Adaptive Aggregation of Flow Records

Pekár, Adrián; Chovanec, Martin; Vokorokos, Liberios; Chovancová, Eva; Fecil'ak, Peter; Michalko, M.

doi:10.4149/cai_2018_1_142

Cited by 22 publications

(7 citation statements)

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“…This should have a better visibility over the monitored system. Data pre‐processing Data filtering– Tools providing different algorithms that allow different types of filtering to be applied to incoming data without losing the information value of the data. –Data consolidation– Tools providing inbound data aggregation based by topic. This can contain data fusion and grouping. Data analysis Data pre‐analytics– Tools that provide machine learning algorithms for flowing data analysis and pattern search in these data. It can be a similar principle to predictive maintenance. –Local database– Local storage of processed data.…”

Section: Edge‐enabled Iot Gateway Criteria Selectionmentioning

confidence: 99%

“…Data analysis Data pre‐analytics– Tools that provide machine learning algorithms for flowing data analysis and pattern search in these data. It can be a similar principle to predictive maintenance. –Local database– Local storage of processed data.…”

Section: Edge‐enabled Iot Gateway Criteria Selectionmentioning

confidence: 99%

“…• Data analysis -Data pre-analytics 20,24,25,29,33 -Tools that provide machine learning algorithms for flowing data analysis and pattern search in these data.…”

Section: Edge-enabled Iot Gateway Criteria Selectionmentioning

confidence: 99%

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Edge‐enabled IoT gateway criteria selection and evaluation

Papcun

Kajáti

Cupkova

et al. 2019

Concurrency and Computation

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Summary In this contribution, we define the concept of transferring intelligence from the cloud to the edge of the network and highlight its importance in modern IoT architectures. It leads to edge‐enabled IoT solutions, where intelligence is distributed to the end devices, ie, edge of the network. The IoT gateway is a middle‐ware between devices and cloud and facilitates computations and communication. We have encountered a research gap and decided to propose our solution on creating an edge‐enabled IoT gateway and evaluating its rating by functionality. In the beginning, we describe the general architecture where we demonstrate the location of the edge‐enabled IoT gateway in edge‐enabled solution. Then, we discuss a selection of major criteria, which could be implemented to any edge‐enabled solution. In the end, we evaluate our proposed edge‐enabled IoT gateway and also compare several scientific works focused on IoT gateway design according to our criteria.

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Section: Edge‐enabled Iot Gateway Criteria Selectionmentioning

confidence: 99%

Section: Edge‐enabled Iot Gateway Criteria Selectionmentioning

confidence: 99%

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Edge‐enabled IoT gateway criteria selection and evaluation

Papcun

Kajáti

Cupkova

et al. 2019

Concurrency and Computation

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“…Some are discarded after they are created, transferred, and used, but a large portion is subsequently archived, extending the total amount of data currently included in ICT. The data transfer itself represents a significant burden for ICT, so we are looking for ways how to model, optimize and minimize it [1] [2]. A large amount of data exists in multiple copies -in uncompressed or compressed forms.…”

Section: Introductionmentioning

confidence: 99%

Δrle

Madoš

Bilanova

Hurtuk

2021

J. inf. organ. sci. (Online)

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Lossless data compression algorithms can use statistical redundancy to represent data using a fewer number of bits in comparison to the original uncompressed data. Run-Length Encoding (RLE) is one of the simplest lossless compression algorithms in terms of understanding its principles and software implementation, as well as in terms of temporal and spatial complexity. If this principle is applied to individual bits of original uncompressed data without respecting the byte boundaries, this approach is referred to as bit-level Run-Length Encoding. Lightweight algorithm for lossless data compression proposed in this paper optimizes bit-level RLE data compression, uses special encoding of repeating data blocks, and, if necessary, combines it with delta data transformation or representation of data in its original form intending to increase compression efficiency compared to a conventional bit-level RLE approach. The advantage of the algorithm proposed in this paper is in its low time and memory consumption which are basic features of RLE, along with the simultaneous increase of compression ratio, compared to the classical bit-level RLE approach.

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“…However, this centralized control does not guarantee that the network performance will not degrade when traffic volume raises [7]. One class of traffic that constantly poses a challenge is Heavy-Hitter (HH) flows-large-volume flows that consume considerably more network resources than other flows combined [7][8][9][10]. One of the consequences of unsupervised forwarding of HHs is that it often leads to network congestion and, subsequently, to overall network performance degradation [7,8,[11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

An Approach Based on Knowledge-Defined Networking for Identifying Heavy-Hitter Flows in Data Center Networks

et al. 2019

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Heavy-Hitters (HHs) are large-volume flows that consume considerably more network resources than other flows combined. In SDN-based DCNs (SDDCNs), HHs cause non-trivial delays for small-volume flows known as non-HHs that are delay-sensitive. Uncontrolled forwarding of HHs leads to network congestion and overall network performance degradation. A pivotal task for controlling HHs is their identification. The existing methods to identify HHs are threshold-based. However, such methods lack a smart system that efficiently identifies HH according to the network behaviour. In this paper, we introduce a novel approach to overcome this lack and investigate the feasibility of using Knowledge-Defined Networking (KDN) in HH identification. KDN by using Machine Learning (ML), allows integrating behavioural models to detect patterns, like HHs, in SDN traffic. Our KDN-based approach includes mainly three modules: HH Data Acquisition Module (HH-DAM), Data ANalyser Module (HH-DANM), and APplication Module (HH-APM). In HH-DAM, we present the flowRecorder tool for organizing packets into flows records. In HH-DANM, we perform a cluster-based analysis to determine an optimal threshold for separating HHs and non-HHs. Finally, in HH-APM, we propose the use of MiceDCER for routing non-HHs efficiently. The per-module evaluation results corroborate the usefulness and feasibility of our approach for identifying HHs.

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Adaptive Aggregation of Flow Records

Cited by 22 publications

References 0 publications

Edge‐enabled IoT gateway criteria selection and evaluation

Edge‐enabled IoT gateway criteria selection and evaluation

Δrle

An Approach Based on Knowledge-Defined Networking for Identifying Heavy-Hitter Flows in Data Center Networks

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