The biennial ACM Symposium on Operating Systems Principles is the world's premier forum for researchers, developers, programmers, and teachers of computer systems technology. Academic and industrial participants present research and experience papers that cover the full range of theory and practice of computer systems software To drive manufacturing innovation, spur job creation, and enhance manufacturing competitiveness, the Advanced Manufacturing Office (AMO) supports applied research, development, and demonstration in crosscutting, platform technologies to decarbonize the industrial sector and promote the development and growth of a resilient manufacturing sector for multiple emerging clean energy fields Oct 29, · Highly Adaptive and Resilient Edges. Continuity of service is a must-have attribute in mission- and safety- critical edge computing applications. For example, in telecommunication networks interruption of communication services is unexceptionable; in oil rigs where continuous monitoring of the safety of on-site workers and the health of multi-million-dollar equipment is a must have; in
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Edge computing is where compute resources, ranging from credit-card-size computers to micro data centers, research papers in operating systems, 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. On October 29,we invited colleagues from academia and industry for a day-long brainstorming session about the future of cloud computing. Edge computing was conceived during those discussions.
Following this meeting, we published the first paper on this topic, in IEEE Pervasive Computing November 1, titled: The Case for VM-based Cloudlets in Mobile Computing. The blog Why a Cloudlet Beats the Cloud for Mobile Apps December 13, was the first article to cover our ideas. In it are described two projects, Cloudlets, research papers in operating systems joint project of Microsoft and Carnegie Mellon University; and MAUI Mobile Assistance Using Infrastructurea 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, guarding against network disconnections, incorporating computing versus communications tradeoff, deciding which methods to offload and which to process locally, research papers in operating systems.
The papers describing Cloudlet and MAUI have been cited over 5, times. Click here for a colorful description of a ten research papers in operating systems look-back. You can read all about it in a separate project page. 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.
Learn more about the Intelligent Edge. Summer Azure IoT Edge availability using Docker Swarms. Summer Declarative specifications for distributed IoT applications. Summer GPU state migration between edges and data centers by reproducing OpenGL states.
Summer Edge-based wireless video surveillance. Summer Vision analytics in real-time with cloud offloads. 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. Along the way, other Microsoft researchers discovered precision agriculture to be a beautiful edge computing application as well.
We are exploring both:. 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. However, getting data from the farm is difficult since there is often no power in the field… Read more. Continuity research papers in operating systems service is a must-have attribute in mission- and safety- critical edge computing applications.
For example, in telecommunication networks interruption of communication services is unexceptionable; in oil rigs link where continuous monitoring of the safety of on-site workers and the health of multi-million-dollar equipment is a must have; in manufacturing, constantly looking-out for production errors that may lead to defective items is also a must-have.
Downtime of edge computing servers, beyond an acceptable threshold, can result in accidents and significant financial loss. The challenge then is to keep research papers in operating systems operational, even when local technicians are absent.
This requires edge servers to be up and running 24x7xwhich is particularly hard when edge-cloud connectivity is unreliable and expensive, research papers in operating systems.
We are developing adaptive and resilient software solutions that enable edge clusters to run continuously. Our goal is to develop a software-based system and architecture that can help keep operations alive by healing the system automatically in the face of machine failures, network disconnections, dynamic application loads, or changes in capacity.
Towards this north star, we have several ongoing projects that take on these problems. HybridKube is a Kubernetes extension for optimal placement of applications across a edge-cloud environment. 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 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 and deep neural network video stream processing MCDNN, MobiSys 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. 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.
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 research papers in operating systems 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, research papers in operating systems.
Our Iridium system SIGCOMM provides research papers in operating systems latency geo-distributed analytics by optimizing placement of both data and tasks of the queries. Follow-on work CLARINET, OSDI considers WAN links with heterogeneous and modest bandwidths, unlike intra-datacenter networks, when deriving query execution plans across the cloud and edge servers.
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.
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.
Cloud providers, such as Microsoft, have two types of edges: On-net edges or Off-net edges. 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, research papers in operating systems. 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.
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 research papers in operating systems of services including: optical character recognition, speech-to-text, path prediction, social computing, language translation, relay, rendezvous, etc. Check out Azure cognitive services. The Project Hawaii team — BACK ROW left to right : Gleb Krivosheev, Philip Fawcett, Ronnie Chaiken; FRONT ROW left to right : Arjmand Research papers in operating systemsJitu PadhyeAlec WolmanVictor Bahl.
A utility tool developed by a student for on-the-go translations, research papers in operating systems. Check out our Gallery for dozens of student created featured projects. Victor Research papers in operating systems. April 16, Cyber-Physical Systems and Internet-of-Things Week — Edge computing and the fourth industrial revolution Victor Bahl. August 20, 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.
July 27, Bleeding Edge of Intelligent Edge — Edge computing: 10 years and counting video Victor Bahl. October 23, IEEE Fourteenth International Conference on Mobile Ad Hoc and Sensor Systems — Live Video Analytics video Victor Bahl.
October 15, Third IEEE International Conference on Collaboration and Internet Computing — Democratizing Video Analytics Victor Bahl. September 29, Emerging Topics in Computing Symposium, University at Buffalo Computer Systems Engineering Dept. May 13, Devices and Networking Summit — Cloud Emergence of Micro Data Centers for Latency Sensitive Computing Victory Bahl. June 27, MSR Summer School on Advances in Wireless Networking — Cloudlets for mobile computing slides Victor Bahl.
November 22, 2nd IEEE International Conference on Cloud Networking Cloudnet — Cloud Emergence of Micro Data Centers for Latency Sensitive Computing Victor Bahl. How Microsoft Got into Edge Computing and Real-Time Video Analytics Microsoft has discussed how it has been developing edge computing over the last decade to create real-time processing and analytics.
Microsoft Azure Picks Dell EMC For Edge Compute And Storage Microsoft created Data Box Edge to ingest and analyze sensor data close to the sensors. It is a standard rack-mounted server chassis design with Xeon dual-processor power with a moderate amount of storage. Microsoft Targets Security, AI, Data, IoT and Edge Computing at Ignite Azure Data Box Edge is a physical network appliance, shipped by Microsoft, that helps businesses uses AI-enabled edge capabilities to analyze, process and transform data before uploading it to the cloud.
Advancing the future of society with AI and the intelligent edge The world is a computer, filled with an incredible amount of data. Bythe average person will generate 1.
Could AI end car accidents? Imagine for a moment that, every week, four to five commercial airplanes crashed in America. Here are a few clues as to why. Why a Cloudlet Beats the Cloud for Mobile Apps Sure, you know cloud computing. But what do you know of Cloudlets?
They may just be a key to the future of mobile computing. International Workshop on Mobile Cloud Computing and Services MCS September University of Southern CaliforniaLos Angles, CA Ganesh Ananthanarayanan September 11, Rice UniversityHouston, Texas Victor Bahl April 28, Washington University St. LouisSt. Louis, Missouri Victor Bahl December 17, Sorbonne UniversitéParis, France Victor Bahl November 20, University College of LondonLondon, U.
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