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Cloud Complexity: Orchestrating Resources in Multi-Cloud Environments

This blog post explores the complexities and benefits of implementing a multi-cloud strategy.

The author outlines the key considerations like cost-saving potential, maintaining clarity across environments, and automation to manage complexity. Different cloud providers are compared for services like serverless functions and Kubernetes cluster deployment times.

The benefits of multi-cloud solutions include:

Improved data privacy and protection through regional storage and IAM customization.

Reduced vendor lock-in risk by facilitating easier migration between cloud providers.

Enhanced global access for geographically dispersed users.

However, challenges include the need for skilled personnel to manage multiple cloud environments and the increased complexity of cost management and security.

The blog concludes by highlighting the growing adoption of multi-cloud strategies and positions Squadcast, an incident management tool, as a VictorOps alternative for streamlining cloud operations.

The cloud revolution is still in its early stages, and migrating to the cloud can be a challenge for many organizations. So, why the upsurge in multi-cloud adoption? This blog explores why this trend deserves your consideration and the challenges it presents.

What is Multi-Cloud?

Multi-cloud strategies come into play when organizations leverage a blend of private and public cloud providers like Azure, AWS, and Google Cloud in their IT infrastructure. Previously, the focus was on cloud migration and vendor selection.

Today, the shift is towards harnessing solutions from multiple cloud vendors. As cloud adoption matures, core functionalities among major players become increasingly similar. However, each vendor offers unique services with variations in delivery.

Considerations include Kubernetes cluster provisioning time, low-latency network availability, storage costs across performance tiers, and Identity and Access Management (IAM) service compatibility. Multi-cloud solutions provide flexibility in selecting desired services, but with inherent complexities.

Here are five key considerations before opting for the multi-cloud model:

  • Cost-Saving Awareness: Ensure your business understands potential cost-saving aspects of navigating multi-cloud complexity.
  • Clarity and Reliability: Invest in tooling to maintain clarity and reliability within the multi-cloud environment.
  • Cloud Exploration: Begin exploring multiple cloud environments to grasp similarities and differences in the context of multi-cloud complexity.
  • Savings Evaluation: Always evaluate areas with the potential for significant savings within the multi-cloud framework.
  • Automation Emphasis: Continuously strive to automate processes to manage the increasing complexity inherent in multi-cloud environments.

Let’s explore some examples to illustrate these nuances and shed light on the intricacies of multi-cloud complexity.

Serverless

Examining current cost factors, Simform offers insights into serverless costs across major cloud service providers — AWS, Azure, and GCP. Serverless functions incur charges based on code execution time. In a hypothetical scenario where a serverless function with 0.5 GB of RAM executes in a second, running 3 million times per month, the cost breakdown is as follows:

  • AWS Lambda: The total monthly cost is approximately $18.74.
  • Azure Function: Costs slightly less at $18 for the same function.
  • Google Cloud Functions: Priced at around $25.15, making it about 40% more expensive than the other two.

It’s crucial to note the inherent volatility in cloud computing costs, with fluctuations anticipated in the near future. However, this analysis underscores the potential for significant cost savings through the strategic use of multi-cloud strategies to navigate the complexities of serverless computing.

Kubernetes Clusters

In the realm of Kubernetes clusters, provisioning time is a crucial factor influencing decision-making, particularly in troubleshooting or conducting iterative Proof of Concepts (POCs). Let’s examine this variable across three major service providers:

  • Amazon Elastic Kubernetes Service (EKS): Approximately 9 minutes for cluster deployment.
  • Azure Kubernetes Service (AKS): Takes about 10 minutes to complete the deployment process.
  • Google Kubernetes Engine (GKE): Remarkably faster at 5 minutes, making it twice as fast as some counterparts.

This discrepancy in deployment times emphasizes the practical significance of such time differentials. Now, let’s explore what it means to implement a multi-cloud strategy and delve into a high-level overview of a multi-cloud system architecture.

As illustrated, extending your on-premise Active Directory (AD) to Azure AD enables authentication and authorization of a Kubernetes cluster running in the Google Cloud. On the backend, logs from Google Kubernetes Engine (GKE) and Azure are forwarded to Splunk, with a similar process for on-premise workloads forwarded to Splunk Enterprise in an AWS environment.

Cloud architecture is a dynamic and evolving process, with the migration of services and solutions adapting to changing market needs. While implementing multi-cloud solutions may seem daunting, it offers long-term benefits, significantly saving organizations resources. The ability to easily redeploy to alternate locations becomes feasible, provided there is flexible tooling and knowledge of diverse environments, highlighting the importance of understanding and navigating multi-cloud complexity.

Potential Gains of Implementation

Data Privacy and Protection:

Privacy and data protection laws like GDPR in the EU can present challenges during organizational migrations. Referring to the earlier diagram example, where various cloud services interact within a multi-cloud framework, workloads with sensitive data may require storage within the Azure compute environment in a European data center.

Utilizing Azure AD simplifies Identity and Access Management (IAM) processes, streamlining operations in Google Cloud and Kubernetes environments, potentially situated in North America. Multi-cloud solutions offer customization of data management to meet the diverse needs of multiple regions.

Reduced Vendor Lock-In Risk:

Many stakeholders rely solely on services from a single cloud provider. Integration within a provider’s ecosystem can lead to difficulty and high costs in exiting or integrating with other providers. Multi-cloud implementations serve to diminish the risk of vendor lock-in and enhance operational flexibility.

Multi-cloud solutions enable the seamless migration of workloads across environments when necessary. Streamlining APIs for cross-cloud communication is essential. Running Proof of Concepts (POCs) for custom architectures ensures compatibility and mitigates challenges.

Global Access:

Availability of cloud services is often tied to the geographical location of the provider’s infrastructure. Despite similar services, variations in data centers and networking accessibility can impact crucial factors like edge computing and latency.

Larger organizations require global access for an enhanced client experience. Forced regional access may hinder the seamless operation of services. Multi-cloud solutions facilitate the scaling of applications to run within different public clouds based on their region and availability.

Investigation of regional data centers significantly benefits workloads of streaming services, enhancing the overall customer experience.

Challenges and Their Mitigations

Multi-cloud solutions, while beneficial, can be complex to implement, requiring careful consideration and planning. Adequate manpower and in-house knowledge are essential to fully leverage the available features of multi-cloud environments.

The challenge lies in tracking multiple rapidly evolving environments, necessitating proactive monitoring and adaptability. Supporting architectural decisions must be constantly adjusted to align with the evolving nature of environments.

Deployment across multiple cloud environments may require software with a single codebase, streamlining infrastructure deployment. Managing hardware and services across several clouds can result in complex cost management, demanding continuous attention.

Staying on top of security and reliability practices is critical to ensuring the integrity of multi-cloud systems. More environments necessitate increased testing, policy and identity management, redundancy, adding to the overall management overhead.

SREs and DevOps professionals play a crucial role in constantly managing and observing services to maintain appropriate incident response times.

The Future of Multi-Cloud

In the near future, an increasing number of organizations are expected to adopt multi-cloud strategies. While acknowledging the challenges, embracing multi-cloud strategies early on positions organizations for success in the cloud-native future, offering enhanced flexibility, scalability, and efficiency.

We trust that you have found value in exploring the intricacies of multi-cloud environments in this blog.

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Squadcast Inc

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Squadcast is a cloud-based software designed around Site Reliability Engineering (SRE) practices with best-of-breed Incident Management & On-call Scheduling capabilities.
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