17 May 2022
Valencia, Spain
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Please note: This schedule is automatically displayed in Central European Standard Time, UTC +2. To see the schedule in your preferred timezone, please select from the drop-down menu to the right, above "Filter by Date."  The schedule is subject to change.

IMPORTANT NOTE: Timing of sessions and room locations are subject to change.

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Sessions [clear filter]
Tuesday, May 17

09:40 CEST

MLExray: Observability for Machine Learning on the Edge - Michelle Nguyen, Stanford
Anyone who’s ever deployed on the edge has had this hope before: “It ran perfectly on my cloud environment, it’ll surely work when I deploy it across these other different environments”. Unfortunately, much of the time, this hope falls flat. This is frustratingly true for those deploying machine learning models on the edge. These models are often painstakingly trained and fine-tuned over months and days to achieve those extra few percentage points of accuracy… Only to see performance drop by over 10% once deployed to an edge device. This session will cover common problems encountered when deploying machine learning models on the edge, and how MLExray, an open-source observability framework created at Stanford, can be used to help debug these issues when they inevitably occur. *MLExray has been accepted into MLSys 2022: https://arxiv.org/pdf/2111.04779.pdf

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Michelle Nguyen

Principal Engineer, New Relic
Michelle Nguyen is a Principal Engineer at New Relic working on Pixie. She was Pixie's first engineer and works across the stack--from Pixie's deployment mechanisms to its distributed query engine. Before Pixie, Michelle was at Trifacta helping build intuitive and interactive UIs... Read More →
avatar for Natalie Serrino

Natalie Serrino

Principal Engineer, New Relic
Natalie Serrino is a Principal Engineer at New Relic working on the Pixie open source project. She focuses on Pixie’s data layer, more specifically, the PxL language, the PxL compiler, and Pixie’s edge query engine for analytics.

Tuesday May 17, 2022 09:40 - 10:15 CEST
Pavilion 4, Room C | Level 2 | Central Forum

10:25 CEST

Introduction to Media Streaming Mesh - Giles Heron, Cisco
Media Streaming Mesh is a new concept for enabling real-time media applications in Kubernetes.  These applications are usually based on RTP rather than TCP and hence are incompatible with web-proxy based service meshes.  In addition they often use out of band TCP control channels (e.g. RTSP, SIP etc.) to negotiate UDP port numbers, and this is incompatible with the kube-proxy ClusterIP NAT. There is, however, a desire from the developers of these platforms to move away from monolithic applications towards the micro-services architecture, and to be able to leverage a cloud-native infrastructure providing load balancing, encryption and observability. Media Streaming Mesh enables this.

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avatar for Giles Heron

Giles Heron

Distinguished Engineer, Cisco
Frank is Distinguished Engineer in Cisco’s Emerging Technologies and Incubation group, driving software and architecture development for Edge platforms, solutions, associated services and applications. He is involved in several open source projects and is a Linux Foundation Networking... Read More →

Tuesday May 17, 2022 10:25 - 10:55 CEST
Pavilion 4, Room C | Level 2 | Central Forum

11:00 CEST

Empower Heterogeneous Edge AI Acceleration with K8s - Tiejun Chen & Zitong Xu, VMware
As an emerging trend in the area of edge computing, edge workloads tend to be managed and orchestrated by k8s. In the meantime, as the the top one workload of edge computing, edge AI accelerations have been enabled by different vendors' edge AI accelerators quickly, including Nvidia edge GPU series, Intel Movidius VPU, Google edge TPU, etc. Actually you can see many ASIC-based edge AI accelerators and even some high-end CPUs used in edge AI. Obviously, edge users have the challenges around empowering these heterogeneous edge AI on the edge with upstream k8s or those edge k8s versions due to missing a general unified framework on k8s. Here we'd like to introduce our unified framework as a plugin to k8s with the following key mechanisms - 1. Extend the Node Feature Discovery to detect edge AI accelerators automatically 2. Unify different vendors' device plugin to provision work nodes according to it's own edge AI accelerator 3. Introduce transparent backend accelerations to boost ML upstream frameworks such as Tensorflow, Pytorch, etc 4. Attaching remote GPU to edge In our project we provide a unified edge AI framework to help k8s empower heterogeneous AI acceleration on the edge.

