Multi-cloud is often framed from the perspective of “lift and shift.” In other words, an organization takes a computing environment previously running on one cloud and transfers it over to a different service provider. Cloud migration may make sense for cost savings or performance benefits and such a shift may be necessary when migrating on-premises data centers to a public cloud.
In theory, lift and shift sounds like it would empower cloud consumers with more choice in the marketplace. However, according to John Gentry, CTO of Virtana, lift and shift is quite rare in practice; it may not be the true driver behind multi-cloud strategies. Moving resources between clouds can result in costly egress fees. Plus, culturally-ingrained cloud preferences become challenging to shake. Unique configurations and add-on software-as-a-service (SaaS) make each cloud more nuanced, requiring new skills and procedures with every migration.
Instead, Gentry advocates for a “fit-for-purpose cloud” strategy. This attitude is less about moving similar workloads between clouds but, instead, pairing particular types of workloads with the appropriate cloud types. According to Gentry, using multiple clouds simultaneously could still help organizations strategically align their digital resources and reduce dependency on a single cloud service provider (CSP). But, the real value of a hybrid multi-cloud setup lies in matching computing environments to different business layers.
At first glance, the ability to move between clouds appears to be driving a new multi-cloud economy where cloud strategists can swiftly migrate resources and optimize intercloud utilization. However, when organizations attempt to shift, they usually experience many hidden costs, Gentry warned. These often pop up around data egress. Or, new data storage costs may take a surprising toll, Gentry added. “A lot of companies look at it from a computing perspective and they don’t account for the storage footprint.”
DevOps in the cloud also introduces new responsibilities and processes that may differ between clouds, continued Gentry. From unique storage operations to managing the life cycle of a virtual machine, each cloud carries unique configuration methods. This makes it more difficult to abandon established procedures in favor of a different computing arrangement.
Nevertheless, Gentry recognizes that we’re starting to see a maturing effect of the multi-cloud concept, especially among large enterprises. “Companies have a multi-cloud strategy—it’s less about moving between clouds than about picking a cloud that has specific advantages for a given workload,” he said. In a fit-for-purpose cloud approach, different workloads correspond to different layers of the business. For example, an organization may require a local data center or private cloud for high-security data. Certain applications may benefit from a distributed global public cloud, whereas other workloads could reap performance benefits and cost savings from a cloud-adjacent edge model. “These specific areas may not have anything to do with one another other than supporting the overall business,” explained Gentry.
Matching Cloud Types to Business Units
So how do we match the workload at hand to the ideal computing arrangement? Gentry identified three emerging cloud types:
- Private cloud: A private core for particular functions that can’t be replatformed or rearchitected. Business areas such as financial records or patient care hold sensitive data that must be stored properly to meet regulations and SLAs.
- Public cloud: End customer-facing, where sensitive data is not an issue. Public distributed cloud is ideal for delivering a highly available front end for customer-facing digital experiences.
- Cloud adjacent/edge: When an environment is kept in a cloud adjacent facility with relatively local geographies. This may sit alongside the public cloud or connect to a front end in a cloud.
For example, Gentry described how a large health care company uses a hybrid multi-cloud setup to support various business divisions. The organization is keeping traditional patient care workloads exclusively on-premises; due to regulatory reasons, it will never see the inside of a cloud. Yet, they are simultaneously building the next generation of applications in a public cloud, using Google Cloud to scale a clinical trial division. Simultaneously they began using Microsoft Azure for their back office. This scenario highlights one peculiar advantage of late-stage cloud adoption, noted Gentry: “They had the advantage of being able to take a more thoughtful approach.”
Gentry described another case of an organization performing genomic sequencing. For data-heavy applications like this, where there is a high dependency on input and output, syncing with a public cloud could be extremely costly and introduce latency. For them, the most cost-effective method was to use a cloud-adjacent or edge model alongside a public cloud.
How to Leverage Fit-For-Purpose Cloud
One situation that may warrant cloud migration is a complete replatforming, noted Gentry. He described one early cloud adopter that rushed into AWS, only to find it wasn’t cost-effective for their needs. Architects then had to make tough decisions to determine what would be repatriated back to local data centers and what should remain in the cloud. “Their only motive going from cloud to cloud was, ‘We made a bad choice and we want to move.’”
Clearly, to match cloud use with business requirements, you must choose the appropriate cloud. AWS is widely adopted for elastic computing and offers a wide product catalog. GCP is offering feature innovation and cost reduction to compete and is going after un- and under-penetrated regions. Azure, on the other hand, will offer licensing advantages if you are a Microsoft shop.
Secondly, to unite IT around a hybrid multi-cloud strategy, you must remove the adversarial state between traditional ops and cloud ops. Classic ops is typically very safety-oriented, using a slow approach with endless tests in pre-production. Traditional ops may produce yearly updates. This is directly opposed to the new cloud ops approach, which emphasizes agility, speed and constant deployment. “They sit in different silos,” Gentry explained.
To solve this dilemma, companies must share technical skillsets between the three main cloud types. “DevOps must become a continuum for all types,” Gentry said. In addition to cultural change, DevOps tools must evolve to monitor workloads across a hybrid multi-cloud estate. This will require integration to tie together tools and deeper observability.