Edge computing provides crucial support to next-generation mobile services that demand high data rates, low latency, and high reliability. The development of high data rate communication has lead to the emergence of cell densification as a key enabler for 5G. Unfortunately, the cost of fiber for backhaul and fronthaul poses hindrance to wide-scale adoption of small cells. Free Space Optics (FSO) offers a cost-effective alternative, though its performance is vulnerable to adverse weather, particularly fog. Recent advances in adaptive optics and automatic steering/tracking have improved FSO reliability by reducing pointing errors and enabling dynamic link reconfiguration, which further supports intelligent traffic diversion and load balancing across edge servers. In this paper, we propose a multi-hop mesh optical access network that integrates fiber-based Passive Optical Networks (PONs) with steerable FSO links to provide reliable and scalable backhaul for dense Radio Access Networks (RANs). A joint optimization framework for dynamic routing and edge server load balancing is developed. Extensive simulations demonstrate that the proposed design ensures fairness, reduces the number of required FSO transceivers, and delivers data rates comparable to established benchmarks.

The use of microservices-based architectures is becoming more prominent due to their advantageous characteristics, such as manageability, scalability, and flexibility. However, their management can be complex, and their performance can be affected by high latencies, which can alter the Service Level Objective (SLO). In order to identify the causes of high latencies, we present a causal modelling framework which is capable of analysing and reconstructing latencies within microservice-based architectures. To this end, we employ causal discovery to identify the causes of latencies. Our model integrates domain knowledge to impose constraints on the causal graph, ensuring the accuracy of the discovered relationships as well as accelerating the causal discovery. To validate our approach, we reconstruct latency metrics using machine learning techniques and demonstrate the effectiveness of our approach by accurately capturing the interrelationships between microservice resources. Our framework provides a better understanding of the causes of latencies that lead to SLO violations, and paves the way for sophisticated mechanisms that enable proactive management of cloud resources.

We consider quantum networks, where entangled-photon pairs are distributed using fibre optic links from a centralized source to entangling nodes. The entanglement is then stored (via an entanglement swap) in entangling nodes' quantum memories until used in, e.g., distributed quantum computing, quantum key distribution, quantum sensing, and other applications. Due to the fibre loss, some photons are lost in transmission. Noise in the transmission link and the quantum memory also reduces fidelity. Thus, entangling nodes must keep updated records of photon-pair arrivals to each destination, and their use by the applications. This coordination requires classical information exchange between each entangled node pair. However, the same fibre link may not admit both classical and quantum transmissions, as the classical channels can generate enough noise (i.e., via spontaneous Raman scattering) to make the quantum link unusable. Here, we consider coordinating entanglement distribution using a standard Internet protocol (IP) network instead, and propose a control protocol to enable such. We analyse the increase in latency from transmission over an IP network, together with the effect of photon loss, quantum memory noise and buffer size, to determine the fidelity and rate of entangled pairs. We characterize the relationship between the latency of the non-ideal IP network and the decoherence time of the quantum memories, providing a comparison of promising quantum memory technologies.

This paper explores the role of data spaces in enabling secure, interoperable data exchange between Network Digital Twins and SDN Controllers. This approach emphasizes data space connectors for faster, scalable, and flexible integration of multiple components across digital ecosystems to work in unison. The proposed approach is to use a common trusted platform (in this paper, the platform used is TRUE Connector) which is responsible for secure information exchange between components(e.g: SDN Controller, Network Digital Twins, Software applications, etc.) in a digital ecosystem, thus encouraging collaboration between multiple technology vendors to provide more efficient network services and encourage interoperability.

We investigate resource allocation for quantum entanglement distribution over an optical network. We characterize and model a network architecture that employs a single broadband quasi-deterministic time-frequency heralded Einstein-Podolsky-Rosen (EPR) pair source, and develop a routing and spectrum allocation scheme for distributing entangled photon pairs over such a network. As our setting allows separately solving the routing and spectrum allocation problems, we first find an optimal polynomial-time routing algorithm. We then employ max-min fairness criterion for spectrum allocation, which presents an NP-hard problem. Thus, we focus on approximately-optimal schemes. We compare their performance by evaluating the max-min and median number of EPR-pair rates assigned by them, and the associated Jain index. We identify two polynomial-time approximation algorithms that perform well, or better than others under these metrics. We also investigate scalability by analyzing how the network size and connectivity affect performance using Watts-Strogatz random graphs. We find that a spectrum allocation approach that achieves higher minimum EPR-pair rate can perform significantly worse when the median EPR-pair rate, Jain index, and computational resources are considered. Additionally, we evaluate the effect of the source node placement on the performance.