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avatar for Tiejun Chen

Tiejun Chen

Technical Leader & Architect, VMware
Tiejun Chen is one technical leader and Architect from ATG, Advanced Technology Group, VMware OCTO. Before joined VMware, he ever worked at Wind River System Linux and Intel OTC - Opensource Technology Center. He ever made many presentations at kubecon China 2021, LC3 China 2017... Read More →

Zitong Xu

Developer, VMware
Zitong Xu is from VMware OCTO - ATG. She is the core developer specific to machine learning and k8s, and many experiences of building edge computing with the partners and the customers.

Tuesday May 17, 2022 11:00 - 11:30 CEST
Pavilion 4, Room C | Level 2 | Central Forum

13:35 CEST

Model Serving at the Edge Made Easier - Paul Van Eck & Animesh Singh, IBM
As edge devices consume the world, the ability to deploy AI models on these devices becomes increasingly vital. Challenges surrounding the management of numerous models across a multitude of edge hosts can be tricky. Not only that, the limited compute power that edge hosts provide makes it necessary to eliminate as much overhead as possible. These are common pain points holding users back from large scale adoption. However, with the combination of ModelMesh with technologies like K3s and MicroShift, the practicality of employing such a system has increased dramatically. As the multi-model serving backend of KServe, ModelMesh offers a small-footprint control-plane for managing model deployments on Kubernetes. Using multi-model runtimes with intelligent model loading/unloading, ModelMesh is able to make the most out of a limited set of resources while still providing the capability to serve many models for inference. Come to this talk to get the edge on edge model serving!

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avatar for Paul Van Eck

Paul Van Eck

Open Source Software Engineer, IBM
Paul Van Eck is a software engineer in the Cognitive OpenTech Group at IBM. Over the past several years, he's been actively involved in open source AI technologies such as Kubeflow and TensorFlow. Currently, Paul is focused on the supporting the deployment of ML models on Kubernetes... Read More →
avatar for Animesh Singh

Animesh Singh

Distinguished Engineer and CTO - Watson Data and AI OSS Platform, IBM
Animesh Singh is CTO and Director for IBM Watson Data and AI Open Technology, responsible for Data and AI Open Technology strategy. Creating, designing and implementing IBM’s Data and AI engine for AI and ML platform, leading IBM`s Trusted AI efforts, driving the strategy and execution... Read More →

Tuesday May 17, 2022 13:35 - 14:05 CEST
Pavilion 4, Room C | Level 2 | Central Forum

14:10 CEST

Separation of Orchestration and Computation in KubeEdge, Understand the Why and How - Harshita Sharma, Kubermatic
With the advent of 5G, the era is going through the exciting phase of bringing Cloud Computing to Edge, with Businesses working on finding an ideal solution to meet their specific demands depending on their reality, use case, and scale.You’ve probably heard about KubeEdge - Kubernetes Native Edge Computing Framework (a CNCF Incubation project).One of the features which makes it a more flexible edge computing platform is making the separation of Orchestration capability in Cloud and Compute functionality in Edge with separate cloud and edge core modules, thus making it scalable and extendable on the cloud and at the same time, the edge can work in offline mode with optimizing resource utilization making it cost-effective.In this presentation, Harshita will talk about how the KubeEgde's Cloud and Edge Components work, communicate, and why this makes it the most compelling edge computing platform currently, based on Kubernetes!

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avatar for Harshita Sharma

Harshita Sharma

Software Engineer, Kubermatic
Harshita Sharma is currently a Kubernetes and Golang Developer at Kubermatic working on multi cluster management and automation - Kubermatic Kubernetes Platform https://github.com/kubermatic/kubermatic. Harshita is an OpenSource Enthusiast and has an interest in Developer Advocacy... Read More →

Tuesday May 17, 2022 14:10 - 14:40 CEST
Pavilion 4, Room C | Level 2 | Central Forum

14:45 CEST

Edge Kubernetes and Healthcare: A Next-Gen Architecture that Scales Across Thousands of Locations - Benjamin Beeman, GE Healthcare & Jeremy Oakey, Spectro Cloud
Edge computing and K8s have the potential to transform every industry, by bringing apps, data and outcomes closer to end-users, but it can be a significant paradigm shift for most enterprises when it comes to operationalization. For the healthcare industry, requirements such as security, availability and performance across thousands of locations are demanding for a new type of architecture and efficient tooling. Join this talk, if you're interested in learning about the requirements and gotchas of running and delivering K8s applications across thousands of edge locations, with 0 tolerance for mistakes. GE Healthcare's Senior Staff Software Architect, Ben Beeman and Spectro Cloud’s CTO, Saad Malik will discuss the lessons and how we need a next generation of edge architecture for the cloud native world. Beyond the theory, we will demo live the open source building blocks of managing the complete lifecycle at the edge, such as Cluster API, Libvirt, Docker and others.

