Welcome to CMG Journal number 137. This issue features six great papers for your reading pleasure.
Our first contributed paper in this issue is the third in a four part series from Ann Dowling and Clea Zolotow, Analytics Techniques for Capacity Management: Forecasting and Modeling is the third of the 4-part series that examines aspects of the Capacity Management process that are conducive to apply Analytics and Capacity Planning. This part explores fundamental forecasting and analytical techniques for capacity planning from the three perspectives of Capacity Management examined in this series’ prior parts: Business, Service, and Component Capacity Management. It examines some of the analytics tools and techniques such as SPSS, SAS, and home-built applications supporting forecasting methodologies.
We also have our usual Winter Issue collection of Late Breaking papers from the CMG Conference.
Our second paper, Multi-tier Application Performance Analysis in the Presence of Software Bottlenecks, from Subhasri Duttagupta, Manoj Nambiar, and Rupinder Virk, describes an analytical model-based technique for predicting performance of a multi-tier system, where the model consists of two layers of queuing networks for software resources and hardware resources. The paper further analyzes performance of two real-life multi-tier enterprise applications that encounter software resource bottlenecks.
Our third paper, Rationale for an IPv6 PDM Extension Header, by Nalini Elkins, looks at the need for performance diagnostic metrics for packets using IPv6 networks. The proposed extension header for IPv6 packets is currently under consideration by the Internet Engineering Task Force (IETF).
Our fourth paper, DevOps for Non-Functional Test Assurance, by Uppara Hampaiah, Mallikarjun Patil, Vijay Paluri, and Srinivas Jandhyala, introduces a SPARTA framework for use by Quality Assurance teams during application development and testing. The paper includes examples of the use of the framework and the benefits acheived.
Our fifth paper, Calibrate Workload Model for Accurate Performance Testing by Benjamin Mao, looks at how to build a workload model that reflects the reality of your production environment, so that you modeling will actually predict the performance that your app will achieve.
Our sixth paper, Implementatioon of Web Services Using Better Performance Improvement Techniques, by Madhusudan Mothe and Kamalakar Burle, looks at web services: what it is, why it is required, how it overcomes the drawbacks of existing technologies, etc.
Thanks to everyone who contributed to this CMG Journal. Even if you have submitted a paper for the Conference, please consider writing a paper for the CMG Journal. You can submit your papers, as well as feedback to us at [email protected].
Thanks again for reading, and we hope you enjoy this issue.