Performance modeling and system management for multi-component online services

TLDR

Dynamic‑content online services consist of multiple interacting components and data partitions spread across server clusters, making performance understanding essential for efficient system management. The study introduces a profile‑driven performance model for cluster‑based multi‑component online services and investigates its application to system‑management tasks such as component placement, capacity planning, and cost‑effectiveness analysis. Offline application profiles capture component resource needs and inter‑component communication, and, combined with a placement strategy, the model predicts throughput and average response time by distinguishing remote invocations from fast‑path calls and measuring network delay from blocking communications. Validation on two J2EE‑based applications shows the model predicts throughput within 13% and average response time within 14% of measured values.

Abstract

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 inter-component 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.