Joint Communication and Computing Resource Allocation in 5G Cloud Radio Access Networks

Ferdouse, Lilatul; Anpalagan, Alagan; Erkucuk, Serhat

doi:10.1109/tvt.2019.2927904

Cited by 37 publications

(21 citation statements)

References 31 publications

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“…This categorization is performed to ensure that the resource allocation methods are well-defined and serve a clear objective. The same case applies to Bouncer where all incoming service requests follow a Poisson arrival scheme [48]. Employing Poisson regression ensures that all events are independent and they don't hinder the path of newly arriving services.…”

Section: Testbed and Topologymentioning

confidence: 99%

Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services

et al. 2019

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Cloud computing is a paradigm that ensures the flexible, convenient and on-demand provisioning of a shared pool of configurable network and computing resources. Its services can be offered by either private or public infrastructures, depending on who owns the operational infrastructure. Much research has been conducted to improve a cloud’s resource provisioning techniques. Unfortunately, sometimes an abrupt increase in the demand for cloud services results in resource shortages affecting both providers and consumers. This uncertainty of resource demands by users can lead to catastrophic failures of cloud systems, thus reducing the number of accepted service requests. In this paper, we present Bouncer—a workload admission control scheme for cloud services. Bouncer works by ensuring that cloud services do not exceed the cloud infrastructure’s threshold capacity. By adopting an application-aware approach, we implemented Bouncer on software-defined network (SDN) infrastructure. Furthermore, we conduct an extensive study to evaluate our framework’s performance. Our evaluation shows that Bouncer significantly outperforms the conventional service admission control schemes, which are still state of the art.

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Section: Testbed and Topologymentioning

confidence: 99%

Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services

et al. 2019

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“…Similarly, OMA supported C-RANs utilize limited number of orthogonal resources considered as communication resources for the small cell users [65]. To improve spectrum efficiency in OMA supported C-RANs, authors in [27] and [76] have considered an underlaid approach of orthogonal resource sharing both for macro cell and small cell users. In the previous chapter, we consider an energy efficient underlaid approach of resource allocation for both small and macro cell users in C-RAN.…”

Section: Scma Supported Cloud-ranmentioning

confidence: 99%

“…However, this underlaid approach increases intra-cell and inter-cell interference levels which limit the data rate of users. Considering the interference issues, authors in [76] have proposed an auction based distributed resource allocation with the aim to improve the data rate of small cell users. Nevertheless, the OMA approach supports limited number of connections due to the use of orthogonal resources.…”

Section: Scma Supported Cloud-ranmentioning

confidence: 99%

Resource Management in Cloud Radio Access Networks

Ferdouse¹

2021

Preprint

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This thesis focuses on resource management both in communication and computing sides of the cloud radio access networks (C-RANs). Communication and computing resources are bandwidth, power, baseband unit servers, and virtual machines, which become major resource allocation elements of C-RANs. If they are not properly handled, they create congestion and overload problems in radio access network and core network part of the backbone cellular network. We study two general problems of C-RAN networks, referred to as communication and computing resource allocation problem along with user association, base band unit (BBU) and remote radio heads (RRH) mapping problems in order to improve energy efficiency, sum data rate and to minimize delay performance of C-RAN networks. In this thesis, we propose, implement, and evaluate several solution strategies, namely posterior probability based user association and power allocation method, double-sided auction based distributed resource allocation method, the energy efficient joint workload scheduling and BBU allocation and iterative resource allocation method to deal with the resource management problems in both orthogonal and non-orthogonal multiple access supported C-RAN networks. In the posterior probability based user association and power allocation method, we apply Bayes theory to solve the multi-cell association problem in the coordinated multi-point supported C-RANs. We also use queueing and auction theory to solve the joint communication and computing resource optimization problem. As the joint optimization problem, we investigate the delay and sum data rate performance of C-RANs. To improve the energy efficiency of C-RANs, we employ Dinkelbach theorem and propose an iterative resource allocation method. Our proposed methods are evaluated via simulations by considering the effect of bandwidth utilization percentage, different scheduling weight, signal-to-interference ratio threshold value and number of users. The results show that the proposed methods can be successfully implemented for 5G C-RANs. Among the various non-orthogonal multiple access schemes, we consider and implement the sparse code multiple access (SCMA) scheme to jointly optimize the codebook and power allocation in the downlink of the C-RANs, where the utilization of sparse code multiple access in C-RANs to improve energy efficiency has not been investigated in detail in the literature. To solve the NP-hard joint optimization problem, we decompose the original problem into two subproblems: codebook allocation and power allocation. Using the graph theory, we propose the throughput aware sparse code multiple access based codebook selection method, which generates a stable codebook allocation solution within a finite number of steps. For the power allocation solution, we propose the iterative level-based power allocation method, which incorporates different power allocation approaches (e.g., weighted and successive interference cancellation ) into different levels to satisfy the maximum power requirement. Simulation results show that the sum data rate and energy efficiency performance of non-orthogonal multiple access supported C-RANs significantly increases with the number of users when the successive interference cancellation aware geometric water-filling based power allocation is used.

