How Much Memory Do I Need? Jack Opgenorth October, 2004

Page 2 Overview z/TPF = Relief, –But How Soon Can You Get There? How Soon Will I Run Out? What Are the Driving Factors? If Necessary, Can You Extend Your Timeline?

Page 3 Z is for Zillions of Bytes Trivia Challenge What’s 2 64 /2 31 GB?  A HEAP! z/TPF 1.1 –Lots of Storage –Lots of other Features, too Do You Want To β Your Business?

Page 4 Running on Empty? Some of Us May Already Know We Have A Problem How Are You Going to Know? –Measure (Last Spring’s Presentation) –Evaluate the Data Get The Brick Stretcher Out! Change Parameters  $ Change Applications $$$

Page 5 TPF Storage Distribution

Page 6 Drivers of Storage Growth Globals –Increased complexity of data market segmentation-creates replication –Volume of Data in Memory Continues to Grow Working Storage –Heap Storage – MBs / ECB –More Engines (16 way) –Larger and more complex data structures –Increased Latency (IO)

Page 7 What Drives Working Storage Demand Application Use of Heap Is One Contributor But Existence Time Is A Significant Contributor N = Φ*R (Little’s Law) Simplified  Working Storage Required (N) = Existence Time (Φ) * Arrival Rate (R) All Values Can Be Obtained From Data Collection Generally, Φ And R Together

Page 8 Are Your Cheeks Full? You’ve Probably Seen The Phone Ads –It’s “Dang-shin, Voce, ‘Nee’, Sie, Usted, You …..” High Utilizations

Page 9 Another Example

Page 10 Another Example- Is It All IO?

Page 11 What Else Is There? What Are We Waiting On? –IO or Someone Else That Looks Like An IO Distributed Processing-Synchronous Calls? Do You Have Time-Out Thresholds For Certain Traffic? Scenario 1 vs. Scenario 2 44% Increase In Existence Time Results (Ouch!) Traffic TypeMsg. Rate Existence Time (sec) Internal Only 10, Distributed Total 10, Traffic TypeMsg. Rate Existence Time (sec) Internal Only 10, Distributed Total 10,

Page 12 Possible Solutions Tighten Up Response Time Requirements –How Many Messages With 10 Sec T.O. Are You Holding Onto? Internal Queuing IO Latency I-Stream Unique 31 Bit Globals? –Increasing I-Streams Reduces VFA –Recall VFA Performance vs. Memory (Blackburn)

Page 13 Some Opportunities for Everyone 1 ST Check Data Collection VFA EFFICIENCY STATISTICS SYSTEM WIDE BSS SUBSYSTEM PROGRAM READS 0.00 PER SECOND 0.00 PER SECOND DATA READS PER SECOND PER SECOND FINDS (WITH I/O) PER SECOND PER SECOND CANDIDATE FILES PER SECOND PER SECOND FORCE FILES 2.70 PER SECOND 2.70 PER SECOND NON-CANDIDATE FILES PER SECOND PER SECOND FILE IMMEDIATES PER SECOND PER SECOND BUFFER UNAVAILABLE 0.00 PER SECOND 0.00 PER SECOND  381 BUFFER USAGE 3.63 PER SECOND 3.63 PER SECOND 1055 BUFFER USAGE PER SECOND PER SECOND 4K BUFFER USAGE PER SECOND PER SECOND ….

Page 14 VFA Buffer Allocation Use Data Available From APAR –File Address –Access Rate Other’s Ideas Used- Aging Time (Residency) Access Density (Spring, 2004) Base Buffer Allocations on Locality Of Reference Don’t Forget About Utilities No Worries in z/TPF

Page 15 Building A Bridge to 64 Bit Architecture Instrumentation is Key –Data Collection Working Storage, CRPA, VFA … No Help for Globals – Approaching 50% of Consumption –Choices for Data in Memory left to Subject Matter Experts Good Choices Can Be Made Creep in Data Structures Diffuse Benefit

Page 16 Global Measurement (Spring, 2004) Tool Samples Memory Access Based on Event Timer (1 second Intervals) Range of Memory Sampled Reports Demonstrate Global Recovery Opportunity

Page 17 Know Your Workload Memory Modeling Is Not Rocket Science –Little’s Law –Apply It –Check With Reality Watch Out For The Other YOU’s CPU Speed Is Not Our Biggest Problem –Know What You Waiting On (IO, Delay, Defer, and Synchronous Traffic

Page 18 Questions ?

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