Implementing a Load-balancing Web Server Using Red Hat Cluster Suite Ya-Lee Tsai 9/17/2018

Project Goals Building a load-balancing web server Performance evaluation 9/17/2018

Steps Setting up a working environment. Implementing load-balancing support in the system. Performance measurement and comparison. 9/17/2018

Load-balancing Web Servers Balancing the load among the servers Providing: High throughput High availability 9/17/2018

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 9/17/2018

Architecture of A Cluster-based Web System 9/17/2018

Architecture of a Distributed Web System 9/17/2018

System Environment Web-serving cluster: Linux cluster DBMS: MySQL Benchmarks: TPC Benchmark 9/17/2018

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 9/17/2018

A Basic LVS Configuration 9/17/2018

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. 9/17/2018

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. 9/17/2018

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 9/17/2018

References: IBM Research Report: the State of the Art in Locally Distributed Web-Server Systems, Valeria Cardellini, Emiliano Casalicchio, Michele Colajanni Www.redhat.Com/manuals/enterprise/RHEL-3-manual/cluster-suite 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 9/17/2018

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 9/17/2018

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 9/17/2018

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 9/17/2018

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 9/17/2018

Streaming Architecture 9/17/2018

Base server bandwidth requirments 9/17/2018