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“…Similar performance can be achieved for uplink by applying the ideas in [ 21 ]. However, due to complexity, latency, connectivity, scalability, and synchronization problems, the deployment of multi-cloud radio-access-networks (M-CRAN) is recently considered in a few works such as [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. For example, Reference [ 27 ] studies the problem of optimization of precoding and joint compression of baseband signals across multiple clusters of BSs in downlink.…”

Section: Introductionmentioning

confidence: 99%

“…This work proposes a distributed iterative solution that achieves the performance of the case all BSs connected to a single BBUP. While the formerly mentioned works assume non-dynamic clustering for each BBUP, the authors of [ 30 ] propose and analyze dynamic clustering approach based on instantaneous CSI, where they also consider the allocation of computation resources of BBUPs as an optimization parameter.…”

Section: Introductionmentioning

confidence: 99%

Degrees-Of-Freedom in Multi-Cloud Based Sectored Cellular Networks

Gelincik

Othman

2020

Entropy

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This paper investigates the achievable per-user degrees-of-freedom (DoF) in multi-cloud based sectored hexagonal cellular networks (M-CRAN) at uplink. The network consists of N base stations (BS) and K ≤ N base band unit pools (BBUP), which function as independent cloud centers. The communication between BSs and BBUPs occurs by means of finite-capacity fronthaul links of capacities C F = μ F · 1 2 log ( 1 + P ) with P denoting transmit power. In the system model, BBUPs have limited processing capacity C BBU = μ BBU · 1 2 log ( 1 + P ) . We propose two different achievability schemes based on dividing the network into non-interfering parallelogram and hexagonal clusters, respectively. The minimum number of users in a cluster is determined by the ratio of BBUPs to BSs, r = K / N . Both of the parallelogram and hexagonal schemes are based on practically implementable beamforming and adapt the way of forming clusters to the sectorization of the cells. Proposed coding schemes improve the sum-rate over naive approaches that ignore cell sectorization, both at finite signal-to-noise ratio (SNR) and in the high-SNR limit. We derive a lower bound on per-user DoF which is a function of μ BBU , μ F , and r. We show that cut-set bound are attained for several cases, the achievability gap between lower and cut-set bounds decreases with the inverse of BBUP-BS ratio 1 r for μ F ≤ 2 M irrespective of μ BBU , and that per-user DoF achieved through hexagonal clustering can not exceed the per-user DoF of parallelogram clustering for any value of μ BBU and r as long as μ F ≤ 2 M . Since the achievability gap decreases with inverse of the BBUP-BS ratio for small and moderate fronthaul capacities, the cut-set bound is almost achieved even for small cluster sizes for this range of fronthaul capacities. For higher fronthaul capacities, the achievability gap is not always tight but decreases with processing capacity. However, the cut-set bound, e.g., at 5 M 6 , can be achieved with a moderate clustering size.

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Joint Communication and Computing Resource Allocation in 5G Cloud Radio Access Networks

Cited by 37 publications

References 31 publications

Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services

Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services

Resource Management in Cloud Radio Access Networks

Degrees-Of-Freedom in Multi-Cloud Based Sectored Cellular Networks

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