Impact of Processing-Resource Sharing on the Placement of Chained Virtual Network Functions

Network Function Virtualization (NFV) provides higher flexibility for network operators and reduces the complexity in network service deployment. Using NFV, Virtual Network Functions (VNF) can be located in various network nodes and chained together in a Service Function Chain (SFC) to provide a specific service. Consolidating multiple VNFs in a smaller number of locations would allow decreasing capital expenditures. However, excessive consolidation of VNFs might cause additional latency penalties due to processing-resource sharing, and this is undesirable, as SFCs are bounded by service-specific latency requirements. In this paper, we identify two different types of penalties (referred as "costs") related to the processingresource sharing among multiple VNFs: the context switching costs and the upscaling costs. Context switching costs arise when multiple CPU processes (e.g., supporting different VNFs) share the same CPU and thus repeated loading/saving of their context is required. Upscaling costs are incurred by VNFs requiring multi-core implementations, since they suffer a penalty due to the load-balancing needs among CPU cores. These costs affect how the chained VNFs are placed in the network to meet the performance requirement of the SFCs. We evaluate their impact while considering SFCs with different bandwidth and latency requirements in a scenario of VNF consolidation.Comment: Accepted for publication in IEEE Transactions on Cloud Computin

Privacy-Friendly Load Scheduling of Deferrable and Interruptible Domestic Appliances in Smart Grids

The massive integration of renewable energy sources in the power grid ecosystem with the aim of reducing carbon emissions must cope with their intrinsically intermittent and unpredictable nature. Therefore, the grid must improve its capability of controlling the energy demand by adapting the power consumption curve to match the trend of green energy generation. This could be done by scheduling the activities of deferrable and/or interruptible electrical appliances. However, communicating the users' needs about the usage of their appliances also leaks sensitive information about their habits and lifestyles, thus arising privacy concerns. This paper proposes a framework to allow the coordination of energy consumption without compromising the privacy of the users: the service requests generated by the domestic appliances are divided into crypto-shares using Shamir Secret Sharing scheme and collected through an anonymous routing protocol by a set of schedulers, which schedule the requests by directly operating on the shares. We discuss the security guarantees provided by our proposed infrastructure and evaluate its performance, comparing it with the optimal scheduling obtained by means of an Integer Linear Programming formulation

Privacy-friendly appliance load scheduling in smart grids

Abstract—The massive integration of renewable energy sources into the power grid ecosystem with the aim of reducing carbon emissions must cope with their intrinsically intermittent and unpredictable nature. Therefore, the grid must improve its capability of controlling the energy demand by adapting the power consumption curve to match the trend of green energy generation. This could be done by scheduling the activities of deferrable electrical appliances. However, communicating the users ’ needs about the usage of the electrical appliances leaks sensitive information about habits and lifestyles of the customers, thus arising privacy concerns. This paper proposes a privacy-preserving framework to allow the coordination of energy con-sumption without compromising the privacy of the users: the ser-vice requests generated by the domestic appliances are diveded in crypto-shares using Shamir Secret Sharing scheme and collected through an anonymous routing protocol based on Crowds by a set of schedulers, which schedule the requests operating directly on the shares. We discuss the security guarantees provided by our proposed infrastructure and evaluate its performance, comparing it with the optimal scheduling obtained through an Integer Linear Programming formulation. I

Up-to-date Key Retrieval for Information Centric Networking

Information Centric Networking (ICN) leverages in-network caching to provide efficient data distribution and better performance by replicating contents in multiple nodes to bring content nearer the users. Since contents are stored and replicated into node caches, the content validity must be assured end-to-end. Each content object carries a digital signature to provide a proof of its integrity, authenticity, and provenance. However, the use of digital signatures requires a key management infrastructure to manage the key life cycle. To perform a proper signature verification, a node needs to know whether the signing key is valid or it has been revoked. This paper discusses how to retrieve up-to-date signing keys in the ICN scenario. In the usual public key infrastructure, the Certificate Revocation Lists (CRL) or the Online Certificate Status Protocol (OCSP) enable applications to obtain the revocation status of a certificate. However, the push-based distribution of Certificate Revocation Lists and the request/response paradigm of Online Certificate Status Protocol should be fit in the mechanism of named-data. We consider three possible approaches to distribute up-to-date keys in a similar way to the current CRL and OCSP. Then, we suggest a fourth protocol leveraging a set of distributed notaries, which naturally fits the ICN scenario. Finally, we evaluate the number and size of exchanged messages of each solution, and then we compare the methods considering the perceived latency by the end nodes and the throughput on the network links

