The managed service provider (MSP) market is extremely competitive and fraught with challenges. MSPs need to offer competitive pricing to make their offerings attractive to customers, but at the same time require the agility to develop and deliver solutions that support the customer journey into the cloud. MSPs also need to meet and exceed service level agreements (SLAs) to maintain their customer base. They also need to avoid vendor lock-in, which may isolate potential customers, simplify their solution stack to streamline management and operations, and ensure their technology is always up to date. Achieving all of this can be impossible without the right foundation in place, which is where software-defined storage (SDS) comes in.

Standardising hardware is the key

SDS is the ideal supporting platform to help MSPs build and deliver solutions and easily manage and operate them. By decoupling hardware and software, SDS allows MSPs to take advantage of their choice of server technology and server architecture. This ensures a vendor-agnostic approach, while at the same time enabling MSPs to standardise on their hardware, using commodity servers, cloud architecture or a combination of the two.

By unifying block, object and file for hypervisors, containers, backup and cloud workloads, the result is a scale-out, resilient and distributed architecture that maximises availability and protects against hardware failure. Essentially, SDS brings significant simplicity into the framework for enhanced predictability in an elastic solution that can scale to thousands of nodes as demand increases, without disrupting service.

The ultimate goal is to establish a fabric that can support a variety of workloads, including virtualisation, containers and backup, and meet today’s data infrastructure challenges through a single domain.

Ideal for MSPs

The backbone of SDS is highly supportive of a multi-tenancy environment and simplifies management of the technology stack that goes with it, regardless of the storage or workloads that are being run. SDS also supports all providers, ensuring there is no vendor lock-in, giving ultimate flexibility in the choice of hardware, development and deployment options.

A single management and presentation layer not only simplifies complexity, it also collapses storage silos into a single management interface to eliminate mass data fragmentation. By deploying a software-defined layer on top of commoditised hardware, across various clusters including virtualised, bare metal, containers and the cloud, a truly agile infrastructure can be created. This also mitigates the risk of technology refreshes, which can be absorbed without creating disruption or downtime.

Virtual disks deliver a utility storage framework on the back end, for granular provisioning of volumes with enterprise storage functionality. This elastic infrastructure eliminates downtime across the spectrum. It also further delivers on agility by automating management of the underlying hardware and infrastructure, removing this from day-to-day administrative tasks.

Focus on customers, not tin

Using SDS, MSPs can solve new infrastructure challenges in a distributed way, enabling seamless cloud integration and adoption, supporting the hybrid cloud journey and allowing for enterprise data protection through data distribution.

SDS provides storage for virtual environments, seamlessly integrates with various container orchestrators, and serves as a single backup storage target, regardless of which backup software solution is in use. This improves efficiency, allowing MSPs to extend their service offerings easily, scale without performance degradation and support their clients’ multi-cloud strategy with persistent, high-availability storage.

MSPs are able to maximise their total cost of ownership, simplicity and scale, which lends itself to multiple use cases. From agile container footprints to micro-services and virtualisation, MSPs are able to drive a single data centre model across regions that provides absolute uptime and high availability. SDS essentially transforms commodity hardware into an elastic, resilient, modern storage solution that allows MSPs to focus on customers and application sets, rather than on the underlying hardware needed to deliver services.

Airtel and AWS Form Strategic Cloud Partnership

Bharti Airtel Ltd. and Amazon Web Services (AWS) announced a strategic partnership aimed at expanding Airtel’s enterprise cloud business in India. The agreement enables Airtel to offer a significantly broader range of cloud products and managed services to its large enterprise and SMB customer base.

At the time of the announcement, Airtel reported serving more than 2,500 large enterprises and over one million emerging businesses through its product portfolio, which includes Airtel Cloud — its multi-cloud product and solutions business.

