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Evaluation of Load Balancing Algorithms and Internet Traffic Modeling for Performance Analysis
By Arthur L. Blais
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Introduction Internet Traffic Modeling Load Balancing Algorithms
Conclusion
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Internet Traffic Modeling
Why is the Internet hard to model? Internet Traffic Characteristics Internet Traffic Models
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Why is the Internet hard to model?
It’s BIG January 2000: > 72 Million Hosts1 Growing Rapidly > 67% per year Constantly Changing Traffic patterns have high variability 1 Source:
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Internet Traffic Patterns
Causes of High variability Client Request Rates Server Responses Network Topology
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Characteristics of Client Request Rate1
Client Sleep Time Inactive Off Time Active Off Time Embedded References 1 Barford and Crovella, Generating Representative Web Workloads for Network and Server Performance Evaluation, Boston University, BU-CS , 1997
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Client Sleep time
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Inactive Off Time Time between requests (Think Time)
Uses a Pareto Distribution Shape parameter: a = 1.5 Lower bound: (k) = 1.0 To create a random variable x: u ~ U(0,1) x = k / (1.0-u)^1.0/ a
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Inactive Off Time
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Active Off Time Time between embedded references
Uses a Weibull Distribution alpha: a = 1.46 (scale parameter) beta: b = (shape parameter) To create a random variable x: u ~ U(0,1) x = a ( -ln( 1.0 – u ) ^ 1.0/b
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Active Off Time
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Embedded References Number of objects in the requested document
(text, audio, video, bit maps) Uses a Pareto Distribution Shape parameter: a = 2.43 Lower bound: (k) = 1.0 To create a random variable x: u ~ U(0,1) x = k / (1.0-u)^1.0/a
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Example HTML Document with Embedded References
<html><head> <title>CS522 F99 Home Page</title> </head> <body background="marble1.jpg"> <BGSOUND SRC="rocky.mid"><embed src="rocky.mid" autostart=true hidden=true loop=false></embed> <td ALIGN=CENTER><img SRC="rainbowan.gif" height=15 width=100%></td>
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Embedded References
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Server Characteristics
File Size Distribution Body – Lognormal Distribution Tail – Pareto Distribution Cache Size Temporal Locality Number of Connections System Performance: CPU speed, disk access time, memory, network interface
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File Size Distribution - Body
Lognormal Distribution Build table with 930 values Range: 92 <= x <= 9020 bytes To create a random variable x: u ~ U(0,1) if ( u <= 93% ) then look up value in table[ u * 1000 ] else use tail distribution
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File Size Distribution - Body
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File Size Distribution - Tail
Pareto Distribution Shape parameter: a = 1.5 Lower Bound: k = 9,020 To create a random variable x: u ~ U(0,1) x = k / (1.0 – u) ^ 1.0/a
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File Size Distribution – Tail
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Self-similarity Fractal-like characteristics: Fractals look the same at all size scales Statistical Self-similarity: Empirical data has similar variability over a wide range of time scales.
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Verification of Self-similarity
Methods Observation Variance Time Plot R/S Plot Periodogram Whittle Estimator
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Self-similarity - Observation
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Variance Time Plot Hurst Parameter H = 1 – b / 2
b: inverse of the slope ½ < H < 1 H = 0.7
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Load Balancing vs. Load Sharing
System avoids having idle processors by placing new processes on idle processors first Load Balancing System attempts to distribute the load equally across all processors based on some global average. Static Processes are placed and executed on only one processor. Dynamic Processes are initially placed on one processor but at some point in time the process may be migrated to another processor based upon some decision criteria.
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Load Balancing Algorithms
Stateless Select a processor without consideration of the system state. Round Robin Random State-based Select a processor based upon some knowledge of the system state. Greedy Subset Stochastic
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Simulation Entities Request Client Load Balance Manager Server
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Request Event Loop
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Experimental Design Cooperative Environment
For each algorithm (round robin, random, greedy, subset, stochastic) Eight Servers with 1, 4 Connections 8, 16, 32, 64, 128, 256,512 Clients 1, 2, 4 Load Balance Managers
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Servers with One Connection
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Global vs. Local Info.
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Servers with Four Connections
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Global vs. Local Info.
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Experimental Design Adversarial Environment
For each algorithm (greedy, subset, stochastic) Eight Servers with 1, 4 Connections 8, 16, 32, 64, 128, 256,512 Clients 4 Load Balance Managers with 1, 2, 3 Random Load Balance Managers as adversaries
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Servers with One Connection
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LBM w/ Adversaries
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Servers with Four Connection
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LBM w/ Adversaries
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Analysis of Experimental Results
Single Connection Global Greedy: x improvement in Response Time Subset: x Stochastic: x Local Greedy: x Subset: x Stochastic: x
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Analysis of Experimental Results – cont.
Four Connections Global Greedy: x improvement in Response Time Subset: x Stochastic: x Local Greedy: x Subset: x Stochastic: x
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Analysis of Experimental Results – cont.
Single Connection w/ Adversaries Greedy: x Subset: x Stochastic: x
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Analysis of Experimental Results – cont.
Four Connection w/ Adversaries Greedy: x Subset: x Stochastic: x
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Conclusions Researched and Developed a Framework for Modeling Internet Traffic for Simulation Experimental Design Analysis of Experimental Results Comparing Five Load Balancing Algorithms
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