1. Implementing a Load-balancing Web Server Using Red Hat Cluster Suite
Ya-Lee Tsai
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2. Project Goals Building a load-balancing web server
Performance evaluation
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3. Steps Setting up a working environment.
Implementing load-balancing support in the system.
Performance measurement and comparison.
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4. Load-balancing Web Servers
Balancing the load among the servers
Providing:
High throughput
High availability
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5. Two Classes of Locally Distributed Architecture for Web Sites
Cluster-based web systems
Virtual IP address
Distributed web systems
IP address visible to client applications
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6. Architecture of A Cluster-based Web System
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7. Architecture of a Distributed Web System
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8. System Environment Web-serving cluster: Linux cluster DBMS: MySQL
Benchmarks: TPC Benchmark
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9. Red Hat Cluster Suite
A cluster of systems to provide highly available web services, database servers, and other types of services
Virtual web server configuration
Using Linux virtual server (LVS) technology
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10. A Basic LVS Configuration
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11. MySQL Database Services
Serve highly available data to applications.
MySQL server packages are installed on each cluster system that will run the service.
Database data is accessed by all cluster members.
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12. TPC Benchmark
TPC defines transaction performance and database benchmarks.
TPC benchmarks measure transaction processing and database performance in terms of how many transactions a given system and a database can perform per unit time.
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13. TPC Benchmarks TPC-C -- OLTP
TPC-H – decision support for AD hoc queries
TPC-W – web e-commerce
TPC-R – decision support for business reporting
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14. References:
IBM Research Report: the State of the Art in Locally Distributed Web-Server Systems,
Valeria Cardellini, Emiliano Casalicchio, Michele Colajanni
Comparing the Memory System Performance of DSS Workloads on the HP V-class and SGI Origin 2000,
Rong Yu, Laxmi Bhuyan, Ravi Iyer
Building Clustered Linux Systems,
Robert W. Lucke
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15. A Synthetic Streaming Workload Generator & Evaluation of Different Streaming Techniques
Reference: GISMO: A Generator of Internet Streaming
Media Objects and workloads
Shudong Jin and Azer Bestavros
Boston University
A preliminary version appears in ACM SIGMETRICS Performance Evaluation Review, November, 2001
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16. Workload Charateristics
Session
the service initiated by a user's request for a transfer and terminated by a user's abortion of an ongoing transfer.
Workload
Session arrivals
Properties of individual sessions
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17. Session arrival Object Populariy Reference locality zipf distribution
a tendency for requests to be concentrated on a few “popular” objects
Reference locality
Heavy-tailed Pareto distribution
Temporal proximity of requests to the same objects
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18. Individual Session Object Size User interactivity
Lognormal distribution
User interactivity
Pareto distribution
Object encoding characteristics
Model the VBR auto-correlation of a streaming object using a self-similar process
Use a heavy-tailed marginal distribution to specify the level of burstiness of the bit rate
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19. Streaming Architecture
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20. Base server bandwidth requirments
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