric Open

Gopal Kakivaya, Xun Lu, Richard Hasha, Shegufta Bakht Ahsan, Todd Pfleiger , et al. · 2018

We describe Service Fabric (SF), Microsoft's distributed platform for building, running, and maintaining microservice applications in the cloud. SF has been running in production for 10+ years, powering many critical services at Microsoft.…

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