Enabling privacy in a gaming framework for smart electricity and water grids

Serious games are potentially powerful tools to influence users' preferences and attitudes. However, privacy concerns related to the misuse of data gathered from the players may emerge in online-gaming interactions. This work proposes a privacy-friendly framework for a gaming platform aimed at reducing energy and water usage, where players are grouped in teams with the challenge of maintaining the aggregated consumption of its members below a given threshold. We discuss a communication protocol which enables the team members to compute their overall consumption with- out disclosing individual measurements. Moreover, the protocol prevents the gaming platform from learning the consumption data and challenge objectives of the players. Correctness and truthfulness checks are included in the protocol to detect cheaters declaring false consumption data or providing altered game results. The security and performance of the framework are assessed, showing that scalability is ensured thanks to the limited data exchange and lightweight cryptographic operations

Imprecise Markov Models for Scalable and Robust Performance Evaluation of Flexi-Grid Spectrum Allocation Policies

The possibility of flexibly assigning spectrum resources with channels of different sizes greatly improves the spectral efficiency of optical networks, but can also lead to unwanted spectrum fragmentation.We study this problem in a scenario where traffic demands are categorised in two types (low or high bit-rate) by assessing the performance of three allocation policies. Our first contribution consists of exact Markov chain models for these allocation policies, which allow us to numerically compute the relevant performance measures. However, these exact models do not scale to large systems, in the sense that the computations required to determine the blocking probabilities---which measure the performance of the allocation policies---become intractable. In order to address this, we first extend an approximate reduced-state Markov chain model that is available in the literature to the three considered allocation policies. These reduced-state Markov chain models allow us to tractably compute approximations of the blocking probabilities, but the accuracy of these approximations cannot be easily verified. Our main contribution then is the introduction of reduced-state imprecise Markov chain models that allow us to derive guaranteed lower and upper bounds on blocking probabilities, for the three allocation policies separately or for all possible allocation policies simultaneously.Comment: 16 pages, 7 figures, 3 table

Modelling Spectrum Assignment in a Two-Service Flexi-Grid Optical Link with Imprecise Continuous-Time Markov Chains

Flexi-grid optical networks (Gerstel et al., 2012) are a novel paradigm for managing the capacity of optical fibers more efficiently. The idea is to divide the spectrum in small frequency slices, and to consider an allocation policy that adaptively assigns one or multiple contiguous slices to incoming bandwidth requests, depending on their size. However, as new requests arrive and old requests are served and return resources to the free pool, the spectrum might become fragmented, leading to inefficiency and unfairness. It is therefore necessary to quantify the performance of a given spectrum allocation policy, for example by determining the probability that a bandwidth request is blocked, in the sense that it cannot be allocated because there are not enough contiguous free slices. To determine blocking probabilities for an optical link with traffic requests of two different sizes and a random allocation policy, Kim et al. (2015) use a Markov chain. Unfortunately, the number of states of this Markov chain grows exponentially with the number of available frequency slices, making it infeasible to determine blocking probabilities for large systems. Therefore, Kim et al. (2015) also consider a second Markov chain, with a highly reduced state space and approximate transition rates, to obtain approximations of these blocking probabilities. In this contribution, we first show how to construct such full and reduced-state Markov chains for two other allocation policies, and compare these with the random policy. Next, we introduce a so-called imprecise Markov chain, which has the same reduced state space but imprecise (interval-valued) transition rates, and explain how it can be used to determine guaranteed upper and lower bounds for --- instead of approximations of --- blocking probabilities, for different families of allocation policies

A privacy-friendly gaming framework in smart electricity and water grids

Serious games can be used to push consumers of common-pool resources toward socially responsible consumption patterns. However, gamified interactions can result in privacy leaks and potential misuses of player-provided data. In the Smart Grid ecosystem, a smart metering framework providing some basic cryptographic primitives can enable the implementation of serious games in a privacy-friendly manner. This paper presents a smart metering architecture in which the users have access to their own high-frequency data and can use them as the input data to a multi-party secure protocol. Authenticity and correctness of the data are guaranteed by the usage of a public blockchain. The framework enables a gaming platform to administer a set of team game activities aimed at promoting a more sustainable usage of energy and water. We discuss and assess the performance of a protocol based on Shamir secret sharing scheme, which enables the members of the teams to calculate their overall consumption and to compare it with those of other teams without disclosing individual energy usage data. Additionally, the protocol impedes that the game platform learns the meter readings of the players (either individual or aggregated) and their challenge objectives