One of the highlights of TFD19 was the visit at VMware’s Palo Alto HQ to hear the latest from the Cloud Management Business Unit. The day was split in two, with the first half focused on the latest advancements of vRealize Operations Manager (a.k.a. vROps) and the last part completely dedicated to Cloud Automation Services (CAS).

Both sessions were demo-heavy and focused more around showing the real capabilities of the products rather than killing the audience with endless PowerPoint decks. John Dias and Cody De Arkland did a terrific  job in presenting their respective solution, I recommend you to visit the TechField Day website and watch the videos: seeing is believing.

Both topics were equally interesting. From my point of view and being a long time vROps user, John’s presentation was useful for taking notes of the “what’s new” features to be tested soon back at work. After an exhausting TFD week, I saved what was left of my energies to focus on CAS. Below are some of my thoughts on it.

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Introduction to RPA

The acronym RPA, which stands for “Robotic Process Automation”, identifies a relatively young type of technology that is becoming more and more popular across the IT industry. While RPA solutions have been available on the market for almost two decades now, their level of maturity has reached a point where they are now widely adopted in almost any business area.

But what is the purpose of RPA? To condense it in just a few lines: RPA is a set of technologies and tools that aims at multiplying the effectiveness of human workers by partnering them with a digital counterpart capable of automating or augmenting the execution of any type of workflow or business process.

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This post is a follow-up to my previous one written in August after my participation to Cloud Field Day 4. In that post, after a brief introduction of Cohesity and the problems their technology solves, I went deep dive on the Cloud-specific features showcased at CFD4.

As a matter of fact, just a few days after returning from CFD4, Cohesity made an impactful announcement, presenting Cohesity Helios. Back then I did not have the time to look into the announcement and write about Helios, but attending a private briefing (presented by Rawlinson Rivera) at VMworld Europe 2018 gave me the opportunity to focus on the solution and briefly report about it.

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Cohesity: a short intro

Cohesity, since its foundation in 2013, has become a popular name in the Enterprise Storage vendor landscape; although initially Cohesity might have been labeled like “just another backup vendor”, this misplaced and simplistic description has certainly been very unfair to them. Cohesity’s completeness of vision goes way beyond that of being just another backup solution provider, putting them instead at the forefront of the “Battle for Secondary Storage”.

The problem that Cohesity is trying to solve is one that is unfortunately very common: the sprawl of unmanaged, uncorrelated and often unused secondary copies of data endlessly generated by organizations. Multiple copies of the same data are created for backups, archives, test and dev, analytics and DR purposes, resulting into unmanageable, inefficient and complex data siloes. Cohesity can ingest all this data, consolidate it efficiently into one single logical container and make it available for any possible use you might think of. Cohesity is a true DataPlatform meant to enable efficient use of secondary storage. While this goal was initially achieved with software defined, hyper-convergent, scalable appliances, the next inevitable step for Cohesity was to abstract the platform’s capabilities from “the iron” and to develop a Virtual Edition of DataPlatform to address ROBO and IoT use cases and, lastly, a Cloud Edition capable of running on AWS, Azure and Google Cloud. All of these implementations share the same distinctive SpanFS File System and the same API-driven, policy-based management interface, enabling Cohesity’s capabilities to extend to any location your data lives on.

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I came across Aviatrix for the first time a few months ago, while I was knee-deep in the preparation of AWS Associate Exams and at the same time researching for a cloud migration project. AWS networking was a major topic of the exams and also an important research area for my assignment at work. It was very clear to me from the very beginning that Cloud Networking is inherently different from traditional networking. Of course, they share the very same foundations but designing and managing networks in any Public Cloud is a very different business than doing the same in your Data Center. In the Cloud there are no routers or switches you can log into, there are no console cables nor SFP connectors, but you have VPCs that you can literally spin up with a few lines of code with all their bells and whistles (including security policies for the workloads they contain).

