73,897 research outputs found
VirtFogSim: A parallel toolbox for dynamic energy-delay performance testing and optimization of 5G Mobile-Fog-Cloud virtualized platforms
It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emergingMobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present VirtFogSim. It is aMATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed VirtFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the placement of the application tasks and the allocation of the needed computing-networking resources under hard constraints on acceptable overall execution times, (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall system; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering, and (v) itsMATLAB code is optimized for running atop multi-core parallel execution platforms. To check both the actual optimization and scalability capabilities of the VirtFogSim toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared
Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges
As a promising paradigm for fifth generation (5G) wireless communication
systems, cloud radio access networks (C-RANs) have been shown to reduce both
capital and operating expenditures, as well as to provide high spectral
efficiency (SE) and energy efficiency (EE). The fronthaul in such networks,
defined as the transmission link between a baseband unit (BBU) and a remote
radio head (RRH), requires high capacity, but is often constrained. This
article comprehensively surveys recent advances in fronthaul-constrained
C-RANs, including system architectures and key techniques. In particular, key
techniques for alleviating the impact of constrained fronthaul on SE/EE and
quality of service for users, including compression and quantization,
large-scale coordinated processing and clustering, and resource allocation
optimization, are discussed. Open issues in terms of software-defined
networking, network function virtualization, and partial centralization are
also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin
note: text overlap with arXiv:1407.3855 by other author
Edge and Central Cloud Computing: A Perfect Pairing for High Energy Efficiency and Low-latency
In this paper, we study the coexistence and synergy between edge and central
cloud computing in a heterogeneous cellular network (HetNet), which contains a
multi-antenna macro base station (MBS), multiple multi-antenna small base
stations (SBSs) and multiple single-antenna user equipment (UEs). The SBSs are
empowered by edge clouds offering limited computing services for UEs, whereas
the MBS provides high-performance central cloud computing services to UEs via a
restricted multiple-input multiple-output (MIMO) backhaul to their associated
SBSs. With processing latency constraints at the central and edge networks, we
aim to minimize the system energy consumption used for task offloading and
computation. The problem is formulated by jointly optimizing the cloud
selection, the UEs' transmit powers, the SBSs' receive beamformers, and the
SBSs' transmit covariance matrices, which is {a mixed-integer and non-convex
optimization problem}. Based on methods such as decomposition approach and
successive pseudoconvex approach, a tractable solution is proposed via an
iterative algorithm. The simulation results show that our proposed solution can
achieve great performance gain over conventional schemes using edge or central
cloud alone. Also, with large-scale antennas at the MBS, the massive MIMO
backhaul can significantly reduce the complexity of the proposed algorithm and
obtain even better performance.Comment: Accepted in IEEE Transactions on Wireless Communication
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