O-RAN introduces intelligent and flexible network control in all parts of the network. The use of controllers with open interfaces allow us to gather real time network measurements and make intelligent/informed decision. The work in this paper focuses on developing a use-case for open and reconfigurable networks to investigate the possibility to predict handover events and understand the value of such predictions for all stakeholders that rely on the communication network to conduct their business. We propose a Long-Short Term Memory Machine Learning approach that takes standard Radio Access Network measurements to predict handover events. The models were trained on real network data collected from a commercial O-RAN setup deployed in our OpenIreland testbed. Our results show that the proposed approach can be optimized for either recall or precision, depending on the defined application level objective. We also link the performance of the Machine Learning (ML) algorithm to the network operation cost. Our results show that ML-based matching between the required and available resources can reduce operational cost by more than 80%, compared to long term resource purchases.

We investigate resource allocation for quantum entanglement distribution over an optical network. We characterize and model a network architecture that employs a single quasi-deterministic time-frequency heralded EPR-pair source, and develop a routing scheme for distributing entangled photon pairs over such a network. We focus on max-min fairness in entanglement distribution and compare the performance of various spectrum allocation schemes by examining both the max-min number of EPR pairs assigned by them and the Jain index associated with this assignment.

In the fight against cyber attacks, Network Softwarization (NS) is a flexible and adaptable shield, using advanced software to spot malicious activity in regular network traffic. However, the availability of comprehensive datasets for mobile networks, which are fundamental for the development of Machine Learning (ML) solutions for attack detection near their source, is still limited. Cross-Domain Artificial Intelligence (AI) can be the key to address this, although its application in Open Radio Access Network (O-RAN) is still at its infancy. To address these challenges, we deployed an end-to-end O-RAN network, that was used to collect data from the RAN and the transport network. These datasets allow us to combine the knowledge from an in-network ML traffic classifier for attack detection to bolster the training of an ML-based traffic classifier specifically tailored for the RAN. Our results demonstrate the potential of the proposed approach, achieving an accuracy rate of 93%. This approach not only bridges critical gaps in mobile network security but also showcases the potential of cross-domain AI in enhancing the efficacy of network security measures.

This paper presents a scalable and efficient solution for secure network design that involves the selection and verification of network paths. The proposal addresses the challenges related to compliance policies by introducing a Proof-of-Transit (PoT) feasible implementation for path-aware programmable networks. Our approach relies on i) a source routing mechanism based on a fixed routeID representing a unique identifier per path, which serves as a key for PoT lookup tables; ii) the "in situ" that allows to collect telemetry information in the packet while the packet traverses a path. The former enables path selection with policy at the edge, while the later allows to perform path verification without extra probe-traffic. A P4 programmable language prototype demonstrates the effectiveness of this approach to protect against deviation attacks with low overhead. The results show its scalability considering the protocol overhead as the path length increases; a significant reduction in network’s forwarding state for fat-tree topologies depending on the workload per path (flows/path). Finally, experimental results show a RTT comparison evaluation, the impact of PoT computation, protection to path deviation and seamless path migration keeping flow protection.

In this paper we propose a hybrid softwarized architecture of Network Function Virtualization (NFV) where Software-Defined Networking (SDN) and Software-Defined Radio (SDR) components are integrated to form a cloud-based communication system. We analyze CPU utilization and power consumption in the OpenIreland testbed for different parameter settings and use case scenarios of this NFV architecture. The experiment results show different behaviour between SDN data plane switching and SDR in terms of CPU utilization and parallelization, which provides insights for processing aggregation and power savings when integrating them together in a cloud-based system. We then propose a power saving scheme with flexible CPU allocation that can reduce the overall power consumption of the system. Our results show that our proposed NFV architecture and its power saving scheme can save up to 20% power consumption compared to conventional scheme where SDN and SDR are separately deployed.

