SlideShare uma empresa Scribd logo
1 de 26
William Louth
    JINSPIRED

      @jxinsight
 www.jinspired.com
william@jinspired.com
Data
Local




Remote   Past   Present   Predicted
Information   Management
   Model        Model
Measurement
Method      Metering      Metric

 Analysis    Causality   Correlation

 Accuracy     Event       Sampled

Allocation    Thread       Process

Assignment    Direct     Apportioned
Model     Activity Resource

Device     Probe      Meter

Develop    Code      Counter

Design    Behavior    Usage

 Data      Group     Metering
Complexity
Space
                        Complexity




               ity
             rs
          ve


                  ism
        Di


                 m
               na
             Dy




                                 Time
HTTP
                                                                                      API

                                                                           ta
                                                                         da
                                                                                     Cloud
                                             data/code                              Service
              data
Application          HTTP      Application                 Activity
                      API
                                               data
                                                                           da
                                                                             ta

                      Cloud
                                                            Cloud        code
                     Service
                                                         Service/Shell               Activity




                                                                                     Cloud
                                                                                  Service/Shell
Client




    sf://.......

cl:clock.time=
                   cl   Response

                        sf:clock.time=
                        sf:cpu.time=
                        sf:io.bytes=
                        sf:charge.unit=
                                          ☁
                                          sf



                                               ☁
                                               ☁
                                               salesforce.com




sf:clock.time=
sf:cpu.time=
sf:io.bytes=
sf:charge.unit=




  Metering
 Management
   Service
Client




  sf://..........
cl:clock.time=
                    cl




                         ☁ Response
                         sf:clock.time=
                         sf:cpu.time=
                         sf:io.bytes=
                         sf:charge.unit=
                         sf:db.time=
                         sf:db.count=
                         s3:clock.time=
                         s3:io.bytes=
                         s3:charge.unit=
                                            ☁
                                            ☁
                                            sf         salesforce.com



                                                             db




                                                       Response




                         ☁
sf:clock.time=
                                                  s3:clock.time=




                         ☁
sf:cpu.time=
                                                  s3:io.bytes=
sf:io.bytes=




                         ☁
                                                  s3:charge.unit=
sf:charge.unit=                             s3
sf:db.time=
sf:db.count=
s3:clock.time=                        aws.amazon.com
s3:io.bytes=
s3:charge.unit=




 Metering
Management
  Service
Control
Awareness
& Adaption
Profile   Protect   Police   Prioritize   Predict   Provision
Self Adaptive Software

Self Adaptive Software evaluates its own behavior
and changes behavior when the evaluation
indicates that it is not accomplishing what the
software is intended to do, or when better
functionality or performance is possible.” DARPA
Evidence
                                                        Observation
                  1



                                                            Self
                                                         Regulated
Action   4   Feedback Loop   2   Relevance   Reaction                 Judgement




                  3


             Consequence
Disturbances




Goals
        Control            Process




                  Sensor
public int func(...) {
                                      QoS
                    reserve(func)   Resource
......
........
.........
.........                               Rate
......                                Limiting
........
...                                     Priority
.....                                  Queueing
...                release(units)

}                                      Reservation
                                          Lanes
User
   WebPage
   Resource
System
Dynamics
+                  _
Inflow                Stock               Outflow


 + _                                        + _


                      Sensor




                +                     _
 Births              Population             Deaths


 +                                           +

       reinforcing                    balancing
           loop                         loop
+

                     _              +                     _
Thread Pool              Reserved        Concurrency          Released



           initial
            size




       +

                     _              +                     _
QoS Resource             Reserved       QoS Reservation       Released



        initial
       capacity
operations            System
      { system execution model }   Dynamics


                                                                  Application
                                                 unification   System Dynamics
                                                                   Software




        development                Application
{ software execution model }        Software
Finally....Faster
Sweeper
                                                                       (Secondary Processor)




                                                         Reduce Friction
                                                       Increase the Speed
     Thrower                            Trajectory
                      Curling Stone                                                               Target
[Primary Processor]                   Predicted Path                            Reduce Curl
                                                                            Straighten the Path
                                                                             Shorten




