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Monday, August 28, 2017

#imPACt 2017 conference program is published. 3. "Rules of Thumb for Response Time Percentiles" (#CMGnews):

See the full program HERE


Again Percentiles! My anomaly detection tool (SonR) now has option to use percentiles to calculate UCL and LCL (Control Limits).



Let's go a listen about  percentiles at the following CMG international conference session:
 Rules of Thumb for Response Time Percentiles: How Risky are they? 
PERF
Whether externally mandated or internally tracked, the enterprise relies on governance of application service response time objectives. In many cases, achieving service requirements in terms of the average response time may not deliver an experience that delights the consumer. The consumer may request a deeper level of governance. Service providers want to achieve the promised objectives and, on the other hand, avoid over-provisioning. This paper explores rules of thumb that can be applied to estimate 90th or 95th percentiles for service response times, based on the measured or predicted mean. The risk assessment behind these recommendations is described in the paper. Various types of networks were modeled and analyzed. Even though classical queueing models rely on strict assumptions (and rarely met in the real world), it was found that the classical M/M/1 model provided a useful upper bound. Another function was evaluated for tighter accuracy.
Presenter bio: Dr. Salsburg is an independent consultant. Previously, Dr. Salsburg was a Distinguished Engineer and Chief Architect for Unisys Technology Products. He was founder and president of Performance & Modeling, Inc. Dr. Salsburg has been awarded three international patents in the area of infrastructure performance modeling algorithms and software. In addition, he has published over 70 papers and has lectured world-wide on the topics of Real-Time Infrastructure, Cloud Computing and Infrastructure Optimization. In 2010, the Computer Measurement Group awarded Dr. Salsburg the A. A. Michelson Award.
Presenter bio: Co-founder and Chief Scientist, BGS Systems, 1975 - 1998

#imPACt 2017 conference program is published. 2. "The Curse of P90..." (#CMGnews):

See the full program HERE

I love Percentiles! My anomaly detection tool (SonR) now has option to use percentiles to calculate UCL and LCL (Control Limits).

Let's go a listen about  percentiles at the following CMG international conference session:

The Curse of P90: An Elegant Way to Overcome it Without Magic 
CAP
Over the decades of development of methodologies and metrics for IT capacity planning and performance analysis, percentile terminology has become the lingua franca of the field. It makes sense: percentiles are easy to interpret, not sensitive to outliers, and directly usable for approximating the distribution of the variable being measured for stochastic simulations. However, depending on which percentile is used, we can miss important information, like multimodality of the metric's distribution. Another, less obvious, downside of relying on percentiles comes into play when we size infrastructure for a high percentile of demand (e.g., p90). Given that it takes time to order, manufacture, receive, and install infrastructure, this means that we need to answer the statistically nontrivial question, "what will this percentile of demand be in one to three years?" This paper discusses the issues that arise in answering it and proposes an elegant way of resolving them.
Presenter bio: Alexander Gilgur is a Data Scientist and Systems Analyst with over 20 years of experience in a wide variety of domains - Control Systems, Chemical Industry, Aviation, Semiconductor manufacturing, Information Technologies, and Networking - and a solid track record of implementing his innovations in production. He has authored and co-authored a number of know-hows, publications, and patents. Alex enjoys applying the beauty of Math and Statistics to solving capacity and performance problems and is interested in non-stationary processes, which make the core of IT problems today. Presently, he is a Network Data Scientist at Facebook and an occasional faculty member at UC Berkeley's MIDS program. He is also a father, a husband, a skier, a soccer player, a sport psychologist, a licensed soccer coach, a licensed professional engineer (PE), and a music aficionado. Alex's technical blog is at http://alexonsimanddata.blogspot.com.


#imPACt 2017 conference program is published. Best Paper CMG India: Performance #AnomalyDetection & Forecasting Model (#CMGnews)




See the full program HERE

Performance Anomaly Detection & Forecasting Model (PADFM) for eRetailer Web application 
EMT
With high performance becoming a mandate, its impact & need for sophisticated performance management is realized by every e-business. Though Application Performance Management (APM) tools has brought down the performance problem diagnosis time to a great extend, these tools don't actually help in detecting the anomalies in the production environment (online or offline mode) and make forecasts on the server performance metrics for capacity sizing. Hence, robust performance anomaly detection and forecasting solution is in demand to detect anomalies in production environment and to provide forecasts on server resource demand to support in server sizing. This paper deals with the implementation of Performance Anomaly detection and Forecasting Model for an online retailer business application using statistical modeling & machine learning techniques that has yielded multi-fold benefit to the business
Presenter bio: I carry about 14+ years of industry wide experience in Performance Testing & Performance Engineering. Am a computer science engineer with Masters in Software Systems (MS) from BITS PILANI, India. I am the Co-Founder & CTO of a US startup, QAEliteSouls LLC (http://qaelitesouls.com) and the founder of a Indian startup, EliteSouls Consulting Services LLP (http://elitesouls.in). I am the CMG India Director for the year 2017.

Cloud Performance Management and Machine Learning are Covered by Greater Boston CMG fall 2017 conference (#CMGnews)

See full agenda HERE

Date and TimeFriday, September 22, 2017, 8:30am - 5:00pm

10:00Performance Management for Cloud Applications (1)Priyanka Arora (MUFG APM)
11:00Benchmarking Machine Learning (2)Rohith Bakkannagari (Mathworks)
2:00Dynatrace journey from monolithic application to Cloud native application (4)Asad Ali (Dynatrace)
3:00Can a robot read your performance reports? Deep learning and machine learning for Performance and Capacity Engineers (5)Anoush Najarian (Mathworks)

Monday, August 14, 2017

I will present at #imPACt2017 conference - "The Model Factory - Correlating Server and Database Utilization with Customer Activity"

The abstract and more info can be found here:

US Patent "SYSTEMS AND METHODS FOR MODELING COMPUTER RESOURCE METRICS", I Trubin et al


Presentation is scheduled:
New Orleans, Louisiana at the 
Loews New Orleans Hotel
Session Number:  362
Subject Area:  CAP
Session Date and Time: 11/8/2017, 2:20 PM-2:50 PM
Room Assignment:  Beauregard

See conference details here: http://cmgimpact.com/ 
Your are welcome to attend!