NSDI '05 Abstract
Performance Modeling and System Management for Multi-component Online
Christopher Stewart and Kai Shen, University of Rochester
Many dynamic-content online services are comprised of multiple interacting
components and data partitions distributed across server clusters.
Understanding the performance of these services is crucial for efficient
system management. This paper presents a profile-driven performance model
for cluster-based multi-component online services. Our offline constructed
application profiles characterize component resource needs and inter-component
communications. With a given component placement strategy,
the application profile can be used to predict system throughput
and average response time for the online service.
Our model differentiates remote invocations from fast-path calls between
co-located components and we measure the network delay caused by blocking
communications. Validation with
two J2EE-based online applications show that our model can predict application
performance with small errors (less than 13% for throughput and less than
14% for the average response time).
We also explore how this performance model can be used to assist system
management functions for multi-component online services, with case
examinations on optimized component placement, capacity planning,
and cost-effectiveness analysis.
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