                                                             Sweeper
                                                       (Secondary Processor)

Mais conteúdo relacionado

Semelhante a Jinspired june2012

StackWatch: A prototype CloudWatch service for CloudStack
StackWatch: A prototype CloudWatch service for CloudStackStackWatch: A prototype CloudWatch service for CloudStack
StackWatch: A prototype CloudWatch service for CloudStackChiradeep Vittal
 
Andrii Dembitskyi "Events in our applications Event bus and distributed systems"
Andrii Dembitskyi "Events in our applications Event bus and distributed systems"Andrii Dembitskyi "Events in our applications Event bus and distributed systems"
Andrii Dembitskyi "Events in our applications Event bus and distributed systems"Fwdays
 
Containerless in the Cloud with AWS Lambda
Containerless in the Cloud with AWS LambdaContainerless in the Cloud with AWS Lambda
Containerless in the Cloud with AWS LambdaRyan Cuprak
 
FlinkForward Asia 2019 - Evolving Keystone to an Open Collaborative Real Time...
FlinkForward Asia 2019 - Evolving Keystone to an Open Collaborative Real Time...FlinkForward Asia 2019 - Evolving Keystone to an Open Collaborative Real Time...
FlinkForward Asia 2019 - Evolving Keystone to an Open Collaborative Real Time...Zhenzhong Xu
 
The Art of The Event Streaming Application: Streams, Stream Processors and Sc...
The Art of The Event Streaming Application: Streams, Stream Processors and Sc...The Art of The Event Streaming Application: Streams, Stream Processors and Sc...
The Art of The Event Streaming Application: Streams, Stream Processors and Sc...confluent
 
Kakfa summit london 2019 - the art of the event-streaming app
Kakfa summit london 2019 - the art of the event-streaming appKakfa summit london 2019 - the art of the event-streaming app
Kakfa summit london 2019 - the art of the event-streaming appNeil Avery
 
PART-3 : Mastering RTOS FreeRTOS and STM32Fx with Debugging
PART-3 : Mastering RTOS FreeRTOS and STM32Fx with DebuggingPART-3 : Mastering RTOS FreeRTOS and STM32Fx with Debugging
PART-3 : Mastering RTOS FreeRTOS and STM32Fx with DebuggingFastBit Embedded Brain Academy
 
Overcoming the Top Four Challenges to Real‐Time Performance in Large‐Scale, D...
Overcoming the Top Four Challenges to Real‐Time Performance in Large‐Scale, D...Overcoming the Top Four Challenges to Real‐Time Performance in Large‐Scale, D...
Overcoming the Top Four Challenges to Real‐Time Performance in Large‐Scale, D...SL Corporation
 
2 years with python and serverless
2 years with python and serverless2 years with python and serverless
2 years with python and serverlessHector Canto
 
Lessons Learnt from Running Thousands of On-demand Spark Applications
Lessons Learnt from Running Thousands of On-demand Spark ApplicationsLessons Learnt from Running Thousands of On-demand Spark Applications
Lessons Learnt from Running Thousands of On-demand Spark ApplicationsItai Yaffe
 
Monitoring Weave Cloud with Prometheus
Monitoring Weave Cloud with PrometheusMonitoring Weave Cloud with Prometheus
Monitoring Weave Cloud with PrometheusWeaveworks
 
Systems Bioinformatics Workshop Keynote
Systems Bioinformatics Workshop KeynoteSystems Bioinformatics Workshop Keynote
Systems Bioinformatics Workshop KeynoteDeepak Singh
 
(CMP407) Lambda as Cron: Scheduling Invocations in AWS Lambda
(CMP407) Lambda as Cron: Scheduling Invocations in AWS Lambda(CMP407) Lambda as Cron: Scheduling Invocations in AWS Lambda
(CMP407) Lambda as Cron: Scheduling Invocations in AWS LambdaAmazon Web Services
 
Pros and Cons of a MicroServices Architecture talk at AWS ReInvent
Pros and Cons of a MicroServices Architecture talk at AWS ReInventPros and Cons of a MicroServices Architecture talk at AWS ReInvent
Pros and Cons of a MicroServices Architecture talk at AWS ReInventSudhir Tonse
 
