Online Incremental Clock Synchronization

Jabbarifar, Masoume; Dagenais, Michel R.; Shameli‐Sendi, Alireza

doi:10.1007/s10922-014-9331-7

Cited by 6 publications

(5 citation statements)

References 27 publications

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“…The absence of a global clock analysis in a distributed system is challenging and may even cause messages to seemingly be received before being issued (message inversion). The convex hull algorithm was proposed by Jabbarifar et al [24] to solve this problem. The idea is to use event matching between different hosts to unify the clock sources.…”

Section: Kernel Modulesmentioning

confidence: 99%

“…In this case, the causality between events is the only way to get a partial ordering, which is enough for the analysis. The fully incremental convex hull synchronization algorithm [24] is used to solve the problem of trace synchronization in distributed systems. The algorithm is based on matching an event a from one trace and an event b from the other.…”

Section: Multilevelmentioning

confidence: 99%

See 1 more Smart Citation

Debugging of Performance Degradation in Distributed Requests Handling Using Multilevel Trace Analysis

Ezzati‐Jivan

Daoud

Dagenais

2021

Wireless Communications and Mobile Computing

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Root cause identification of performance degradation within distributed systems is often a difficult and time-consuming task, yet it is crucial for maintaining high performance. In this paper, we present an execution trace-driven solution that reduces the efforts required to investigate, debug, and solve performance problems found in multinode distributed systems. The proposed approach employs a unified analysis method to represent trace data collected from the user-space level to the hardware level of involved nodes, allowing for efficient and effective root cause analysis. This solution works by extracting performance metrics and state information from trace data collected at user-space, kernel, and network levels. The multisource trace data is then synchronized and structured in a multidimensional data store, which is designed specifically for this kind of data. A posteriori analysis using a top-down approach is then used to investigate performance problems and detect their root causes. In this paper, we apply this generic framework to analyze trace data collected from the execution of the web server, database server, and application servers in a distributed LAMP (Linux, Apache, MySQL, and PHP) Stack. Using industrial level use cases, we show that the proposed approach is capable of investigating the root cause of performance issues, addressing unusual latency, and improving base latency by 70%. This is achieved with minimal tracing overhead that does not significantly impact performance, as well as O log n query response times for efficient analysis.

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Section: Kernel Modulesmentioning

confidence: 99%

Section: Multilevelmentioning

confidence: 99%

Debugging of Performance Degradation in Distributed Requests Handling Using Multilevel Trace Analysis

Ezzati‐Jivan

Daoud

Dagenais

2021

Wireless Communications and Mobile Computing

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“…In this case, the causality between events can be used F I G U R E 3 Client and OSD analysis before and after clock synchronization [Colour figure can be viewed at wileyonlinelibrary.com] to get partial ordering, which is sufficient to have meaningful trace data. In this article, we used the fully incremental convex hull algorithm 32 to synchronize the traces. The algorithm is based on the causality between the events of the different traces.…”

Section: Trace Correlationmentioning

confidence: 99%

Performance analysis of distributed storage clusters based on kernel and userspace traces

Daoud

Dagenais

2020

Softw Pract Exp

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Distributed storage systems are commonly used in modern computing. They are highly scalable and offer data replication and fault tolerance. The complexity of those systems makes them difficult to debug using traditional tools. The existing tools are able to evaluate the overall performance of such systems but they do not provide enough information to find the root cause of performance issues. In this article, we propose a tracing-based performance analysis framework for storage clusters. We use a tracing strategy that reduces the tracing overhead in production systems. The traces collected from the different storage nodes are correlated and used to generate a data model that represents the cluster. Userspace tracing is used to gather data from the storage daemons, while Kernel tracing is used to provide detailed information about operating system internals such as disk queues, network queues and process scheduling. Efficient data structures are used to store the model and to generate metrics and graphical views. Our tool is used in different real world scenarios and is able to investigate interesting performance problems including I/O latencies, data replication and storage nodes failures.

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“…We use the fully incremental convex hull synchronization algorithm to achieve offline trace synchronization, introduced by [18] for distributed traces synchronization. Each guest trace is processed individually and synchronized according to the host's trace whose timeline is taken as a reference.…”

Section: Event Matchingmentioning

confidence: 99%

Fine-grained preemption analysis for latency investigation across virtual machines

2014

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This paper studies the preemption between programs running in different virtual machines on the same computer. One of the current monitoring methods consist of updating the average steal time through collaboration with the hypervisor. However, the average is insufficient to diagnose abnormal latencies in time-sensitive applications. Moreover, the added latency is not directly visible from the virtual machine point of view. The main challenge is to recover the cause of preemption of a task running in a virtual machine, whether it is a task on the host computer or in another virtual machine. We propose a new method to study thread preemption crossing virtual machines boundaries using kernel tracing. The host computer and each monitored virtual machine are traced simultaneously. We developed an efficient and portable trace synchronization method, which is required to account for time offset and drift that occur within each virtual machine. We then devised an algorithm to recover the root cause of preemption between threads at every level. The algorithm successfully detected interactions between multiple competing threads in distinct virtual machines on a multi-core machine.

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Online Incremental Clock Synchronization

Cited by 6 publications

References 27 publications

Debugging of Performance Degradation in Distributed Requests Handling Using Multilevel Trace Analysis

Debugging of Performance Degradation in Distributed Requests Handling Using Multilevel Trace Analysis

Performance analysis of distributed storage clusters based on kernel and userspace traces

Fine-grained preemption analysis for latency investigation across virtual machines

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