In this work we analyse and demonstrate the coexistence of digital coherent and analogue radio over fibre signals over an access-metro transmission network and field fibre. We analyse how the spectral proximity of the two signals and the non-ideal filter alignment of typical telecomms-grade ROADMs affect the signal performance. Our results show that coexistence is indeed possible, although performance deteriorates with the increase in number of ROADMs in the network topology. Thus, while today's access-metro networks will be able to support future 5.5 and 6G cell densification operating at mmWave and THz frequency, using spectral efficient analogue radio over fibre transmission, there will be trade-offs to be considered. In our experiment setup, we show that the limit for ARoF accessible performance is reached after transmission over 3 ROADMs and a total of 49 km of fibre.

We design and demonstrate a method for early detection of Denial-of-Service attacks. The proposed approach takes advantage of the OpenRAN framework to collect measurements from the air interface (for attack detection) and to dynamically control the operation of the Radio Access Network (RAN). For that purpose, we developed our near-Real Time (RT) RAN Intelligent Controller (RIC) interface. We apply and analyze a wide range of Machine Learning algorithms to data traffic analysis that satisfy the accuracy and latency requirements set by the near-RT RIC. Our results show that the proposed framework is able to correctly classify genuine vs. malicious traffic with high accuracy (i.e., 95%) in a realistic testbed environment, allowing us to detect attacks already at the Distributed Unit (DU), before malicious traffic even enters the Centralized Unit (CU).

Internet of Things (IoT) systems require highly scalable infrastructure to adaptively provide services to meet various performance requirements. Combining Software-Defined Networking (SDN) with Mobile Edge Cloud (MEC) technology brings more flexibility for IoT systems. We present a four-tier task processing architecture for MEC and vehicular networks, which includes processing tasks locally within a vehicle, on neighboring vehicles, on an edge cloud, and on a remote cloud. The flexible network connection is controlled by SDN. We propose a CPU resource allocation algorithm, called Partial Idle Resource Strategy (PIRS) with Vehicle to Vehicle (V2V) communications, based on Asymmetric Nash Bargaining Solution (ANBS) in Game Theory. PIRS encourages vehicles in the same location to cooperate by sharing part of their spare CPU resources. In our simulations, we adopt four applications running on the vehicles to generate workload. We compare the proposed algorithm with Non-Cooperation Strategy (NCS) and All Idle Resource Strategy (AIRS). In NCS, the vehicles execute tasks generated by the applications in their own On-Board Units (OBU), while in AIRS vehicles provide all their CPU resources to help other vehicles’ offloading requests. Our simulation results show that our PIRS strategy can execute more tasks on the V2V layer and lead to fewer number of task (and their length) to be offloaded to the cloud, reaching up to 28% improvement compared to NCS and up to 10% improvement compared to AIRS.

The open-radio access network (O-RAN) embraces cloudification and network function virtualization for base-band function processing by dis-aggregated radio units (RUs), distributed units (DUs), and centralized units (CUs). These enable the cloud-RAN vision in full, where multiple mobile network operators (MNOs) can install their proprietary or open RUs, but lease on-demand computational resources for DU-CU functions from commonly available open-clouds via open x-haul interfaces. In this paper, we propose and compare the performances ofmin-max fairnessandVickrey-Clarke-Groves (VCG) auction-based x-haul and DU-CU resource allocation mechanisms to create a multi-tenant O-RAN ecosystem that is sustainable for small, medium, and large MNOs. The min-max fair approachminimizes the maximum OPEX of RUsthrough cost-sharing proportional to their demands, whereas the VCG auction-based approachminimizes the total OPEX for all resources utilized while extracting truthful demands from RUs. We consider time-wavelength division multiplexed (TWDM) passive optical network (PON)-based x-haul interfaces where PON virtualization technique is used to flexibly provide optical connections among RUs and edge-clouds at macro-cell RU locations as well as open-clouds at the central office locations. Moreover, we design efficient heuristics that yield significantly better economic efficiency and network resource utilization than conventional greedy resource allocation algorithms and reinforcement learning-based algorithms.

