eWeekChat

A7: Yes and no. There are still workloads in private #datacenters that are ripe for moving. Most customers are hybrid/multicloud - i.e., they have some applications & infrastructure in data centers that they manage w/ the rest spread across multiple public #cloud platforms.

A7: dont believe the hype :-), data centers will be around for a while, we see many customers requiring hybrid architectures especially in finserv and healthcare

A7: No. There is significant amount of critical infrastructure that continues to run in Data centers. If the workloads are large, simple on design and predictable in terms of elasticity & growth, the economics are not in favor of a cloud migration. Hybrid cloud is a solution.

A7: there won't ever be a one-size-fits-all solution for compute. variety to models will continue to persist. all clouds of some sort, but different models: multi-cloud, single-cloud, hybrid, on-prem, etc.

#HybridisHeretoStay

@GaneshJKRam That attitude seems to be growing.

A7: Multi-cloud includes the datacenter! It is an important part of a MC strategy. Not only often from a cost perspective, but HW innovation and best of breed choice. For companies where their infrastructure stack is part of their differentiation--it will always be critical.

A7. I think any Enterprise that has been around for 5+ years knows hybrid & multi-cloud together are the new normal & are here to stay. Edge is a good example of this, so are the on-premise ones (hospital floor, manufacturing floor, onsite maintenance etc.)

There will be a consolidation of data centers for large enterprises in particular - footprints will come down - bit it is not going away.

@codyhosterman The traditional datacenter though is changing. Introducing the cloud operating model for resources will be the future.

@codyhosterman +1, i view it the same way, its part of the overall MC architecture

@codyhosterman - agree. It is changing - but not fully replaceable yet.

agree as well with @codyhosterman, multi-cloud includes all cloud models. its about the arch not the owner/location/size.

This does bring up an interesting operating model choice. Do I start with cloud operational model and homogenize that to DC, or the other way around. Or are these 2 going to be different altogether.

Multi-Cloud means now many big centralized cloud providers, mini-regionalized clouding providers , Edge Cloud Providers and yes many Data Centers.

@GaneshJKRam Completely agree. The key question isn't will there be private data centers. It's what percentage of overall enterprise infrastructure footprint will they represent. Over (a long period of) time, will be small percentage.

@ramsba Cost model is different. So, which direction you come from Data center to Cloud or Cloud to Data center (we have seen people moving in both directions due to Cost , operational cost and also Speed/Agility being the reasons.

Q7: Data centers are not going away yet. Remember many companies that used to be traditional tech companies, are not becoming Cloud Companies e.g. Agriculture, Logistics, etc. and such companies need specialization and distribution that require them to have their own data centers

@codyhosterman I agree. Data center ownership is changing. Now it is not just traditional IT companies, but high value, regulatory heavy industries transforming and need Data Centers to build what they can't get just in time from cloud provdiers

A6: Machine Learning has been democratized well across clouds but larger AI is not – capabilities are very different. Edge is also a big usecase for us– there is a strong need for being very close to the customer for latency – driver here “being global and local” at the same time

A6: i see those as big drivers. higher performance edge applications like AI inferencing and retraining are driving the need for multi-cloud support across geos

A6:very much so, in the security space we see higher need for AI solutions that leverage user behavior across multicloud applications to determine risk and take action if needed

A6. Oh related but also different. I think use-cases like industry 4.0, autonomous capabilities, gaming, drones etc do make legitimate edge use-cases. However there's also the edge or AI-washing - things that make you go "is that really needed ?"

A6: Edge (Cloud) is an augment to the Cloud , it is easy to start there and functions like CDN, WAF, BOT, DDOS, real-Time Analytics , multi-cloud observability can give instant benefits without moving to hosting the compute and data in other clouds

A6: Well AI certainly--different clouds are specializing in different types from a service perspective, so as different clouds create different competencies we will see this grow. This also drives data mobility needs. Edge will need to feed data to the right cloud

@GaneshJKRam +1. Interesting that many of the edge use-cases are really about latency and proximity.

@ilakshmis totally agree on edge being part of Cloud leveraging optimization tools to improve experience

A6: we see customers using Cloud data/ML for training large data sets and bring the models at edge for use cases described above, autonomous cars, , Personalized in-store , in-stadium experiences, etc.

A6:  Leveraging #AI-powered intelligence, #ML or #analytics across multiple clouds is important. With increasingly complex operational needs of modern environments, and increasing volume of data - #AI/ML is realistically the only cost-effective way to tackle that.

