Edge Computing

Edge Computing

Established: October 29, 2008




Edge computing is where compute resources, ranging from credit-card-size computers to micro data centers, are placed closer to information-generation sources, to reduce network latency and bandwidth usage generally associated with cloud computing. Edge computing ensures continuation of service and operation despite intermittent cloud connections. Industries ranging from manufacturing to healthcare are eager to develop real-time control systems that use machine learning and artificial intelligence to improve efficiencies and reduce cost. We are exploring this new computing paradigm by identifying and addressing emerging technology and business model challenges.

A Brief History of Edge Computing

In October 2008, Microsoft Research invited colleagues from academia and industry for a daylong brainstorming session about the future of cloud computing. Edge computing was conceived during this session. Attendees included Victor Bahl (organizer, Microsoft Research), Ramón Cáceres (AT&T Labs), Nigel Davies (Lancaster University, U.K.), Mahadev Satyanarayanan (Carnegie Mellon University), and Roy Want (Intel Research). Following this meeting, we published the first paper on this topic, in IEEE Pervasive Computing (November 1, 2009) titled: The Case for VM-based Cloudlets in Mobile Computing.

The blog Why a Cloudlet Beats the Cloud for Mobile Apps (December 13, 2009) was the first article to cover our ideas. In it are described two projects, Cloudlets, a joint project of Microsoft and Carnegie Mellon; and MAUI (Mobile Assistance Using Infrastructure), a Microsoft Research project. In Cloudlets, we investigated fast virtual machine (VM) synthesis on the edge; in MAUI, we explored a .NET programming model for computational offloads to the edge. Many of the ideas we explored have withstood the test of time. For example, the decisions about which methods could be offloaded and which needed to be processed locally to guard against disconnected operation. The papers for the Cloudlet and the MAUI projects have been cited over 4,500 times.

Since then, having made the case for edge computing in the research community (see Faculty Summit keynote), industry (see: Network World interview) and internally in Microsoft (see Intelligent Edge), we have been focusing on live-video analytics as the “killer” app for edge computing. You can read all about it in a separate project page.

Listen to Victor Bahl’s Podcast,  A brief history of networking (and a bit about the future too), where he shares some fascinating stories and gives an inside look at Edge Computing.

The Intelligent Edge

Microsoft product groups coined the term The Intelligent Edge. The Intelligent Edge is a capability that enables Microsoft customers to enjoy a seamless experience and compute capabilities wherever their data exists—in the cloud or offline. Microsoft is making it easier for developers to build apps that use edge technology, by open sourcing the Azure IoT Edge Runtime, which allows customers to modify the runtime and customize applications.

Types of Edges

Learn more about the Intelligent Edge.

Recent Activity

Keynote at the Cyber-Physical Systems & Internet-of-Things Week 2019 in Montreal, Canada (Victor Bahl, April 16, 2019)
• Plenary on “AI/ML for Communication Networks” at the ICNC 2019 conference in Honolulu, HI (Victor Bahl, Feb. 19, 2019)
• “From Intelligent Cloud to Intelligent Edge” Workshop at UIUC CSL (Organizers: Shadi Noghabi & Yuanchao Shu, Feb. 7, 2019)
• Opening plenary at the 14th annual UIUC CSL Student Conference (Victor Bahl, Feb. 6, 2019)
Working on the edge, Nature Electronics Q/A with Victor Bahl (Jan. 15, 2019)
Arjmand Samuel, Principle Program Manager of Azure IoT Edge, gave the keynote at ACM/IEEE SEC 2018 (Oct. 26, 2018)
Blog on the 10th anniversary of edge computing by Victor Bahl (Oct. 19, 2018)
Shadi Noghabi from UIUC joins Microsoft Research and our project as a full time Researcher (Oct. 1, 2018)
Ganesh Ananthanarayanan named program co-chair of ACM/IEEE SEC 2019 in Washington D.C (Sept. 9, 2018)
• Our VideoEdge paper accepted with impressive review scores in ACM/IEEE SEC 2018 (July 31, 2018)
• Check out our workshop: At the bleeding edge of Intelligent Edges in Redmond WA (July 27, 2018)
• Edge was a hot-topic in HotCloud 2018 in Boston, MA. Ganesh Ananthanarayanan was PC co-chair of this USENIX workshop (July 9, 2018)
• Rashmi Misra, GM IoT & AI Solution and Rushmi Malaviarachchi, Partner GPM, Windoes IoT to keynote at MSR’s upcoming workshop




