Cloud And Fog Computing In 5G Mobile Networks: ...
Fog computing involves extending cloud capabilities, such compute, storage and networking services, through various nodes and IoT gateways. A major benefit of the technology includes the ability to run applications closer to the end user, processing data received from multiple endpoints. It is also more scalable in comparison to edge computing, providing an overall picture of a network based on information provided by various data points.
Cloud and Fog Computing in 5G Mobile Networks: ...
Communication in 5G networks will be driven by high-frequency millimeter waves. A general 5G cellular network would connect mobile users to a base station, which would be connected to the core network. Although low latency radio interfaces would provide sub-millisecond communication between the end user and base station, forwarding cloud-based application requests from the core network to the cloud would significantly increase latency. Consequently, 5G networks require moving processing power closer to the end user, which can be accomplished with fog computing.
With the constricted resources of mobile terminals, 5G technology would provide challenging services and applications, especially for FC . 5G promises to overcome faults of resource constraint present in wireless computing network by offering enhanced resource-studded with an array of dazzling services . This heavily focuses on premier quality wireless communication with an offer of superior spectral efficiency. As demonstrated in , a propped-up Fog Radio Access Network (F-RAN) would merge FC into a diversified cloud radio access network. Moreover, resolving the current challenges of the centralized base-band unit pool, it also finds a solution for the challenges related to Cloud Radio Access Network (C-RAN) with the constricted front haul. It does this by making the actual-time collaboration available between flexible, cooperative radio resource management and radio signal processing at the devices that are at the edge.
FC is one of the modern technologies which have gained rapid growth in a brief period. FC enables the effective processing of data retrieved from smart devices. It brings computing paradigm from the cloud to the edge of the network with computing capabilities. However, massive data generation from numerous sensors and actuators in the Internet of Things (IoT) has first networks to a bottleneck [7,8]. Thus, the need to improve computing server architecture to handle the continuous growing demands of smart devices is pertinent. Therefore, in this study, some cases of fog computing on 5G networks are discussed. Currently, using FC technology in 5G systems has much vision, and the same goes for the 5G network itself. Therefore, to actualize this vision, a few of the research areas needed to be addressed. This study provides comprehensive knowledge about FC technologies that are used to serve as an enabling computing technology to the 5G network [3,6]. The study elaborates on dynamic issues in the field of computing network technologies, and information is provided on how to remedy these for future recommendations in the area of research and computing network technologies. In addition, this study will contribute to providing the knowledge that helps to emerge the modern technologies to deliver efficient data dissemination services. For example, the concept of Network Function Virtualization (NFV) in heterogeneous networks using FC as an enabler to 5G is not clear in present times since there is a need for improvement in implementing NFV for mobile communication networks as explored in earlier studies [9,10,11]. Moreover, a large amount of energy is required for the processing of more substantial applications . Minimizing energy consumption is key for commercializing FC technology in 5G systems. Another challenge is the pricing policies because different parties require different prices of resources for the use of FC services . Thus, finely granulated accounting management and price policy are needed in resource management of the FC and 5G ecosystem. Finally, the issue of visibility of data to the third party by 5G networks makes privacy and security a pertinent point in FC architectures . These issues and challenges can be addressed using FC in 5G networks. It is evident that both technologies, i.e., FC and 5G, are compatible with each other. However, combining both concepts is envisaged to be the future of Internet services. The present study aims to find a better solution and a more sustainable future for humanity.
Executing intensive applications outside mobile devices is more feasible than locally executing applications devices, and remote execution of offloaded mobile applications is supported by the provision of computational resources in closed proximity of end users. Sensitive and real-time computation is leading to service quality degradation, round-trip delay, and network congestion. This issue was resolved with the help of the EC concept . The basic concept of EC is to bring close data sources to the computation facilities, which is done by enabling data processing at the edge network as a localized computing paradigm . Thus, a swift response to the request is needed for raw bulk data sent to core networks. However, it does not spontaneously give a response to other cloud-based services that are focused on the requirement of the end users . In summary, MEC integrates Mobile and Cloud computing with wireless communication networks. It is performed to improve the Quality of Experience (QoE) and creates new business opportunities for both cloud service providers and network operators. MEC and FC can enable the edge network. Precisely, FC is a component of computational infrastructure that can be extended for both core and edge networks. It is evident from existing studies that FC supports IoT, Artificial Intelligence (AI), 5G, and many other programs that require high network bandwidth, advance security, constraint resources, and ultra-low latency. FC is the process of extending computing enterprise networks to the edge rather than working and hosting from a centralized cloud. It allows smooth computing operations, increased storage, and better network services between end users and computing data centers. Mostly, Fog computing is confused with edge computing. However, FC leverages EC but not the other way around as presumed by many people. FC gives way for orchestration, distribution, management, and services to secure resources across or between devices residing at the edge of a system-level architecture . Contrasting to the concept of FC, EC places small servers or clouds at the edge by relying on separate nodes, where each node runs in a silo that requires data transported back through the cloud for peer-to-peer traffic . As a result, the latency for service delivered to real-time IoT applications will be minimized to a great extent. FC can extend cloud-based services like IaaS, PaaS, SaaS to the edge of the network, unlike EC. thus, it is better to consider a good potential and well-structured computing technology for IoT to be compared as well as an enabler to 5G network technology compared to other computing paradigms. A SWOT analysis is explained in this study to have a summary of the future industrial requirement of the EC and FC.
Offloading: Data will exponentially continue to increase with the advent of 5G networks. However, Internet Service Providers (ISP) are seeking measures to prevent congestion on Internet usage. Offloading data traffic free up network capacity as well as providing high QoS. In this section, some reviews are made of recently proposed offloading techniques in fog computing based on different modalities. In , the authors proposed a scheme that is based on transmission time and energy consumption. This scheme was applied to a mobile device communicating with fog. This fog can forward the task to the cloud or execute it on its own. If executed on its own (within the fog), it experiences transmission delay of inputted data, and execution time is dependent on the processing power of the fog. If the processing task requires offloading to the cloud, delay remains the same. However, the transmission delay will include a communication from fog to cloud. Moreover, the energy consumption by fog in delegating executed tasks to cloud and energy consumption by the cloud to execute the task is used up. In a fog-only scenario, total energy consumption is based on idle energy consumption of user devices that is necessary to transfer input bits to the fog. In an event where the cloud is also involved, the additional energy consumption would be that of fog transmitting the tasks to the cloud and the cloud then executing them.
It is clear we will see cloud and distributed computing resources at new locations in the future. Outside the two main anchors today, centralized cloud data centers and premises based private clouds. The new market is shaped between the two extremes. Where four nomenclatures circulate:
Tied to mobile network platforms we see two movements. Performance critical parts of the mobile core network functions can benefit from being distributed closer to end-customers. And a movement in the opposite direction where virtualized radio network functions are centralized closer to the core network. Where both these movements depend on a distributed cloud.
Third use cases were compute resources, and the relation between on device and cloud-based computing can be disrupted. Where costly on-device compute, and associated power consumption, can be moved from devices to a nearby distributed cloud. For any stationary device without a strong natural connection to wired power and connectivity. Where distributed clouds provide significant leaps down in device cost and increase in battery life times.
MEC refers to computing at the edge of a network. ETSI described MEC as any location in a network where the compute and other resources and services are available closer to the user than the central data center or cloud. 041b061a72