Exploiting Caching and Multicast for 5G Wireless Networks

The landscape toward 5G wireless communication is currently unclear, and, despite the efforts of academia and industry in evolving traditional cellular networks, the enabling technology for 5G is still obscure. This paper puts forward a network paradigm toward next-generation cellular networks, targeting to satisfy the explosive demand for mobile data while minimizing energy expenditures. The paradigm builds on two principles; namely caching and multicast. On one hand, caching policies disperse popular content files at the wireless edge, e.g., pico-cells and femto-cells, hence shortening the distance between content and requester. On other hand, due to the broadcast nature of wireless medium, requests for identical files occurring at nearby times are aggregated and served through a common multicast stream. To better exploit the available cache space, caching policies are optimized based on multicast transmissions. We show that the multicast-aware caching problem is NP-hard and develop solutions with performance guarantees using randomized-rounding techniques. Trace-driven numerical results show that in the presence of massive demand for delay tolerant content, combining caching and multicast can indeed reduce energy costs. The gains over existing caching schemes are 19% when users tolerate delay of three minutes, increasing further with the steepness of content access pattern

Learning the optimal synchronization rates in distributed SDN control architectures

Since the early development of Software-DefinedNetwork (SDN) technology, researchers have been concernedwith the idea of physical distribution of the control plane to ad-dress scalability and reliability challenges of centralized designs.However, having multiple controllers managing the networkwhile maintaining a “logically-centralized” network view bringsadditional challenges. One such challenge is how to coordinatethe management decisions made by the controllers which isusually achieved by disseminating synchronization messages ina peer-to-peer manner. While there exist many architecturesand protocols to ensure synchronized network views and drivecoordination among controllers, there is no systematic method-ology for deciding the optimal frequency (or rate) of messagedissemination. In this paper, we fill this gap by introducingthe SDN synchronization problem: how often to synchronize thenetwork views for each controller pair. We consider two differentobjectives; first, the maximization of the number of controllerpairs that are synchronized, and second, the maximization of theperformance of applications of interest which may be affectedby the synchronization rate. Using techniques from knapsackoptimization and learning theory, we derive algorithms withprovable performance guarantees for each objective. Evaluationresults demonstrate significant benefits over baseline schemes thatsynchronize all controller pairs at equal rate

MACS: deep reinforcement learning based SDN controller synchronization policy design

In distributed software-defined networks (SDN), multiple physical SDN controllers, each managing a network domain, are implemented to balance centralised control, scalability, and reliability requirements. In such networking paradigms, controllers synchronize with each other, in attempts to maintain a logically centralised network view. Despite the presence of various design proposals for distributed SDN controller architectures, most existing works only aim at eliminating anomalies arising from the inconsistencies in different controllers' network views. However, the performance aspect of controller synchronization designs with respect to given SDN applications are generally missing. To fill this gap, we formulate the controller synchronization problem as a Markov decision process (MDP) and apply reinforcement learning techniques combined with deep neural networks (DNNs) to train a smart, scalable, and fine-grained controller synchronization policy, called the Multi-Armed Cooperative Synchronization (MACS), whose goal is to maximise the performance enhancements brought by controller synchronizations. Evaluation results confirm the DNN's exceptional ability in abstracting latent patterns in the distributed SDN environment, rendering significant superiority to MACS-based synchronization policy, which are 56% and 30% performance improvements over ONOS and greedy SDN controller synchronization heuristics

Bringing SDN to the mobile edge

Nowadays, Software Defined Network (SDN) architectures and applications are revolutionizing the way wired networks are built and operate. However, little is known about the potential of this disruptive technology in wireless mobile networks. In fact, SDN is based on a centralized network control principle, while existing mobile network protocols give emphasis on the distribution of network resources and their management. Therefore, it is challenging to apply SDN ideas in the context of mobile networks. In this paper, we propose methods to overcome these challenges and make SDN more suitable for the mobile environment. Our main idea is to combine centralized SDN and distributed control in a hybrid design that takes the best of the two paradigms; (i) global network view and control programmability of SDN and (ii) robustness of distributed protocols. We discuss the pros and cons of each method and highlight them in an SDN prototype implementation built using off-The-shelf mobile devices

Optimal algorithms for hierarchical web caches

Hierarchical topologies have been applied in many existing systems that provide public IPTV or massive content delivery services. The efficient operation of these services requires massive bandwidth resources. Data caching has emerged as an effective way in reducing bandwidth consumption and accelerating content access. In hierarchical caching systems requests for content are routed upwards until they reach a cache that stores a copy of the requested file. When the requested file is found, it is sent on the reverse path to the client. In this work, we focus on the problem of caching redundant copies of content in intermediate caches on the reverse path in order to minimize the bandwidth consumption within a given time horizon. The above problem is known to be NP-hard. However, we show that replacing the cache capacity constraints by a cost term paid each time we store a file in a cache, results to the tractable problem of minimizing the overall bandwidth and caching expenses. We use its optimal solution to establish a novel algorithm for the efficient solution of the original problem. We furthermore study the case that segments of encoded versions of the files instead of only complete files are allowed to be stored at the caches. We show that this problem is of polynomial complexity. Numerical experiments for typical popularity distributions reveal the performance distance between the proposed algorithms and heuristic algorithms that are commonly applied nowadays. © 2013 IEEE

Surviving in a competitive market of information providers

As the processing and transport capacity of the information and communication technologies (ICT) infrastructure increased vastly the last few years, the bottleneck of the information exchange process moved to the end points of the process, i.e. The consumers and the producers of information. On one hand there is the limited time that a consumer has to access the information and on the other hand there is the minimum utility level that a provider needs to provide to the society of consumers to cover it's investment cost. In this paper we present a novel decision model for a set of competing providers that wish to enter a market. It may happen that due to the competition, some competitors will not be able to cover their investment cost and therefore will disappear. We analyze the optimum way of forming the market, in order to maximize the aggregate utility of it. We show that this problem is NP-complete and present a linear programming rounding heuristic algorithm to solve it. Besides, we study a game where every player (provider) is to choose whether to join the market or not. We compute the price of anarchy of the game and present a heuristic algorithm that belongs to the family of best response dynamic algorithms. Systematic experiments on a real world data set have demonstrated the effectiveness of our proposed approach. © 2013 IEEE