science

Intelligent workload management

Intelligent workload management is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Intelligent workload management rather than just read about it. In short: Intelligent workload management (IWM) is a paradigm for IT systems management arising from the intersection of dynamic infrastructure, virtualization, identity management, and the discipline of software appliance development. IWM enables the management and optimization of computing resources in a secure and compliant manner across physical, virtual and cloud environments to deliver business services for end customer…

Key takeaways

Intelligent workload management belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Intelligent workload management to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Intelligent workload management from memory before moving on to harder problems.

Reference excerpt

Intelligent workload management (IWM) is a paradigm for IT systems management arising from the intersection of dynamic infrastructure, virtualization, identity management, and the discipline of software appliance development. IWM enables the management and optimization of computing resources in a secure and compliant manner across physical, virtual and cloud environments to deliver business services for end customers. The IWM paradigm builds on the traditional concept of workload management whereby processing resources are dynamically assigned to tasks, or "workloads," based on criteria such as business process priorities (for example, in balancing business intelligence queries against online transaction processing), resource availability, security protocols, or event scheduling, but extends the concept into the structure of individual workloads themselves.

Definition of "workload" In the context of IT systems and data center management, a "workload" can be broadly defined as "the total requests made by users and applications of a system." However, it is also possible to break down the entire workload of a given system into sets of self-contained units. Such a self-contained unit constitutes a "workload" in the narrow sense: an integrated stack consisting of application, middleware, database, and operating system devoted to a specific computing task. Typically, a workload is "platform agnostic," meaning that it can run in physical, virtual or cloud computing environments. Finally, a collection of related workloads which allow end users to complete a specific set of business tasks can be defined as a "business service."

Making workloads "intelligent" A workload is considered "intelligent" when it a) understands its security protocols and processing requirements so it can self-determine whether it can deploy in the public cloud, the private cloud or only on physical machines; b) recognizes when it is at capacity and can find alternative computing capacity as required to optimize performance; c) carries identity and access controls as well as log management and compliance reporting capabilities with it as it moves across environments; and d) is fully integrated with the business service management layer, ensuring that end user computing requirements are not disrupted by distributed computing resources, and working with current and emergent IT management frameworks.

Intelligent workloads and security in the cloud The deployment of individual workloads and workload-based business services in the "hybrid distributed data center," - including physical machines, data centers, private clouds, and the public cloud - raises a host of issues for the efficient management of provisioning, security, and compliance. By making workloads "intelligent" so that they can effectively manage themselves in terms of where they run, how they run, and who can access them, intelligent workload management addresses these issues in a way that is efficient, flexible, and scalable. The 1989 seminal work by D.F. Ferguson, Y. Yemini, and C. Nikolaou "Microeconomic Algorithms for Load Balancing in Distributed Computing Systems" developed a theory by which workloads could be made "intelligent" to manage themselves. This theory has since been patented and was commercialized by the Boston-based company, VMTurbo, in 2009.

See also Cloud computing Dynamic infrastructure Identity management Portable application Software appliance Virtual appliance

References

Worked examples

Example 1 — a first encounter with Intelligent workload management

Start with the simplest possible case. Write down what Intelligent workload management claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Intelligent workload management before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Intelligent workload management ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Intelligent workload management

In research: Intelligent workload management appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Intelligent workload management in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Intelligent workload management is common in secondary-school and first-year university syllabi. It links to neighbouring topics Information technology management, so understanding it makes those chapters shorter.
In everyday life: Look for Intelligent workload management outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Intelligent workload management in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Intelligent workload management means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Intelligent workload management out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Intelligent workload management in simple terms?

Why does Intelligent workload management matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Intelligent workload management?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Intelligent workload management.