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A Framework for Satisfying the Performance Requir irements of Contain inerized Software Systems Through Mult lti-Versionin ing
Sara Gholami Alireza Goli Cor-Paul Bezemer Hamzeh Khazaei
A Framework for Satisfying the Performance Requir irements of - - PowerPoint PPT Presentation
A Framework for Satisfying the Performance Requir irements of Contain inerized Software Systems Through Mult lti-Versionin ing Alireza Goli Cor-Paul Bezemer Hamzeh Khazaei Sara Gholami 1 / 28 An example problem: The Sla lashdot Effect 2
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Sara Gholami Alireza Goli Cor-Paul Bezemer Hamzeh Khazaei
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Load Balancer Load Balancer Load Balancer Load Balancer
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Ideal case experiment Recommender with multiple training (Only heavy weight) Adaptive experiment Adaptive load distribution (Mix of heavy and light weight) Worst case experiment Recommender with single training (Only light weight)
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Ideal case experiment Multimedia responses only (Only heavy weight) Adaptive experiment Adaptive load distribution (Mix of heavy and light weight) Worst case experiment Text responses only (Only light weight)
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Ideal case exp.
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Worst case exp.
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Adaptive exp.
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Ideal case exp.
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Worst case exp.
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Adaptive exp.
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Adaptive exp.
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Adaptive exp.
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$METRIC $OPERATOR $THRESHOLD, (version $VERSION_NAME perc =$PERCENTAGE;)+ For example: RT > 0.4, version recommender:HeavyWeight perc=40; version recommender:LightWeight perc=60;
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docker service create [$OPTIONS] $IMAGE1 $REPLICATION1 … $IMAGEn $REPLICATIONn For example, docker service create e REGISTRY_HOST=host_ip e REGISTRY_PORT=1000 10.2.5.26 Network recommender 8080 rules.txt sgholami/teastore-recommender:HeavyWeight 1 sgholami/teastore-recommender:LightWeight 1