What the Partnership Covers

Under the agreement, Airtel Cloud will establish an AWS Cloud Practice backed by AWS Professional Services. The collaboration is structured around three main pillars:

• AWS Cloud Practice: Airtel Cloud will develop a dedicated AWS practice, giving enterprise customers access to AWS Professional Services expertise through an Airtel-led engagement model.
• Differentiated Airtel Cloud Products: New cloud products and capabilities will be built by combining AWS services with Airtel’s own data centre infrastructure, network assets, and telecoms offerings — aiming to produce solutions not available from either company alone.
• Integrated Sales and Support: Enterprise customers will receive a unified sales, consulting, and support experience from both Airtel and AWS teams, along with enhanced security, scalability, and cloud management capabilities.

Why This Matters: India’s Growing Public Cloud Market

The timing of the partnership reflects broader momentum in India’s enterprise technology sector. According to International Data Corporation (IDC) figures cited at the time of the announcement, India’s public cloud services market was valued at approximately $3.4 billion in 2020 and was projected to reach $7.1 billion by 2024 — representing significant compound annual growth.

For Airtel, partnering with AWS positions the telco to compete more aggressively for a share of this expanding market, moving beyond traditional connectivity services into higher-margin managed cloud and professional services.

The Telco-Cloud Convergence Trend

The Airtel–AWS deal is part of a broader global trend in which telecommunications operators leverage their existing network infrastructure, data centre footprint, and enterprise customer relationships to compete in the cloud services space. Telcos bring several natural advantages to this market:

• Network edge infrastructure: Proximity to customers enables low-latency cloud delivery, particularly relevant for hybrid and edge computing use cases.
• Existing enterprise relationships: Large telcos already hold long-term contracts with enterprises for connectivity, making cross-selling cloud services a lower-friction opportunity.
• Data centre assets: Many telcos operate their own data centres, which can be integrated with hyperscaler infrastructure to create differentiated hybrid offerings.

By combining Airtel’s network and data centre capabilities with AWS’s cloud platform and professional services expertise, the partnership is designed to address the full cloud adoption lifecycle for Indian enterprises — from initial migration consulting through to ongoing managed operations.

Implications for Enterprise DevOps Teams in India

For DevOps and platform engineering teams operating within Indian enterprises, this kind of partnership can have practical implications:

• Broader procurement options: Enterprises already purchasing connectivity services from Airtel may be able to consolidate cloud procurement under a single vendor relationship.
• Access to AWS services through a local partner: Airtel’s Professional Services layer, backed by AWS, could provide localised support for cloud migrations, architecture reviews, and DevOps toolchain implementations.
• Hybrid and multi-cloud architectures: The combination of Airtel’s on-premises data centre capabilities with AWS public cloud services is particularly suited to enterprises with regulatory or latency requirements that prevent a full public-cloud migration.

Video Overview

HCL Commerce Moves to Google Cloud

HCL Technologies and Google Cloud announced an expanded partnership that makes Google Cloud the preferred cloud platform for HCL Commerce. The agreement is designed to give businesses access to Google’s global infrastructure combined with HCL’s commerce platform, enabling more scalable, data-driven customer experiences across industries.

According to the two companies, the integration aims to help organisations execute digital transformation strategies more rapidly — particularly relevant as retail and e-commerce businesses accelerate their shift to cloud-native operations.

What the Partnership Covers

• Preferred cloud platform: Google Cloud becomes the primary infrastructure provider for HCL Commerce deployments, offering global edge presence, elasticity, and reliability.
• Multi-cloud and hybrid deployments via Anthos: HCL has stated its intention to leverage Google’s Anthos platform to support multi-cloud and hybrid-cloud HCL Commerce deployments, giving enterprises flexibility in where and how they run workloads.
• Joint product roadmap: The launch of HCL Commerce on Google Cloud is described as the first of several planned joint offerings, with future focus areas including application modernisation, data centre optimisation, and database value unlock.
• Global partner ecosystem: HCL’s implementation partner network will support delivery of the combined commerce solution across verticals.

Executive Perspectives

Darren Oberst, HCL Corporate Vice President and Head of Software, framed the collaboration in terms of business resilience:

“The collaboration between Google Cloud and HCL Commerce is helping customers rapidly execute their digital transformation strategy that is rooted in the new normal. With the support of our global implementation partner ecosystem, we can now deliver a proven, comprehensive commerce solution across all industries, handling the challenges of today and in the future.”

