One of the recent methods for effectively utilising the available bandwidth of the network as conditions change is in-network packet trimming. It has advantages for applications which rely on low-latency and low-loss, such as video streaming. Packet trimming allows a network node to reduce the size of packets as they travel across the network, by removing some of the content, during the journey of each packet. In our previous work, we presented a system for layered SVC video streaming, and showed how the trimming process can be successfully implemented and utilised as a virtualized edge deployed function. If trimming is needed, there is a task which has to unpack all the video content and meta-data, including associated significance values, then determine which data to remove, and finally repack and rebuilding a new packet. Conversely, a simple scheme for packet trimming, in the form of a basic buffer cutting process, has been implemented in switches. This process keeps the first bytes of a packet, and drops the rest. Here we investigate if a buffer cutting process can be used for layered video, and describe the issues arising and the design aspects needed to adapt from packet rebuilding to packet cutting.

Supporting ubiquitous deployment of built-in Internet service with Software Defined Networking (SDN), Network Function Virtualization (NFV), and Low Earth Orbit (LEO) satellite constellations has been widely accepted as one of the key technologies for the next-generation communication services. By integrating terrestrial and space network capabilities, new design features are introduced to existing network ecosystems. For instance, terrestrial Virtual Network Functions (VNFs) can now be hosted on satellites, utilizing satellite highways. This requires expanding the roles of the control units, originally responsible for terrestrial data planes, to include space-based counterparts. As a result, seamless integration of Service Function Chains (SFCs) across satellite constellations and terrestrial control units becomes a challenge due to the topology dynamics caused by high-speed LEO satellites. In this paper, we propose the Geosynchronous Service Function Chaining (GSFC) scheme to facilitate programmable, Internet SFC operations based on LEO satellite network environments. The key idea is to cluster adjacent LEO satellites to represent logical VNF containers at the fixed positions, where the initial VNFs at the region are continuously filled up by the traversing satellite payload functions in a predictable manner. With this design, the ground-based controllers can maintain the space-terrestrial SFCs without being affected by the constantly shifting satellite VNFs, and thereby large-scale and complex recalculation for the routing policies is avoided. The design principle introduces a ground-breaking approach to space Internet protocol stacks, facilitating robust routing for SFC operations across integrated space and terrestrial networks. Our simulation results verify the feasibility of the proposed GSFC-based VNF orchestration mechanism and reveal the trade-offs in both data and control plane performance.

The utilization of the packet trimming technique as part of 6G has previously been described. It allows a network node to reduce packet sizes, by removing some of the content, during the journey of each packet. This approach has been successful in many ways, but can be limited when there is very little bandwidth. In this paper, we propose an approach using an architecture suitable for 6G, which overcomes some of these issues by removing extra content from the packets, when network conditions are restricted.

We propose ACCT, an end-to-end learning-based congestion control mechanism for Internet flows that aims to achieve an optimal sending rate through collaboration between applications and the network elements along the network path. Specifically, an ACCT sender can exploit explicit feedback from the network about its ability to satisfy the application's desired sending rate. Unlike existing schemes, ACCT does not blindly adjust its sending rate to either of these signals; instead, it considers them advisory because the bottleneck may be at routers that do not support ACCT signaling. To detect such bottlenecks and strictly control the end-to-end delay for latency-sensitive applications, ACCT actively measures the end-to-end queuing delay and regulates its sending rate to keep queuing delay within an acceptable margin. Finally, ACCT heavily relies on a Reinforcement Learning (RL) agent to adjust the aggressiveness of its sending rate. We propose a novel approach to formulate the RL agent, exploiting multiple reward functions simultaneously to train the agent. We implemented the ACCT congestion control module in NS-3, which communicates with the RL agent via a technology-agnostic protocol. We evaluated performance across various network scenarios in both wired and wireless networks. Across all experiments, ACCT significantly outperforms commonly used TCP variants such as CUBIC; it provides network fairness when competing with other ACCT flows, and finally, ACCT flows coexist well when they carry heterogeneous traffic.

