The Future of Workloads: A Cloud Perspective

Aug 06, 2020

Infrastructure and Cloud Technology | 4 min READ

With the worldwide public cloud service market expected to grow to USD 331.2 billion in 2022 and seven in 10 companies already running at least half of their workloads on the cloud, it is safe to say that the future is cloud-first. What’s driving this rapid uptake of cloud technology? Data.

Kapil Uniyal

Former Vice President & Global HBU Head

Infrastructure, Security and Cloud Technology Services

Birlasoft

The rate at which organizations today are bombarded with operational and financial data is posing a challenge concerning data management while presenting an opportunity to gather actionable data insights. By using an appropriate cloud deployment model, companies can manage critical data effectively and harness the proven benefits of the cloud – reliability, cost-effectiveness, scalability, and flexibility. Another reason why cloud adoption is on the rise can be attributed to the rise of the BYOD worker and the need for accessing business-critical data on the go. And companies are making significant investments in deploying cloud-native applications and replacing legacy software with SaaS offerings to empower the mobile workforce.

Interestingly, an industry-by-industry comparison unearths unexpected trends in cloud spending. According to IDC, professional services, discrete manufacturing, and banking will account for one-third of all public cloud spending from 2018 to 2023. Investment in infrastructure-as-a-service (IaaS), in particular, will be higher for industries focused on building data and compute-intensive services. That can be linked back to the agility that cloud solutions deliver when it comes to mining large volumes of data and running in-depth analytics on them.

Managing workloads in the cloud

2019 saw an increased interest in the adoption of hybrid cloud deployment models characterized by the combination of a private cloud with a third-party, public cloud service. That way, organizations could move their workloads between the private cloud and the public cloud based on business requirements such as data security compliance or the need for decreased response latency. For instance, transactional workloads could be run on-premise, while the analytics workloads run on the cloud, providing superior flexibility.

"As organizations move their workloads to cloud infrastructure, data security will be the top priority for most CIOs. The traditional security operations center (SOC) will be replaced with next-generation tools monitoring the security posture round the clock and relaying in-depth insights through intuitive dashboards. The use of DevSecOps that promotes ‘security by design’ will also see significant uptake to ensure security vulnerabilities across the application ecosystem are eliminated during the development stage."

- Kapil Uniyal

We often discuss which workloads are ideal for being migrated to the cloud. And the parameters to be considered are always centered around the need for scalability. Web applications that experience spikes in the workload, batch processing tasks that require dedicated processing power, and disaster recovery setups – are all good candidates for cloud migration. According to some reports, backup and recovery comprise close to 15 percent of the cloud budget. And that makes absolute business sense. Procuring and maintaining a replica of production-quality infrastructure can prove to be extraordinarily cost-intensive and wasteful. When a disaster situation strikes, companies can just scale up the DR infrastructure on the cloud and prevent downtime, resulting in seamless business continuity. On the other hand, some workloads do not lend themselves well to a cloud deployment – applications that require very low latency over the network or ones that rely on unique hardware configurations not offered by cloud providers. Moreover, some databases associated with very high I/O rates and network throughputs may also prove to be wrong candidates for the cloud.

The future of cloud management will be defined by AIOps – the application of artificial intelligence (AI) to IT operations. Intelligent multi-layered technology platforms will be able to capture the cloud administrator’s intent regarding business metrics and operational efficiency and translate them to orchestrate physical and virtual resources, ensuring SLA achievement. AIOps will especially prove useful in multi-cloud environments.

The rise of the multi-cloud

From relying entirely on leading monolithic cloud service providers such as AWS, Microsoft Azure, and Google Cloud Platform, companies are looking to move to specialized public cloud offerings delivering significant improvements in running compute and storage workloads better.

These specialized or boutique providers offer IaaS, PaaS, or microservices, that are more agile, cost-efficient, and reliable. As a result, the demand for multi-cloud management tools such as Microsoft Azure Arc, Cisco CloudCenter Suite, and Google Cloud Anthos is on the rise. Those tools can be deployed to ensure the unified discovery, monitoring, security, and troubleshooting of workloads.

Several industry reports indicate that that most companies do not have the in-house skills and strategy to manage a multi-cloud environment effectively. That implies companies looking to drive cloud ROI will have to bridge the talent gap, ensure close business-IT alignment, and enable robust change management and governance. Another way to achieve that is to partner with a proven cloud implementation partner with diverse experience of kickstarting complex multi-/hybrid-cloud environments and extensive artillery of cloud assessment tools, templates, and accelerators.

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