The New Stack Analysts

Service meshes have emerged as essential tools in managing deployments on containers and microservices. Indeed, one would be hard pressed to find DevOps teams that have successfully deployed on Kubernetes without the observability and management capabilities they offer give the immense complexity involved in such a project.

The key role service meshes play in the cloud native world also accounts for why they were a major topic discussed during KubeCon + CloudNativeCon in Barcelona and how Envoy, Istio, Linkerd, Aspen Mesh and other projects will continue to serve as open source alternatives.

Indeed, the announcements at KubeCon about Microsoft’s Service Mesh Interface (SMI) specification and how Solo.io has created what it calls “the first reference implementations” for SMI were arguably the most important newsworthy developments during the conference.

Solo.io’s founder and CEO Idit Levine was also on hand to put service meshes into perspective as one of the panel guests during The New Stack pancake breakfast in this podcast about services meshes held during the first of the Barcelona conference. Hosted by Alex Williams, founder and editor in chief, and co-hosted by Libby Clark, editorial director, of The New Stack, the other guests on hand included, in addition to Levine:

Cliff Grossner, executive director research and technology fellow, IHS Markit;

Pere Monclus, vice president and CTO, networking and security, VMware;

Florian Dudouet, product owner and cloud engineer, Swisscom;

Lee Calcote, founder, Layer5, and author of “The Enterprise Path to Service Mesh Architectures.”

Watch on YouTube: https://www.youtube.com/watch?v=c2v9zvBR7Ds

A blog post by Saad Ali, senior software engineer at Google, drew considerable attention early year when Ali first described Kubernetes GA in “Container Storage Interface (CSI) for Kubernetes GA.  In that post, Ali described how “CSI was developed as a standard for exposing arbitrary block and file storage systems to containerized workloads on Container Orchestration Systems (COs) like Kubernetes.” Among other things, CSI protects backwards compatibility with protection by a Kubernetes deprecation policy, Ali wrote.

The implications of CSI, as well as Kubernetes storage and the evolution of containers, were the subject of a podcast episode of The New Stack Analyst hosted by Alex Williams, founder and editor-in-chief of The New Stack, with Janakiram MSV, a TNS correspondent and principal of Janakiram & Associates. Joining Ali as a guest was  Anand Babu "AB" Periasamy, co-founder and CEO at MinIO. The podcast was broadcast during DockerCon 2019, Docker's flagship user conference, which recently took place in San Francisco.

Christopher Liljenstolpe is the founder and chief technology officer of Tigera, a provider of cloud native security and networking software. He formed Tigera to offer commercial support for Project Calico, a control plane he created for cloud native applications. 

In this episode of The New Stack Analysts podcast, TNS Managing Editor Joab Jackson and TNS contributing analyst Janakiram MSV talk with Liljenstolpe about Calico's creation, overlay networks, service meshes and IPv6.

Key Takeaways:

Originally created for OpenStack, Calico was designed to make it easy to get data packets from one part of the network to another, using the Internet technologies like IP routing, rather than switching, virtual networks, overlay networks or other complex approaches.

Since this form of networking offers only a coarse-grained isolation across nodes, so Calico uses real-time distributed filtering engines to control which nodes can communicate with one another, in effect acting as a network policy enforcement tool.

Anticipating containers, Calico was designed for very dynamic environments, and can manage hundreds of thousands of end-points that can change location at any time.

It is hard to believe for many, as it is for this writer, that Cloud Foundry has existed for more than a decade after it was founded in 2008. Since its beginning, it certainly has more than established itself as a platform as a service (PaaS) of choice for deploying and scaling open source applications. It  has also certainly played a role in the growing momentum in the adoption of Kubernetes, microservices, cloud native and other new technologies as well as many other open source tools that continue to help transform DevOps practices.

In this episode of The New Stack Analysts podcast, we recorded a panel discussion held during a pancake breakfast at Cloud Foundry Summit North America earlier this month in Philadelphia with Cloud Foundry as the featured topic.

Hosted by The New Stack’s Alex Williams, founder and editor-in-chief, and co-hosted by Joab Jackson, managing editor, panel invitees discussed Cloud Foundry’s evolution over the past few years and the key role it continues to play in the open source community.

The panelists included: Abby Kearns, executive director, of the Cloud Foundry Foundation; Cornelia Davis, Vice President of Technology, Pivotal; Daniel Jones, CTO for EngineerBetter; Rick Rioboli, Senior Vice President and CIO, for Comcast; and Stephen O'Grady, an analyst for Redmonk.

