Performance Analysis of Computer Systems and Networks Prof. Varsha Apte.

Performance Analysis of Computer Systems and

Networks

Prof. Varsha Apte

Example: On-line Service

Client

Server

•What questions about performance can we ask?•Why should we ask them?•How can we answer them?

…What

SoftwareResponse timeBlockingQueue lengthThroughput Utilization

NetworkPacket Delay, Message DelayLoss RateQueue Length“Goodput”UtilizationDelay Jitter

...Why

Sizing (Hardware, network)

Setting configuration parameters

Choosing architectural alternatives

Comparing algorithms

Determining bottlenecks

Guaranteeing QoS will be met

Example: Estimating end-to-end delay

Client

Server

Measure it!At the clientAt the server

Simulate it – Write (or use) a computer program that simulates the behaviour of the system and collects statistics

Analyse it “with pen and paper”

Let's try (delay)!Assume Web service

Dissecting the response time

Delay components:

Client Processing (prepare request)

Connection Set-up

Sending the request

Server processing the request

Sending the response

Client processing (display response)

...Dissecting delaysConnection Set-up (assume TCP): SYN—SYNACK1 Round-trip time before request can be sent

Sending the request½ RTT for request to reach server

At the server:Queuing Delay for server threadProcessing delay (once request gets server thread)

Thread will also be in CPU job “queue” or disk queue

Sending the response back

...Dissecting delays

Delay components of “RTT”:Queuing delay at (at each link)Packet processing delay (at each node)Packet transmission delay (at each link)Link propagation delay

Delay- observations

Many delays are fixed Propagation delayPacket processing delayFor a given packet size, transmission delay

Some are variableNotably, Queuing Delay

Key Concept

Fundamental concept: Contention for a resource leads to users of the resource spending time queuing, or in some way waiting for the resource to be given to them. The calculation of this time, is what requires sophisticated models, because this time changes with random changes in the system - e.g. traffic volumes, failures, etc. and because it depends on various system mechanisms.

•Focus of this workshop: Queuing Delay

Queuing Systems

An Introduction to Elementary Queuing Theory

What/Why is a Queue?

The systems whose performance we study are those that have some contention for resources

If there is no contention, performance is in most cases not an issue When multiple “users/jobs/customers/ tasks” require the same resource, use of the resource has to be regulated by some discipline

…What/Why is a Queue?

When a customer finds a resource busy, the customer may

Wait in a “queue” (if there is a waiting room)Or go away (if there is no waiting room, or if the waiting room is full)

Hence the word “queue” or “queuing system”

Can represent any resource in front of which, a queue can form

In some cases an actual queue may not form, but it is called a “queue” anyway.

Examples of Queuing SystemsCPU

Customers: processes/threads

DiskCustomers: processes/threads

Network Link Customers: packets

IP Router Customers: packets

ATM switch:Customers: ATM cells

Web server threads Customers: HTTP requests

Telephone lines:Customers: Telephone Calls

Elements of a Queue

ServerWaiting Room/ Buffer/Queue

Queueing Discipline

Customer Inter-arrival time

Service time

Elements of a Queue

Number of Servers

Size of waiting room/buffer

Service time distribution

Nature of arrival “process”Inter-arrival time distributionCorrelated arrivals, etc.

Number of “users” issuing jobs (population)

Queuing discipline: FCFS, priority, LCFS, processor sharing (round-robin)

Elements of a Queue

Number of Servers: 1,2,3….

Size of buffer: 0,1,2,3,…

Service time distribution & Inter-arrival time distribution

Deterministic (constant)ExponentialGeneral (any)

Population: 1,2,3,…

Queue Performance Measures

Queue Length: Number of jobs in the system (or in the queue)

Waiting time (average, distribution): Time spent in queue before service

Response time: Waiting time+service time

Utilization: Fraction of time server is busy or probability that server is busy

Throughput: Job completion rate

Queue Performance Measures

Let observation time be TA = number of arrivals during time TC = number of completions during time TB = Total time system was busy during time T

Then:Arrival Rate = = A/TThroughput = C/TUtilization = ρ = B/TAverage service time = = B/CService rate = 1/

Basic RelationshipsIn “steady-state” for a stable system without loss (i.e. infinite buffer system)

Completionrate = Arrival Rate, since “in-flow = out-flow”)

If arrival rate > service rate, then Utilization =

B/T = (B/C) x (C/T) = Average Service Time x Completion Rate = = for a loss-less system.