  • Portrait of Zephyr Yao

    Zephyr Yao

    UC Irvine

    Summer 2018 | Azure IoT Edge availability using Docker Swarms

  • Portrait of Enrique  Saurez Apuy

    Enrique Saurez Apuy

    Georgia Tech

    Summer 2017 | Edge-based scalable real-time video analytics system

  • Portrait of Jack Kolb

    Jack Kolb

    UC Berkeley

    Summer 2017 | Declarative specifications for distributed IoT applications

  • Portrait of Shadi Noghabi

    Shadi Noghabi


    Summer 2016, Summer 2017 | Monitoring Azure IoT Edge & software offloading to the Azure cloud

  • Portrait of Chien-Chun Hung

    Chien-Chun Hung


    2016-2017 | Query optimizing for edge based streaming video analytics system

  • Portrait of Giulio Grassi

    Giulio Grassi

    Sorbonne Université

    Summer 2015 | Edge based video analytics in automobiles

  • Portrait of Kiryong Ha

    Kiryong Ha


    Summer 2014 | GPU state migration between edges and data centers by reproducing OpenGL states

  • Portrait of Aakanksha Chowdhery

    Aakanksha Chowdhery


    Summer 2014 | Edge-based wireless video surveillance

  • Portrait of Tiffany Chen

    Tiffany Chen


    Summer 2013 | Vision analytics in real-time with cloud offloads

  • Portrait of Eduardo Cuero

    Eduardo Cuero


    Summer 2009, Summer 2011 | Automatic cloud offloading & cloud augmented high-quality gaming on SmartPhones


Research Themes


From the very beginning, we have maintained that the most compelling applications for edge computing are ones that require low latency responses or ones where the network to the cloud is expensive or inadequate. In this context, we asserted that the “killer app” for edge computing is live video analytics. Along the way, other Microsoft researchers discovered precision agriculture to be a beautiful edge computing application as well. We are exploring both:

Live Video Analytics

Live Video Analytics Scenrios

Large-scale video processing is a grand challenge representing an important frontier for analytics, what with videos from factory floors, traffic intersections, police vehicles, and retail shops. Read more.

Precision Agriculture

Photography depicts Microsoft's FarmBeats technology uses AI and IoT to help increase farm productivity.

We believe that data, coupled with the farmer’s knowledge and intuition, can help increase farm productivity and help reduce costs. However, getting data from the farm is difficult since there is often no power in the field… Read more.

Offloading Computations

We have been exploring the fundamental trade-off between computation and communications to enable a new class of cpu-, gpu- and data-intensive applications that seamlessly augment the cognitive abilities of users by exploiting speech recognition, NLP, vision, machine learning, and augmented reality (Project Maui, Mobisys 2010). We have made significant progress in overcoming the energy and computation limitations of sensors, handhelds, and wearables. In subsequent research we demonstrated how important special-purpose workloads can also leverage cloud offload: for GPU-intensive rendering applications (Project Kahawai, MobiSys 2015) and deep neural network video stream processing (MCDNN, MobiSys 2016).

Project MAUI

Image of chess being played on phone to describe Project Maui

Mobile Assistance Using Infrastructure (MAUI) was the first system to demonstrate fine-grained code offload to nearby edge server(s) with minimal programmer effort. Watch the video.

Project Kahawai

Image of person on computer

Kahawai enables high-quality gaming on mobile devices, such as tablets and smartphones, by offloading a portion of the GPU computation to server-side infrastructure. Watch the video.

Geo-distributed Edge Analytics

Edge servers located in thousands of locations and managed by the same administrative entity offer powerful computing resources for cloud providers. Our research on low-latency edge analytics explores how best to use these resources. For example, the old approach of aggregating all the data from sensors to a single data center negatively impacts the timeliness of the analytics. But, running queries over geo-distributed inputs using the current intra-DC analytics frameworks also results in high query response times because these frameworks cannot cope with the relatively low and variable capacity of the WAN links. Our Iridium system (SIGCOMM 2015) provides low latency geo-distributed analytics by optimizing placement of both data and tasks of the queries. Follow-on work (CLARINET, OSDI 2016) considers WAN links with heterogeneous and modest bandwidths, unlike intra-datacenter networks, when deriving query execution plans across the cloud and edge servers.

Edge Availability

While edge computing is touted as the next big evolution in cloud computing, little is said about edge availability. Customers use the cloud for the operation of their businesses and they expect the cloud to be available 24x7x365. Should they not expect the same from their edge servers? The problem of edge availability is like the truck-roll problem, that is, with tens of thousands of edges in different locations, what happens when an edge goes down? Whose responsibility is it to fix it? Should the cloud provider send some expert to take care of it? The answer to these questions is closely related to the business model of edge computing. We are investigating these questions and building technology that provides guarantees to the edge computing users, guarantees that are at par to the service level agreements (SLA) that cloud providers offer. We are also investigating whether it makes sense to use the same metrics for edge availability as we have been using for cloud availability.

