Cloud-Scale Application Performance Monitoring with SDN and NFV

2015 ◽  
Author(s):  
Guyue Liu ◽  
Timothy Wood
Keyword(s):  
Performance Monitoring ◽  
Application Performance

scholarly journals An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1590
Author(s):  
Arnak Poghosyan ◽  
Ashot Harutyunyan ◽  
Naira Grigoryan ◽  
Clement Pang ◽  
George Oganesyan ◽  
...  
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Transfer Learning ◽  
Performance Monitoring ◽  
Nonlinear Problems ◽  
Time Series Forecasting ◽  
Stationary Time Series ◽  
Learning Gain ◽  
Application Performance ◽  
Cloud Applications

The main purpose of an application performance monitoring/management (APM) software is to ensure the highest availability, efficiency and security of applications. An APM software accomplishes the main goals through automation, measurements, analysis and diagnostics. Gartner specifies the three crucial capabilities of APM softwares. The first is an end-user experience monitoring for revealing the interactions of users with application and infrastructure components. The second is application discovery, diagnostics and tracing. The third key component is machine learning (ML) and artificial intelligence (AI) powered data analytics for predictions, anomaly detection, event correlations and root cause analysis. Time series metrics, logs and traces are the three pillars of observability and the valuable source of information for IT operations. Accurate, scalable and robust time series forecasting and anomaly detection are the requested capabilities of the analytics. Approaches based on neural networks (NN) and deep learning gain an increasing popularity due to their flexibility and ability to tackle complex nonlinear problems. However, some of the disadvantages of NN-based models for distributed cloud applications mitigate expectations and require specific approaches. We demonstrate how NN-models, pretrained on a global time series database, can be applied to customer specific data using transfer learning. In general, NN-models adequately operate only on stationary time series. Application to nonstationary time series requires multilayer data processing including hypothesis testing for data categorization, category specific transformations into stationary data, forecasting and backward transformations. We present the mathematical background of this approach and discuss experimental results based on implementation for Wavefront by VMware (an APM software) while monitoring real customer cloud environments.

AN MPI PERFORMANCE MONITORING INTERFACE FOR CELL BASED COMPUTE NODES

2009 ◽  
Vol 19 (04) ◽  
pp. 535-552
Author(s):  
HIKMET DURSUN ◽  
KEVIN J. BARKER ◽  
DARREN J. KERBYSON ◽  
SCOTT PAKIN ◽  
RICHARD SEYMOUR ◽  
...  
Keyword(s):  
Performance Monitoring ◽  
Parallel Applications ◽  
Cell Processor ◽  
Worst Case ◽  
Application Performance ◽  
Cell Broadband Engine ◽  
The Family ◽  
Function Call ◽  
Local Store ◽  
And Function

In this paper, we present a methodology for profiling parallel applications executing on the family of architectures commonly referred as the "Cell" processor. Specifically, we examine Cell-centric MPI programs on hybrid clusters containing multiple Opteron and IBM PowerXCell 8i processors per node such as those used in the petascale Roadrunner system. We analyze the performance of our approach on a PlayStation3 console based on Cell Broadband Engine—the CBE—as well as an IBM BladeCenter QS22 based on PowerXCell 8i. Our implementation incurs less than 0.5% overhead and 0.3 µs per profiler call for a typical molecular dynamics code on the Cell BE while efficiently utilizing the limited local store of the Cell's SPE cores. Our worst-case overhead analysis on the PowerXCell 8i costs 3.2 µs per profiler call while using only two 5 KiB buffers. We demonstrate the use of our profiler on a cluster of hybrid nodes running a suite of scientific applications. Our analyses of inter-SPE communication (across the entire cluster) and function call patterns provide valuable information that can be used to optimize application performance.

scholarly journals An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning

2021 ◽  
Author(s):  
Arnak Poghosyan ◽  
Ashot Harutyunyan ◽  
Naira Grigoryan ◽  
Clement Pang ◽  
George Oganesyan ◽  
...  
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Performance Monitoring ◽  
Time Series Forecasting ◽  
Stationary Time Series ◽  
Learning Gain ◽  
Application Performance ◽  
Stationary Time ◽  
Forecasting System ◽  
Cloud Applications

One of the key components of application performance monitoring (APM) software is 2 AI/ML empowered data analytics for predictions, anomaly detection, event correlations and root 3 cause analysis. Time series metrics, logs and traces are three pillars of observability and the valuable 4 source of information for IT operations. Accurate, scalable and robust time series forecasting and 5 anomaly detection are desirable capabilities of the analytics. Approaches based on neural networks 6 (NN) and deep learning gain increasing popularity due to their flexibility and ability to tackle complex 7 non-linear problems. However, some of the disadvantages of NN-based models for distributed cloud 8 applications mitigate expectations and require specific approaches. We demonstrate how NN-models 9 pretrained on a global time series database can be applied to customer specific data using transfer 10 learning. In general, NN-models adequately operate only on stationary time series. Application 11 to non-stationary time series requires multilayer data processing including hypothesis testing for 12 data categorization, category specific transformations into stationary data, forecasting and backward 13 transformations. We present the mathematical background of this approach and discuss experimental 14 results from the productized implementation in Wavefront by VMware (an APM software) while 15 monitoring real customer cloud environments.

scholarly journals FDM CLOUD BASED APPLICATION PERFORMANCE MONITORING SYSTEM OF THE CADETS WHILE ON BOARD SHIP TRAINING FOR THE PHILIPPINE MERCHANT MARINE ACADEMY IN SAN NARCISO ZAMBALES

2017 ◽  
Vol 10 (04) ◽  
pp. 725-729
Author(s):  
Froilan D. Mobo
Keyword(s):  
Monitoring System ◽  
Performance Monitoring ◽  
Present System ◽  
Merchant Marine ◽  
Real Time System ◽  
Application Performance ◽  
Descriptive Research ◽  
System Composition ◽  
The One ◽  
Board Ship

The main thrust of the study is to develop a cloud-based application performance monitoring system of the cadets while on board ship training for the Philippine Merchant Marine Academy in San Narciso, Zambales which intended to serve as an alternative system that can be used by the target school in monitoring the performance of the cadets while on ship board and determine its effectiveness. The researcher has used the descriptive research method wherein the study is focused on the present situations. It involves the recording, description and the presentation of the present system, composition and phenomena. The Statement of the problem focused in obtaining the effectiveness of the proposed cloud based application in performance monitoring system of the cadets for the Philippine Merchant Marine Academy, San Narciso, Zambales. Findings showed that the Department of Shipboard Training needs a real time system that can monitor the cadets while on board ship training. Based on the conclusion of this study, the researcher recommends the development of the cloud based application which will monitor the performance of the cadets while on board ship training and lastly, the developed system was tested during the implementation and met no errors. Based on the findings, the number of respondents as group per course is in parallel to the ratio of the students currently enrolled at PMMA. Majority of the respondents for both the groups came from the existing Group whose ages range from 19-21 years old.As revealed, the majority of the respondents for both the groups are males from the Proposed Group. In the proposed system out of one hundred percent respondents, thirty or 93.5% cadets; while the officials are nine or 6.5% of the one hundred, from the total of thirty-nine respondents.

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