Cryptographic hash functions are widely used to provide from digital time stamping to authenticity and digital signatures, mapping an extensive collection of messages into a small set of message digests and help to secure network connection and data, consequently consuming CPU resources. P4 enables data plane customisation using a high-level programming language to facilitate in-network computing development across diverse hardware targets, including Network Interface Cards (NICs). Currently, most P4 targets do not implement secure hash functions due to a lack of hardware instructions or the absence of formal functions to expose their native hardware-based implementation. Moreover, many applications and protocols cannot be instantiated using in-network computing due to stringent requirements based on these hash functions. In order to empower the security and other hash-based applications, in this paper we propose and implement a P4 shared object library for a secure hash algorithm 2 (SHA-2). Our goal is to enable SHA-2 to be used as an embedded Network Function (eNF), overcoming the lack of support in a SmartNIC architecture, in order to address the latency and throughput requirements of Service Function Chain (SFC) forwarding performance within the Network Function Virtualization (NFV) paradigm. Thus, our prototype is evaluated against kernel-level Open vSwitch (OvS) and user-space Data Plane Development Kit (DPDK) implementations. The outcomes demonstrate different tradeoffs over each platform, from the randomness added by the OS to the high cost of executing the aforesaid function using a network programmable device, leading us to highlight the best choice for each specific application.

Achieving high Quality of Service (QoS) is one of the important goals in the latest 5G Heterogeneous Networks (HetNets) environments. However, ensuring fairness among users with Reduced Power Consumption (RPC) is a major challenge. Although several studies have examined the joint issue of User Association (UA), Resource Allocation (RA), and Power Allocation (PA), there is still no optimal solution that achieves QoS fairness and RPC with low complexity and processing time. This paper proposes the Power-Performance Efficient Adaptive Genetic Algorithm (P2EAGA) for solving the UA-RA-PA problem in HetNets. The UA-RA sub-problem is formulated as a ‘0/1’ Multiple Knapsack Problem (MKP) with constraints on the maximum capacity of Base Stations (BSs) along with the transport block size index. Its sub-optimal solution is given as input to the second MKP, formulated to solve the PA sub-problem with constraints on the total BS power capacity. Simulation results show that P2EAGA outperforms existing schemes in terms of variability, fairness, RPC, and QoS, including throughput, packet loss ratio, delay, and jitter. Simulation results also show that P2EAGA generates solutions that are very close to the optimal global solution compared to the Default Genetic Algorithm.

The recently proposed open-radio access network (O-RAN) architecture embraces cloudification and network function virtualization techniques to perform the base-band function processing by dis-aggregated radio units (RUs), distributed units (DUs), and centralized units (CUs). This enables the cloud-RAN vision in full, where mobile network operators (MNOs) could install their proprietary RUs, but then lease on-demand computational resources for the processing of DU and CU functions from commonly available open-cloud (O-Cloud) servers via open x-haul interfaces due to variation of load over the day. This creates a multi-tenant scenario where multiple MNOs share networking as well as computational resources. In this paper, we propose a framework that dynamically allocates x-haul and DU/CU resources in a multi-tenant O-RAN ecosystem with min-max fairness guarantees. This framework ensures that a maximum number of RUs get sufficient resources while minimizing the OPEX for their MNOs. Moreover, in order to provide an access network architecture capable of sustaining low-latency and high capacity between RUs and edge-computing devices, we consider time-wavelength division multiplexed (TWDM) passive optical network (PON)-based x-haul interfaces where the PON virtualization technique is used to provide a direct optical connection between end-points. This creates a virtual mesh interconnection among all the nodes such that the RUs can be connected to the Edge-Clouds at macro-cell RU locations as well as to the O-Cloud servers at the central office locations. Furthermore, we analyze the system performance with our proposed framework and show that MNOs can operate with a better cost-efficiency than baseline greedy resource allocation with uniform cost-sharing.

