Paper Info
Title
R-Capriccio: a capacity planning and anomaly detection tool for enterprise services with live workloads
Abstract
As the complexity of IT systems increases, performance management and capacity planning become the largest and most difficult expenses to control. New methodologies and modeling techniques that explain large-system behavior and help predict their future performance are now needed to effectively tackle the emerging performance issues. With the multi-tier architecture paradigm becoming an industry standard for developing scalable client-server applications, it is important to design effective and accurate performance prediction models of multi-tier applications under an enterprise production environment and a real workload mix. To accurately answer performance questions for an existing production system with a real workload mix, we design and implement a new capacity planning and anomaly detection tool, called R-Capriccio, that is based on the following three components: i) a Workload Profiler that exploits locality in existing enterprise web workloads and extracts a small set of most popular, core client transactions responsible for the majority of client requests in the system; ii) a Regression-based Solver that is used for deriving the CPU demand of each core transaction on a given hardware; and iii) an Analytical Model that is based on a network of queues that models a multi-tier system. To validate R-Capriccio, we conduct a detailed case study using the access logs from two heterogeneous production servers that represent customized client accesses to a popular and actively used HP Open View Service Desk application.
Year
DOI
Venue
2007
10.1007/978-3-540-76778-7_13
Middleware
Keywords
Field
DocType
live workloads,client access,performance issue,capacity planning,performance question,client request,performance management,enterprise production environment,real workload mix,future performance,accurate performance prediction model,core client transaction,enterprise service,anomaly detection tool,client server,anomaly detection,production system
Anomaly detection,Workload,Computer science,Server,Capacity planning,Service desk,Performance management,Database,Scalability,Distributed computing,Application server
Conference
Volume
ISSN
ISBN
4834
0302-9743
3-540-76777-0
Citations 
PageRank 
References 
32
1.98
15
Authors
5
Name
Order
Citations
PageRank
Qi Zhang141422.77
Ludmila Cherkasova23041205.44
Guy Mathews3342.36
Wayne Greene4342.36
Evgenia Smirni51857161.97