Energy Efficiency of 5G Mobile Networks in Hybrid Fog and Cloud Computing Environment (original) (raw)
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5G is the next generation cellular network that aspires to achieve substantial improvement on quality of service, such as higher throughput and lower latency. Edge computing is an emerging technology that enables the evolution to 5G by bringing cloud capabilities near to the end users (or user equipment, UEs) in order to overcome the intrinsic problems of the traditional cloud, such as high latency and the lack of security. In this paper, we establish a taxonomy of edge computing in 5G, which gives an overview of existing state-of-the-art solutions of edge computing in 5G on the basis of objectives, computational platforms, attributes, 5G functions, performance measures, and roles. We also present other important aspects, including the key requirements for its successful deployment in 5G and the applications of edge computing in 5G. Then, we explore, highlight, and categorize recent advancements in edge computing for 5G. By doing so, we reveal the salient features of different edge computing paradigms for 5G. Finally, open research issues are outlined.
Fog Computing as a Support for 5G Network
Journal of Emerging research and solutions in ICT, 2016
5G will enable new future Internet of Services (IoSs) paradigms such as Anything as a Service (AaaS), where devices, terminals, machines, also smart things and robots will become innovative tools that will produce and will use applications, services and data. However, 5G will have to support huge mobile traffic volumes, and will also have to deal with the rapid increase of new and complex applications and services. On the other hand, Fog Computing, which extends Cloud Computing to the edge of the network, with its service orchestration mechanisms offers virtually unlimited dynamic resources for computation, storage and service provision, that will overcome the constraints of smart mobile devices. 5G in the fog computing environment will create opportunities for companies to deploy many new real-time services that cannot be delivered over current mobile and wireless networks. This paper evaluates Fog Computing as a support mechanism for 5G Network in terms of latency, throughput, and energy efficiency.
IEEE Access, 2014
This paper focuses on energy efficiency aspects and related benefits of radio-accessnetwork-as-a-service (RANaaS) implementation (using commodity hardware) as architectural evolution of LTE-advanced networks toward 5G infrastructure. RANaaS is a novel concept introduced recently, which enables the partial centralization of RAN functionalities depending on the actual needs as well as on network characteristics. In the view of future definition of 5G systems, this cloud-based design is an important solution in terms of efficient usage of network resources. The aim of this paper is to give a vision of the advantages of the RANaaS, to present its benefits in terms of energy efficiency and to propose a consistent system-level power model as a reference for assessing innovative functionalities toward 5G systems. The incremental benefits through the years are also discussed in perspective, by considering technological evolution of IT platforms and the increasing matching between their capabilities and the need for progressive virtualization of RAN functionalities. The description is complemented by an exemplary evaluation in terms of energy efficiency, analyzing the achievable gains associated with the RANaaS paradigm.
Fog Computing Service Orchestration Mechanisms for 5G Networks
Journal of Internet Technology, 2018
5G network will enable new future Internet of Services paradigms such as Anything as a Service, where devices, terminals, machines, also smart things and robots will become innovative tools that will produce and will use applications, services and data. However, the emerging applications in the context of the Internet of Everything introduce high mobility, high scalability, real-time, and low latency requirements that raise new challenges on the services being provided to the users. Fortunately, Fog Computing or briefly Fog, which extends Cloud Computing to the edge of the network, with its service orchestration mechanisms offers virtually unlimited dynamic resources for computation, storage and service provision, that will effectively cope with the requirements of the forthcoming services. 5G in the fog computing environment will create opportunities for companies to deploy many new real-time services that cannot be delivered over current mobile and wireless networks. This paper ev...
Fog computing in 5G networks: an application perspective
Cloud and Fog Computing in 5G Mobile Networks: Emerging advances and applications
Fifth generation (5G) cellular network promises to offer to its users submillisecond latency and 1 gigabit per second transmission speed. However, the current cloud based computation and data delivery model do not allow these quality of service (QoS) guarantees to be efficiently harnessed, due to the number of hops of wired networks between the 5G base stations and the cloud, that leads to a significant increase in latency. Forwarding all the data generated by devices directly to the cloud may devour the bandwidth and lead to congestion. Therefore, it is necessary that processing be hosted near the devices, close to the source of the data, so that the high speed transmission of 5G can be utilized and data can be processed and filtered out by the time it reaches the cloud. This bringing down of computation, storage and networking services to the network edge opens up many new research areas of applying Fog computing over cellular network architecture. This chapter discusses the advantages of extending the cloud services to the edge by presenting use-cases which can be realized by fog computing over 5G networks.
