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Multimedia content streaming from Internet-based sources emerges as one of the most demanded services by wireless users. In order to alleviate excessive traffic due to multimedia content transmission, many architectures (e.g., small cells, femtocells, etc.) have been proposed to offload such traffic to the nearest (or strongest) access point also called "helper". However, the deployment of more helpers is not necessarily beneficial due to their potential of increasing interference. In this work, we evaluate a wireless system which can serve both cacheable and non-cacheable traffic. More specifically, we consider a general system in which a wireless user with limited cache storage requests cacheable content from a data center that can be directly accessed through a base station. The user can be assisted by a pair of wireless helpers that exchange non-cacheable content as well. Files not available from the helpers are transmitted by the base station. We analyze the system throughput and the delay experienced by the cached user and show how these performance metrics are affected by the packet arrival rate at the source helper, the availability of caching helpers, the caches parameters, and the users request rate by means of numerical results. Funding Agencies|Linkoping University; CENIIT; ELLIIT

The Internet of Things (IoT) should be able to react with minimal human intervention and contribute to the Artificial Intelligence (AI) era requiring real-time and scalable operation under heteroge ...

Internet of Things (IoT) devices can be equipped with multiple heterogeneous network interfaces. An overwhelmingly large amount of services may demand some or all of these interfaces' available resources. Herein, we present a precise mathematical formulation of assigning services to interfaces with heterogeneous resources in one or more rounds. For reasonable instance sizes, the presented formulation produces optimal solutions for this computationally hard problem. We prove the NP-Completeness of the problem and develop two algorithms to approximate the optimal solution for big instance sizes. The first algorithm allocates the most demanding service requirements first, considering the average cost of interfaces resources. The second one calculates the demanding resource shares and allocates the most demanding of them first by choosing randomly among equally demanding shares. Finally, we provide simulation results giving insight into services splitting over different interfaces for both cases., IEEE Internet of Things Journal

The emerging vision of smart cities necessitates the use of Internet of Things (IoT) network devices to implement sustainable solutions that will improve the operations of urban areas. A massive amount of smart cities services may demand allocation of computational resources, such as processing power or storage, that IoT devices offer. Within this context, we present an IoT network device comprising interfaces with one specific computational resource available. The efficient utilization of available IoT resources would improve the Quality of Service (QoS) of the IoT network that serves the smart city. All resource allocations must be completed within a given scheduling window and every service is parametrized by a pricing weight function to indicate its tolerance to be served at the beginning of the scheduling window. We propose a mathematical optimization formulation to minimize the total cost of allocating all demands within the scheduling window considering the tolerance level of each service at the same time. Moreover, we prove that the problem is computationally hard and we provide numerical results to gain insight into the impact of different pricing weight functions on the allocations’ distribution within the scheduling window.

In the Internet of Things (IoT), devices and gateways may be equipped with multiple, heterogeneous network interfaces which should be utilized by a large number of services. In this work, we model the problem of assigning services' resource demands to a device's heterogeneous interfaces and give a Mixed Integer Linear Program (MILP) formulation for it. For meaningful instance sizes the MILP model gives optimal solutions to the presented computationally-hard problem. We provide insightful results discussing the properties of the derived solutions with respect to the splitting of services to different interfaces., 7 pages, 2 figures

We consider a cost minimization problem for High Volume Servers (HVS) equipped with Virtual Machines (VMs) to serve Virtual Network Functions (VNF) demands for resources. Given a scheduling period, a central scheduler decides which VM to use for each VNF demand. Each VM can be activated or disabled with different costs. Each VNF has a delay-weighted pricing function to indicate its completion time tolerance. We prove the NP-completeness of the problem and develop an algorithm based on Lagrangian relaxation and subgradient optimization to deal with this computational complexity. Finally, our numerical results demonstrate our algorithm’s effectiveness compared to two benchmarks.