CloudIQ

Bhaumik, Sourjya; Chandrabose, Shoban Preeth; Jataprolu, Manjunath Kashyap; Kumar, G.Hari Hara; Muralidhar, Anand; Polakos, P.; Srinivasan, Vikram; Woo, T.Y.C.

doi:10.1145/2348543.2348561

Cited by 192 publications

(31 citation statements)

References 7 publications

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“…which depends on the number of resource blocks N RB and the modulation techniques (M CS) where both of them change linerly with the delay [36].…”

Section: G Delay Analysismentioning

confidence: 99%

Based on 6G, Fog Radio Access Network Behaviour Analysis

Abdelnaby,

Abd-Alaaty,

Nashat

et al. 2024

Preprint

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The introduction of a new generation of cellular networks each decade necessitates the development of new architectural designs. A novel paradigm called fog communication has been put out to make use of the throughput and latency advantages of edge computing. We propose architecture for new generation 6G with the aid of fog computing and mathematical model research to evaluate the effectiveness of such ideas. In particular, the performance of 6G Radio Access Nework in terms of, power consumption (PC), energy efficiency (EE), and delay with fog architecture is evaluated in comparison to that of previous generation 5G. Fog Radio Access Networks (F-RANs) have been shown in the literature to offer improved latency performance, but use a lot of power, reducing their EE in contrast to their regular cloud equivalents. This paper demonstrates another perspective or new term (fully photonics) or introducing photonics component to the network. However, the quantity of deployed fog devices detremines the degree of degradation, which have an immediate impact on the PC.The entrance of full photonics will elliminate this degradation and will improve the network performance In all of the aforementioned aspects.This study demonstrates adding a fog architecture and optics component and studying the network behaviour from three perspectives: time delay, EE, and PC.

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“…which depends on the number of resource blocks N RB and the modulation techniques (M CS) where both of them change linerly with the delay [36].…”

Section: G Delay Analysismentioning

confidence: 99%

Based on 6G, Fog Radio Access Network Behaviour Analysis

Abdelnaby,

Abd-Alaaty,

Nashat

et al. 2024

Preprint

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“…In any case, the total Round-Trip-Time (RTT) from RU to BBU limits the size and diameter of clusters. The typical RTT budget is 3 due to Hybrid-ARQ delay requirements [2], [29]. This delay budget includes the processing time as well as fronthaul transmission delay between RU and BBU.…”

Section: Problem Statementmentioning

confidence: 99%

“…when both stations have the same remaining resources, irrespective of whether this quantity is high or low. Moreover, given a fixed difference ( − ) 2 , the square of the 3D-distance is proportional to the square of the 2D-distance; as a result, RUs in a small geographical distance from each other are favored to collaborate, whereas large 2D-distance prohibits any collaboration.…”

Section: Traffic-aware Ru Clustersmentioning

confidence: 99%

“…In the special case with = 2, for (1) = (̄ (1),̄ (1),̄ (1)) the -th centroid obtained at time-slot = 1, we search for ′ (2) = (̄ ′ (2),̄ ′ (2),̄ ′ (2)), the 2 closest centroid to ′ (2) from an execution at = 1. The resulting robust centroid is the average of each dimension of these two centroids (1) and ′ (2). We finally remap the RRHs according to the new centroids using the 3 distance.…”

Section: Robustnessmentioning

confidence: 99%

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Hyperbolic K-means for traffic-aware clustering in cloud and virtualized RANs