For loss-full systems, if p = fraction of requests lost, (1 – p)

Throughput of a system, Utilization x Service Rate =

(C/T) = (B/T) x (C/B)

Little’s Law: N = R

Average number of customers in a queuing system = Throughput x Average Response Time

Applicable to any “closed boundary” that contains queuing systems

Some other assumptions

Also, if L is the number in queue (not in service), and W is waiting time:

Simple Example (Server)Assume just one server (single thread)Requests come in @ 3 requests/secondRequest processing time = 250 ms.Utilization of server?

75%Throughput of the server?

3 reqs/secondWhat if requests come in 5 reqs/second?

Utilization = 100%, Throughput = 3 reqs/secondWaiting time (for 3 reqs/second?)

L/3, where L is queue length. But what is L?Need Queuing

Queuing Systems Notation

X/Y/Z/A/B/CX: Inter-arrival time distribution

Distributions denoted by D (Deterministic), M (Exponential) or G (General)

Y: Service time distributionZ: Number of ServersA: Buffer size B: Population sizeC: Discipline

E.g.: M/G/4/50/2000/LCFS

Classic Single Server Queue: M/M/1

Exponential service time

Exponential inter-arrival timeThis is the “Poisson” arrival process.

Single Server

Infinite buffer (waiting room)

FCFS disciplineCan be solved very easily , using theory of Markov chains

Exponential Distribution

Memory-less distribution Distribution of remaining time does not depend on elapsed time

Mathematically convenient

Realistic in many situations (e.g. inter-arrival times of calls)

X is EXP()

P[X < t] = 1 – e-t

Average value of X = 1/

0 1 2 3 4 5 6 7 8 9 10t

CDF PDF

Exponential <-> Poisson

When distribution of inter-arrival time is Exponential, the “arrival process” is a “Poisson” process.Properties of Poisson process with parameter

If Nt = Number of arrivals in (0,t]; then P[Nt = k] = t e-t/k!

Superposition of Poisson processes is a Poisson processSplitting of Poisson process results in Poisson processes

Important Result!

M/M/1 queue results

Let be arrival rate and be service time, and = 1/ be service rate

Utilization

Mean number of jobs in the system/(1-)

ThroughputAverage response time (Little’s law):R = N/

Response Time Graph

Graph illustrates typical behaviour of response time curve

tau =0.5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

M/M/1 queue results

For M/M/1 queue, a formula for distribution of response time is also derived.

M/M/1 response time is exponentially distributed, with parameter (1-), i.e.

P [Response Time < t] = 1 – e-(t

M/G/1 single server queue

General service time distribution

Mean number in system =N = Where standard deviation/mean of service timeCalled the Pollaczek-Khinchin (P-K) mean value formula.

Mean response time by Little’s law

M/G/1 delay

Mean response time by Little’s lawR = N/ =

For constant service time (M/D/1 queue):

M/D/1-Mean response time= M/D/1-Mean waiting time =

Queue Length by P-K formula

Coefficient of variation for:

•Det: 0

•Uniform(10-50): 0.222

•Erlang-2: 0.5

•Exp: 1

•Gen: 3

0 0.2 0.4 0.6 0.8 1

Det Erlang-2 Unif(10,50) Exp General

Multiple server queue: M/M/c

One queue, c serversUtilization, ca= Average number of busy

servers.Queue length equation exists (not

shown here)For c = 2, queue length is: 2 - Average Response Time? Little’s Law! For c = 2, R = N/-

Important quantity: termed traffic intensity or offered load

Finite Buffer Models: M/M/c/K

c servers, buffer size K (total in the system can be c+K)

If a request arrives when system is full, request is dropped

For this system, there will be a notion of loss, or blocking, in addition to response time etc. Blocking probability (p) is probability that arriving request finds the system full

Response time is relevant only for requests that are “accepted”

...Finite Buffer QueuesArrival rate: Service rate:Throughput?– p)

Utilization?c

Blocking probability? Queue length (N)?Formula exists (from queuing model)

Waiting time? (Little's law = N/)

Finite Buffer Queue:Asymptotic Behavior

As offered load increases (infinity)Utilization () ThroughputcBlocking probabilityp 1

Queue lengthN c+ K

Waiting time K/(c + 1/

Finite Buffer (Loss Models)

M/M/c/0: Poisson arrivals, exponential service time, c servers, no waiting room.

Represents which kind of systems?Circuit-switched telephony! (Servers are lines or trunks, Service time is termed “holding time”)

Interesting measure for this queue: probability that arriving call finds all lines busy

Erlang-B formula

Blocking probability (probability that arriving call finds all s servers busy) =

(as/s!) / [sum(k from 0 to s) {ak/k!}]

Examples

Example-1

You are developing an application server where the performance requirements are:

Average response time < 3 seconds

Forecasted arrival rate = 0.5 requests/second

What should be the budget for service time of requests in the server?

