Energy management in communication networks: a journey through modelling and optimization glasses

The widespread proliferation of Internet and wireless applications has produced a significant increase of ICT energy footprint. As a response, in the last five years, significant efforts have been undertaken to include energy-awareness into network management. Several green networking frameworks have been proposed by carefully managing the network routing and the power state of network devices. Even though approaches proposed differ based on network technologies and sleep modes of nodes and interfaces, they all aim at tailoring the active network resources to the varying traffic needs in order to minimize energy consumption. From a modeling point of view, this has several commonalities with classical network design and routing problems, even if with different objectives and in a dynamic context. With most researchers focused on addressing the complex and crucial technological aspects of green networking schemes, there has been so far little attention on understanding the modeling similarities and differences of proposed solutions. This paper fills the gap surveying the literature with optimization modeling glasses, following a tutorial approach that guides through the different components of the models with a unified symbolism. A detailed classification of the previous work based on the modeling issues included is also proposed

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Server resource dimensioning and routing of service function chain in NFV network architectures

The Network Function Virtualization (NFV) technology aims at virtualizing the network service with the execution of the single service components in Virtual Machines activated on Commercial-off-the-shelf (COTS) servers. Any service is represented by the Service Function Chain (SFC) that is a set of VNFs to be executed according to a given order. The running of VNFs needs the instantiation of VNF instances (VNFI) that in general are software components executed on Virtual Machines. In this paper we cope with the routing and resource dimensioning problem in NFV architectures. We formulate the optimization problem and due to its NP-hard complexity, heuristics are proposed for both cases of offline and online traffic demand. We show how the heuristics works correctly by guaranteeing a uniform occupancy of the server processing capacity and the network link bandwidth. A consolidation algorithm for the power consumption minimization is also proposed. The application of the consolidation algorithm allows for a high power consumption saving that however is to be paid with an increase in SFC blocking probability

Analysis of algorithms for online routing and scheduling in networks

We study situations in which an algorithm must make decisions about how to best route and schedule data transfer requests in a communication network before each transfer leaves its source. For some situations, such as those requiring quality of service guarantees, this is essential. For other situations, doing work in advance can simplify decisions in transit and increase the speed of the network. In order to reflect realistic scenarios, we require that our algorithms be online, or make their decisions without knowing future requests. We measure the efficiency of an online algorithm by its competitive ratio, which is the maximum ratio, over all request sequences, of the cost of the online algorithm\u27s solution to that of an optimal solution constructed by knowing all the requests in advance.;We identify and study two distinct variations of this general problem. In the first, data transfer requests are permanent virtual circuit requests in a circuit-switched network and the goal is to minimize the network congestion caused by the route assignment. In the second variation, data transfer requests are packets in a packet-switched network and the goal is to minimize the makespan of the schedule, or the time that the last packet reaches its destination. We present new lower bounds on the competitive ratio of any online algorithm with respect to both network congestion and makespan.;We consider two greedy online algorithms for permanent virtual circuit routing on arbitrary networks with unit capacity links, and prove both lower and upper bounds on their competitive ratios. While these greedy algorithms are not optimal, they can be expected to perform well in many circumstances and require less time to make a decision, when compared to a previously discovered asymptotically optimal online algorithm. For the online packet routing and scheduling problem, we consider an algorithm which simply assigns to each packet a priority based upon its arrival time. No packet is delayed by another packet with a lower priority. We analyze the competitive ratio of this algorithm on linear array, tree, and ring networks