Flexible Design of Millimeter-Wave Cache Enabled Fog Networks

Abstract

Ultra-densification, millimeter wave (mmW) communications, and proactive network-edge caching, utilized within mmW fog networks (mmFNs), are foreseen to provide tangible gains for broadband access, network capacity, and latency. However, caching implementation in mmFN imposes high capital expenditure (CAPEX) due to the ultra-high density of base stations (BSs). For a given caching CAPEX, it may be more efficient to install higher capacity caches in a fraction of the BSs than installing smaller capacity caches in every BSs. In the former case, wireless self-backhauling of mmW systems can be exploited to share the cache contents stored in a given cache enabled BSs (CE-BSs) with other BSs in the network. In this regards, this paper develops a mathematical model, based on stochastic geometry, to study the tradeoff between the cache size and intensity of CE-BSs on the probability that requested popular contents are retrieved from the network edge, denoted as the hit probability. Assuming a power-law inverse relationship between the cache size and intensity of CE-BSs, an optimization problem is formulated and solved for the intensity of CE-BSs and probabilistic file placement in caches such that the hit probability is maximized. The results show that neither installing small caches in every BS nor having sufficiently high capacity caches (i.e., that confine all popular files) installed in small number of BSs exploit the full potential of mmFN. Instead, there exists an optimal balance between the cache size and intensity of CE-BSs, which depends on the network parameters such as the applied caching strategy, required rate, total intensity of BSs, popular content distribution, and cache size/intensity relationship.

Understanding Interdependencies among Fog System Characteristics

“…Contemporary IT use cases impose growing requirements on networks, for example, with healthcare applications that cannot afford any delay in processing [17], Augmented Reality (AR) and VR applications requiring extraordinary large bandwidth [18,19], and IoT use cases that generate a huge amount of data, requiring high reliability and a low latency guarantee across heterogeneous devices in a scalable manner [20][21][22][23]. Modern enterprises combining edge and cloud computing increasingly struggle with the growing requirements so that more and more often quality of service requirements cannot be met [24], real-time performance and reliability become insufficient [25], or an inefficient network architecture leads to congested connections and connectivity issues [9,17,20].…”

“…Description Relevance Design measures to improve the system on the characteristic Latency (55) Average required time from a request to its response including time spent on communication and processing for the entire fog system within and across its layers [48] Modern applications such as VR require near realtime processing and communication due to the speed of changes in their environment [3,17,35] Deploy more fog nodes closer to edge devices [35], Target low utilization of fog nodes to manage peak loads [57], Introduce more capable connections with larger bandwidths [58], Improve distribution of loads across fog nodes [59] Data load (27) Amount of data that needs to be transferred or stored within the fog system [60] Modern applications and an increased number of devices and sensors produce massive amounts of data to be managed in the fog system [60,61] Cache content near to end-users [19,62], Filter and aggregate data in lower levels of the system [63], Forward only prioritized/critical data, Compress data [63], More peer-to-peer communication [64] Security (26) The ability of fog systems to maintain availability, integrity, and confidentiality by defending against unauthorized interception, interruption, modification, and fabrication [65] Fog systems usually encompass a multitude of nodes, sensors, and actors in potentially critical domains such as mobility and healthcare in which any security breach can have a severe impact [48,61] Introduce peer trust models [66], Limit access to the fog system [49], Introduce authentication mechanisms for every layer [44], Introduce trust mechanisms for edge devices and fog nodes [56,67], Allow only validated edge devices or for nodes or registered persons to access the system [68], Constant monitoring of activities to check for abnormal behavior [49], Introduce blockchain-based strategies [61] Interoperability…”

“…In addition, having spare bandwidth across the system allows scaling faster if the data load of additional edge devices and fog nodes can be handled without causing congestions (S11.2). Spare bandwidth, on the other hand, decreases the cost-efficiency (T11.3) of a system as utilization decreases and potentially more bandwidth is set up than required [19]. Further, if the increase in bandwidth depends on certain communication technology and protocols to be used, interoperability of the system is decreased (T11.4) as entities within the system need to adhere to related standards.…”

Caching and Multicasting for Fog Radio Access Networks

“…In [8], transmission-aware caching strategies were proposed for the distributed and centralized modes of F-RAN aiming at minimizing the mean download delay of the end-users. In [9], the authors proposed a probabilistic caching strategy that maximizes the hit probability in self-backhauled millimeter-wave F-RAN. The optimal cache design presented in [10] maximises the fractional offloaded traffic (FOT) and successful transmission probability (STP) for joint transmission and parallel transmission strategies.…”