ML for Edge

Our colleagues in Microsoft Research India are developing a library of efficient machine learning (ML) algorithms that can run on resource-constrained edge and IoT devices ranging from the Arduino to the Raspberry Pi. The thesis is that IoT devices and sensors don’t have to be “dumb” i.e. they can do more than just sense their environment and transmit their readings to the cloud, which is where the traditional decision making intelligence resides. Instead, an alternative paradigm is where even tiny, resource-constrained IoT devices run ML algorithms locally. This enables important scenarios overcoming concerns around connectivity to the cloud, latency, energy, privacy, and security. Read more


Cloud providers, such as Microsoft, have two types of edges: On-net edges or Off-net edges. On-net edges are generally easier to operate, manage and maintain as they are on the cloud provider’s network. In contrast, Off-net edges are connected to the cloud via the Internet, which may include several ISPs. Managing and operating such edges can be challenging due to the vagaries of the Internet. We are investigating problems to improve the network connectivity to our Off-net edges and the networking between the edges and sensors. Furthermore, edges provide us an opportunity to (re) investigate old ideas around low-latency, secure, overlay networking.


Cloud companies spend large amounts of money to physically secure their millions of servers located in their many data centers. In contrast, edge computing servers may or may not be physically secured. This opens the possibility of malicious attacks on the edge and cloud infrastructure. While a lot has been done to physically secure assets in the cloud, we are investigating techniques to do the same for our edge assets. Security and trust require authenticity and integrity, so we are investigating the use of sensors and specialized hardware in combination with new programming abstractions and system support for building secure and trusted edges. This research builds on our prior work on trusted sensors (MobiSys 2012, ASPLOS ’14) and recent product offering (Azure Sphere).

Cloud Services

Before the dawn of edge computing, which has brought about a major cloud computing paradigm shift in the industry, we developed, deployed and operated a cloud service-store under the banner of Project Hawaii. With it we empowered developers to build sophisticated, cloud-enhanced applications for their resource constraint devices. Our cloud service store included a variety of services including: optical character recognition, speech-to-text, path prediction, social computing, language translation, relay, rendezvous, etc. for Windows, Android, & IOS devices. Over 60 universities used our services as a teaching aid for senior and graduate-level mobile + cloud computing courses. 2015 onward similar cloud services were commercialized by all major cloud providers under the banner of cognitive services. Check out Azure cognitive services. Historically speaking, Project Hawaii was the first to show how cloud/edge can be used in conjunction with a resource-constraint mobile device to augment human abilities.

Project Hawaii

Project Hawaii group photo

The Project Hawaii team – BACK ROW (left to right): Gleb Krivosheev, Philip Fawcett, Ronnie Chaiken; FRONT ROW (left to right): Arjmand Samuel, Jitu Padhye, Alec Wolman, Victor Bahl. Read more.


Gallery image of Project Hawaii

A utility tool developed by a student for on-the-go translations. Project Hawaii’s OCR & S2T services, and Bing Translator were used. Check out our Gallery for dozens of student created featured projects. Read more.


April 15, 2019 | Cyber-Physical Systems and Internet-of-Things Week 2019
Here’s whey you should embrace edge computing | Victor Bahl

February 6, 2019 | Fourteenth Annual University of Illinois Urbana Champaign CSL Student Conference
Better together: the intelligent edge + the intelligent cloud | Victor Bahl

August 20, 2018 | SIGCOMM Workshop on Big Data Analytics and Machine Learning
Democratizing Video Analytics – The quest for the holy trinity of low latency, low cost, and high accuracy | Ganesh Ananthanarayanan

August 20, 2018 | SIGCOMM Workshop on Mobile Edge Communications
Edge computing: a historical perspective & direction (slides) | Victor Bahl

July 27, 2018 | Bleeding Edge of Intelligent Edge
Edge computing: 10 years and counting (video) | Victor Bahl

October 23, 2017 | IEEE Fourteenth International Conference on Mobile Ad Hoc and Sensor Systems
Live Video Analytics (video) | Victor Bahl

October 15, 2017 | Third IEEE International Conference on Collaboration and Internet Computing
Democratizing Video Analytics | Victor Bahl