Kalyan Kumar, HCL Chief Technology Officer and Corporate Vice President, highlighted the broader product collaboration:

“The HCL Google Cloud Business Unit is enabling product collaboration across HCL Software and Google Cloud. The launch of HCL Commerce on Google Cloud is the first among a number of planned joint offerings built around application modernisation, data centre and database value unlock.”

Kevin Ichhpurani, Google Cloud Corporate Vice President, emphasised the customer experience angle:

“It is more important than ever for firms spanning all industries to deliver strong, customer-centric eCommerce experiences. We’re proud that Google Cloud infrastructure will power HCL Commerce, helping businesses leverage the elasticity and reliability of Google Cloud and ultimately delivering positive eCommerce experiences for customers around the world.”

Why Anthos Matters for Hybrid E-Commerce Deployments

The planned use of Google’s Anthos platform is a technically significant element of this partnership. Anthos is Google’s managed application platform that lets organisations run containerised workloads consistently across on-premises infrastructure, Google Cloud, and other public clouds. For enterprise retailers with existing on-premises investments — common in the HCL Commerce customer base — Anthos provides a migration path that avoids a hard cutover to public cloud, instead enabling a gradual or permanent hybrid model.

This is particularly relevant for e-commerce platforms that must handle highly variable traffic spikes (such as seasonal peaks) while keeping sensitive transactional data under tighter control where compliance requires it.

Broader Context: Google Cloud’s Partner Expansion Strategy

The HCL announcement was part of a wider pattern of Google Cloud deepening enterprise partnerships in mid-2020. Around the same period, Google Cloud expanded its relationship with Dell Technologies, which included the launch of a joint hybrid cloud storage system capable of handling high-performance workloads of up to 50 petabytes.

That Dell-Google solution was designed for workloads requiring high throughput and low-latency file storage — such as video streaming services supporting 4K content — and allowed movement of demanding workloads in a single filesystem between on-premises Dell infrastructure and Google Cloud.

Kris Day, Dell Vice President of Modern Data Centre for Asia Pacific and Japan, described the strategic intent:

“Our new advancements affirm our belief that cloud is an accelerator for innovation, as well as underpin our go-to-market strategy of bringing the cloud operating model spanning edge, private and public clouds with consistent infrastructure and operations. These Dell Technologies Cloud updates are designed to support our customers across Asia Pacific and Japan who are investing in multi-cloud strategies that include hybrid, public and private cloud environments to meet different needs for a variety of data workloads — mission-critical applications, AI and ML workloads, as well as developer environments.”

Key Takeaways for DevOps and Platform Engineers

• HCL Commerce on Google Cloud signals growing enterprise appetite for fully managed, cloud-native commerce infrastructure rather than self-hosted deployments.
• Anthos as a bridge layer is becoming a recurring pattern for ISVs looking to support hybrid deployments without forking their product for each environment.
• Multi-cloud is a selling point, not just a technical option — vendors are explicitly calling out multi-cloud flexibility as a business requirement, not just an architectural preference.
• Platform partnerships drive go-to-market: Joint business units (like the HCL Google Cloud Business Unit mentioned here) are increasingly the vehicle through which cloud providers and ISVs co-develop and co-sell solutions.

What Is Confidential Computing?

Confidential computing is a security model that uses hardware-based mechanisms to isolate and protect data while it is in use—that is, while code is actively processing it inside CPU memory. The goal is to ensure that even privileged software layers (the hypervisor, the host operating system, or a cloud provider’s own administrators) cannot access the plaintext data being worked on.

The term and the surrounding ecosystem are governed by the Confidential Computing Consortium (CCC), a project under the Linux Foundation. Members include Microsoft, Intel, Google, IBM, and other major technology vendors, who collaborate to standardize TEE interfaces and promote open-source tooling for enclave development.

Azure DCsv2-Series VMs and Microsoft’s Role

Microsoft’s DCsv2-Series virtual machines are designed specifically for confidential computing workloads. According to Microsoft, launching this VM series made it one of the first cloud providers to offer virtualized confidential computing as a generally available product. The VMs run on physical hosts equipped with Intel SGX-capable processors, and each VM can allocate a portion of protected memory—called Enclave Page Cache (EPC)—for enclave execution.

