Fri. Nov 27th, 2020
Cloud Performance

Boost the performance of enterprise cloud applications with the right mix of services and design choices. Consider these elements to optimize your workloads.

Cloud Performance
Cloud Performance

Cloud Performance-Enterprises strive for optimal application performance, but achieving it is no accident.

In an on-premises, host-based IT setting, enterprises must deliver properly tuned resources to meet performance goals. Cloud computing complicates these considerations because it limits how much a user can tailor the infrastructure and other available features.

While there is no single cloud architecture that guarantees peak performance for every application, several services and practices can boost cloud performance.

1. Select appropriate instances

Organizations see a profound result if they understand their workload’s resource needs and provision an instance type with appropriate characteristics for it. VMs are the most common instance type in the cloud, though container variants are proliferating quickly.

The goal is to right-size the instance with the best allotment of virtual CPUs (vCPUs), memory and specialized characteristics. If the instance is too big, the extra resources have little benefit on the cloud workload performance and will ultimately waste money. If the instance is too small, it will impair performance — if the workload runs at all.

Cloud providers offer myriad instance types — each with a unique mix of vCPUs, memory, storage and networking. These VMs can be tailored for specific tasks. For example, AWS’ A1 instances suit scale-out and ARM workloads. On the other hand, M4 instances balance resources for different applications. There are cloud instances optimized for compute-intensive workloads, memory-intensive applications, AI and more. This array of choices makes it essential to pick the right one.

2. Implement autoscaling services

Enterprises traditionally approached scaling as an ad hoc effort. Since the IT resources were limited and within the business’s ownership, there was little need to make scaling fast, dynamic or autonomous.

However, public cloud computing is dynamic. The public cloud offers the potential to add or remove instances and related resources on demand. AWS, Google Cloud Platform (GCP) and Microsoft Azure each offer load balancing and autoscaling.

Organizations must implement the appropriate rule set to decide when and what to scale if they want to enhance cloud performance. In many cases, monitoring services track load characteristics, such as average vCPU utilization. When the workload exceeds a defined utilization threshold, the monitoring alert triggers the autoscaling service, which follows a predefined plan to add resources and set load-balancing preferences. When the load drops below a certain threshold, the autoscaling service can reverse the process and withdraw unneeded resources.

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Article Credit: TechTarget

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