Overview : The DevOps methodology embodies two core philosophies: decreasing the lead time of software deployment and the automation of delivery and testing. DevOps emerged as a practical response to the agile development movement, in contrast with traditional, phase-based or “waterfall” development, which is inefficient and labor-intensive. Traditional methods should be phased out, and companies...

Tech has had a tremendous impact on the way today’s businesses seek continued growth and improvement. No matter what business they are in, executives everywhere are investing in technology that improves their business processes, gets them ahead of the competition and widens their margins. Ultimately, the return on that investment is determined by how well technology supports a business’ ability to...

Capacity management, considered by top analyst firms to be an essential process in any large IT organization, is often so complex that in today’s accelerated business world it cannot be effectively implemented. Changing priorities, increasing complexity and scalable cloud infrastructure have made traditional models for capacity management less relevant. A new paradigm for capacity management is...

At an application level with Vityl Capacity Management In this guide, John Miecielica of Metavante, provides a step-by-step example showing how he uses Vityl Capacity Management to analyze IT resource consumption at an application level. This key capability is especially important in today’s environments where multiple applications run on a server or multiple servers might be required to implement...

In this paper we present an introductory analysis of throughput scalability for update intensive workloads (such as measured by the TPC-C or TPC-W benchmarks) and how that scaling is limited by serialization effects in the software-hardware combination that comprises any platform.

In this online article Dr. Gunther digs down into the UNIX kernel to find out how load averages (the “LA Triplets”) are calculated and how appropriate they are as capacity planning metrics.

This is the second in a two part-series where we explore the use of UNIX load averages in performance analysis and capacity planning.

This is an unexpected Part 3 to the discussion about the UNIX load average metric answering the question of where the weight factor comes from.

This article is about delusions that arise from incorrect interpretation of load test measurements.

This paper discusses avoiding calculation results that are more precise than is justified by precision of corresponding measurement input data.

This article explains how correlated or bunched requests can impact capacity planning results.

This article explains how correlated or bunched requests can impact capacity planning results.

This is the final online article concerning the concept of application scalability. Here, you will learn how to determine value of the parameters that control scalability.

This is a little quiz to test your understanding of the triplet of numbers that appear in the UNIX® load average (LA) performance metric.