September 29, 2017 | Emerging Topics in Computing Symposium, University at Buffalo Computer Systems Engineering Dept. 50th Anniversary
Live Video Analytics the Perfect Edge Computing Application | Victor Bahl

December 10, 2016 | IEEE International Performance Computing and Communications Conference
Democratization of Streaming Video Analytics & the Emergence of Edge Computing (video) | Victor Bahl

May 13, 2015 | Devices and Networking Summit 2015
Cloud 2020: Emergence of Micro Data Centers for Latency Sensitive Computing | Victory Bahl

March 10, 2015 | IEEE Wireless Communications and Networking Conference (WCNC) 2015
Cloud 2020: Emergence of Micro Data Centers (Cloudlets/Edges) for Latency Sensitive Computing (slides) | Victor Bahl

February 19, 2015 | IEEE International Conference on Computing, Networking and Communications (ICNC) 2015
Cloud 2020: Emergence of Micro Data Centers (Cloudlets/Edges) for Latency Sensitive Computing (slides) | Victor Bahl

June 27, 2014 | MSR Summer School on Advances in Wireless Networking
Cloudlets for mobile computing (slides) | Victor Bahl

November 22, 2013 | 2nd IEEE International Conference on Cloud Networking (Cloudnet) 2013
Cloud 2020: Emergence of Micro Data Centers for Latency Sensitive Computing | Victor Bahl


Events (we helped organize)

Research Community Service


2017 – Present | Associate Editor (Victor Bahl): ACM Transactions on Internet of Things
2017 – Present | Associate Editor (Victor Bahl): IEEE Transactions on Service Computing
2013 – Present | Advisory Board Member (Victor Bahl): IEEE Internet of Things Journal
2007-2018 | Editorial Board Member (Victor Bahl): Foundations and Trends® in Networking

Conferences & Workshops

2019 | Program Committee Co-Chair (Ganesh Ananthanarayanan): The Fourth ACM/IEEE Symposium on Edge Computing
2018 | Program Committee Co-Chair (Victor Bahl): The Third ACM/IEEE Symposium on Edge Computing
2018 | Invited Speaker (Ganesh Ananthanarayanan): IEEE Sarnoff Symposium
2018 | Program Committee Co-Chair (Ganesh Ananthanarayanan<): 10th USENIX Workshop on Hot Topics in Cloud Computing
2016 | Advisor & Steering Committee Member (Victor Bahl): NSF Workshop on Grand Challenges in Edge Computing
2014 – Present | (Founding) Steering Committee Member (Victor Bahl): ACM/IEEE Symposium on Edge Computing
2010-2015 | (Founding) Steering Committee Member (Victor Bahl): ACM workshop on Mobile Cloud Computing and Services (MCS)

Distinguished Seminars (on Edge Computing)

September 2018 | University of Southern California, Los Angles, CA | Ganesh Ananthanarayanan
September 11, 2017 | Rice University, Houston, Texas | Victor Bahl
April 28, 2017 | Washington University St. Louis, St. Louis, Missouri | Victor Bahl
December 17, 2014 | Sorbonne Université, Paris, France | Victor Bahl
November 20, 2014 | University College of London, London, U.K.| Victor Bahl
October 3, 2014 | Yale University, New Haven, Connecticut | Victor Bahl

Panels (on Edge Computing)

February 19, 2019 | AI/ML for Communication Networks | IEEE Intl. Conf. on Computing, Networking & Communication | Honolulu, Hawaii, USA | Victor Bahl
October 12, 2017 | Enabling Technologies for Edge Computing | Second ACM/IEEE Symposium on Edge Computing | San Jose, California, USA | Victor Bahl

Microsoft Blog

Microsoft Azure enables a new wave of edge computing. Here’s how.

We are going through a technology transformation that is unlocking new scenarios that were simply not possible before. Smart sensors and connected devices are breathing new life into industrial equipment from factories to farms, smart cities to homes, while new devices are increasingly cloud connected by default – whether it’s a car or a refrigerator.

Microsoft Azure Blog | September 24, 2018

Advancing the future shape of systems research at the Faculty Summit 2018

The 19th Microsoft Research Faculty Summit yet again demonstrated its unique place in the world of computer science in gathering thought leaders, state-of-the-art ideas, new products and a sense of the possible under one roof as industry and leading academic researchers came together to share vision and purpose.

Microsoft Research Blog | August 17, 2018

A brief history of networking (and a bit about the future too) with Dr. Victor Bahl

If your idea of a great job includes pursuing untethered research, shepherding brilliant researchers and helping shape the long-term strategy of one of the largest tech companies in the world… oh, and also publishing prolifically, authoring patents, winning awards and speaking around the world…

Microsoft Research Podcast | August 15, 2018

Microsoft will invest $5 billion in IoT. Here’s why.

