Adapting System Execution Traces for Validation of Distributed System QoS Properties

Data Mining System Execution Traces to Validate Distributed System Quality-of-Service Properties

Knowledge Discovery Practices and Emerging Applications of Data Mining - Advances in Data Mining and Database Management ◽

10.4018/978-1-60960-067-9.ch009 ◽

2010 ◽

pp. 174-197 ◽

Cited By ~ 1

Author(s):

James H. Hill

Keyword(s):

Data Mining ◽

Quality Of Service ◽

Distributed System ◽

Real Life ◽

System Quality ◽

Mining System ◽

Execution Traces ◽

Data Mining System ◽

Software Lifecycle

System Execution Modeling (SEM) tools enable distributed system testers to validate Quality-of-Service (QoS) properties, such as end-to-end response time, throughput, and scalability, during early phases of the software lifecycle. Analytical capabilities of QoS properties, however, are traditionally bounded by a SEM tool’s capabilities. This chapter discusses how to mine system execution traces, which are a collection of log messages describing events and states of a distributed system throughout its execution lifetime, generated by distributed systems so that the validation of QoS properties is not dependent on a SEM tool’s capabilities. The author uses a real-life case study to illustrate how data mining system execution traces can assist in discovering potential performance bottlenecks using system execution traces.

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Traces Synchronization in Distributed Networks

Journal of Computer Systems Networks and Communications ◽

10.1155/2009/190579 ◽

2009 ◽

Vol 2009 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Eric Clément ◽

Michel Dagenais

Keyword(s):

Distributed System ◽

Large Time ◽

Least Square ◽

System Level ◽

Time Interval ◽

Execution Traces ◽

Novel Approach ◽

Least Square Fit ◽

Time Offset ◽

The Individual

This article proposes a novel approach to synchronize a posteriori the detailed execution traces from several networked computers. It can be used to debug and investigate complex performance problems in systems where several computers exchange information. When the distributed system is under study, detailed execution traces are generated locally on each system using an efficient and accurate system level tracer, LTTng. When the tracing is finished, the individual traces are collected and analysed together. The messaging events in all the traces are then identified and correlated in order to estimate the time offset over time between each node. The time offset computation imprecision, associated with asymmetric network delays and operating system latency in message sending and receiving, is amortized over a large time interval through a linear least square fit over several messages covering a large time span. The resulting accuracy is such that it is possible to estimate the clock offsets in a distributed system, even with a relatively low volume of messages exchanged, to within the order of a microsecond while having a very low impact on the system execution, which is sufficient to properly order the events traced on the individual computers in the distributed system.

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Data Mining System Execution Traces to Validate Distributed System Quality-of-Service Properties

Data Mining ◽

10.4018/978-1-4666-2455-9.ch039 ◽

2013 ◽

pp. 751-774

Author(s):

James H. Hill

Keyword(s):

Data Mining ◽

Quality Of Service ◽

Distributed System ◽

Real Life ◽

System Quality ◽

Mining System ◽

Execution Traces ◽

Data Mining System ◽

Software Lifecycle

System Execution Modeling (SEM) tools enable distributed system testers to validate Quality-of-Service (QoS) properties, such as end-to-end response time, throughput, and scalability, during early phases of the software lifecycle. Analytical capabilities of QoS properties, however, are traditionally bounded by a SEM tool’s capabilities. This chapter discusses how to mine system execution traces, which are a collection of log messages describing events and states of a distributed system throughout its execution lifetime, generated by distributed systems so that the validation of QoS properties is not dependent on a SEM tool’s capabilities. The author uses a real-life case study to illustrate how data mining system execution traces can assist in discovering potential performance bottlenecks using system execution traces.

Download Full-text