July 2003 SPECTS 2003 1

Network-Level Impacts on User-Level Web Performance

Carey Williamson

Nayden Markatchev

University of Calgary

SPECTS 2003 2July 2003

Introduction

Blessing made the Internet available to the

masses shields users from the low-layer

technical details of networking provides seamless exchange of

information, in a time-independent, location-independent, and platform-independent manner

Curse made the Internet available to the

masses placed a lot of stress on the

Internet infrastructure traffic volume, sustained growth demands on the TCP/IP protocol

suite (i.e.,TCP is not really a good “fit” for Web traffic demands)

“The Web has been both a blessing and a curse.” -- CLW 2001

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Related Work: TCP and the Web

Persistent-connection HTTP [Mogul 1995] Larger TCP initial window size [Allman et al 1998] TCP “fast start” to reduce Web transfer latency

[Padmanabhan/Katz 1998] Parallel (concurrent) TCP connections supported

in most Web browsers today (e.g., 4) Ensemble-TCP to manage aggregation of TCP

connections to same dest. [Eggert et al 2000] Rate-based pacing of TCP packets for the Web

[Aggarwal et al 2000] [Ke/Williamson 2000] Context-aware TCP/IP [Williamson/Wu 2002]

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Motivation Most of the current Web performance literature

is focused on either: Web caching simulation studies (i.e., with an

application-layer view, focusing on hit ratios, but ignoring network-level issues and protocol effects); or

TCP performance studies (i.e., packet-level studies, but often focusing on throughput for bulk transfers, rather than response times for (short) Web transfers)

Our Objective: To explore the relationships between TCP, network-level effects, Web caching, and user-perceived Web response time

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Research Methodology Overview Network simulation (ns2) Synthetic Web workloads (WebTraff) Simple network model:

two-level Web proxy caching hierarchysettable parameters for link capacity,

propagation delay, cache hit ratio, etc Packet-level simulation study (TCP Reno) Performance metric: object transfer time

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Network ModelWebServer

Proxy1

Proxy2

Clients

C1

C2

C3

d1

d2

d3

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Network ModelWebServer

Proxy1

Proxy2

Clients

(Hit at Proxy1)

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Network ModelWebServer

Proxy1

Proxy2

Clients

(Hit at Proxy2)

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Network ModelWebServer

Proxy1

Proxy2

Clients

(Download from server)

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Simulation Model Assumptions Two-level Web proxy caching hierarchy All Web content is cacheable static content Data transfers are unidirectional toward the

clients (i.e., we ignore the HTTP request step) One-way TCP model (i.e., models the data

transfer only, using DATA/ACK; no SYN/FIN) TCP Reno, with segment size of 512 bytes Proxy caches behave as store-and-forward

routers (on a per-packet basis)

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Simulation Methodology

Multi-step process:Workload generation using WebTraff (makes a

time-ordered sequence of 5000 Web object transfer sizes, with desired request arrival rate)

Modify workload file to randomly associate transfers with either Proxy1, Proxy2, or Server based on desired cache hit ratios (HR1, HR2)

Use the ns2 network simulator to model the TCP transfers on the desired network model

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Experimental DesignFactors Levels

Link Capacity C (Mbps) 10, 100, 1000

Propagation Delay d (msec) 1, 5, 10, 30, 60

Request Arrival Rate (req/sec) 10, 20, 40, 80

Child Proxy Hit Ratio HR1 20%, 30%, 40%

Parent Proxy Hit Ratio HR2 7%, 10%, 15%

Full-factorial experiment (540 possible combinations)

Performance metric: TCP transfer time for each Web object download (plotted versus transfer size)

NetworkParameters

WorkloadParameters

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Web Workload Model

5000 HTTP transfers synthetically generated by the WebTraff tool [Markatchev/Williamson 2002]

Poisson arrival process assumed for Web requests Four different request arrival rates considered:

Light: 10 req/sec (approx. 0.77 Mbps offered load) Moderate: 20 req/sec (approx. 1.54 Mbps offered load) Medium: 40 req/sec (approx. 3.08 Mbps offered load) Heavy: 80 req/sec (approx. 6.16 Mbps offered load)

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Web Workload Characteristics

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Baseline Scenario Link Capacity