Orchestration of MEC Computation Jobs and Energy Consumption Challenges in 5G and Beyond
IEEE Access, 2022
Mobile Edge Computing (MEC) technology philosophy inspires the next generation mobile networks to provide cloud computing capabilities in addition to a diverse range of Information Technology (IT) services with ultra-low latency and higher bandwidth at the edge. One of the most common challenges of 5G-MEC is the management and orchestration across all networks and infrastructure resources as well as end-to-end quality of experience. The decentralized architecture of MEC with independent and non-collaborative servers results in the situation of having underutilized servers with wasted energy. Moreover, the consequences of having highly utilized servers with highly consumed energy are not only the incapability to accommodate all the load of the computing jobs and the dramatic increase in the total OPEX cost, but it also creates some environmental problems. Orchestrating servers' workload and control offloading the computation jobs is one of the technical advantages of MEC since it satisfies the increasing requirements of modern mobile applications while optimizing the energy consumption and cost. In this work, we consider cluster-based energy-aware offloading framework. The proposed work consists of dual-tier domain divided into clusters of Edge Servers ES s. We have presented the results of our simulation as a proof of our concept that the formulated adaptive strategy to minimize the optimization problem calculation per cluster reduces the energy consumption and enhances the quality of experience while achieving the conservation of the related computing and storage resources cost. INDEX TERMS MEC, IT, 5G, OPEX (operating expense), edge servers.
Dynamic energy savings in Cloud-RAN: An experimental assessment and implementation
2017 IEEE International Conference on Communications Workshops (ICC Workshops), 2017
Cloud Radio Access Network (C-RAN) is one of the most promising network architectures for next-generation mobile communication. Integrating multiple Base Band Units (BBUs) for signal processing in centralized BBU pools provides the opportunity to manage the utilization of processing resources more flexibly and in a more energy-efficient way. This paper demonstrates two approaches for dynamic energy saving in heterogeneous C-RAN networks by switching BBUs from sleep mode to operational mode, depending on changes in data traffic demand. To wake up BBUs, Wake-on-LAN (WoL) packets are sent either by the Remote Radio Head (RRH) in the first approach or by the controller in a BBU pool in the second approach. We implement and compare, in terms of wake-up latency, experimental prototypes using Software Defined Radio (SDR) for both approaches. Aiming at compliance to current LTE standards, the design and implementation of these prototypes has the potential to be applied to a larger scale C-RAN architecture in next-generation commercial mobile networks, including, but not limited to, 5G networks.
Enabling green computing in cloud environments: Network virtualization approach toward 5G support
Transactions on Emerging Telecommunications Technologies, 2018
Virtualization technology has revolutionized the mobile network and widely used in 5G innovation. It is a way of computing that allows dynamic leasing of server capabilities in the form of services like SaaS, PaaS, and IaaS. The proliferation of these services among the users led to the establishment of large-scale cloud data centers that consume an enormous amount of electrical energy and results into high metered bill cost and carbon footprint. In this paper, we propose three heuristic models namely Median Migration Time (MeMT), Smallest Void Detection (SVD) and Maximum Fill (MF) that can reduce energy consumption with minimal variation in SLAs negotiated. Specifically, we derive the cost of running cloud data center, cost optimization problem and resource utilization optimization problem. Power consumption model is developed for cloud computing environment focusing on liner relationship between power consumption and resource utilization. A virtual machine migration technique is considered focusing on synchronization oriented shorter stop-and-copy phase. The complete operational steps as algorithms are developed for energy aware heuristic models including MeMT, SVD and MF. To evaluate proposed heuristic models, we conduct experimentations using PlanetLab server data often ten days and synthetic workload data collected randomly from the similar number of VMs employed in PlanetLab Servers. Through evaluation process, we deduce that proposed approaches can significantly reduce the energy consumption, total VM migration, and host shutdown while maintaining the high system performance.