From a DevOps perspective, workloads targeted at these VMs must be either:

• Enclave-aware applications written or ported using the Intel SGX SDK, Open Enclave SDK, or similar frameworks.
• Lift-and-shift confidential workloads where a compatibility layer (such as a library OS) runs an unmodified application inside an enclave.

How Intel SGX Creates Trusted Execution Environments

Intel Software Guard Extensions (SGX) is a set of CPU instructions that allow an application to create private memory regions called enclaves. Key properties of SGX enclaves include:

• Memory encryption: The CPU encrypts enclave memory automatically. Data decrypts only inside the physical CPU package, so DRAM snooping yields only ciphertext.
• Isolation from privileged software: The OS, hypervisor, and BIOS cannot read enclave contents, even with ring-0 access or physical access to the server.
• Remote attestation: A relying party (e.g., a client sending sensitive data) can cryptographically verify that a specific, unmodified enclave is running on genuine SGX hardware before sending secrets to it.
• Sealing: An enclave can encrypt data to a key derived from the enclave’s identity and the platform, so sealed secrets can only be reopened by the same enclave on the same hardware.

Intel describes SGX as the most widely researched and deployed application isolation technology in this category. The Open Enclave SDK, maintained as an open-source project within the CCC, provides a cross-platform API so developers can write enclave code that targets SGX (and other TEE types) without being locked to a single hardware vendor’s SDK.

Why the "Data in Use" Gap Matters

Traditional cloud security controls protect data at rest and in transit well. The remaining exposure occurs the moment a cloud service decrypts data to process it. At that point, a compromised hypervisor, a malicious insider with administrative access, or a memory-scraping attack could theoretically read the plaintext. Regulatory frameworks such as GDPR, HIPAA, and financial sector requirements make this a compliance concern, not just a theoretical one.

Confidential computing shrinks this attack surface by keeping data encrypted even while in use. The attack surface moves down to the CPU’s microarchitecture—a much harder target than software-layer exploits.

Practical Use Cases

Confidential computing unlocks several scenarios that are difficult or impossible to accomplish safely with conventional cloud architectures:

Multi-Party Computation

Multiple organizations can contribute sensitive datasets to a shared computation (for example, banks pooling transaction records for fraud-detection analytics) without any party exposing raw data to the others or to the cloud operator. Each party’s data enters the enclave encrypted; results exit, but intermediate plaintext never leaves the enclave boundary.

Confidential Machine Learning

Multiple parties can collaborate to train a machine learning model on combined datasets—such as anonymized health records from different providers—without sharing the underlying data. The enclave enforces that only the agreed-upon model training code runs on the combined data.

Secure Database Query Processing

Database engines running inside enclaves can process encrypted queries without the database administrator or cloud provider being able to view query contents or result sets. This removes the need to fully trust the database operator with sensitive business data.

Trusted Remote Virtual Machines

Enterprises running workloads in a partner’s or third-party cloud environment can use remote attestation to verify that their code is running in an intact enclave on genuine SGX hardware before transmitting secrets, removing dependence on contractual trust alone.

The Confidential Computing Consortium and Open-Source Ecosystem

The Confidential Computing Consortium, hosted under the Linux Foundation, sets the governance rules for interoperability standards and open-source projects related to TEEs. Its primary open-source outputs include:

• Open Enclave SDK: A cross-platform SDK for writing enclave applications that can target multiple TEE technologies.
• Enarx: A runtime for deploying applications in TEEs without rewriting them, supporting multiple hardware backends.
• Gramine (formerly Graphene-SGX): A library OS allowing unmodified Linux applications to run inside SGX enclaves.

The nonprofit governance model is intended to prevent any single vendor from controlling the standards, ensuring interoperability across cloud platforms and TEE implementations from different hardware vendors.