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avatar for Jeremy Oakey

Jeremy Oakey

VP of Sales Engineering, Spectro Cloud
Jeremy Oakey is the VP of Sales Engineering for Spectro Cloud. Jeremy was formerly an executive at Cisco, leading cloud product management and technical marketing for a portfolio of products and was brought to Cisco through the acquisition of CliQr where he was the VP of Strategic... Read More →
avatar for Benjamin Beeman

Benjamin Beeman

Senior Staff Software Architect, GE Healthcare
Benjamin Beeman is a Senior Software Engineer with a broad experience on development languages and open source frameworks and tools. His skillset in programming languages spans from C#, C++, Java, Object Oriented Design, JavaScript, Python, OpenCL, OpenGL, MAndroid Development, HTML... Read More →

Tuesday May 17, 2022 14:45 - 15:15 CEST
Pavilion 4, Room C | Level 2 | Central Forum

15:35 CEST

Managing WebAssembly apps with Kubernetes
Developers can leverage container tools such as Kubernetes, Docker and CRI-O to deploy, manage, and run lightweight WebAssembly applications. In this talk, there will be a demonstration of how Kubernetes ecosystem tools work with WasmEdge WebAssembly applications. Compared with Linux containers, WebAssembly could be 100x faster at startup, have a much smaller memory and disk footprint, and have a better-defined safety sandbox. However, the trade-off is that WebAssembly requires its own language SDKs, and compiler toolchains, making it a more constrained developer environment than Linux containers. WebAssembly is increasingly used in Edge Computing scenarios where it is difficult to deploy Linux containers or when the application performance is vital. One of the great advantages of Linux application containers is the rich ecosystem of tools. The good news is that you can use the exact same tools to manage WebAssembly applications, enabling Linux containers and WebAssembly apps to run side-by-side in the same system.

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avatar for Shivay Lamba

Shivay Lamba

Developer Relations Engineer, Meilisearch
Shivay Lamba is a software developer specializing in DevOps, Machine Learning and Full Stack Development. He is an Open Source Enthusiast and has been part of various programs like Google Code In and Google Summer of Code as a Mentor and has also been a MLH Fellow. He has also interned... Read More →

Tuesday May 17, 2022 15:35 - 16:05 CEST
Pavilion 4, Room C | Level 2 | Central Forum

16:10 CEST

Building a Multi-Node Cluster with K3s Using ARM Devices - Sergio Méndez, Universidad de San Carlos de Guatemala
Edge computing is coming as a way to improve user experience for applications, processing data near to the source. Sometimes in edge computing you share data between local machines and the cloud. If you are starting an edge computing project, you could be interested in taking advantage of containers on the edge. Here is where K3s shines to implement edge computing solutions on low resources environments that commonly use ARM devices. In this talk we are going to explore the key elements and best practices to build a K3s cluster on the edge using ARM devices. The talk will show how to build this cluster using Raspberry devices, showing how to configure your local network and a bare metal load balancer with MetalLB, and install other cloud native storage blocks such as Longhorn or OpenEBS. This to finally get a production ready K3s cluster. The talk also provides a quickstart to build edge computing environments for your needs, mentioning different use cases and challenges of this kind when building edge solutions.

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avatar for Sergio Méndez

Sergio Méndez

Professor, Universidad San Carlos de Guatemala
Sergio Méndez is a systems engineer and professor of operating systems at San Carlos of Guatemala university. His work at the university is related to teaching and researching cloud native technologies with his students. He has experience working on DevOps, and MLOps using open source... Read More →

Tuesday May 17, 2022 16:10 - 16:40 CEST
Pavilion 4, Room C | Level 2 | Central Forum
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