OSMC 2016 - ZMON Zalandos OS approach to monitoring in the cloud and DCs by J...
OSMC 2016 - ZMON Zalandos OS approach to monitoring in the cloud and DCs by J...OSMC 2016 - ZMON Zalandos OS approach to monitoring in the cloud and DCs by J...
OSMC 2016 - ZMON Zalandos OS approach to monitoring in the cloud and DCs by J...NETWAYS
 
OSMC 2016 | ZMON: Zalando's OS approach to monitoring in the cloud and DCs by...
OSMC 2016 | ZMON: Zalando's OS approach to monitoring in the cloud and DCs by...OSMC 2016 | ZMON: Zalando's OS approach to monitoring in the cloud and DCs by...
OSMC 2016 | ZMON: Zalando's OS approach to monitoring in the cloud and DCs by...NETWAYS
 
#lspe: Dynamic Scaling
#lspe: Dynamic Scaling #lspe: Dynamic Scaling
#lspe: Dynamic Scaling steveshah
 
Deep Dive into SpaceONE
Deep Dive into SpaceONEDeep Dive into SpaceONE
Deep Dive into SpaceONEChoonho Son
 

Semelhante a Jinspired june2012 (20)

StackWatch: A prototype CloudWatch service for CloudStack
StackWatch: A prototype CloudWatch service for CloudStackStackWatch: A prototype CloudWatch service for CloudStack
StackWatch: A prototype CloudWatch service for CloudStack
 
Andrii Dembitskyi "Events in our applications Event bus and distributed systems"
Andrii Dembitskyi "Events in our applications Event bus and distributed systems"Andrii Dembitskyi "Events in our applications Event bus and distributed systems"
Andrii Dembitskyi "Events in our applications Event bus and distributed systems"
 
Containerless in the Cloud with AWS Lambda
Containerless in the Cloud with AWS LambdaContainerless in the Cloud with AWS Lambda
Containerless in the Cloud with AWS Lambda
 
FlinkForward Asia 2019 - Evolving Keystone to an Open Collaborative Real Time...
FlinkForward Asia 2019 - Evolving Keystone to an Open Collaborative Real Time...FlinkForward Asia 2019 - Evolving Keystone to an Open Collaborative Real Time...
FlinkForward Asia 2019 - Evolving Keystone to an Open Collaborative Real Time...
 
The Art of The Event Streaming Application: Streams, Stream Processors and Sc...
The Art of The Event Streaming Application: Streams, Stream Processors and Sc...The Art of The Event Streaming Application: Streams, Stream Processors and Sc...
The Art of The Event Streaming Application: Streams, Stream Processors and Sc...
 
Kakfa summit london 2019 - the art of the event-streaming app
Kakfa summit london 2019 - the art of the event-streaming appKakfa summit london 2019 - the art of the event-streaming app
Kakfa summit london 2019 - the art of the event-streaming app
 
PART-3 : Mastering RTOS FreeRTOS and STM32Fx with Debugging
PART-3 : Mastering RTOS FreeRTOS and STM32Fx with DebuggingPART-3 : Mastering RTOS FreeRTOS and STM32Fx with Debugging
PART-3 : Mastering RTOS FreeRTOS and STM32Fx with Debugging
 
AWS Lambda Deep Dive
AWS Lambda Deep DiveAWS Lambda Deep Dive
AWS Lambda Deep Dive
 
Overcoming the Top Four Challenges to Real‐Time Performance in Large‐Scale, D...
Overcoming the Top Four Challenges to Real‐Time Performance in Large‐Scale, D...Overcoming the Top Four Challenges to Real‐Time Performance in Large‐Scale, D...
Overcoming the Top Four Challenges to Real‐Time Performance in Large‐Scale, D...
 
