DCMIX: Generating Mixed Workloads for the Cloud Data Center

Transcription

1 DCMIX: Geeratig Mixed Workloads for the Cloud Data Ceter XigWag Xiog, Lei Wag, WaLig Gao, Rui Re, Ke Liu, Che Zheg, Yu We, YiLiag Istitute of Computig Techology, Chiese Academy of Scieces Bech 2018, Seattle, USA INSTITUTE OF COMPUTING TECHNOLOGY

2 Outlie Motivatio & challeges What is DCMIX? System Etropy Experimet Coclusio

3 Cloud Data Ceter Co-locatig workloads o shared resources State-of-Practise % Higher utilizatios but poor user experiece (Resources Cotetios) System Utilizatios CPU Utilizatio Memory badwidth utilizatio Disk badwidth utilizatio S U Workloads: Redis+Sort+MD5+ WordCout 0 Request Latecy AVG 95th 99th

4 Bechmarkig Cloud data ceter More ad more efforts try to do it: user experieces & system utilizatios Cache Allocatio Techology, Liux Cotaiers Techology Bechmark is the first step Measurig the Co-locatig workloads system Bechmarks: vedors ad users embrace it From the view of users: User Experieces (latecy, tail latecy) From the view of vedors: utilizatios ad cotetios of the target system State-of-Art Bechmarks Geeratig multiple workloads idividually BigBech, BigDataBech, TailBech, CloudSuite Sythetic Co-locatig workloads accordig to the trace SWIM, CloudMix sythetic workloads Co-locatig Stadaloe DCMIX real workloads How to geerate real Co-locatig workloads ad measure the correspodig system?

5 Challeges ad Our cotributios Workloads Mixed Patters Bechmarks: DCMIX a) 17 typical cloud data ceter workloads, latecies of workloads rage from microsecods to miutes. b) Mixture of serial executio ad parallel executio. Metrics Challeges Metrics: the system etropy a) The joit etropy of four system-level performace metric b) Obtaiigeasily & ot iterfere withworkloads Experimets: four differet modes o the X86 platform a) Mixed mode V.s. Stadaloe: the latecy icreased 7 times, ad resource utilizatios icreased 10 times. b) The isolatio mechaism has some efforts uder the mixed mode, especially the CPUaffiity mechaism.

6 Outlie Motivatio & challeges What is DCMIX? System Etropy Experimet Coclusio

7 DCMIX s Framework

8 DCMIX s Workloads Workloads Olie services Data aalytic workloads Applicatio fields Big data AI High performace computig Trasactio databases

9 DCMIX s Workloads(Cot d) Coverage both i applicatio fields ad latecy

10 DCMIX s Workload Geerator Mixture executio Serial executio Parallel executio User-defied cofiguratios Request itesity(i.e. QPS) Number of requests Number of warm-up requests Data size of jobs Threads umber of jobs

11 Outlie Motivatio & challeges What is DCMIX? System Etropy Experimet Coclusio

12 Motivatios Metrics that the cloud vedor cosidered System resource utilizatios: beefits CPU utilizatio Memory badwidth utilizatio Disk I/O utilizatio Network badwidth utilizatio System resource cotetios :disturbaces The system etropy the ucertaity associated with resources usage

13 The Defiitio of System Etropy Based o Shao etropy the ucertaity associated with a radom variable H X =. p x log 5 p x 7 9 Joit etropies of four system-level metrics CPU utilizatio Memory badwidth utilizatio Disk I/O utilizatio HNetwork S = Hbadwidth C + M utilizatio + H D + H(N)

14 System Etropy Calculatio The sum of four elemet s etropies H S = H C + H M + H D + H(N) Elemet s etropies based o Shao etropy H X =. p x log 5 p x 7 9 p c = Num(c)

15 Outlie Motivatio & challeges What is DCMIX? System Etropy Experimet Coclusio

16 Experimet Cofiguratios Two Nodes: The Cliet Node workloads geerator The Server Node target ode Data aalytic workloads(8gb text data) Sort, MD5, WordCout Redis Requests 50,000 requests per secod Cofiguratios of The Server Node

17 Experimetal Methodology Four modes: Stadaloe Mixed Stadaloe Mixed Redis x86 Sort Wordcout x86 MD5 Redis Mixed-Tied Mixed-tied Mixed-Docker Sort MD5 Wordcout Redis 23 cores 1 core x86 Mixed-Docker Sort MD5 Wordcout Redis Cotaier #1 Cotaier #2 x86

18 Metrics User-observed metrics Latecy System level metrics CPU utilizatio Memory badwidth utilizatio Disk badwidth utilizatio Network badwidth utilizatio Micro-architectural metrics L1I Cache Miss L2 Cache Miss L3 Cache Miss DTLB Cache Miss ITLB Cache Miss The System etropy

19 Latecy Behaviors , ,520 1,169 s µ Stadaloe Mixed 10 Mixed-Tied Mixed-Docker 1 AVG 90th 95th 99th 99.9th Latecy Co-locatig deploymet without ay isolatio mechaism icurs the high latecy.

20 System Resource Utilizatios % Stadaloe Mixed Mixed-Tied Mixed-Docker 0 CPU Utilizatio Memory badwidth utilizatio Disk badwidth utilizatio Network badwidth utilizatio Co-locatig deploymet ca prompt the resource utilizatios.

21 The System Etropy y p tro 20 e m15 ste sy e 10 th 5 0 Stadaloe Mixed Mixed-Tied Mixed-Docker The etropy of mixed modes is much larger tha that of the Stadaloe mode.

22 Micro-architecture Behaviors I K P M Stadaloe Mixed Mixed-Tied Mixed-Docker The micro-architecture metrics ca t reflect the system resource cotetio.

23 The Experimet Summary Co-locatio brigs higher resource utilizatio ad loger latecy 10 times resource utilizatio 7 times the average latecy & 14 times the 99.9th latecy The system etropy ca reflect the resource cotetio System etropy i Stadaloe mode is 6 times that i Mixed mode Isolatio mechaisms ca reduce the iterferece betwee Co-locatig workloads CPU-affiity mechaism reduces iterferece.

24 Coclusio DCMIX Ø Co-locatig workloads forcloud data ceter Ø 17 typical cloud data ceter workloads, latecies of workloads rage from microsecods to miutes. Ad mixture of serial executio ad parallel executio Ø will ope sourced soo l l X86 versio RISC-V versio

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