C1 = C2 = C3 = 10 Mbps Propagation Delay

d1 = 1 msec; d2 = 5 msec; d3 = 30 msec Hit Ratios

HR1 = 40%; HR2 = 15% Request Arrival Rate

Light: 10 requests/sec

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Simulation Results (Baseline Scenario)

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Simulation Results (Baseline Scenario)

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Simulation Results (Baseline Scenario)

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Simulation Results (Baseline Scenario)

“slower”

“faster”

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Simulation Results (Baseline Scenario)

Queueing Delays

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Simulation Results (Baseline Scenario)

Packet Losses/Retransmissions

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Results Interpretation

TCP slow start is evident (for large RTT) The “width” of steps increases exponentially The vertical separation reflects propagation

delay component of RTT Queuing delays, packet losses, timeouts,

and retransmissions manifest themselves as deviations from the normal structure

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Effects of Network Link Capacity

To model current network infrastructures, we considered four sets of link capacities: C1 =10 Mbps, C2 =10 Mbps, C3 =10 Mbps (baseline) C1 =100 Mbps, C2 =10 Mbps, C3 =10 Mbps C1 =100 Mbps, C2 =100 Mbps, C3 =10 Mbps C1 =1000 Mbps, C2 =100 Mbps, C3 =10 Mbps

This models increasingly faster client network access to the Internet, while the WAN backbone to the server remains slow (10 Mbps)

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Results for Link CapacityC1 = 10 Mbps (baseline)

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Results for Link CapacityC1 = 100 Mbps (upgrade)

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Effect of Propagation Delay

Values for propagation delayd1 = 1 msec, d2 = 5 msec, d3 = 30 msec d1 = 1 msec, d2 = 5 msec, d3 = 60 msecd1 = 1 msec, d3 = 10 msec, d3 = 30 msec d1 = 1 msec, d2 = 10 msec, d3 = 60 msec

Representing LAN, MAN, WAN scenarios

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Propagation Delay (d2 = 5 msec)

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Propagation Delay (d2 = 10 msec)

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Effect of Request Arrival Rate

Vary the offered load: 10 requests/sec20 requests/sec40 requests/sec80 requests/sec

Makes network more and more congested

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Effect of Request Arrival Rate (Light Offered Load: 10 req/sec)

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Effect of Request Arrival Rate (Moderate Offered Load: 20 req/sec)

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Effect of Request Arrival Rate (Medium Offered Load: 40 req/sec)

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Effect of Request Arrival Rate (Heavy Offered Load: 80 req/sec)

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Effect of Cache Hit Ratio

Vary the Cache Hit Ratio at each of the Web proxy caches in the simulated network “Good”: HR1 = 40%, HR2 = 15% (baseline) “Average”: HR1 = 30%, HR2 = 10% “Poor”: HR1 = 20%, HR2 = 7%

Assess user-perceived Web response time for fairly realistic range of possible cache hit ratios, and consideration of “cache filter effects”

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Effect of Cache Hit Ratio(“Good” HR1 = 40%; HR2 = 15%)

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Effect of Cache Hit Ratio(“Average” HR1 = 30%; HR2 = 10%)

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Effect of Cache Hit Ratio(“Poor” HR1 = 20%; HR2 = 7%)

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Effect of Cache Hit Ratio

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Effect of Cache Management Policy

Suppose that the two caches are coordinated using a size-based thresholding policy

One cache for “small” items One cache for “large” items Is this a good idea?

Scenario considered: Child Proxy: items <= 8 KB Parent Proxy: items > 8 KB Same hit ratios as in baseline

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Cache Management Policies(Default Policy; C1 = 10 Mbps)

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Cache Management Policies(Threshold Policy; C1 = 10 Mbps)

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Cache Management Policies(Default Policy; C1 = 100 Mbps)

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Cache Management Policies(Threshold Policy; C1 = 100 Mbps)

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Summary of Simulation Results for Cache Management Policies

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Summary and Conclusions Packet-level network simulation study of

TCP effects on user-perceived Web perf. Link capacity, propagation delay, network

congestion, and TCP protocol behaviors can all have significant impact on the user-perceived Web response time

Relationship between Web cache hit ratio and user-perceived response time tricky

Cache management and placement hard!

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The End!

Thanks for your attention!

For more information:Email: {nayden,carey}@cpsc.ucalgary.ca