System Performance & Scalability i206 Fall 2010 John Chuang

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John Chuang3 Computing Trends  Multi-core CPUs  Data centers  Cloud computing  What are the drivers? -scalability, availability, cost-effectiveness

John Chuang4 Lecture Outline  Performance Metrics  Availability  Queuing theory -M/M/1 queue  Scalability -M/M/m queue

John Chuang5 What is Performance?  Users want fast response time and high availability  Managers want happy users, and many of them, while minimizing cost  What are standard measures of system performance?

John Chuang6 Performance Metrics  Response time (seconds)  Throughput (MIPS, Mbps, TPS,...)  Resource utilization (%)  Availability (%)

John Chuang7 Availability Down-time per yearOne hour down-time per: 90%36 days9 hours 99%3.7 days4.1 days 99.9%9 hours41.6 days 99.99%53 minutes1.14 years %5 minutes11.41 years Availability = MTTF / (MTTF + MTTR) -Mean-time-to-failure (MTTF) -Mean-time-to-recover (MTTR)

John Chuang8 Response Time ClientServer Formulate request Message latency Processing time Interpret response Network Queuing time Adapted from: David Messerschmitt

John Chuang9 Queuing Theory 1. Arrival Process 2. Service Time Distribution 3. Number of Servers 4. System Capacity 5. Customer Population 6. Service Discipline Source: Raj Jain

John Chuang10 Kendall’s Notation (1953)  A/B/c/k/N/D -A: arrival process -B: service time distribution -c: number of servers -k: system capacity -N: population size -D: service discipline M: Markov (exponential, memoryless, random, Poisson) D: deterministic E: Erlang H: hyper-exponential G: general FCFS: first come first served FCLS: first come last served RR: round-robin etc. 1. Arrival Process 2. Service Time Distribution 3. Number of Servers 4. System Capacity 5. Customer Population 6. Service Discipline

John Chuang11 Example Systems  M/M/1/ / /FCFS (simplified as M/M/1) -Markovian (Poisson, memoryless) arrival -Markovian service time -1 server -Infinite server capacity -Infinite arrival stream -First-come-first-serve discipline  Other examples: -M/M/1/k (finite capacity) -M/M/m (m servers) -G/D/1 (arbitrary arrival, deterministic service time) 8 8

John Chuang12 M/M/1 Queue  Poisson arrival, with average arrival rate of jobs/sec  Poisson service, with average service rate of  jobs/sec  Single server with infinite queue  System utilization (hopefully < 1):  = /   Average number of jobs in system: N =  n·p n =  /(1 -  )  System throughput (if  < 1) : X =  Average response time (from Little’s Law): R = N/X = 1/(  - )

John Chuang13 Example: Web Server  Web server receives 40 requests/second  Web server can process 100 requests/second  What is server utilization?  At any given time, how many requests are at server (waiting plus being processed)?  What is the mean total delay at server (waiting plus processing)?  What happens when traffic rate doubles?

John Chuang14 Example: Web Server  = 40 requests/second   = 100 requests/second  Utilization =  = /  = 40/100 = 40%  # of requests = N =  /(1 -  ) = 0.67  Average time spent at server = R = N/X = 0.67/40 = 17ms

John Chuang15 Example: Traffic Doubled  = 80 requests/second   = 100 requests/second  Utilization =  = /  = 80/100 = 80%  # of requests = N =  /(1 -  ) = 4  Average time spent at server = R = N/X = 4/80 = 50ms (more than doubled!)

John Chuang16 Approaching Congestion  = 99 requests/second   = 100 requests/second  Utilization =  = /  = 99/100 = 99%  # of requests = N =  /(1 -  ) = 99  Average time spent at server = R = N/X = 99/99 = 1 second!

John Chuang17 Utilization Affects Performance

John Chuang18 M/M/1/k Queue (Finite Capacity)   = /   N =  /(1-  ) – (k+1)  k+1 /(1-  k+1 )  R = N/X = N/ eff -where eff = (1-P k ) = effective arrival rate -and P k =  k (1-  )/(1-  k+1 ) = probability of a full queue  Loss rate = - eff

John Chuang19 M/M/1/k Response Time

John Chuang20 M/M/1/k Throughput

John Chuang21 Lecture Outline  Performance Metrics  Availability  Queuing theory -M/M/1 queue  Scalability -M/M/m queue

John Chuang22 Scalability  The capability of a system to increase total throughput under an increased load when resources (typically hardware) are added -Cost of additional resource -Performance degradation under increased load

John Chuang23 Scalability Example  Original web server: can process  requests/sec; accepts requests at /sec  Now request rate increases to 10 /sec and web server is swamped (  = 10 /  )!  Need to add new hardware!

John Chuang24 Which is better?  Option 1: One big web server that can process 10  requests/sec  Option 2: Ten web servers, each can process  requests/sec; each accepts 10% of requests ( /sec per server)  Option 3: Ten web servers, each can process  requests/sec; share single queue (load balancer) that accepts requests at 10 /sec

John Chuang25                        Option 1: M/M/1 queue with big server Option 2: (ten M/M/1 queues) Option 3: M/M/10 queue

John Chuang26 M/M/m Queue (m Servers)   = /m   N = m  +  /(1-  ) where and 

John Chuang27 Which is Better? Option 1 (M/M/1 big) Option 2 (ten M/M/1) Option 3 (M/M/10) Utilization (  ) 0.5 Number of requests (N) 11* Response Time (R) 2ms20ms10.07ms m = 10;  = 100; = 50 Remember: Scalability is not just about performance!