This paper presents DNS++, a re-design of the Internet's name resolution system that addresses dynamic information and privacy. DNS++ uses a pub/sub overlay to send updates about a given service to interested clients, allowing them to (re)select between replicas according to their requirements, as updates about services and their features dynamically change. Since third-party brokers in the overlay are not always trusted for the confidentiality of the content flowing through them, clients' privacy is preserved in DNS++ through homomorphic encryption. Brokers are prevented from accessing encrypted service information but can perform homomorphic match and forward service updates to relevant clients through the overlay accordingly. Assuming that forwarding tables in each broker are implemented via ordered data structures, the time required for adding a new client's subscription, and to perform homomorphic match between existing subscriptions and service updates, would grow logarithmically with the number of entries within a table. This is shown by our performance evaluation, which confirms that DNS++ is feasible to be deployed with an acceptable performance overhead.

Despite the breakthroughs in end-to-end encryption that keeps the content of Internet data confidential, the fact that packet headers contain source and IP addresses remains a strong violation of users' privacy. This paper describes a routing mechanism that allows for connections to be established where no provider, including the final destination, knows who is connecting to whom. The system makes use of inter-domain source routing with public key cryptography to establish connections and simple private symmetric encryption in the data path that allows for fully stateless packet transmission. We discuss the potential implications of real deployment of our routing mechanism in the Internet.

In a packet network, the routes taken by traffic can be determined according to predefined objectives. Assuming that the network conditions remain static and the defined objectives do not change, mathematical tools such as linear programming could be used to solve this routing problem. However, networks can be dynamic or the routing requirements may change. In that context, Reinforcement Learning (RL), which can learn to adapt in dynamic conditions and offers flexibility of behavior through the reward function, presents as a suitable tool to find good routing strategies. In this work, we train an RL agent, which we call R2L, to address the routing problem. The policy function used in R2L is a neural network and we use an evolution strategy algorithm to determine its weights and biases. We tested R2L in two different scenarios: static and dynamic networks conditions. In the first, we used a 16-node network and experimented with different reward functions, observing that R2L was able to adapt its routing behavior accordingly. Finally, in the second experiment, we used a 5-node network topology where a given link's transmission rate changed during the simulation. In this scenario, we observed that R2L was able to deliver a competitive performance, compared to heuristic benchmarks, with changing network conditions.

It has been claimed that the deployment of fog and edge computing infrastructure is a necessity to make high-performance cloud-based applications a possibility. However, there are a large number of middle-ground latency-sensitive applications such as online gaming, interactive photo editing and multimedia conferencing that require servers deployed closer to users than in globally centralised clouds but do not necessarily need the extreme low-latency provided by a new infrastructure of micro data centres located at the network edge, e.g., in base stations and ISP Points of Presence. In this paper we analyse a snapshot of today's data centres and the distribution of users around the globe and conclude that existing infrastructure provides a sufficiently distributed platform for middle-ground applications requiring a response time of 20-200 ms. However, while placement and selection of edge servers for extreme low-latency applications is a relatively straightforward matter of choosing the closest, providing a high quality of experience for middle-ground latency applications that use the more widespread distribution of today's data centres, as we advocate in this paper, raises new management challenges to develop algorithms for optimising the placement of and the per-request selection between replicated service instances.

In this work, we propose a Deep Learning (DL) based solution to the problem of routing traffic flows in computer networks. Routing decisions can be made in different ways depending on the desired objective and, based on that objective function, optimal solutions can be computed using a variety of techniques, e.g. with mixed integer linear programming. However, determining these solutions requires solving complex optimization problems and, thus, cannot be typically done at runtime. Instead, heuristics for these problems are often created but designing them is non-trivial in many cases. The routing framework proposed here presents an alternative to the design of heuristics, whilst still achieving good performance. This is done by building a DL model trained on the optimal decisions over flows from known traffic demands. To evaluate our solution, we focused on the problem of network congestion, even though a wide range of alternative objectives could be fitted into this framework. We ran experiments using two publicly available datasets of networks with real traffic demands and showed that our solution achieves close-to-optimal network congestion values.

Advanced Internet services increasingly rely on many components to implement their functionality. These composite services have three important features: they are expensive to deploy, components need to be placed intelligently close to the users to improve quality of experience and they will potentially consume significant amounts of bandwidth. This paper presents Triptych, a multi-objective optimisation framework that tries to optimise according these three dimensions to help the three main stakeholders in the Internet ecosystem: users, application providers and network providers. Triptych implements evolutionary computation approaches for this complex problem, which simultaneously optimise service deployment costs, latency-based user utility and network congestion. These algorithms provide possible operating points, bringing important tools for network managements and resource allocation. A large set of simulations under different scenarios are provided to validate the algorithms.