Today, we are announcing that we will invest $5 billion in the Internet of Things over the next four years. The reason we are doing this is simple: Our goal is to give every customer the ability to transform their businesses, and the world at large, with connected solutions.

Microsoft IoT Blog | April 4, 2018

Unpacking the future of IoT at the 2017 Summer Institute

Leading researchers from across industry and academia are meeting this week in Snoqualmie, Washington, to hash out their vision for this interconnected world and a plan to turn this vision into reality.

Microsoft Research Blog | July 31, 2017

Jammin’ with Cloud-Enabled Apps

Enter your Windows Phone or Windows 8 app in the Project Hawaii Mobile Code Jam Challenge. But you’d better act quickly—the Code Jam is featured in the upcoming IEEE Consumer Communications & Networking Conference…

Microsoft Research Blog | October 25, 2012

Project Hawaii XAPFest 2011 Awards Hawaiian Trip

Working as an intern at Microsoft has many benefits, but a vacation in Hawaii is not usually one of them.All of the presentations we saw this year were very impressive, which made it tough to pick a final winner.

Microsoft Research Blog | September 9, 2011

The Cloud, to go: Project Hawaii

An important component of Project Hawaii is engaging with universities around the world. This enables professors and students to work on projects reflective of the increasingly interconnected relationship between mobile devices and the cloud.

Microsoft Research Blog | September 21, 2010

In the News

What is edge computing, and why does it matter to you?

The words “intelligent edge” or “edge computing” sound, well, cutting-edge and may evoke a feeling of pushing the technological envelope even for those who don’t know what the terms mean. At the core of edge

Windows Central | January 28, 2019

How we created edge computing

Edge computing processes data on infrastructure that is located close to the point of data creation. Mahadev Satyanarayanan recounts how recognition of the potential limitations of centralized, cloud-based processing led to this new approach to computing.

Nature Electronics | January 16, 2019

Working on the edge

I think of edge computing as a new computing paradigm in which a device, ranging from a small sensor or actuator to a virtual reality headset, offloads its computations to a processor, which is part of another distinct, independent device, over a network.

Nature Electronics | January 16, 2019

Microsoft supports Carnegie Mellon’s edge computing research

Microsoft and Carnegie Mellon were already pretty close, their research organizations have been collaborating on edge computing since 2008. As part of this announcement, Microsoft is also joining the Open Edge Computing Initiative, which funds the Living Edge Lab

Geekwire | November 14, 2018

Could AI end car accidents?

Imagine for a moment that, every week, four to five commercial airplanes crashed in America. In reality, a similar number of people die per week in traffic accidents, but, for the most part, those deaths don’t resonate with us in the same way.

Technical.ly | September 6, 2017

Microsoft Build 2017 buzzword bingo: On the edge

What Microsoft is doing in the edge computing/distributed computing space may be a big topic at this week’s Build 2017 developer conference. Here are a few clues as to why.

ZDNet | September 6, 2017

Microsoft researcher: Why Micro Datacenters really matter to mobile's future

Microsoft Research distinguished scientist Victor Bahl has been spreading the word about Micro Datacenters, also known by the adorable name “cloudlets”, as a key concept for optimizing the performance and usefulness of mobile and other networked devices via the cloud.

Network World | September 3, 2015

Intelligent cameras can put an end to always-on surveillance

Many cities are packed with cameras pointlessly recording everything they see, but smart algorithms could allow them to keep only footage that matters. It was being in London that got Victor Bahl started. The UK is awash with millions of closed-circuit TV cameras, and as Bahl walked around the city, he realised that much of what the cameras record would never be of interest to anyone.

New Scientist | September 2, 2015

Why a Cloudlet Beats the Cloud for Mobile Apps

Sure, you know cloud computing. You also know a bit about so-called “private clouds,” which enterprises and government agencies are exploring as an option to combine the power and scale of virtualized cloud architectures with security and control over data. But what do you know of Cloudlets? They may just be a key to the future of mobile computing.

Shepherd’s Pi | December 13, 2009

The Case for VM-based Cloudlets in Mobile Computing

Resource poverty is a fundamental constraint that severely limits the class of applications that can be run on mobile devices. This constraint is not just a temporary limitation of current technology, but is intrinsic to mobility. In this paper, we put forth a vision of mobile computing that breaks free of this fundamental constraint.

IEEE Pervasive Computing | November 1, 2009