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Workload Optimization

Chapter Description

Workload Optimizer is an analytical decision engine that generates actions that drive the IT environment toward a desired state where workload performance is assured and cost is minimized. In this sample chapter from Cisco Intersight: A Handbook for Intelligent Cloud Operations, you will explore how Workload Optimizer helps applications perform well while simultaneously minimizing cost (in a public cloud) and optimizing resource utilization (on premises) while also complying with workload policies.

Targets and Configuration

For Workload Optimizer to generate actions, it needs information to analyze. It accesses the information it needs via API calls to targets, as configured under the Admin tab (refer to Chapter 1). The information gathered from infrastructure targets—metadata, telemetry, and metrics—must be both current and actionable.

The number of possible data points available for analysis is effectively infinite, and Workload Optimizer gathers only data that has the potential to lead to or impact a decision. This distinction is important as it can help explain why a given target is or is not available or supported. In theory, anything with an API could be integrated as a target, but the key question would be “What decision would Workload Optimizer make differently if it had this information?”

One of the great advantages of this approach—and the economic abstraction that underpins the decision engine—is that it scales. Human beings are easily overwhelmed by data, and more data usually just means more noise that confuses the situation. In the case of Workload Optimizer’s intelligent decision engine, the more data it has from a myriad of heterogeneous sources, the smarter it gets. More data in this case means more signal and better decisions.

Workload Optimizer accesses its targets in three basic ways (see Figure 9-1):

  • Making direct API calls from the Intersight cloud to other cloud services and platforms such as Amazon Web Services, Microsoft Azure, and AppDynamics SaaS (that is, directly cloud to cloud)

  • Communicating directly with targets that natively run Device Connector

  • Via the Assist function of Intersight Appliance, which enables Workload Optimizer to communicate with on-premises infrastructure natively lacking Device Connector (that is, most third-party hardware and software) that otherwise would be inaccessible behind an organization’s firewall.

Figure 9-1

Figure 9-1 Communication with public cloud services and on-premises resources

It is therefore possible to use Workload Optimizer as a purely SaaS customer, as a purely on-premises customer, or as a mix of both.

While all communication to targets occurs via API calls, without any traditional agents required on the target side, Kubernetes clusters do require a unique setup step: deploying Kubernetes Collector on a node within the target cluster. Collector runs with a service account that has a cluster administrator role and runs Device Connector, essentially proxying communications to and commands from Intersight and the native cluster kubelet or node agent. In this respect, Collector allows the insertion of Device Connector into any Kubernetes cluster, whether on premises or in the public cloud.

One of the richest sources of workload telemetry for Workload Optimizer comes from application performance management tools such as Cisco’s AppDynamics. As noted earlier, the core focus of Workload Optimizer is application resource management. However, for an application to truly perform well, it needs more than just the right physical resources at the right time; it also needs to be written and architected well.

AppDynamics provides developers and applications IT teams with a detailed logical dependency map of the application and its underlying services, fine-grained insight into individual lines of problematic code and poorly performing database queries and their impact on actual business transactions, and guidance in troubleshooting poor end-user experiences. Figure 9-2 illustrates the combination of application performance management (that is, assuring good code and architecture) and Workload Optimizer’s application resource management (that is, the right resources at the right time for the lowest cost).

Figure 9-2

Figure 9-2 AppDynamics integration into Workload Optimizer

Cisco Full Stack Observability (FSO) expands on the visibility, insights, and action capabilities of the Workload Optimizer and AppDynamics combination and enhances it with wide area network and end-user monitoring intelligence from Cisco ThousandEyes. The FSO solution currently addresses numerous critical business use cases, such as customer digital experience monitoring and cloud-native application monitoring, and more product integrations and use cases are on the way.

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