CS 640 1Network Performance Measurement and AnalysisOutlineMeasurementTools and Techniues!orkload "enerationAnalysis#asic statistics$ueuin" modelsSimulationCS 640 %Measurement and Analysis O&er&iew'Si(e) com*le+ity and di&ersity of the ,nternet makes it &ery difficult to understand cause-effect relationshi*s'Measurement is necessary for understandin" current system .eha&ior and how new systems will .eha&e/0ow) when) where) what do we measure1'Measurement is meanin"less without careful analysis/Analysis of data "athered from networks is uite different from work done in other disci*lines' Measurement2analysis ena.les models to .e .uilt which can .e used to effecti&ely de&elo* and e&aluate new techniues/Statistical models/$ueuin" models/Simulation models CS 640 34eterminin" What to Measure'#efore any measurements can take *lace one must determine what to measure'There are many commonly used network *erformance characteristics/5atency/Throu"h*ut/6es*onse time/Arri&al rate/7tili(ation/#andwidth/5oss/6outin"/6elia.ilityCS 640 4Measurement ,ntroduction',nternet measurement is done to either analy(e2characteri(e network *henomena or to test new tools) *rotocols) systems) etc8'Measurin" ,nternet *erformance is easier said than done/!hat does 9*erformance: mean1/!orkload ;what and where youority of ,nternet traffic'Trace-.ased workloads/Ca*ture traces and re*lay them/#lack-.o+ method'Synthetic workloads/A.straction of actual o*eration/May not ca*ture all as*ects of workload'Analytic workloads/Attem*t to model workload *recisely/Hery difficultCS 640 IS76FJ !e. !orkload Fenerator'Scala.le 76l Fenerator/Analytic workload "enerator/#ased on 1% em*irically deri&ed distri.utions of !e. .rowsin" .eha&iror/J+*licit) *arameteri(ed models/Ca*tures 9hea&y-tailed: ;hi"hly &aria.le= *ro*erties of !e. workloads/!idely used'S76FJ com*onents@/Statistical distri.ution "enerator/0y*er Te+t Transfer Protocol ;0TTP= reuest "eneratorCS 640 K!orkload characteristics ca*tured in S76FJCharacteristic Com*onent Model System ,m*actDile Si(e #ase file - .ody 5o"normal Dile System L#ase file - tail Pareto LJm.edded file 5o"normal LSin"le file1 5o"normal LSin"le file % 5o"normal L6euest Si(e #ody 5o"normal Network LTail Pareto L4ocument Po*ularity Ai*fCaches) .uffersTem*oral 5ocality 5o"normal Caches) .uffersODD Times Pareto LJm.edded 6eferences Pareto ON Times LSession 5en"ths ,n&erse Faussian Connection times#D JD1 JD% Off time SD Off time #D JD1CS 640 10S76FJ ArchitectureSURGE Client SystemSURGE Client SystemSURGE Client SystemLANON/OFF ThreadON/OFF ThreadON/OFF Thread Web Server SystemCS 640 11S76FJ and SPJC!e.K6 e+ercise ser&ers &ery differently Sur"eSPJC!e.K6-505101520253035400 200 400 600Packets per SecondPercent CPU UtilizationSPECWeb96SURGECS 640 1%Analy(in" Measured 4ata'Analy(in" measured data in networks is ty*ically done usin" statistical methods/Selectin" a**ro*riate analysis method;s= is critical'A&era"in"'4is*ersion ;&aria.ility='Correlations'6e"ression analysis'4istri.utional analysis'Dreuency analysis'Princi*al-com*onent analysis'Cluster analysis'Jach form of analysis has stren"ths and weaknessesCS 640 13Self-Similar Nature of Network Traffric'!8 5eland) M8 Tau) !8 !illin"er) 48 !ilson) On the Self-Similar Nature of Ethernet Traffic, IEEE/ACM TON) 1KK48/#aker Award winner'H8 Pa+son) S8 Dloyd) Wie-Area Traffic!The "ailure of #oi$$on Moeling, IEEE/ACM TON) 1KK?8 'M8 Cro&ella) A8 #esta&ros) Self-Similarit% in Worl Wie We& Traffic! Evience an #o$$i&le Cau$e$, IEEE/ACM TON) 1KKG8 CS 640 14$ueuin" Models'One of the key modelin" techniues for com*uter systems in "eneral/Hast literature on ueuin" theory/Nicely suited for network analysis/Prof8 Mary Hernon is our local e+*ert'Fenerally) ueuin" systems deal with a situation where >o.s ;of which there are many= wait in line for a resource ;of which there are few=/$ueuin" theory can ena.le us to determine res*onse time/J+am*les1CS 640 1?$ueuin" Models contd8'J+am*le@*ackets arri&in" at a router / how can we determine how lon" it takes for *ackets to .e forwarded .y the router1'Characteristics necessary to s*ecify a ueuin" system/Arri&al *rocess/Ser&ice time distri.ution/Num.er of ser&ers/System ca*acity ;num.er of .uffers=/Po*ulation si(e/Ser&ice disci*line/Mendal notation@A2S2m2#2M2S4'6es*onse time C waitin" time B ser&ice time'Dor sta.ility) mean arri&al rate must .e less than mean ser&ice rateCS 640 165ittleo.s in system C arri&al rate L mean res*onse time /Treats a system as a .lack .o+/A**lies whene&er num.er of >o.s enterin" the system euals num.er of >o.s lea&in" the system'No >o.s created or lost inside system/Can .e e+tended to include systems with finite .uffers'J+am*le@A&era"e forwardin" time in a router is 100 microseconds) ,2O rate for *ackets is 100k8!hat is the mean num.er of *ackets .uffered in the router1CS 640 1GSimulation Models'Simulation is one of the most common2im*ortant methods of analysis2modelin"/Ty*ically an a.straction of the system under consideration/Can *ro&ide si"nificant insi"ht to system