2 years with python and serverless
2 years with python and serverless2 years with python and serverless
2 years with python and serverless
 
Lessons Learnt from Running Thousands of On-demand Spark Applications
Lessons Learnt from Running Thousands of On-demand Spark ApplicationsLessons Learnt from Running Thousands of On-demand Spark Applications
Lessons Learnt from Running Thousands of On-demand Spark Applications
 
Monitoring Weave Cloud with Prometheus
Monitoring Weave Cloud with PrometheusMonitoring Weave Cloud with Prometheus
Monitoring Weave Cloud with Prometheus
 
BlazeDS
BlazeDSBlazeDS
BlazeDS
 
Systems Bioinformatics Workshop Keynote
Systems Bioinformatics Workshop KeynoteSystems Bioinformatics Workshop Keynote
Systems Bioinformatics Workshop Keynote
 
(CMP407) Lambda as Cron: Scheduling Invocations in AWS Lambda
(CMP407) Lambda as Cron: Scheduling Invocations in AWS Lambda(CMP407) Lambda as Cron: Scheduling Invocations in AWS Lambda
(CMP407) Lambda as Cron: Scheduling Invocations in AWS Lambda
 
Pros and Cons of a MicroServices Architecture talk at AWS ReInvent
Pros and Cons of a MicroServices Architecture talk at AWS ReInventPros and Cons of a MicroServices Architecture talk at AWS ReInvent
Pros and Cons of a MicroServices Architecture talk at AWS ReInvent
 
OSMC 2016 - ZMON Zalandos OS approach to monitoring in the cloud and DCs by J...
OSMC 2016 - ZMON Zalandos OS approach to monitoring in the cloud and DCs by J...OSMC 2016 - ZMON Zalandos OS approach to monitoring in the cloud and DCs by J...
OSMC 2016 - ZMON Zalandos OS approach to monitoring in the cloud and DCs by J...
 
OSMC 2016 | ZMON: Zalando's OS approach to monitoring in the cloud and DCs by...
OSMC 2016 | ZMON: Zalando's OS approach to monitoring in the cloud and DCs by...OSMC 2016 | ZMON: Zalando's OS approach to monitoring in the cloud and DCs by...
OSMC 2016 | ZMON: Zalando's OS approach to monitoring in the cloud and DCs by...
 
#lspe: Dynamic Scaling
#lspe: Dynamic Scaling #lspe: Dynamic Scaling
#lspe: Dynamic Scaling
 
Deep Dive into SpaceONE
Deep Dive into SpaceONEDeep Dive into SpaceONE
Deep Dive into SpaceONE
 

Mais de nlwebperf

MeasureWorks - eCommerce Live - Designing Time & Conversion
MeasureWorks -  eCommerce Live - Designing Time & ConversionMeasureWorks -  eCommerce Live - Designing Time & Conversion
MeasureWorks - eCommerce Live - Designing Time & Conversionnlwebperf
 
Nimbuzz march2012
Nimbuzz march2012Nimbuzz march2012
Nimbuzz march2012nlwebperf
 
Nimsoft Web performance monitoring
Nimsoft Web performance monitoringNimsoft Web performance monitoring
Nimsoft Web performance monitoringnlwebperf
 
Hyves: Mobile app development with HTML5 and Javascript
Hyves: Mobile app development with HTML5 and JavascriptHyves: Mobile app development with HTML5 and Javascript
Hyves: Mobile app development with HTML5 and Javascriptnlwebperf
 
NLCMG - Performance is good, Understanding performance is better
NLCMG - Performance is good, Understanding performance is better NLCMG - Performance is good, Understanding performance is better
NLCMG - Performance is good, Understanding performance is better nlwebperf
 
2deHands.be - Tuning a Big Classifieds Site
2deHands.be - Tuning a Big Classifieds Site2deHands.be - Tuning a Big Classifieds Site
2deHands.be - Tuning a Big Classifieds Sitenlwebperf
 

Mais de nlwebperf (7)

MeasureWorks - eCommerce Live - Designing Time & Conversion
MeasureWorks -  eCommerce Live - Designing Time & ConversionMeasureWorks -  eCommerce Live - Designing Time & Conversion
MeasureWorks - eCommerce Live - Designing Time & Conversion
 
Fashiolista
FashiolistaFashiolista
Fashiolista
 
Nimbuzz march2012
Nimbuzz march2012Nimbuzz march2012
Nimbuzz march2012
 
Nimsoft Web performance monitoring
Nimsoft Web performance monitoringNimsoft Web performance monitoring
Nimsoft Web performance monitoring
 