Fog Computing: An Efficient Platform for the Cloud-resource Management
JETIR, 2019
Datacenters in cloud paradigm are storing enormous data size, and have an impact on both the energy consumptions as well service costs. Although centralized cloud computing is still more convenient, feasible platform for most of real-time applications and services but not the best. As in centralized distributed environment, resource provision and optimal usage of the configurable resources is somewhat neglected and quality of services are ignored, oversized data communication with low bandwidth network capability leads to the congestion in network data transportation, high latencies, delays and network jitters. Lack of proper resource provisioning leads to the unsophisticated energy management which burdens and laden high energy costs on the end-users. These heavy sized cloud macro data centers have negative impact on the environment as these exchange excessive heat while processing. Recent times marvelous research is being taking place that will provide the new horizons to explore and exploit the new horizons of the distributed technologies. The latest one in the town is the Cisco's fog-edge computing and is being considered as the next step in the distributed computing paradigm. Here in this paper, we the authors, consider Fog computing as a convenient and an energy efficient computational platform for resource provisioning, and its impact on optimal costs for the computing resources like services and applications.. Keywords-Centralized Cloud, Real-Time applications, Resource Provisioning 1. Introduction Distributed Cloud computing is the core platform for various evolving technologies likes IoT, Mist, Edge and Fog computing as it sophisticatedly changing ways, the various resources, computational processing, network bandwidth and unlimited storage is being offered with minimal interference and less costs on demand basis [1]. Fog computing main purpose is to take the computational processing and intelligence at the edge of network rather than at macro-core cloud network. Fog is mini-cloud with advantageous features reducing the flaws and limitations i.e. bottlenecks, high latencies, geographical and widely dispersed aerial locations of cloud computing. Fog computing is convenient platform for IoT enabled scenarios i.e. smart (grids, cities, connected vehicles, traffic lighting systems), geographically distributed, real-time sensitive latency bounded applications and act as an intermediate layer between cloud and edge computing, serves various benefits and is now considered to be the future alternative of it[2]. The storage and computational power is being severed at the nano data centers (micro servers) in fog-edge network, rather than sending it to the centralized core cloud network lowering the further latency and time delays, Since the data transformation from the end user devices to the macro-core data centers is a cumbersome process and is energy inefficient, the more is the hops count, the more delay jitters are occurring and cause the highest latencies and time delays. In this paper the more attention is on applications running in fog-edge network and are energy efficient compared to same applications and services running over entirely core-cloud centralized network [3]. The efficient energy management model is of importance to monitor and control the energy consumptions. Proliferation in technology paved more opportunities and possibilities to minimize the energy constraints by enabling the portable less power consuming and high performing RFID tagged end devices enable to make energy efficient systems [4]. These systems make possible cost feasibility and portability, reduction in physical shape and size (Sensors, Actuators, Network-adapters, Switches, Routers, and Fog-Smart Gateways) [5]. In this article our motto is to consider and introduce Fog platform as a novel platform for some of cloud constraints like flexibility, interactivity, scalability, and interoperability among various fog-edge devices, lowering the energy management costs. In this paper, we the authors present the computing resource management with fog computing and the remaining portion of the paper is being categorized in sections. Section I is an introductory part, Section II present the related work, while as Section III present the research challenges, Section IV describe briefly on the problem statement, Section V, fog-based cloud resource management and finally in Section VI, we conclude the paper.
Quality Evaluation of Cloud and Fog Computing Services in 5G Networks
Advances in Wireless Technologies and Telecommunication, 2019
Because of the increased computing and intelligent networking demands in 5G network, cloud computing alone encounters too many limitations, such as requirements for reduced latency, high mobility, high scalability, and real-time execution. A new paradigm called fog computing has emerged to resolve these issues. Fog computing distributes computing, data processing, and networking services to the edge of the network, closer to end users. Fog applied in 5G significantly improves network performance in terms of spectral and energy efficiency, enable direct device-to-device wireless communications, and support the growing trend of network function virtualization and separation of network control intelligence from radio network hardware. This chapter evaluates the quality of cloud and fog computing services in 5G network, and proposes five algorithms for an optimal selection of 5G RAN according to the service requirements. The results demonstrate that fog computing is a suitable technology solution for 5G networks.