Architectural Considerations for DevOps Teams

Before planning a confidential computing deployment on Azure DCsv2-Series VMs, DevOps and platform engineering teams should evaluate:

• EPC size constraints: SGX enclave memory (EPC) is limited and managed by the CPU. Applications requiring large in-memory datasets may need architectural changes to process data in smaller chunks.
• Performance overhead: Context switches between trusted and untrusted execution zones and EPC paging introduce latency. Benchmark your specific workload; overhead varies significantly by access pattern.
• Attestation infrastructure: Remote attestation requires integrating with Intel’s attestation service or Microsoft Azure Attestation. This is a non-trivial pipeline addition.
• SDK and language support: SGX development is most mature for C and C++. Higher-level language support (Rust, Go, Python) exists through library OS approaches but adds complexity.
• Patching cadence: SGX has been subject to microarchitectural side-channel research (such as Spectre variants targeting enclaves). Keeping SGX platform software and microcode updated is essential.

Summary

Azure Confidential Computing on DCsv2-Series VMs, powered by Intel SGX, addresses the historically unprotected attack surface of data in use. By running sensitive workloads inside CPU-enforced encrypted enclaves, organizations can reduce reliance on organizational trust in cloud administrators, satisfy regulatory requirements for data confidentiality, and enable entirely new categories of multi-party and collaborative computation. For DevOps engineers, the practical challenges lie in EPC capacity planning, attestation pipeline integration, and the additional complexity of enclave-aware application development—areas worth investing in for workloads where data sensitivity is high.

Data centre strategies for cloud-scale software are fundamentally different from those designed to support traditional, on-premises enterprise applications. Whether workloads are hosted on public IaaS, in a private cloud, or in a managed private cloud, infrastructure planners need to account for characteristics that make modern cloud software architecturally distinct. This article breaks down those characteristics and their practical implications for infrastructure design.

Why Cloud-Scale Software Demands a Different Infrastructure Approach

Traditional enterprise software was typically deployed for a known, bounded user population on dedicated hardware. Cloud-scale software inverts nearly every one of those assumptions: user populations are large and variable, deployments are shared, release cycles are compressed, data volumes are enormous, and analytical workloads are central rather than peripheral. Each of these factors has direct consequences for how data centres must be designed and operated.

1. Application Multi-Tenancy

Cloud software is inherently multi-tenant: many users from different organisations share the same underlying infrastructure while maintaining strict logical separation of their private data. This architectural pattern places specific demands on data centre infrastructure:

• Consistent performance isolation. A surge in activity from one tenant must not degrade the experience of others. Infrastructure must provide predictable, isolated performance under variable aggregate load.
• Shared-resource efficiency. Multi-tenancy only delivers its cost and operational benefits when the infrastructure can efficiently pool and reallocate compute, memory, network, and storage resources across tenants in real time.
• Security boundaries. Logical separation must be enforced at every layer — networking, storage, and application runtime — to prevent data leakage between tenants.

Hyperconverged infrastructure (HCI) and software-defined storage platforms have become popular choices for multi-tenant deployments precisely because they allow fine-grained quality-of-service policies to be applied per tenant or per workload.

2. Agile Development and DevOps Velocity

One of the core advantages of centralised, cloud-scale infrastructure is the ability to ship new features frequently — weekly or even daily release cycles are common in mature DevOps organisations. This cadence creates infrastructure requirements that are easy to overlook during initial capacity planning:

• Programmable, API-driven provisioning. Teams cannot afford to wait days for manually provisioned test or staging environments. Infrastructure must expose APIs that CI/CD pipelines can consume to spin environments up and tear them down automatically.
• Immutable deployments and rapid rollback. Container-based and immutable-infrastructure patterns require storage and networking layers that support fast image distribution and stateless compute scaling.
• Observability from day one. High-frequency releases increase the probability of regressions reaching production. Infrastructure must expose rich telemetry — metrics, logs, and traces — that development teams can consume alongside application-level signals.

Infrastructure that requires lengthy change-management cycles or manual intervention becomes a bottleneck that negates the value of the DevOps practices built on top of it. The data centre strategy must treat infrastructure-as-code as a first-class requirement, not an afterthought.