Hyves: Mobile app development with HTML5 and Javascript
Hyves: Mobile app development with HTML5 and JavascriptHyves: Mobile app development with HTML5 and Javascript
Hyves: Mobile app development with HTML5 and Javascript
 
NLCMG - Performance is good, Understanding performance is better
NLCMG - Performance is good, Understanding performance is better NLCMG - Performance is good, Understanding performance is better
NLCMG - Performance is good, Understanding performance is better
 
2deHands.be - Tuning a Big Classifieds Site
2deHands.be - Tuning a Big Classifieds Site2deHands.be - Tuning a Big Classifieds Site
2deHands.be - Tuning a Big Classifieds Site
 

Último

Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxLoriGlavin3
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxNavinnSomaal
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxLoriGlavin3
 
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESSALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESmohitsingh558521
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningLars Bell
 

Último (20)

Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptx
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptx
 
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESSALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine Tuning
 

Jinspired june2012

  • 1. William Louth JINSPIRED @jxinsight www.jinspired.com william@jinspired.com
  • 3. Local Remote Past Present Predicted
  • 4. Information Management Model Model
  • 6. Method Metering Metric Analysis Causality Correlation Accuracy Event Sampled Allocation Thread Process Assignment Direct Apportioned
  • 7. Model Activity Resource Device Probe Meter Develop Code Counter Design Behavior Usage Data Group Metering
  • 9. Space Complexity ity rs ve ism Di m na Dy Time
  • 10.
  • 11. HTTP API ta da Cloud data/code Service data Application HTTP Application Activity API data da ta Cloud Cloud code Service Service/Shell Activity Cloud Service/Shell
  • 12. Client sf://....... cl:clock.time= cl Response sf:clock.time= sf:cpu.time= sf:io.bytes= sf:charge.unit= ☁ sf ☁ ☁ salesforce.com sf:clock.time= sf:cpu.time= sf:io.bytes= sf:charge.unit= Metering Management Service
  • 13. Client sf://.......... cl:clock.time= cl ☁ Response sf:clock.time= sf:cpu.time= sf:io.bytes= sf:charge.unit= sf:db.time= sf:db.count= s3:clock.time= s3:io.bytes= s3:charge.unit= ☁ ☁ sf salesforce.com db Response ☁ sf:clock.time= s3:clock.time= ☁ sf:cpu.time= s3:io.bytes= sf:io.bytes= ☁ s3:charge.unit= sf:charge.unit= s3 sf:db.time= sf:db.count= s3:clock.time= aws.amazon.com s3:io.bytes= s3:charge.unit= Metering Management Service
  • 15. Profile Protect Police Prioritize Predict Provision
  • 16. Self Adaptive Software Self Adaptive Software evaluates its own behavior and changes behavior when the evaluation indicates that it is not accomplishing what the software is intended to do, or when better functionality or performance is possible.” DARPA
  • 17. Evidence Observation 1 Self Regulated Action 4 Feedback Loop 2 Relevance Reaction Judgement 3 Consequence
  • 18. Disturbances Goals Control Process Sensor
  • 19. public int func(...) { QoS reserve(func) Resource ...... ........ ......... ......... Rate ...... Limiting ........ ... Priority ..... Queueing ... release(units) } Reservation Lanes
  • 20. User WebPage Resource
  • 22. + _ Inflow Stock Outflow + _ + _ Sensor + _ Births Population Deaths + + reinforcing balancing loop loop
  • 23. + _ + _ Thread Pool Reserved Concurrency Released initial size + _ + _ QoS Resource Reserved QoS Reservation Released initial capacity
  • 24. operations System { system execution model } Dynamics Application unification System Dynamics Software development Application { software execution model } Software
  • 26. Sweeper (Secondary Processor) Reduce Friction Increase the Speed Thrower Trajectory Curling Stone Target [Primary Processor] Predicted Path Reduce Curl Straighten the Path Shorten Sweeper (Secondary Processor)