The fifth-generation of mobile radio technologies is expected to be agile, flexible, and scalable while provisioning ultra-reliable and low-latency communication (uRLLC), enhanced mobile broadband (eMBB), and massive machine type communication (mMTC) applications. An efficient way of implementing these is by adopting cloudification, network function virtualization, and network slicing techniques with open-radio access network (O-RAN) architecture where the base-band processing functions are disaggregated into virtualized radio unit (RU), distributed unit (DU), and centralized unit (CU) over front/mid-haul interfaces. However, cost-efficient solutions are required for designing front/mid-haul interfaces and time-wavelength division multiplexed (TWDM) passive optical network (PON) appears as a potential candidate. Therefore, in this paper, we propose a framework for the optimal placement of RUs based on long-term network statistics and connecting them to open access-edge servers for hosting the corresponding DUs and CUs over front/mid-haul interfaces while satisfying the diverse QoS requirements of uRLLC, eMBB, and mMTC slices. In turn, we formulate a two-stage integer programming problem and time-efficient heuristics for users to RU association and flexible deployment of the corresponding DUs and CUs. We evaluate the O-RAN deployment cost and latency requirements with our TWDM-PON-based framework against urban, rural, and industrial areas and show its efficiency over the optical transport network (OTN)-based framework.

The development of Multi-access edge computing (MEC) has resulted from the requirement for supporting next generation mobile services, which need high capacity, high reliability and low latency. The key issue in such MEC architectures is to decide which edge nodes will be employed for serving the needs of the different end users. Here, we take a fresh look into this problem by focusing on the minimization of migration events rather than focusing on maximizing usage of resources. This is important because service migrations can create significant service downtime to applications that need low latency and high reliability, in addition to increasing traffic congestion in the underlying network. This paper introduces a priority induced service migration minimization (PrISMM) algorithm, which aims at minimizing service migration for both high and low priority services, through the use of Markov decision process, learning automata and combinatorial optimization. We carry out extensive simulations and produce results showing its effectiveness in reducing the mean service downtime of lower priority services and the mean admission time of the higher priority services.

A key challenge related to Radio Access Network (RAN) slicing is deciding how to efficiently map radio resources from the physical radio to realise RAN slices, known as the virtual wireless network embedding problem. To the best of our knowledge, this is the first paper to model and derive an analytical solution for embedding heterogeneous RAN slices with different waveforms, numerologies and Radio Access Technologies (RATs) with resources isolated down to the Physical (PHY) layer, ultimately enabling secure technology-agnostic RAN as a Service (RANaaS). First, we assess how current virtual wireless network embedding solutions model the allocation of radio resources to realise RAN slices. Then, we propose a graph-based model for embedding heterogeneous RAN slices that considers the guard bands required to ensure isolation in the frequency domain. This approach is transparent to the type and granularity of radio resources of each RAN slice, and can be extended to support RAN slices with new waveforms, numerologies and RATs. Next, we introduce a resource management optimisation problem solved at the Network Provider (NP) to determine the optimal embedding of RAN slices that maximises the total useful bandwidth occupied by tenants; and we propose three different heuristic algorithms to obtain solutions in near real-time. We compare their performance against the analytical solution using different metrics, and our results show that the best heuristic depends on the NP’s business model, e.g., using the Greedy Algorithm (GA) to increase resource utilisation or the Nearest Neighbour Algorithm (NNA) to increase the number of allocated RAN slices.

The latest Heterogeneous Network (HetNet) environments, supported by 5th generation (5G) network solutions, include small cells deployed to increase the traditional macro-cell network performance. In HetNet environments, before data transmission starts, there is a user association (UA) process with a specific base station (BS). Additionally, during data transmission, diverse resource allocation (RA) schemes are employed. UA-RA solutions play a critical role in improving network load balancing, spectral performance, and energy efficiency. Although several studies have examined the joint UA-RA problem, there is no optimal strategy to address it with low complexity while also reducing the time overhead. We propose two different versions of simulated annealing (SA): Reduced Search Space SA ($RS^{3}A$) and Performance-Improved Reduced Search Space SA ($PIRS^{3}A$), algorithms for solving UA-RA problem in HetNets. First, the UA-RA problem is formulated as a multiple knapsack problem (MKP) with constraints on the maximum BS capacity and transport block size (TBS) index. Second, the proposed$RS^{3}A$and$PIRS^{3}A$are used to solve the formulated MKP. Simulation results show that the proposed scheme$PIRS^{3}A$outperforms$RS^{3}A$and other existing schemes such as Default Simulated Annealing (DSA), and Default Genetic Algorithm (DGA) in terms of variability and DSA and$RS^{3}A$in terms of Quality of Service (QoS) metrics, including throughput, packet loss ratio (PLR), delay and jitter. Simulation results show that$PIRS^{3}A$generates solutions that are very close to the optimal solution.

