Skype is a very popular VoIP software which has recently attracted the attention of the research community and network operators. Following a closed source and proprietary design, Skype protocols and algorithms are unknown. Moreover, strong encryption mechanisms are adopted by Skype, making it very difficult to even glimpse its presence from a traffic aggregate. In this paper, we propose a framework based on two complementary techniques to reveal Skype traffic in real time. The first approach, based on Pearson's Chi-Square test and agnostic to VoIP-related traffic characteristics, is used to detect Skype's fingerprint from the packet framing structure, exploiting the randomness introduced at the bit level by the encryption process. Conversely, the second approach is based on a stochastic characterization of Skype traffic in terms of packet arrival rate and packet length, which are used as features of a decision process based on Naive Bayesian Classifiers. In order to assess the effectiveness of the above techniques, we develop an off-line cross-checking heuristic based on deep-packet inspection and flow correlation, which is interesting per se. This heuristic allows us to quantify the amount of false negatives and false positives gathered by means of the two proposed approaches: results obtained from measurements in different networks show that the technique is very effective in identifying Skype traffic. While both Bayesian classifier and packet inspection techniques are commonly used, the idea of leveraging on randomness to reveal traffic is novel. We adopt this to identify Skype traffic, but the same methodology can be applied to other classification problems as well.

Considering the exponential increase in mobile traffic, requiring denser cellular access networks, the use of renewable energy (RE) to power base stations (BSs) may contribute to reduction of the huge operational cost faced by mobile network operators (MNOs) due to power supply. Furthermore, the smart grid (SG) paradigm is deeply changing the energy market, envisioning an active interaction between grid and customers. Hence, MNOs can combine renewable-powered BSs with properly designed energy management strategies to improve the interaction with the SG, with the twofold objective of reducing the energy bill and providing ancillary services. We propose a stochastic model to investigate a renewable-powered mobile network dynamically adapting its energy consumption to accomplish SG requests, and receiving rewards in return, by exploiting two techniques. First, resource on demand (RoD) can dynamically switch off unneeded BSs. Second, an energy management policy takes efficient decisions about using/harvesting locally produced RE, depending on SG requests. The proposed techniques highly increase the probability of responding to ancillary service demands, up to 90%, also depending on the RE system size, with RoD raising, by up to 20%, the probability of accomplishing SG requests. Consequently, cost saving can become equal to the total cost or even greater, providing revenues to the MNO.

Geographically distributed data centers (DCs) offer promising business opportunities to both big companies that own several sites and multi-owner inter-cloud infrastructures. In these scenarios, workload management is a particularly challenging task, since the autonomy of single DCs should be preserved while global objectives, such as cost reduction and load balance, should be achieved. In this paper, a hierarchical approach for workload management in geographically distributed DCs is presented. The proposed solution is composed of two algorithms devoted to workload assignment and migration. Both algorithms are based on the computation of a simple function that represents the cost of running some workload in the different sites of the distributed DC. The framework requires a very limited exchange of state information among the sites and preserves the autonomy of single DCs and, at the same time, allows for an integrated management of heterogeneous platforms. Performance is analyzed for a specific infrastructure composed of four DCs, with two goals: 1) load balance and 2) energy cost reduction. Results show that the proposed approach smoothly adapts the workload distribution to variations of energy cost and load, while achieving the desired combination of management objectives.

While designed for being energy efficient, the deployment of 5G networks will further increase Radio Access Networks (RANs) energy consumption with the twofold effect to raise sustainability issues and increase operational costs for Mobile Network Operators (MNOs). However, the energy waste occurring during low traffic periods can be mitigated through Advanced Sleep Modes (ASMs) that make the BSs enter into progressively deeper and less consuming sleep modes. Deep sleep modes, unfortunately, have longer reactivation times, and may jeopardize service quality. In this paper, focusing on 5G latency requirements in low traffic periods, we propose a framework to dynamically adapt the ASM configuration settings to the actual traffic load so as to meet a desired constraint on the average BS reactivation delay.