Predicting Bandwidth Utilization on Network Links Using Machine Learning

<div>IoT devices (wireless sensors, actuators, computer devices) produce large volume and variety of data and the data</div><div>produced by the IoT devices are transient. In order to overcome the problem of traditional IoT architecture where</div><div>data is sent to the cloud for processing, an emerging technology known as fog computing is proposed recently.</div><div>Fog computing brings storage, computing and control near to the end devices. Fog computing complements the</div><div>cloud and provide services to the IoT devices. Hence, data used by the IoT devices must be cached at the fog nodes</div><div>in order to reduce the bandwidth utilization and latency. This chapter discusses the utility of data caching at the</div><div>fog nodes. Further, various machine learning techniques can be used to reduce the latency by caching the data</div><div>near to the IoT devices by predicting their future demands. Therefore, this chapter also discusses various machine</div><div>learning techniques that can be used to extract the accurate data and predict future requests of IoT devices.</div>

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Data Caching at Fog Nodes Under IoT Networks: Review of Machine Learning Approaches

10.36227/techrxiv.12091410.v1 ◽

2020 ◽

Author(s):

Riya Tapwal ◽

Nitin Gupta ◽

Qin Xin

Keyword(s):

Machine Learning ◽

Fog Computing ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Bandwidth Utilization ◽

Data Caching ◽

Learning Techniques ◽

Future Demands ◽

Iot Devices ◽

And Control

<div>IoT devices (wireless sensors, actuators, computer devices) produce large volume and variety of data and the data</div><div>produced by the IoT devices are transient. In order to overcome the problem of traditional IoT architecture where</div><div>data is sent to the cloud for processing, an emerging technology known as fog computing is proposed recently.</div><div>Fog computing brings storage, computing and control near to the end devices. Fog computing complements the</div><div>cloud and provide services to the IoT devices. Hence, data used by the IoT devices must be cached at the fog nodes</div><div>in order to reduce the bandwidth utilization and latency. This chapter discusses the utility of data caching at the</div><div>fog nodes. Further, various machine learning techniques can be used to reduce the latency by caching the data</div><div>near to the IoT devices by predicting their future demands. Therefore, this chapter also discusses various machine</div><div>learning techniques that can be used to extract the accurate data and predict future requests of IoT devices.</div>

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Using machine learning to assess short term causal dependence and infer network links

Chaos An Interdisciplinary Journal of Nonlinear Science ◽

10.1063/1.5134845 ◽

2019 ◽

Vol 29 (12) ◽

pp. 121104 ◽

Cited By ~ 3

Author(s):

Amitava Banerjee ◽

Jaideep Pathak ◽

Rajarshi Roy ◽

Juan G. Restrepo ◽

Edward Ott

Keyword(s):

Machine Learning ◽

Short Term ◽

Causal Dependence ◽

Network Links

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SortCache

ACM Transactions on Architecture and Code Optimization ◽

10.1145/3473332 ◽

2021 ◽

Vol 18 (4) ◽

pp. 1-24

Author(s):

Sriseshan Srikanth ◽

Anirudh Jain ◽

Thomas M. Conte ◽

Erik P. Debenedictis ◽

Jeanine Cook

Keyword(s):

Machine Learning ◽

Sparse Data ◽

Graph Processing ◽

Cache Management ◽

Bandwidth Utilization ◽

The Past ◽

Machine Learning Applications ◽

On Chip ◽

Access Patterns ◽

Memory Architectures

Sparse data applications have irregular access patterns that stymie modern memory architectures. Although hyper-sparse workloads have received considerable attention in the past, moderately-sparse workloads prevalent in machine learning applications, graph processing and HPC have not. Where the former can bypass the cache hierarchy, the latter fit in the cache. This article makes the observation that intelligent, near-processor cache management can improve bandwidth utilization for data-irregular accesses, thereby accelerating moderately-sparse workloads. We propose SortCache, a processor-centric approach to accelerating sparse workloads by introducing accelerators that leverage the on-chip cache subsystem, with minimal programmer intervention.

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Load Balancing Oriented Predictive Routing Algorithm for Data Center Networks

Future Internet ◽

10.3390/fi13020054 ◽

2021 ◽

Vol 13 (2) ◽

pp. 54

Author(s):

Yazhi Liu ◽

Jiye Zhang ◽

Wei Li ◽

Qianqian Wu ◽

Pengmiao Li

Keyword(s):

Load Balancing ◽

Data Center ◽

Load Balance ◽

Routing Algorithm ◽

Round Robin ◽

Data Center Networks ◽

Bandwidth Utilization ◽

Time Flow ◽

Data Flows ◽

Network Links

A data center undertakes increasing background services of various applications, and the data flows transmitted between the nodes in data center networks (DCNs) are consequently increased. At the same time, the traffic of each link in a DCN changes dynamically over time. Flow scheduling algorithms can improve the distribution of data flows among the network links so as to improve the balance of link loads in a DCN. However, most current load balancing works achieve flow scheduling decisions to the current links on the basis of past link flow conditions. This situation impedes the existing link scheduling methods from implementing optimal decisions for scheduling data flows among the network links in a DCN. This paper proposes a predictive link load balance routing algorithm for a DCN based on residual networks (ResNet), i.e., the link load balance route (LLBR) algorithm. The LLBR algorithm predicts the occupancy of the network links in the next duty cycle, according to the ResNet architecture, and then the optimal traffic route is selected according to the predictive network environment. The LLBR algorithm, round-robin scheduling (RRS), and weighted round-robin scheduling (WRRS) are used in the same experimental environment. Experimental results show that compared with the WRRS and RRS, the LLBR algorithm can reduce the transmission time by approximately 50%, reduce the packet loss rate from 0.05% to 0.02%, and improve the bandwidth utilization by 30%.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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