Host Hypervisor Trace Mining for Virtual Machine Workload Characterization

Nemati, Hani; Azhari, Seyed Vahid; Dagenais, Michel R.

doi:10.1109/ic2e.2019.00024

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…Virtual Machine state transitions have also been used as a nonintrusive estimation of VM Load to classify huge amounts of VM instances when dealing with performance issues in a cloud (Nemati et al, 2019). An agentless technique for VM feature extraction is proposed.…”

Section: Related Workmentioning

confidence: 99%

Server load estimation by Burr distribution mixture analysis of TCP SYN response time

Pedro

Rosu²,

Vergara

2023

Journal of Network and Computer Applications

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Section: Related Workmentioning

confidence: 99%

Server load estimation by Burr distribution mixture analysis of TCP SYN response time

Pedro

Rosu²,

Vergara

2023

Journal of Network and Computer Applications

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“…To overcome this limitation, Vaswani et al [6] introduced the Transformer, a sequence-to-sequence model based solely on the inter-attention and self-attention mechanisms. The selfattention allows relating any two positions in a sequence 3 GRU is similar to LSTM but requires fewer parameters. regardless of their distance thus allowing for a significant increase in performance in most natural language processing tasks at the cost of a quadratic complexity with respect to the sequence length.…”

Section: Related Workmentioning

confidence: 99%

“…Most machine learning methods take a vector of numerical features as input. Hand-crafted features of traces have been proposed, but no representation seems to work universally well or to encapsulate the true underlying explanatory factors [2,3,4]. Instead of relying on hand-crafted features, neural networks learn how to extract meaningful features for the task.…”

Section: Introductionmentioning

confidence: 99%

On Improving Deep Learning Trace Analysis with System Call Arguments

Fournier¹,

Aloise²,

Azhari³

et al. 2021

Preprint

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Kernel traces are sequences of low-level events comprising a name and multiple arguments, including a timestamp, a process id, and a return value, depending on the event. Their analysis helps uncover intrusions, identify bugs, and find latency causes. However, their effectiveness is hindered by omitting the event arguments. To remedy this limitation, we introduce a general approach to learning a representation of the event names along with their arguments using both embedding and encoding. The proposed method is readily applicable to most neural networks and is task-agnostic. The benefit is quantified by conducting an ablation study on three groups of arguments: call-related, process-related, and time-related. Experiments were conducted on a novel web request dataset and validated on a second dataset collected on pre-production servers by Ciena, our partnering company. By leveraging additional information, we were able to increase the performance of two widely-used neural networks, an LSTM and a Transformer, by up to 11.3% on two unsupervised language modelling tasks. Such tasks may be used to detect anomalies, pre-train neural networks to improve their performance, and extract a contextual representation of the events.

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“…A practical approach is to cluster them based on system resources consumption behaviors, thus grouping the faulty behaviors based on the extent to which they compete over resources to serve user requests. In a recent work, Nemati et al [17] extracted high-level features from low-level traces of virtual machines and grouped them using two-stage k-means. Their method provided insights into the different behaviors and potential anomalies.…”

Section: Introductionmentioning

confidence: 99%

Automatic Cause Detection of Performance Problems in Web Applications

Fournier

Ezzati‐Jivan

Aloise

et al. 2019

2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)

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The execution of similar units can be compared by their internal behaviors to determine the causes of their potential performance issues. For instance, by examining the internal behaviors of different fast or slow web requests more closely, and by clustering and comparing their internal executions, one can determine what causes some requests to run slowly or behave in unexpected ways. In this paper, we propose a method of extracting the internal behavior of web requests as well as introduce a pipeline that detects performance issues in web requests and provides insights into their root causes. First, low-level and fine-grained information regarding each request is gathered by tracing both the user space and the kernel space. Second, further information is extracted and fed into an outlier detector. Finally, these outliers are then clustered by their behavior, and each group is analyzed separately. Experiments revealed that this pipeline is indeed able to detect slow web requests and provide additional insights into their true root causes. Notably, we were able to identify a real PHP cache contention issue using the proposed approach.

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Host Hypervisor Trace Mining for Virtual Machine Workload Characterization

Cited by 7 publications

References 15 publications

Server load estimation by Burr distribution mixture analysis of TCP SYN response time

Server load estimation by Burr distribution mixture analysis of TCP SYN response time

On Improving Deep Learning Trace Analysis with System Call Arguments

Automatic Cause Detection of Performance Problems in Web Applications

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