Djeddal

Touzari

Giovanidis

et al. 2021

Computer Communications

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As the internet and connected objects gain more and more in popularity, serving the ever-increasing data traffic becomes a challenge for the mobile operators. The traditional cellular radio access network (RAN), where each base station is co-located with its own processing unit and is responsible for a specific geographic area, has evolved first with the so-called Cloud RAN (C-RAN), and is currently undergoing further architectural evolution under the virtualized RAN (vRAN), Software-Defined RAN (SD-RAN), and Open RAN (O-RAN) architectures. In all these versions, the data processing units can be dynamically centralized into a pool and shared between several base stations, enlarging the geographical view for scheduling and resource allocation algorithms. For instance, resource utilisation is improved by avoiding resource idling during off-peak hours. C-RAN and vRAN gains depend strongly on the clustering scheme of radio units (RRHs and RUs). In this paper, we propose a novel radio clustering algorithm that takes into account both the traffic demand and the position of stations, by using the hyperbolic distance in 3-dimensions. We introduce a modified K-means clustering algorithm, called Hyperbolic K-means, and show that this generates geographically compact RU clusters with traffic charge equally shared among them. Application of our algorithm on real-world mobile data traffic, collected from the cities of Nantes and Lille in France, shows an increase in resource utilisation by 25%, and a reduction in deployment cost by 15%, compared to the standard RAN. Furthermore, the performance of our Hyperbolic K-means algorithm is compared extensively against alternative C-RAN clustering proposals from the literature and is shown to outperform them, in resource utilisation as well as in cost reduction.

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“…One processor core for the receiver, assuming 16-QAM on the uplink, and approximately 1 core for the transmitter processing with 64-QAM on the downlink, are required to meet the HARQ deadlines for a fully loaded system. Processing load is mainly dominated by uplink and increases with growing PRBs and MCSs [1,4]. Furthermore, the ratio and variation of downlink processing load to that of uplink also grows with the increase of PRB and MCS.…”

Section: Number Of Cpu Cores Per Bbumentioning

confidence: 99%

Towards a Cloud-Native Radio Access Network

Nikaein

Schiller

Favraud

et al. 2016

Studies in Big Data

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Commoditization and virtualization of wireless networks are changing the economics of mobile networks to help network providers, e.g. Mobile Network Operator (MNO), Mobile Virtual Network Operator (MVNO), move from proprietary and bespoke hardware and software platforms towards an open, cost-effective, and flexible cellular ecosystem. In addition, rich and innovative local services can be efficiently materialized through cloudification by leveraging the existing infrastructure. In this work, we present a Radio Access Network as a Service (RANaaS), in which a Cloudified Centralized Radio Access Network (C-RAN) is delivered as a service. RANaaS describes the service life-cycle of an on-demand, elastic, and pay as you go RAN instantiated on top of the cloud infrastructure. Due to short deadlines in many examples of RAN, the fluctuations of processing time, introduced by the virtualization framework, have a deep impact on the C-RAN performance. While in typical cloud environments, the deadlines of processing time cannot be guaranteed, the cloudification of C-RAN, in which signal processing runs on general purpose processors inside Virtual Machines (VMs), is a challenging subject. We describe an example of real-time cloudified LTE network deployment using the OpenAirInterface (OAI) LTE implementation and OpenStack running on commodity hardware. We also show the flexibility and performance of the platform developed. Finally, we draw general conclusions on the RANaaS provisioning problem in future 5G networks.Abstract Commoditization and virtualization of wireless networks are changing the economics of mobile networks to help network providers, e.g. Mobile Network Operator (MNO), Mobile Virtual Network Operator (MVNO), move from proprietary and bespoke hardware and software platforms towards an open, cost-effective, and flexible cellular ecosystem. In addition, rich and innovative local services can be efficiently materialized through cloudification by leveraging the existing infrastructure. In this work, we present a Radio Access Network as a Service (RANaaS), in which a Cloudified Centralized Radio Access Network (C-RAN) is delivered as a service. RANaaS describes the service life-cycle of an on-demand, elastic, and pay as you go RAN instantiated on top of the cloud infrastructure. Due to short deadlines in many examples of RAN, the fluctuations of processing time, introduced by the virtualization framework, have a deep impact on the C-RAN performance. While in typical cloud environments, the deadlines of processing time cannot be guaranteed, the cloudification of C-RAN, in which signal processing runs on general purpose processors inside Virtual Machines (VMs), is a challenging subject. We describe an example of real-time cloudifed LTE network deployment using the OpenAirInterface (OAI) LTE implementation and OpenStack running on commodity hardware. We also show the flexibility and performance of the platform developed. Finally, we draw general conclusions on the RANaaS provisioning problem in future 5G networks.

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CloudIQ

Cited by 192 publications

References 7 publications

Based on 6G, Fog Radio Access Network Behaviour Analysis

Based on 6G, Fog Radio Access Network Behaviour Analysis

Hyperbolic K-means for traffic-aware clustering in cloud and virtualized RANs

Towards a Cloud-Native Radio Access Network

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