Gigaom Logo White

Our Analysts

Chris Grundemann

Chris Grundemann

Chris has well over a decade of experience as both a network engineer and solution architect designing, building, securing, and operating large IP, Ethernet, and Wireless Ethernet networks. He has direct experience with service provider and enterprise environments, design and implementation projects, for-profit and not-for-profit organizations, big picture strategic thinking and detailed tactical execution, and standards and public policy development bodies. Chris frequently works with C-level executives and senior engineering staff at internet and cloud service providers, media and entertainment companies, financials, healthcare providers, retail businesses, and technology start-ups.

Chris holds eight patents in network technology and is the author of two books, an IETF RFC, a personal weblog, and a multitude of industry papers, articles, and posts. In addition to being the lead research analyst for all networking and security topics at GigaOm, he is the co-host of Utilizing AI, the Enterprise AI podcast. He is also a cofounder and Vice President of IX-Denver and Chair of the Open-IX Marketing committee. Chris has given presentations in 34 countries on 5 continents and is often sought out to speak at conferences, NOGs, and NOFs the world over.

Currently based in West Texas, Chris can be reached via Twitter.

Featured Content

Cloud networking software enables data transmission within and between clouds by deploying and orchestrating virtual networking functions. Cloud networking is entirely software driven, with each virtual function playing a role in defining how the cloud entities communicate at a logical level, and enabling connectivity between different data centers and cloud providers.

Cloud networking software enables data transmission within and between clouds by deploying and orchestrating virtual network functions (VNFs). Cloud networking is entirely software driven, each virtual appliance playing a role in defining how the cloud entities communicate among themselves at a logical level, but also enabling connectivity across different data centers and cloud providers.

Industry analysts predict that, within a few years, up to three-quarters of the data generated by enterprises will be created outside of traditional centralized data centers or public clouds. Fueled by emerging AI, AR/VR, and IoT use cases, remote devices and end users are producing massive volumes of data, creating an ever-increasing demand for low-latency, high-throughput infrastructure. The need for data to be processed and secured as close to the point of origin as possible redefines how businesses plan, deploy, and operate their IT environment.

Global Low-Power Wide Area Network (LPWAN) providers offer Internet of Things (IoT) connectivity as a service over widespread geographical areas. In this report, we’ll be assessing vendors who operate in the unlicensed spectrum, which is a set of frequency bands in the Industrial, Scientific, and Medical (ISM) range, and do not require the vendors to apply for or purchase at auction licenses from national telecommunications regulators.

Developed in-house from the ground up and released in February 2019, Grey Matter is an enterprise-proven, universal service mesh networking platform offering zero-trust security, exceptional Layer 3, 4, and 7 visibility, unmatched business intelligence for modern governance, risk, and compliance (GRC) control, and automated performance optimization. Addressing many of the challenges introduced by a service-based architecture (SBA), Grey Matter combines proprietary algorithms with open source technologies, enabling granular service mesh-enabled observability, analytics, and automation to optimize traffic throughput across on-premises, multi-cloud, or hybrid environments with or without the use of Kubernetes.

Managing networks is challenging, time-consuming, and resource-intensive. Despite enhancements to network observability and manageability, enabled by the introduction of artificial intelligence (AI) and machine learning (ML), many network management tasks still require manual intervention by highly-trained personnel. And with post-COVID-19 “new normal” efforts split between supporting full-scale, on-premises environments and full-scale, remote access for home-based employees, NetOps teams need to work smarter by utilizing fully automated solutions based on proven DevOps principles.

gigaom-logo-dark

Do you want to work with Chris Grundemann? Contact Us.

Company