The magnanimous shift to cloud-native has brought with it obvious changes and shifts in how DevOps’ manage application deployments. Key among the challenges are observability and monitoring, logging, routing and, of course, security. As a way to keep this all under control, service meshes are increasingly seen not only as extremely useful extra layers to have — but as a necessity production pipelines, deployments and operations running on microservices and Kubernetes live and die by.

This was the main theme of a The New Stack makers podcast hosted by Alex Williams, founder and editor-in-chief of The New Stack and co-hosted by Sriram Subramanian, founder and principal at CloudDon. From Instana, which offers application performance management (APM) solutions for microservices; Williams and Subramanian were joined by Mirko Novakovic, Instana CEO and co-founder, and Michele Mancioppi, Instana senior technical product manager.

It is now widely assumed that making the shift to microservices and cloud-native environments will only lead to great things — and yet. As the rush to the cloud builds in momentum, many organizations are rudely waking up to more than a few difficulties along the way, such as how Kubernetes and serverless platforms on offer often remain a work in progress.

“We are coming to all those communities and basically pitching them to move, right? We tell them, ‘look, monolithic is very complicated — let’s move to microservices,’ so, they are working very, very hard to move but then they discover that the tooling is not that mature,” Idit Levine, founder and CEO of solo.io, said. “And actually, there are many gaps in the tooling that they had or used it before and now they’re losing this functionality. For instance, like logging or like debugging microservices is a big problem and so on.”

Levine, whose company offers service mesh solutions, also described how service meshes were designed to “solve exactly this problem,” during a podcast episode of The New Stack Analyst hosted by Alex Williams, founder and editor-in-chief of The New Stack, with Janakiram MSV, The New Stack Correspondent and principal of Janakiram & Associates.

Monitoring and observability will also play a major in this brave new AI/ML landscape with Kubernetes and microservices. This was the main theme of a podcast Alex Williams, founder and editor-in-chief of The New Stack, hosted, with Janakiram MSV, The New Stack Correspondent and principal of Janakiram & Associates, as the co-host.

Irshad Raihan, director of product marketing at Red Hat, was the guest who spoke about the role of data and observability in AI/ML, in addition to how DevOps is changing for AI/ML, especially with increasing availability of direct data and data streaming.

I've got to admit it's getting better (Better)

A little better all the time (It can't get no worse)

Paul McCartney and John Lennon, respectively, sum up the two ways to look at the future of artificial intelligence (AI) and machine learning — with optimism over limitless potential or pessimism over increased job loss and income equality. The New Stack's Libby Clark and Jennifer Riggins sat down (via Zoom) with The New York Times's Martin Ford, author of Architects of Intelligence: The truth about AI from the people building it, and Ofer Hermoni, chair of the technical advisory council for The Linux Foundation’s Deep Learning Foundation projects, to talk about the current state of AI, how it will scale, and its consequences. 

The last year alone has seen major advancements in deep learning, machine learning, and neural networks — frameworks for machine learning algorithms to work together and process complex data inputs. However, as Ford points out in this podcast, we are only at the start of the ethical implications of AI, including the implications of reduced privacy, potential weaponization, and the unconscious bias that is feeding much of the data going into these models.

On today’s episode of The New Stack Analysts podcast, TNS founder Alex Williams is joined by Janakiram MSV, a principal analyst with Janakiram & Associates, and Steve Burton, VP of Marketing at Harness.io to discuss not only the effects containers and Kubernetes have had on realizing our DevOps dreams, but also how machine learning is taking it to the next level with the evolution of AIOps.

"In the last five years, DevOps has actually matured. So, we started with VMs, and DevOps was all about provisioning and configuration management and then, eventually CI/CD came in and Jenkins became the front and center of build management and release management, but that entire game was taken to the next level when containers became mainstream," said Janakiram. "We have evolved. Basically the current phase is driven predominately by container orchestration managers like Kubernetes that makes it extremely easy to spin up a staging environment or a test environment. And then we have Docker images as the unit of deployment. That fundamentally changes the game."

This week on The New Stack Analysts podcast, we take a closer look at the appeal of using virtual machines in Kubernetes environments.

The discussion was sparked by a popular blog post penned last month by Pivotal Principal Technologist Paul Czarkowski. The problem with basic Docker-styled containers is that they do not offer sufficient security in multitenant environments, where multiple deployments intermingle on the same set of Kubernetes-controlled servers. So we spoke with Czarkowski to learn more of his thinking.

Linux containers all rely on a shared kernel from the kernel, and isolation is provided by the kernel through namespaces. The Kubernetes API, however, is not secured, and most K8s components are not aware of the tenants. This is forcing service providers to provision Kubernetes workloads for different clients as separate clusters, not taking full advantage of the full savings that Kubernetes could provide by pooling workloads on the same cluster, Czarkowski argued.