Answer: <1.2 seconds.

Example-2

If you have two servers, is it better to split the incoming requests into two queues

and send them to each server Or, put them in one queue, and the first in

queue is sent to whichever server is idle.Or, replace two servers by one server,

twice as fast. for minimizing response times?

Example-2 contd.

Verify intuition by model. Let be arrival rate, and be service timeCalculate response times, and order cases

by response times.

Answer: R3 < R2 < R1

Example-3: ATM Link Model

Assume ATM link, Poisson arrivals, infinite buffer

Link b/w: 10 MbpsPacket size: 53 bytesPacket transmission time = 42.4 sPacket inter-arrival time = 50 s. Assume Poisson arrivals

Example-3 contd.

Delay through link: node processing delay (negligible) + queuing delay (waiting time)+ transmission delay + propagation delay

Queuing Delay?M/D/1 delay = (42.4)(42.4/50) / (2 x (1-

42.4/50)) = 118.2 ms

Example-4: Multi-threaded Server

Assume multi-threaded server. Arriving requests are put into a buffer. When a thread is free, it picks up the next request from the buffer.

Execution time: mean = 200 msTraffic = 30 requests/secHow many threads should we configure? (assume enough hardware).

Traffic Intensity = 30 x 0.2 = 6 = Average number of busy servers At least 6

Response time = (Which formula?)

...Example-4

Related question: estimate average memory requirement of the server.

Likely to have: constant component + dynamic componentDynamic component is related to number of active threadsSuppose memory requirement of one active thread = MAvg. memory requirement= constant + M* 6

Example-5: Hardware Sizing

Consider the following scenario:An application server runs on a 24-CPU

machineServer seems to peak at 320 transactions

per secondWe need to scale to 400.Hardware vendor recommends going to 32

CPU machine.Should you?

First do bottleneck analysis! Suppose logs show that at 320 transactions per second, CPU utilization is 67% - What is the problem? What is solution?

Most likely Explanation: Number of threads in server is < number of CPUs

Possible diagnosis: Server has 16 threads configured Each thread has capacity of 20 transactions per

second Total capacity: 320 reqs/second. At this load, 16 threads will be 100% busy average

CPU utilization will be 16/24=67%

Solution: Increase number of threads – no need for CPUs.

Part II

Applications to Software Performance Testing and Measurement, Network Models and Service Performance

Software Performance Testing

Typical Scenario

M clients issue requests, wait for response, then “think”, then issue request again

Let 1/ be mean think time, 1/ be mean service time.

Scenario is actually of “closed” queuing network

Clients

Server

Observations

Throughput? Arrival rate?At steady-state, “flow” through both queues is equalServer throughput = Server utilization X Service rate

U X Request is generated by each user once every [think time + response time] = 1/ (1/+ R)

Overall request arrival rate = M / (1/ + R) = U *

Clients

Server

Observations

M / (1/ + R) = U * = Throughput

Response time = number of clients/Throughput – think time

As number of clients increase, can be shown to tend to: M/ – 1/.

Linear function of M Increase M by 1 Response time increases by 1/

Clients

Server

Metrics from model

Saturation number (number of clients after which system is “saturated”)

M* = 1 + /

Case Study

Software Performance Measurement

Queuing Networks

Jobs arrive at certain queues (open queuing networks)

After receiving service at one queue (i) , they proceed to another server (j), with some probability pij, or exit

Open Queuing Network - measures

Maximum Throughput

Bottleneck Server

Throughput

Open Queuing Network - measures

Total time spent in the system before completion (overall response time, from the point of view of the user)

start finish

Open Queuing Network: Example 1

W: Web Server

A1, A2: Application Server 1, 2

D1, D2: Database Server 1,2

D2A2pw0

...Open Queuing Network- Example 1

Each server has different service time

But what is the request rate arriving to each server?

Need to calculate this using flow equations

Equations for Average number of visits before leaving the server network

vA1 = pwA1. vw+ vD1 , vD1 = pA1D1 . vA1

vA2 = pwA2. vw + vD2 , vD2 = pA2D2 . vA2

Vw = 1 + pA1w. vA1 + pA2w. vA2

Spreadsheet calculation showsHow bottleneck server changesHow throughput changesHow response time changes

Results are accurate for only some types of networks, for others, they are approximate

Performance Analysis of Computer Systems and Networks Prof. Varsha Apte.

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