july 2003spects 2003 1 network-level impacts on user-level web performance carey williamson nayden...
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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
SPECTS 2003 3July 2003
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]
SPECTS 2003 4July 2003
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
SPECTS 2003 5July 2003
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
SPECTS 2003 10July 2003
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)
SPECTS 2003 11July 2003
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
SPECTS 2003 12July 2003
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
SPECTS 2003 13July 2003
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)
SPECTS 2003 15July 2003
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
SPECTS 2003 22July 2003
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
SPECTS 2003 23July 2003
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)
SPECTS 2003 26July 2003
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
SPECTS 2003 29July 2003
Effect of Request Arrival Rate
Vary the offered load: 10 requests/sec20 requests/sec40 requests/sec80 requests/sec
Makes network more and more congested
SPECTS 2003 34July 2003
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”
SPECTS 2003 39July 2003
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
SPECTS 2003 45July 2003
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!