Rethinking Your Cloud Journey As A Continuous Cycle

As we’ve discussed in previous posts, viewing the cloud journey as a linear destination is a misconception. Too often, businesses take a fragmented approach to their cloud strategy, focusing efforts in individual phases with different vendors. 

For example, migrations often are outsourced to a managed service provider, infrastructure as code blueprints are deployed internally on an ad-hoc basis and cost management is procured from a third-party independent software vendor (ISV), where optimizations are viewed from an in-cloud perspective as opposed to a long-term product roadmap / application portfolio outlook. Users are trained and certified on multiple platforms and spend an endless amount of time engaging with account managers across tools.

Clearly, this approach can lead to operational inefficiencies and siloed goals rather than a united, 360° view of organization-wide objectives and transformational goals.

So What Is The Cloud Lifecycle, And Why Should You Care?

The cloud lifecycle is a continuous journey of realizing IT transformation by adopting tools that enable organizations with cloud-native capabilities. The lifecycle sequence enables organizations to accelerate and optimize time to cloud, time to application modernization and time to new product / service delivery. With each new cycle, enterprises get closer to maximum performance at an optimal cost. 

According to the McKinsey Research Report “Creating value with the cloud”, cloud adoption reduces IT costs by 35-40%, scales IT processes, adds flexibility in on-demand application management, and improves quality of service (QoS) with a potential 70% reduction in total incidents. However, this level of optimization can only be realized when viewing the cloud lifecycle comprehensively in four key phases: Plan, Build, Manage and Optimize. 

Cloud Lifecycle Phase 1: Plan

The Plan phase usually starts with determining the optimal cloud strategy for the involved department, business unit and / or organization. This requires mapping the current infrastructure, understanding the workloads per virtual machine (VM), mapping application dependencies, understanding network, storage and compute usage, and prioritizing workloads as per phased application rollouts. The initial misconception is a bias toward lift and shift migrations.

Most organizations have multi-year plans to scale workloads. The priority of these workloads may be more accurately forecasted when understanding which blueprints are potentially being orchestrated to best provision the deployment of VMs and applications and understanding in-cloud application dependencies that require application tagging or clustering for optimal instance right sizing. 

For example, security-based applications may require blueprints where security and compliance policies have been instituted for low latency validations. In-cloud gap analysis allows you to understand the overall performance of the migration, workload or application group, preparing you for your next wave or series of waves of migrations. 

cloudamize platform

(Source: Cloudamize)

Cloud Lifecycle Phase 2: Build

The Build phase usually focuses on optimizing the time for development, testing and production by deploying infrastructure as code blueprints. This requires the successful migration of such environments to the cloud or across clouds. Complexities arise on the basis of the level of automation, the location of environments and in-cloud validation of successful blueprint deployments via tests related to compliance, linting, licensing, Git, architecture, and security, among others. Successful implementation of build allows for fewer production errors, reduced technical debt, more automation via version control and optimized application uptime.

When executed in isolation, Build automates DevOps on a piecemeal basis. But when Build is preceded by Plan and followed by Manage and Optimize, the end result is systematic orchestration of blueprints on a timed cadence, assuming migration workloads, application dependencies, automation priorities and testing across all policies and standards that enable optimized governance. 

(Source: Cloudamize)

Cloud Lifecycle Phase 3: Manage

The Manage phase focuses on marrying performance expectations with cost goals. Continuous cost predictability and optimization consistently rank as top goals among CFOs, CIOs and CISOs, next to security.  Today, 85% of organizations overspend on their intended budget because of fewer reserved instance purchases, lack of chargebacks, confusion in SKUs (500K+ across all cloud service providers), fear of innovation prevention and / or lack of prioritization. 

Manage prevents unintended or accidental overspending by right sizing your cloud configurations to ensure that your network, compute and storage inventory are efficiently allocated without overprovisioning for bursty behavior. True Manage value may be harnessed when juxtaposing savings across Plan and Manage options. For example, an asset management company with 840 VMs and 180 applications in AWS was able to perform a cloud cost comparison when comparing lift and shift migrations, right sizing compute and storage and pre-purchasing 3 years’ worth of reserved instances at 4.2 million, 2.6 million and 1.7 million USD in cloud costs respectively. 

(Source: Cloudamize)

Cloud Lifecycle Phase 4: Optimize

The Optimize phase focuses on the integration of AIOps to ensure that gap analysis is in a state of constant evaluation for more competitive advantages. According to Gartner, AIOps refers to multi-layered technology platforms that automate and enhance IT operations by 1) using analytics and machine learning to analyze big data collected from various IT operations tools and devices, in order to 2) automatically spot and react to issues in real time. AIOps can be thought of as Continuous Integration and Deployment (CI/CD) for core IT functions.

AI and machine learning are powerful tools that solve for the speed, scale and complexity challenges associated with cloud transformation and application modernization. AIOps greatly accelerates the time to mine data, scale GPUs and automate inferences around cloud strategies, blueprint deployments and right sizing recommendations. 

Other outcomes from a successful Optimize phase implementation include app-centric root cause identification, data correlation maps, automated cost calibrations and proactive remediations against outages, breaches and incidents.

(Source: AWS Re:Invent)

The Platform that Powers the Cloud Lifecycle

In summary, the cloud lifecycle is a multi-year journey that requires a 360° outlook of your current infrastructure, application pipeline and product roadmap. Enterprise level cloud adoption has been on the rise in recent years. With it is the expectation of more granularity, speed, scale and automation. 

Choosing the right single, cloud-agnostic and app-centric platform that operates across the aforementioned phases is vital to successful cloud journey navigation. Cloudamize positions organizations to cycle seamlessly  through these phases by:  

    • Supporting the initiation of a 360° analysis with our Plan feature
    • Supporting the sequential path to DevOps automation via our Build feature
    • Providing cost management capabilities in AWS with unique functionality to automate tags with app-centric cost visibility

To learn more and request a demo of our platform, contact us today.

  • Cloudamize