In-Network Computing is a promising field that can be explored to leverage programmable network devices to offload computing towards the edge of the network. This has created great interest in supporting a wide range of network functionality in the data plane. Considering a networked robotics domain, this brings new opportunities to tackle the communication latency challenges. However, this approach opens a room for hardware-level exploits, with the possibility to add a malicious code to the network device in a hidden fashion, compromising the entire communication in the robotic facilities. In this work, we expose vulnerabilities that are exploitable in the most widely used flexible framework for writing robot software, Robot Operating System (ROS). We focus on ROS protocol crossing a programmable SmartNIC as a use case for In-Network Hijacking and In-Network Replay attacks, that can be easily implemented using the P4 language, exposing security vulnerabilities for hackers to take control of the robots or simply breaking the entire system.

Multi-access edge computing nodes are being developed in support of next generation applications, which require high capacity, high reliability and low latency. One important problem that the research community has recently focused on is the allocation strategy of applications to the different MEC server nodes. In our approach, rather than focusing on maximizing usage of resources, we focus on the minimization of migration events, which can create significant service downtime to applications that need low latency and high reliability, in addition to increasing traffic congestion in the underlying network. This paper introduces a priority induced service migration minimization (PrISMM) algorithm, which aims at minimizing service migration for both high and low priority services, through the use of learning automata. We carry out extensive simulations and produce results showing its effectiveness in reducing the mean service downtime of lower priority services and the mean admission time of the higher priority services.

Recently, the use of heterogeneous small-cell networks to offload traffic from existing cellular systems has attracted considerable attention. One of the significant challenges in heterogeneous networks (HetNet) is cross-tier interference, which becomes significant when macro-cell users (MUE) are in the vicinity of femtocell base stations (FBS). Indeed, the femtocell will cause significant interference to MUEs on the macrocell downlink (DL) while MUEs will induce hefty interference to the femtocell on the macrocell uplink (UL). Substantial work has focused on offloading and interference mitigation in HetNets; yet, none of them has considered the impact of cross-tier interference on quality of service (QoS), quality of experience (QoE). This paper proposes the Quality Efficient Femtocell Offloading Scheme (QEFOS) that selects the users most affected by the interference encountered and offloads them to nearby FBSs. QEFOS testing shows substantial improvements in terms of QoS and QoE perceived by users in heavy cross-tier interference scenarios in comparison with alternative approaches. In particular QEFOS's impact on throughput, packet loss ratio (PLR), peak-to-signal-noise ratio (PSNR), and structural similarity identity matrix (SSIM) was assessed.

The legacy security defense mechanisms cannot resist where emerging sophisticated threats such as zero-day and malware campaigns have profoundly changed the dimensions of cyber-attacks. Recent studies indicate that cyber threat intelligence plays a crucial role in implementing proactive defense operations. It provides a knowledge-sharing platform that not only increases security awareness and readiness but also enables the collaborative defense to diminish the effectiveness of potential attacks. In this paper, we propose a secure distributed model to facilitate cyber threat intelligence sharing among diverse participants. The proposed model uses blockchain technology to assure tamper-proof record-keeping and smart contracts to guarantee immutable logic. We use an open-source permissioned blockchain platform, Hyperledger Fabric, to implement the blockchain application. We also utilize the flexibility and management capabilities of Software-Defined Networking to be integrated with the proposed sharing platform to enhance defense perspectives against threats in the system. In the end, collaborative DDoS attack mitigation is taken as a case study to demonstrate our approach.

Reconfiguration procedures in optical transmission systems assisted with artificial intelligence (Al) present an innovative approach towards the mitigation of network resources mismanagement. Regression and classification tools have been studied in recent years with the aim to predict performance metrics such as Bit Error Rate (BER) and Optical Signal-to-Noise Ratio (OSNR). We have generated synthetic OSNR labeled data, which has been used for training a Support Vector Machine (SVM) classifier, in order to predict the OSNR performance upon provisioning a wavelength channel (lightpath). Information on the active lightpaths in the network is used to train the learning model, together with network topology configuration features. Our results demonstrate a 96.2% multi-class classification accuracy to predict QoT of unestablished lightpaths in topology independent (generic) scenarios.