Multi-user video conferencing is a ubiquitous technology. Increasingly end-hosts in a conference are assisted by cloud-based servers that improve the quality of experience for end users. This paper evaluates the impact of strategies for placement of such servers on user experience and deployment cost. We consider scenarios based upon the Amazon EC2 infrastructure as well as future scenarios in which cloud instances can be located at a larger number of possible sites across the planet. We compare a number of possible strategies for choosing which cloud locations should host services and how traffic should route through them. Our study is driven by real data to create demand scenarios with realistic geographical user distributions and diurnal behaviour. We conclude that on the EC2 infrastructure a well chosen static selection of servers performs well but as more cloud locations are available a dynamic choice of servers becomes important.

The dominant application in today's Internet is content streaming, which is increasingly relying on caches to meet the stringent conditions on the latency between content servers and end-users. These systems routinely face the challenges of limited bandwidth capacities and network server failures, which degrade caching performance. In this paper, we study the problem of optimally allocating content over a resilient caching network, in which each cache may fail under some situations. Given content request rates and multiple routing paths, we formulate an optimization problem to maximize the expected caching gain, i.e., the reduction of latency due to intermediate caching. The offline version of this problem is NP-hard. We first propose a centralized, offline algorithm and show that a solution with (1-1/e) approximation ratio to the optimal can be constructed. We then propose a distributed ascent algorithm based on the concave relaxation of the expected gain. Informed by the results of our analysis, we finally propose a distributed resilient caching algorithm (DR-Cache) that is simple and adaptive to network failures. We show numerically that DR-Cache significantly outperforms other candidate algorithms under synthetic requests, as well as real world traces over a class of network topologies.

The advent of virtualization and IaaS have revolutionized the telecom industry via SDN/NFV. A new wave of cloud-native PaaS promises to further improve SDN/NFV performance, portability, and cost-efficiency. In this poster, we highlight a work in progress being done in the 5G-MEDIA project [2], which pioneers the application of the serverless paradigm to NFV in the context of media intensive applications in 5G networks. Motivational use cases include tele-immersive gaming, mobile journalism and UHD content distribution. For example, consider a next-gen e-sport, in which bouts between gamers last only a few minutes. FaaS offers a clear cost-efficiency benefit for hosting such applications. An architecture is shown in Fig. 1. It includes i) an Application/Service Development Kit (SDK) to enable access to media applications development tools; ii) a Service Virtualization Platform (SVP) to run the ETSI MANO framework, the Media Service MAPE optimization component and the VIM and WIM plugins to enable NFVIs integration; iii) different NFVIs to execute media-specific VNFs. FaaS VIM is implemented for integration of FaaS with the rest of the MANO stack. It allows mixing FaaS and "regular" VNFs within the same media forwarding graph. For reference implementation, Apache OpenWhisk [1] and Kubernetes are used. The main challenge is extending the programming model to support groups of actions communicating over a network, while retaining the simplicity of FaaS. The project is supported by EU H2020 R&I program (Grant Agreement No 761699).

The emergence of distributed clouds opens up new research challenges for service deployment. Composite services consist of multiple components, potentially located in different geographical locations, which need to be interconnected and invoked in the correct order according to the overall service work-flow. The placement of composite services over distributed cloud node locations raises new challenges for efficient deployment and management. In this paper, we design exact models of the composite service placement problems using mixed integer linear program, and compare these to solutions based on genetic algorithms. We use a utility function, based initially on latency metrics, to evaluate the quality of service (QoS) of the deployed composite service. By maximizing the utility with respect to deployment cost, our approach can provide good QoS for users while satisfying budget constraints for service providers. Based on simulations using real data-center locations and traffic demand patterns, we show that our algorithms are scalable under a range of scenarios.

This paper is focused on techniques for maximizing utility across all users within a total network transit cost budget. We present a new method for selecting between replicated servers distributed over the Internet. First, we introduce a novel utility framework that factors in quality of service metrics. Then we design an optimization algorithm, solvable in polynomial time, to allocate user requests to servers based on utility while satisfying network transit cost constraints, mapping service names to service instance locators. We then describe an efficient, low overhead distributed model which only requires knowledge of a fraction of the data required by the global optimization formulation. Next, a load-balancing variant of the algorithm is explored that substantially reduces blocking caused by congested servers. Extensive simulations show that our method is scalable and leads to higher user utility compared with mapping user requests to the closest service replica, while meeting network traffic cost constraints. We discuss several options for real-world deployment that require no changes to end-systems based on either the use of SDN controllers or extensions to the current DNS system.

