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The emerging paradigm of Software Defined Network (SDN) and virtualization technology promises an efficient solution for network providers to deploy services. Adopting them not only facilitates network management but also helps reduce the cost of maintaining network infrastructure. However, despite these advantages, there are still obstacles that must be overcome before SDN and virtualization can advance to reality in industrial deployments. In this paper, we focus on two well-researched issues, namely controller-switch assignment and Virtual Network Function (VNF) placement. Unlike prior works, our purpose is to jointly solve these two problems, accounting for the complex and counter-intuitive manner they are related to each other. We present a service chain aware framework (SACO) that enables the controller-switch association in a multi-controller network regarding the relationship of switches via their connected VNFs that implement service components of the chain. We also propose a model and formulate the joint optimization problem of dynamic controller-switch mapping and VNF allocation. We apply the Lyapunov optimization framework to transform a long-term optimization problem into a series of real-time problem and employ the Markov approximation method to find a near-optimal solution. Simulation results show that our service chain aware approach improves the system cost up to 10 ~ 43% compared to the state-of-the-art solutions.

Software in IoT devices needs to be improved regularly to adapt security issues and new user requirements. In advanced IoT networks, devices employ the component-based software architecture in which components can be updated at run-time, such devices can download software components from neighbors, enabling fast distribution of updates in the entire network. One of the most energy consuming operations in the update process is flash re-writing in which the order of re-writing components into the flash memory is decisive for energy consumption. In this paper, we propose a mechanism that schedules updates in an entire IoT network to minimize the energy consumption, while satisfying the deadline constraint for updating all the devices. We mathematically formulate the problem of energy efficient update scheduling as an optimization problem with a novel energy model of the update process, then propose an algorithm to approximate the optimal schedule for updating all devices in the network. We examine the proposed algorithm in three different network instances including a tree, a partial mesh and a full mesh topology. Simulation results illustrate that our algorithm can obtain a near optimum which is, in the best case, only 3.2% different from the minimum.

Smart home gateway has to process different types of network traffic generated from several devices in an optimal way to meet their QoS requirements. However, the fluctuation of network traffic distributions results in packets concurrency. Current QoS-aware scheduling methods in the smart home networks do not consider concurrent traffic in their scheduling solutions. This paper presents an analytic model for a QoS-aware scheduling optimization of concurrent smart home network traffic with mixed arrival distributions and using probabilistic queuing disciplines. We formulate a hybrid QoS-aware scheduling problem for concurrent traffics in smart home network, and propose an innovative queuing design based on the auction economic model of game theory to provide a fair multiple access over different communication channels/ports. Our experiments show the proposed solution achieves an improvement of 14% of packets that meet their required delay and 57% of delay for different number of concurrent flows in the system.

Smart home gateways have to forward multi-sourced network traffic generated with different distributions and with different Quality of Service (QoS) requirements. Most of the current QoS-aware scheduling methods consider only the conventional priority metrics based on the IP Type of Service (ToS) field to make decision for bandwidth allocation. Such priority-based scheduling methods are not optimal to provide both QoS and QoE (quality of experience) since higher-priority traffic do not necessary require higher stringent delay than lower-priority traffic. To solve the gaps between QoS and QoE, we propose a new queuing model for QoS-level Pair traffic with mixed arrival distributions in Smart Home network (QP-SH) to make a dynamic QoS-aware scheduling decision which meets delay requirements of all traffic while preserves their degrees of criticality. A new metric which combines, the ToS field and the maximum number of packets that can be processed by the system's service during the maximum required delay, is defined. Our experiments show the proposed solution provides an improvement regarding the number of packets that meet their priorities and their maximum delays as well as the mean number of packets in the system.

Adaptive Link Rate (ALR) is widely used to save energy consumption of network by adjusting the link rate according to the carried traffic through a network-level optimization of the flow allocation process. Existing ALR solution is mainly reactive, in which link speed is changed only when new traffic demand is requested. Also, they focus on energy consumption, and do not consider the cost of changes in the network (e.g., change in traffic routes, and link rates). Once bandwidth has been allocated for a demand, the link rate remains constant during the entire session. Therefore, this solution may result in sub-optimal schemes and requires multiple re-optimizations as traffic flows are fluctuating during the session, hence reducing the overall network performance. In this paper, we improve the ALR with a multiple-step-ahead method to optimize link rates based on forecasting traffic demand predictively. We formulate the proposed Predictive ALR (PALR) as an Integer Linear Programming (ILP) model and then design a heuristic simulated annealing (SA) -based algorithm to solve it. Our experimental results show our approach provides energy saving while it decreases on average 18% of link state transition and 11% of the flow reroutings compared to the original ALR.