3. Elastic and Cost-Efficient Scale

Cloud-scale businesses are built on the assumption of growth: more users, more connected devices, more data. Data centre infrastructure must scale seamlessly and predictably to match that growth. This has two interrelated dimensions.

Technical Scalability

Infrastructure must scale horizontally without architectural re-engineering. Storage, compute, and networking capacity should all be addable in incremental units without requiring application downtime or data migration. Scale-out architectures — distributed storage clusters, disaggregated compute pools, software-defined networking — are better suited to this requirement than traditional scale-up, monolithic designs.

Cost Scalability

One well-known challenge with public IaaS is that storage and compute costs tend to grow nearly linearly with workload volume, making it difficult to realise economies of scale at the infrastructure layer as the business grows. A well-designed data centre strategy — whether built on private cloud, colocation, or a hybrid model — should aim to improve cost efficiency per unit of workload as scale increases, rather than simply tracking it linearly. This typically means evaluating:

• Total cost of ownership (TCO) of owned or leased infrastructure versus on-demand public cloud spend at projected scale.
• Reserved or committed-use pricing models on public IaaS for predictable baseline workloads.
• Hybrid architectures that run steady-state workloads on owned infrastructure while bursting to public cloud for unpredictable peaks.

4. High-Performance Analytics

Analytics is no longer a secondary, batch-oriented workload running overnight in a data warehouse. For modern cloud-scale applications, real-time analytics is a core part of the product experience and the operational backbone of the business:

• Personalisation engines in eCommerce and streaming platforms make real-time decisions based on user behaviour data at massive scale.
• Business intelligence in enterprise SaaS applications surfaces insights that must reflect near-current state of operational data.
• Machine learning inference for AI-enabled features — recommendations, fraud detection, image recognition — runs continuously against live data streams.
• Conversational interfaces and chatbots rely on low-latency model inference to maintain acceptable response times.

These workloads share a common infrastructure requirement: very high I/O throughput combined with low, consistent latency. All-flash storage arrays and NVMe-based storage architectures have become key enablers for delivering analytics performance at the scale cloud software demands. Emerging NVMe-over-Fabrics (NVMe-oF) networking protocols promise to extend the low-latency characteristics of local NVMe storage across a shared network fabric, enabling storage disaggregation without the latency penalty of traditional SAN or NAS.

Navigating a Complex Ecosystem

Product, DevOps, IT, and finance teams charting data centre strategies for cloud-scale applications face an increasingly complex decision landscape. The technology choices — hyperconverged infrastructure, software-defined storage, bare-metal cloud, colocation, public IaaS, hybrid architectures — are not mutually exclusive, and the right answer is rarely a single vendor or a single deployment model.

A practical evaluation framework should test candidate strategies against each of the four dimensions above:

1. Multi-tenancy: Does the infrastructure enforce performance isolation and data security between tenants without requiring over-provisioning?
2. DevOps agility: Can the infrastructure be provisioned, configured, and decommissioned entirely through APIs and infrastructure-as-code tooling?
3. Scale economics: Does the cost model improve — or at least hold flat — as workload volume grows, rather than scaling linearly?
4. Analytics performance: Can the storage and compute layers deliver the throughput and latency required by real-time analytical workloads at production scale?

Infrastructure vendors and cloud service providers that can credibly address all four dimensions will be well positioned to support the next generation of cloud-scale software. Those that excel on compute or networking alone, but create bottlenecks at the storage or orchestration layer, will increasingly be disqualified from shortlists as engineering teams become more sophisticated in their evaluations.

Key Takeaways

• Cloud-scale software requires infrastructure designed for multi-tenancy, rapid deployment cycles, elastic scale, and high-performance analytics — not simply more of what supported traditional enterprise software.
• DevOps velocity is only achievable when the underlying infrastructure is fully API-driven and can be managed as code.
• Cost efficiency at scale requires deliberate architectural choices; public IaaS alone does not automatically deliver economies of scale.
• Real-time analytics workloads are increasingly central to cloud software products and demand low-latency, high-throughput storage infrastructure such as all-flash and NVMe-based systems.
• Most production environments will adopt hybrid strategies, mixing public IaaS, private cloud, and colocation to balance agility, performance, and cost.