This implies a few considerations. First and foremost, the expectations of Cloud Engineers are very different from those of Network Engineers: Cloud Engineers can set up VPCs in minutes but they can be easily frustrated by their on-prem Network counterparts lagging weeks behind to provide VPN connectivity and BGP route distribution to the Data Center. Then there is the skills gap to be filled: Cloud Engineering Teams are usually small and manned by all-round technologists rather than specialists, very often there is no Network Guru in Cloud Teams capable of citing RFCs by memory, so there is a need to keep things simple, yet they must work “as they should”. Finally, in Public Clouds is very easy to lose control and become victims of the VPC sprawl; managing Cloud Networking at scale is probably the biggest challenges of all.

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I have written a couple of posts (here and here) about Datrium around Tech Field Day 14 back in May;  at that time I was intrigued by their fresh and unusual approach to resolve the challenges associated with both the traditional “non-converged” and the “hyper-converged” infrastructure philosophies, but at the very same time I expressed my concerns about the maturity of their solution. I was eager to see their promising technology mature and today I am very pleased to acknowledge the efforts that Datrium have been making since that day, away from the glamour of the spotlight. Datrium just made a big push and only three months after their TFD showcase they introduced not one but two major technology updates.

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OpenIO is a very young company with a history already behind it: although on the market only since 2015, the company’s founders started developing the core technology back in 2006, as part of a project for a major Telco. The code was open-sourced in 2012, then forked and finally productized and presented to customers in its current form. OpenIO is based in Lille, France, with offices in San Francisco and Tokyo and plans for expansion in the next coming months.

OpenIO’s proposition could be quickly and very unfairly labeled as YAOSS – Yet Another Object Storage Solution, while in reality it is way more than just that. To better understand why, let’s start from a very high level description of the current state of the storage market, the typical use cases for object storage systems and how they are quickly evolving.

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A few years after their introduction, HyperConverged systems are now a reality, slowly but steadily eroding market shares from the traditional server/array platforms. They promised scalability, ease of deployment, operational and architectural simplification. While they mostly delivered on those promises, HCI systems introduced some new limitations and pain points. Probably the most relevant is a consequence of the uniqueness of HCI systems’ architecture where multiple identical nodes – each one providing compute, storage and data services – are pooled together. This induces an inter-dependency between them, as VM data must be available on different nodes at the same time to guarantee resiliency in case of failure. Consequently, HCI nodes are not stateless: inter-node, east-west communications are required to guarantee that data resiliency policies are applied. Unfortunately, this statefulness also has other consequences: when a node is brought down, either by a fault or because of a planned maintenance task, so is the storage that comes with it and data must be rebuilt or relocated to ensure continued operations.

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ClearSky is a Boston-based startup founded in 2014 by industry veterans Lazarus Vekiarides and Ellen Rubin; ClearSky comes with a unique proposition, which – if successful – might revolutionize the way primary storage is consumed. I introduced ClearSky in my previous TFD14 preview article where I described their solution; the objective is to reduce drastically the Data Center footprint of traditional primary storage by shifting it to the Cloud while at the same time simplifying DR operations and ensuring accessibility of data from any location. This outcome seemed to be impossible to achieve due to the strict latency requirements that primary storage inherently carries, but ClearSky has found an elegant and effective solution to this conundrum. However, there is one caveat here and it will be evident in the following paragraph.

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This article is a follow up to my TFD14 Turbonomic preview; at that time I knew very little about Turbonomic and that post was a collection of thoughts and impressions I gathered looking at the product from a distance. I am happy to say that after the TFD presentation, my understanding of the solution is clearer and the initial good impressions are confirmed.

Turbonomic is – in their own words – an “Autonomic Platform”; the play on words here is the merge between Automation and Economy, that is because Turbonomic uses the “Supply Chain” metaphor, where every element in the infrastructure “buys” resources from the underlying components and “sells” upstream, leveraging at the same time automation to ensure that the apps are always performing in their “Desired State”.

The objective is to “assure the applications performance” regardless of where the app is running (in the Private, Public or Hybrid Cloud). Coming from an operations background I know well how difficult it is to keep an infrastructure running within ideal parameters: any single intervention – no matter how apparently insignificant – leads to an imbalance in the infrastructure and this, in turn, leads to a deviation from those optimal parameters. What happens is that app performances are less predictable and corrective actions must be taken to return to the “Desired State”. This is what is called the “Break-Fix” loop, which requires continuous human intervention.

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