Power allocation for cache-aided small-cell networks with limited backhaul

Cache-aided small-cell network is becoming an effective method to improve the transmission rate and reduce the backhaul load. Due to the limited capacity of backhaul, less power should be allocated to users whose requested contents do not exist in the local caches to maximize the performance of caching. In this paper, power allocation is considered to improve the performance of cache-aided small-cell networks with limited backhaul, where interference alignment (IA) is utilized to manage interferences among users. Specifically, three power allocation algorithms are proposed. First, we come up with a power allocation algorithm to maximize the sum transmission rate of the network, considering the limitation of backhaul. Second, in order to have more users meet their rate requirements, a power allocation algorithm to minimizing the average outage probability is also proposed. In addition, in order to further improve the users’ experience, a power allocation algorithm that maximizes the average satisfaction of all the users is also designed. Simulation results are provided to show the effectiveness of the three proposed power allocation algorithms for cache-aided small-cell networks with limited backhaul

Caching Placement Strategies for Dynamic Content Delivery in Metro Area Networks

Video-on-Demand (VoD) traffic explosion has been one of the main driving forces behind the recent Internet evolution from a traditional connection-centric architecture towards the new content-centric paradigm. To cope with this evolution, caching of VoD contents closer to the users in core, metro and even metro-access optical network equipment is regarded to be a prime solution that could help mitigating this traffic growth. However, the optimal caches placement and dimensioning is not univocal, especially in the context of a dynamic network, as it depends on various parameters, such as network topology, users behavior and content popularity. In this paper, we focus on a dynamic VoD content delivery scenario in a metropolitan network implementing different caching strategies. We evaluate the performance of the various caching strategies in terms of network-capacity occupation showing the savings in resource occupation in each of the network segments. We also evaluate the effect of the distribution of the storage capacity on the overall average number of hops of all requests. The obtained numerical results show that, in general, a significant amount of network resources can be saved by enabling content caching near to end-users. Moreover, we show that blindly providing caching capability in access nodes may result unnecessary, whereas a balanced storage distribution between access and metro network segments provides the best performance

Live Data Analytics with Collaborative Edge and Cloud Processing in Wireless IoT Network

Recently, big data analytics has received important attention in a variety of application domains including business, finance, space science, healthcare, telecommunication and Internet of Things (IoT). Among these areas, IoT is considered as an important platform in bringing people, processes, data and things/objects together in order to enhance the quality of our everyday lives. However, the key challenges are how to effectively extract useful features from the massive amount of heterogeneous data generated by resource-constrained IoT devices in order to provide real-time information and feedback to the endusers, and how to utilize this data-aware intelligence in enhancing the performance of wireless IoT networks. Although there are parallel advances in cloud computing and edge computing for addressing some issues in data analytics, they have their own benefits and limitations. The convergence of these two computing paradigms, i.e., massive virtually shared pool of computing and storage resources from the cloud and real-time data processing by edge computing, could effectively enable live data analytics in wireless IoT networks. In this regard, we propose a novel framework for coordinated processing between edge and cloud computing/processing by integrating advantages from both the platforms. The proposed framework can exploit the network-wide knowledge and historical information available at the cloud center to guide edge computing units towards satisfying various performance requirements of heterogeneous wireless IoT networks. Starting with the main features, key enablers and the challenges of big data analytics, we provide various synergies and distinctions between cloud and edge processing. More importantly, we identify and describe the potential key enablers for the proposed edge-cloud collaborative framework, the associated key challenges and some interesting future research directions