@arnlopez - security is clearly an area where we see a lot of AI based solutions across clouds. Again, some clouds have far better offerings than the others.

@iLakshmiS Good ones - these make legit cases for edge. Gaming, industry 4.0 manufacturing use-cases are increasingly getting popular...

A6 AI/ML is clearly growing enormously in enterprise adoption. However, it's one of the areas with the greatest difference in functionality among the big three cloud providers. That provides opportunities to pick and choose for use case and drives multi-cloud.

@bernardgolden But also makes trying to develop an ML app that is portable pretty challenging

Media and Entertainment , that includes basically anything that has interactive experience (gaming, live streaming, user generated contents based platforms , are all using Edge successfully. Security and Privacy is also being enforced at Edge for all industries.

@ramsba Heh. Edgewashing. Nice.

@bernardgolden Agree.. each cloud provider is on a journey of their own and ML works as best as the data the cloud provider is subscribed to or allowed to use. So, the value the Enterprise gets is also different from each cloud.

A6 Edge can be more or less like a hyperscale environment. If it is an air gap environment, that can be more challenging in terms of how to run a control plane.

AI and ML capabilities of the cloud are attractive but the required models are data hungry and will bind customers to the cloud provider indefinitely. Multi-cloud portability to have pricing leverage can be absolutely fundamental.

A5: (1/2) Choose tools that work across multiple clouds. If you are using a single native cloud, the provider tools may suffice but the reality is most enterprise customers (1) don’t want to be tied to a single cloud (2) may need capabilities that require multiple cloud platforms

A5: lots and lots of internal discovery and assessment, you need to know where all of your data is, what applications are business critical, do this first, then document and deploy your MC platforms

A5: Cross cloud portability needs to be a design consideration for any greenfield application & part of the test plan. Make opensource or cloud-native technology choices that are standards based & not proprietary ones wherever possible. Use a common DevOps toolchain across clouds

A5. Unfortunately there isn't a thing called "right blueprint" for multi-cloud. Too many nuances and considerations (tech stack, latency, operational tools, skill-set) ... and each ends up creating their own version.

A5: Be consistent where you can. application delivery pipelines, languages, deployment mechanisms. Consistency that still allows for taking advantage of that cloud choice benefits and not abstracting away it's value.

A5: understand what your workload needs are, prioritize your pain points, know the strengths/offerings of clouds under consideration

A5: (2/2) Also the reality is that most #enterprises are hybrid and will have some assets and systems in their data centers and others in #cloud or #SaaS. Tooling from discovery to observability to optimization should ideally support that hybrid scenario.

That said - start small with a few workloads that need to operate across clouds, decide if they are independent parallel clouds or you need interaction. If latter, ensure the experience is good before scaling out. Cloud elasticity is great and all but if you don't start correctly

A5: dont add security post deployment, include this in your overall deployment strategy, integrate security into your multicloud strategy with stakeholder buy in

it becomes unmanageable fairly quickly

@arnlopez - well said. Need to understand all the assets and identify what needs to be multicloud and why so?

@arnlopez True that. I wished multiple elements of infrastructure (security, networking etc) were shifted left in the decision making process

agree with the comments about consistency, making it as seamless as possible by avoiding unnecessary differences between offerings

@arnlopez Agreed. Both security and observability need to be built-in upfront.

multi-Cloud may not be for all, as it may be complex for you to manage, so start with One Cloud, identify why you need it , see the gains, then move to multi-cloud only if you do need.

@ramsba great point on starting out small and building from there. don't boil the ocean

@GaneshJKRam Agreed as well. And discover those assets and dependencies as well as map them to business services.

if you do not have Dev Teams and Cloud Practitioners in house, get trained before you start . You can start with Edge Cloud for CDN, Load Balancer, WAF , BOT etc. So, you can start by leveraging these acceleration and security and data and compute from centralized clouds.

A6 Examine the complete app architecture prior to beginning building to ensure dependencies on a single cloud are identified and portability requirements to other clouds are defined and designed. Post-implementation redesign is expensive and error-prone.

@bernardgolden The challenge with this is convincing decision makers to adopt an approach that triages apps to find candidates where some degree of modernization is warranted versus lift-n-shift only. Hard to optimize spend or do cost takeout w/out touching the app architecture.