Performance Modeling and Benchmarking of Event-Based ... - DVS

Chapter 1
Introduction
1.1 Motivation
Event-based systems (EBS) have been gaining attention in many domains of industry. With
the advent of ambient intelligence and ubiquitous computing, many new applications of EBS
have been proposed [101], for example, in the areas of transport information monitoring [15,
206], event-driven supply chain management [33, 1, 196], ubiquitous (wireless) sensor environments
[2, 30, 179], environmental monitoring, ambient assisted living, and location-based
services [59, 35, 102, 100]. Many of these novel event-based applications are highly distributed
and data intensive and hence pose some serious performance and scalability challenges. With
the increasing popularity of EBS and their gradual adoption in mission critical areas, performance
issues are becoming a major concern. The performance and scalability of event-based
middleware (used to process real-time event data) are of crucial importance for the successful
adoption of such applications in the industry, and methodologies are needed to guarantee an
adequate quality-of-service (QoS) level.
As a consequence, EBS have to be subjected to a rigorous performance analysis at all stages
of an application’s life cycle. To meet QoS requirements, techniques for predicting system
performance as a function of configuration and workload are needed. Common performance
metrics of interest are, for example, expected event notification latency as well as utilization and
message throughput of the various system components (e.g., event brokers, network links). Since
the components of EBS are loosely coupled and communicate asynchronously, the understanding
of these metrics may differ from traditional performance engineering. However, obtaining such
information is essential in order to determine the optimal system topology, configuration, and
capacity for providing adequate QoS to applications at a reasonable cost. Moreover, given
the dynamics of most EBS applications, it is important that the performance of the system
is continuously monitored and analyzed during operation to help anticipate changes in the
workload and take corrective actions to ensure that QoS requirements are satisfied.
The goal of this thesis is to develop novel approaches to analyze and predict the behavior of
EBS and their performance and scalability under load. To achieve this, we focus on workload
characterization, benchmarking and performance modeling.
1.2 Problem Statement
EBS are often used in business critical environments and thus their reliability is crucial for
the whole IT infrastructure. A certain QoS level has to be ensured. Since EBS are loosely
coupled, highly distributed, data intensive and often heterogeneous systems, this is a very challenging
task. The dynamics of most EBS applications and their underlying middleware makes
1