Cloud computing has revolutionized service delivery by providing on-demand invocation and elasticity. To reap these benefits, computation has been displaced from client devices and into data centers. This partial centralization is undesirable for applications that have stringent locality requirements, e.g., low latency. This problem could be addressed with large numbers of smaller cloud resources closer to users. However, as cloud computing diffuses from within data centers and into the network, there will be a need for cloud resource allocation algorithms that operate on resource-constrained computational units that serve localized subsets of customers. In this paper, we present a mechanism for service provisioning in distributed clouds where applications compete for resources. The mechanism operates by enabling execution zones to assign resources based on Vickrey auctions and provides high-quality probabilistic models that applications can use to predict the outcomes of such auctions. This allows applications to use knowledge of the locality distribution of their clients to accurately select the number of bids to be sent to each execution zone and their value. The proposed mechanism is highly scalable, efficient, and validated by extensive simulations.

This paper describes TARDIS (Traffic Assignment and Retiming Dynamics with Inherent Stability) which is an algorithmic procedure designed to reallocate traffic within Internet Service Provider (ISP) networks. Recent work has investigated the idea of shifting traffic in time (from peak to off-peak) or in space (by using different links). This work gives a unified scheme for both time and space shifting to reduce costs. Particular attention is given to the commonly used 95th percentile pricing scheme.

This paper considers fundamental measurements which drive TCP flows: throughput, RTT and loss. It is clear that throughput is, in some sense, a function of both RTT and loss. In their seminal paper Padyhe et al [1] begin with a mathematical model of the TCP sliding window evolution process and come up with an equation showing that TCP throughput is (roughly) proportional to 1/RTT√p where p is the probability of packet loss. Their equation is shown to be consistent with data gathered on several links. This paper takes the opposite approach and analyses a large number of packet traces from well-known sources in order to create a data-driven estimate of the functions which relate TCP, loss and RTT. Regression analysis is used to fit models to connect the quantities. The fitted models show different behaviour from that expected in [1].

When designing distributed systems and Internet protocols, designers can benefit from statistical models of the Internet that can be used to estimate their performance. However, it is frequently impossible for these models to include every property of interest. In these cases, model builders have to select a reduced subset of network properties, and the rest will have to be estimated from those available. In this paper we present a technique for the analysis of Internet round trip times (RTT) and its relationship with other geographic and network properties. This technique is applied on a novel dataset comprising ~19 million RTT measurements derived from ~200 million RTT samples between ~54 thousand DNS servers. Our main contribution is an information-theoretical analysis that allows us to determine the amount of information that a given subset of geographic or network variables (such as RTT or great circle distance between geolocated hosts) gives about other variables of interest. We then provide bounds on the error that can be expected when using statistical estimators for the variables of interest based on subsets of other variables.

There is growing evidence that mutually beneficial outcomes can be achieved when content distribution overlays and their underlying ISPs collaborate through open interfaces. We further contribute to this body of work by considering consolidated topology construction strategies that integrate the information provided by multiple ISPs. We focus on situations with potentially conflicting, asymmetric preference costs, since these situations are expected to benefit more from the tradeoffs provided by consolidation to produce an overlay topology with desirable global properties. In this paper we develop a generic model for the multi-domain consolidation of ISP preferences expressed as costs for pairwise peer connections, where peers are grouped into clusters based on topology criteria. Using this model, we propose two consolidated topology construction strategies: Shared Cost, designed to provide a tradeoff for preference cost asymmetries, and Low Cost, designed to reduce the overall preference cost that the overlay imposes on all its underlying ISPs. We evaluate these two models through extensive simulations over a wide range of ISP and peer cluster sizes, and we show that preference consolidation can provide ISPs with outcomes more aligned with their preferences than those provided by non-consolidated operation.

Although direct reciprocity (Tit-for-Tai) contribution systems have been successful in reducing freeloading in peer- to-peer overlays, it has been shown that, unless the contribution network is dense, they tend to be slow (or may even fail) to converge. On the other hand, current indirect reciprocity mechanisms based on reputation systems tend to be susceptible to sybil attacks, peer slander and whitewashing. In this paper we present PledgeRoute, an accounting mechanism for peer contributions that is based on social capital. This mechanism allows peers to contribute resources to one set of peers and use this contribution to obtain services from a different set of peers, at a different time. PledgeRoute is completely decentralised, can be implemented in both structured and unstructured peer-to-peer systems, and it is resistant to the three kinds of attacks mentioned above. To achieve this, we model contribution transitivity as a routing problem in the contribution network of the peer-to-peer overlay, and we present arguments for the routing behaviour and the sybilproofness of our contribution transfer procedures on this basis. Additionally, we present mechanisms for the seeding of the contribution network, and a combination of incentive mechanisms and reciprocation policies that motivate peers to adhere to the protocol and maximise their service contributions to the overlay.

