Correlation-Model-Based Reduction of Monitoring Data in Data Centers

Peng, Xuesong; Pernici, Barbara

doi:10.5220/0005794803950405

Cited by 7 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instead, implicit relations can be detected using data mining and machine learning techniques. While several approaches exist for extracting association rules between attributes of a dataset, the detection of dependencies between numerical values is not trivial (Peng and Pernici, 2016 ). Reduction enables to regenerate the original information, although with some approximations.…”

Section: Adaptation Actions For Data Managementmentioning

confidence: 99%

Improving Health Monitoring With Adaptive Data Movement in Fog Computing

et al. 2020

Self Cite

View full text Add to dashboard Cite

Pervasive sensing is increasing our ability to monitor the status of patients not only when they are hospitalized but also during home recovery. As a result, lots of data are collected and are available for multiple purposes. If operations can take advantage of timely and detailed data, the huge amount of data collected can also be useful for analytics. However, these data may be unusable for two reasons: data quality and performance problems. First, if the quality of the collected values is low, the processing activities could produce insignificant results. Second, if the system does not guarantee adequate performance, the results may not be delivered at the right time. The goal of this document is to propose a data utility model that considers the impact of the quality of the data sources (e.g., collected data, biographical data, and clinical history) on the expected results and allows for improvement of the performance through utility-driven data management in a Fog environment. Regarding data quality, our approach aims to consider it as a context-dependent problem: a given dataset can be considered useful for one application and inadequate for another application. For this reason, we suggest a context-dependent quality assessment considering dimensions such as accuracy, completeness, consistency, and timeliness, and we argue that different applications have different quality requirements to consider. The management of data in Fog computing also requires particular attention to quality of service requirements. For this reason, we include QoS aspects in the data utility model, such as availability, response time, and latency. Based on the proposed data utility model, we present an approach based on a goal model capable of identifying when one or more dimensions of quality of service or data quality are violated and of suggesting which is the best action to be taken to address this violation. The proposed approach is evaluated with a real and appropriately anonymized dataset, obtained as part of the experimental procedure of a research project in which a device with a set of sensors (inertial, temperature, humidity, and light sensors) is used to collect motion and environmental data associated with the daily physical activities of healthy young volunteers.

show abstract

Section: Adaptation Actions For Data Managementmentioning

confidence: 99%

Improving Health Monitoring With Adaptive Data Movement in Fog Computing

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…A recent work [38] uses a correlation-based method to reduce data center's monitoring data. The authors identify the correlation between different measurement metrics using Bayesian network models learned from historical data and proposed to use linear regression between correlated metrics.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Data Center's Telemetry Reduction and Reconstruction Using Markov Chain Models

Baig

Iqbal

Berral

et al. 2019

IEEE Systems Journal

View full text Add to dashboard Cite

Large-scale data centers are composed of thousands of servers organized in interconnected racks to offer services to users. These data centers continuously generate large amounts of telemetry data streams (e.g., hardware utilization metrics) used for multiple purposes, including resource management, workload characterization, resource utilization prediction, capacity planning, and real-time analytics. These telemetry streams require costly bandwidth utilization and storage space, particularly at medium-long term for large data centers. This paper addresses this problem by proposing and evaluating a system to efficiently reduce bandwidth and storage for telemetry data through real-time modeling using Markov chain based methods. Our proposed solution was evaluated using real telemetry datasets and compared with polynomial regression methods for reducing and reconstructing data. Experimental results show that data can be lossy compressed up to 75% for bandwidth utilization and 95.33% for storage space, with reconstruction accuracy close to 92%.

show abstract