Value-added services (e.g., overlaid video advertisements) have become an integral part of today's Content Delivery Networks (CDNs). To offer cost-efficient, scalable and more agile provisioning of new value-added services in CDNs, Network Functions Virtualization (NFV) paradigm may be leveraged to allow implementation of fine-grained services as a chain of Virtual Network Functions (VNFs) to be placed in CDN. The manner in which these chains are placed is critical as it both affects the quality of service (QoS) and provider cost. The problem is however, very challenging due to the specifics of the chains (e.g., one of their end-points is not known prior to the placement). We formulate it as an Integer Linear Program (ILP) and propose a cost efficient Proactive VNF placement and chaining (CPVNF) algorithm. The objective is to find the optimal number of VNFs along with their locations in such a manner that the cost is minimized while QoS is met. Apart from cost minimization, the support for large-scale CDNs with a large number of servers and end-users is an important feature of the proposed algorithm. Through simulations, the algorithm's behavior for small-scale to large-scale CDN networks is analyzed.

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Wireless sensor network (WSN) typically has energy consumption restriction. Designing energy-aware routing protocol can significantly reduce energy consumption in WSNs. Energy-aware routing protocols can be classified into two categories, energy savers and energy balancers. Energy saving protocols are used to minimize the overall energy consumed by a WSN, while energy balancing protocols attempt to efficiently distribute the consumption of energy throughout the network. In general terms, energy saving protocols are not necessarily good at balancing energy consumption and energy balancing protocols are not always good at reducing energy consumption. In this paper, we propose an energy-aware routing protocol (ERP) for query-based applications in WSNs, which offers a good trade-off between traditional energy balancing and energy saving objectives and supports a soft real time packet delivery. This is achieved by means of fuzzy sets and learning automata techniques along with zonal broadcasting to decrease total energy consumption.

Providing networks with QoS guarantees is one of the key issues to support current and future expected clients' demands. In this scenario, QoS routing is definitely critical as being responsible for defining those optimal routes supporting traffic forwarding throughout the whole network. This paper proposes two new QoS-aware RWA algorithms dealing with the routing inaccuracy problem, aiming at reducing blocking probability while limiting signaling overhead and balancing network load. The proposed algorithms extend the work already published by the authors on prediction based routing by adding a novel fuzzy-based technique featuring a powerful tool for modeling uncertainty. The proposed algorithms are compared with a well-known RWA algorithm and results show the benefit of introducing the fuzzy techniques in the RWA selection.Peer ReviewedPostprint (published version

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In the distributed cloud paradigm, data centers are geographically dispersed and interconnected over a widearea network. Due to the geographical distribution of data centers, communication networks play an important role in distributed clouds in terms of communication cost and QoS. Large-scale, processing-intensive tasks require the cooperation of many VMs, which may be distributed in more than one data center and should communicate with each other. In this setting, the number of data centers serving the given task and the network distance among those data centers have critical impact on the communication cost, traffic and even completion time of the task. In this paper, we present the NACER algorithm, a Network-Aware Cost-Efficient Resource allocation method for optimizing the placement of large multi-VM tasks in distributed clouds. NACER builds on ideas of the A * search algorithm from Artificial Intelligence research in order to obtain better results than typical greedy heuristics. We present extensive simulation results to compare the performance of NACER with competing heuristics and show its effectiveness.