Peer-to-peer networks providing QoS-enabled services are sensitive to hidden action situations, where the actions of a server peer are hidden from the peers who receive services from it. This is because server peers can choose to strategically minimize their effort, and client peers may be unable to distinguish between cases where the server exerted insufficient effort and cases where the server kept its advertised effort levels but the end-to-end conditions in the network were sufficiently adverse. We propose a principal-agent model for hidden action that gives server peers sufficient incentives to meet their advertised effort levels, without client peers having to decide for each transaction whether the outcome was due to server behavior or network conditions. This allows peers to draft contracts that provide incentives for truthful revelation of QoS capabilities, and to have predictable transaction quality. We then exemplify the model for the case of a mesh-based, pull-oriented streaming system with low delay requirements. For this case, we show how to estimate the model parameters a function of the prevailing network conditions, and how to enforce contract fulfillment through a reciprocative strategy.

In this paper we introduce the concepts of Network Planes and Parallel Internets, with the objective of designing and implementing a lightweight solution for viable end-to-end QoS provisioning. The proposed solution can be deployed with very small incremental additions to the existing best-effort Internet. Through Network Plane engineering and interconnection, mainly by means of intra- and inter-domain routing differentiation, end-to-end service differentiation across the Internet can be achieved.

IP Differentiated Services is widely seen as the framework to provide quality of service (QoS) in the Internet in a scalable fashion. However many issues have still not been fully addressed, such as: the way per-hop behaviours can be combined to provide end-to-end services; the specification of admission control and resource reservation mechanisms; and the role of management plane functionality and its integration with the control and data planes. This paper presents the service management aspects of an integrated control and management architecture for supporting end-to-end QoS-based IP services in next generation networks. It introduces a two-phased approach for service negotiation, namely, service subscription followed by service invocation, and describes the interworking between service and resource management based on the concept of a resource provisioning cycle.

As the Internet evolves, a key consideration is support for services with guaranteed quality of service (QoS). The proposed differentiated services (DiffServ) framework, which supports aggregate traffic classes, is seen as the key technology to achieve this. DiffServ currently concentrates on control/data plane mechanisms to support QoS but also recognises the need for management plane aspects through the bandwidth broker (BB). In this paper we propose a model and architectural framework for supporting end-to-end QoS in the Internet through a combination of both management and control/data plane aspects. Within the network we consider control mechanisms for traffic engineering (TE) based both on explicitly routed paths and on pure node-by-node layer 3 routing. Management aspects include customer interfacing for service level specification (SLS) negotiation, network dimensioning, traffic forecasting and dynamic resource and routing management. All these are policy-driven in order to allow for the specification of high-level management directives. Many of the functional blocks of our architectural model are also features of BBs, the main difference being that a BB is seen as driven purely by customer requests whereas, in our approach, TE functions are continually aiming at optimising the network configuration and its performance. As such, we substantiate the notion of the BB and propose an integrated management and control architecture that will allow providers to offer both qualitative and quantitative QoS-based services while optimising the use of underlying network resources.

This paper addresses a number of issues related to the evolution of the TMN framework in terms of the associated interface technology. A review of the management model is followed by an analysis of the challenges raised by emerging distributed object architectures which may result in highly distributed management systems; from these, fundamental requirements for CORBA-based TMN agent architectures are derived. The paper describes two approaches for realising CORBA-based management agents that attempt to fulfil these requirements. Both approaches have been validated through implementation while operational experiences with the two approaches are presented.

This paper proposes a hierarchical approach to the routing issue in ATM-based broadband multi-service networks. We show how distributed route selection algorithms embedded within network nodes may be managed by a TMN system, by virtue of a management service called load balancing, to increase their adaptivity to network wide conditions and therefore their effectiveness. The paper elaborates on the issues involved, presents specific algorithms and a TMN-compliant management architecture. The proposed management service influences the routing decisions by conveying network-wide information and contributes to the network load balancing by directing the routing algorithms to route traffic to the least congested network areas. Results regarding the effect of the proposed management system on the network operation are also presented.