As the capacity of the optical access networks increases, the case for sharing this capacity amongst multiple operators becomes stronger. In addition to the capital and operating expenditure savings that infrastructure sharing can provide for the operators, providing a higher degree of infrastructure customization will be a strong motivator for operators to participate in the sharing ecosystem. Thanks to the network virtualization technologies, the higher degree of control over the infrastructure can be a motivator for the new virtual operators to join. Given this control, each operator will make decisions for their share of the resources according to their policies. However, when it comes to the infrastructure provider to aggregate all these decisions, ensuring trust becomes vital. It is essential to study the incentives of all the operators and design a sharing mechanism that incentivizes truthfulness. In this paper, we propose such an auction mechanism to monetize the exchange of excess capacity between network operators to increase resource efficiency. The proposed market design is based on a sealed-bid VCG auction for homogeneous multi-item goods with a reserve price. Through market simulations, we show that our proposed market design can achieve all the fundamental economic properties of a market including, truthful value announcing, individual rationality and weak budget balance.

The increasing success of Video on Demand services poses several challenges to both network operators and service providers. Due to their heavy bandwidth requirement and the high concentration of requests during peak times, implementing VoD services efficiently without compromising the Quality of Service of customers is not a trivial task. To alleviate this problem, several caching strategies have been proposed in literature.

SUMMARY We consider a hybrid electronic packet switched and optical circuit switched interconnection network for future high performance computing and datacenter systems. Given the logical task‐to‐task communication graph of an application, our objective is to cluster the logical parallel tasks to compute resources and configure the (reconfigurable) optical part of the hybrid interconnect to efficiently serve application communication requirements. We formulate the clustering and topology configuration problem in such a network, prove that it is NP‐complete, and provide an optimal algorithm to solve it based on an integer linear programming formulation. The integer linear programming algorithm is used to optimally solve small‐scale instances of the problem for the purpose of obtaining performance bounds. Aiming at large‐scale, we also present a heuristic based on simulated annealing that trades‐off performance for responsiveness. We measure the performance of a hybrid interconnect employing the proposed algorithm using real workloads, as well as extrapolated traffic, and compare it against application mapping on conventional fixed, electronic‐only interconnects based on toroidal topologies. Copyright © 2013 John Wiley & Sons, Ltd.

We report on an emerging application focused on the design of resilient long reach passive optical networks using combinatorial optimisation techniques. The objective of the application is to determine the optimal position and capacity of a set of metro nodes. We specifically consider dual parented networks whereby each customer must be associated with two metro nodes. An important property of such a placement is resilience to single node failure. Therefore excess capacity should be provided at each metro node in order to ensure that customers can be redistributed amongst the metro sites. Our application, as well as finding optimal node placements, can compute the minimum level of excess capacity on all metro nodes. In this paper we present three alternative approaches to optimal metro node placement. We present a detailed analysis of the impact of different placement approaches on the distribution of excess capacity throughout the network. We show that preferential distributions occur in practice, based on a case-study in Ireland. Finally we show that load and excess capacity provision are independent of each other.

We present an application focused on the design of resilient long-reach passive optical networks. We specifically consider dual parented networks whereby each customer must be connected to two metro sites via a local exchange sites. An important property of such a placement is resilience to single metro node failure. The objective of the application is to determine the optimal position of a set of metro-nodes such that the total optical fibre length is minimised. We prove that the decision variant of this problem is NP-Complete. We present three alternative combinatorial optimisation approaches to finding an optimal metro node placement using: a mixed integer linear programming formulation of the problem, a hybrid approach that uses clustering as a preprocessing step, and, finally, a local search approach. We consider a detailed case-study based on a network for Ireland. The hybrid approach scales well and finds solutions that are close to optimal, with a runtime that is two orders-of-magnitude better than the MIP model. The local search approach is consistently good on all benchmarks.