Link rate adaptation is an effective means to save energy consumption of network elements by adjusting the link rate according to the carried traffic through a network-level optimization of the flow allocation process. Unfortunately, current adaptation approaches are mainly reactive, in which link speed is changed only when new traffic demand is requested. Once bandwidth has been allocated for a demand, link rate remains constant during the entire session. This approach may result in sub-optimal energy efficiency schemes and requires multiple re-optimizations as traffic flows are fluctuating during the session, hence reducing the overall network performance. In this paper, we propose a multiple-step-ahead method to predictively optimize link rates based on forecasting traffic demand. We formulate the link adaptive energy efficiency as a MIP model and propose a heuristic simulated annealing algorithm to solve it. Our experimental results show our approach provides energy saving while it significantly decreases the number of re-optimizations in the energy-aware routing.

The last few years have witnessed a significant increase in the deployment of heterogeneous Internet of Things (IoT) networks. IoT devices send data with different requirements such as tolerated delay and data rates. Emerging multi-interface IoT devices bring the flexibility of connecting to multiple heterogeneous access networks, which thus improves the network capacity. However, each network interface has its own constraints in terms of network coverage, capacity, packet loss rates, etc. An efficient utilization of the available multiple interfaces in IoT gateways would improve the network performance. Therefore, it is crucial to design a flow assignment mechanism to select the appropriate interface that best satisfies the flow’s requirements and maximizes the amount of data accepted by an IoT gateway. In this work, we model and formulate the optimized flow assignment problem (OFAP) in a multi-interface IoT gateway. Then, we develop two heuristic algorithms to find a feasible solution for OFAP. The first algorithm is based on the greedy approach and the second uses dynamic programming to assign flows to interfaces. We provide simulation results that show the effectiveness of our algorithms.

This paper investigates an efficient placing mechanism for placing Virtual Network Function (VNF) on the cloud networks to enable the construction of Internet of Things (IoT) service chains with minimal resource usage cost. In particular, we propose a model taking IoT network infrastructure into account. Such the network composed of numerous sensors with constrained resource, dynamic connectivity toward multi-homing IoT gateways makes the problem of optimally placing VNFs with expected Quality of Service (QoS) more challenging and has not been considered in prior works. From the model, we formulate a non-convex Integer Programming (IP) placement problem and devise a batching Markov approximation placement (BMAP) algorithm to find the optimal solution. Simulation results show that the proposed approach improves the cost compared to those that do not consider the IoT network.

Accelerated Software Defined Networking (SDN) adoption makes SDN paradigm emerging in the state of the art. Especially, the combination of SDN and network functions virtualization (NFV) becomes a promising trend in deploying virtual network services for network operators. Although SDN can decouple networks into the control plane and the data plane to obtain flexible operation and programmability, there are many open issues that need to be addressed for SDN deployments, such as i) where to place SDN controllers in a given network, ii) how to assign connections from controllers to switches in terms of satisfying multiple objectives (e.g., resource utilization, failure, quality of services, etc.). In this work, we focus on the efficient assignment between SDN controllers and switches to guarantee a low operational cost, quality of services, and fault tolerance, in which the complexity of virtual links in network services, an omitted factor in current works, is considered and addressed. We formulate an optimization problem for dynamic controller/switch mapping (DCSM) in the network virtualization. We then proposed approximation algorithms in terms of relaxing the binary variables to solve the NP-hard problem, DCSM, in both centralized and distributed mechanisms. We also create various simulation schemes to evaluate our methods where they outperform state-of-the-art methods.

Summary The environmental impacts of data centers that provide information and communication technologies (ICTs) services are strongly related to electricity generation. With the increasing use of ICT, many data centers are expected to be built, causing more absolute impacts on the environment. Given that electricity distribution networks are very complex and dynamic systems, an environmental evaluation of future data centers is uncertain. This study proposes a new approach to investigate the consequences of future data center deployment in Canada and optimize this deployment based on the Energy 2020 technoeconomic model in combination with life cycle assessment methodology. The method determines specific electricity sources that will power the future Canadian data centers and computes related environmental impacts based on several indicators. In case-study scenarios, the largest deployment of data centers leads to the smallest impact per megawatt of data centers for all of the environmental indicators. It is found that an increase in power demand by data centers would lead to a reduction in electricity exports to the United States, driving the United States to generate more electricity to meet its energy demand. Given that electricity generation in the United States is more polluting than in Canada, the deployment of data centers in Canada is indirectly linked to an increase in overall environmental impacts. However, though an optimal solution should be found to mitigate global greenhouse gas emissions, it is not clear whether the environmental burden related to U.S. electricity generation should be attributed to the Canadian data centers.

Providing multitenant, multiaccess, and multiservice solutions in communities is a key enabler to sustain innovations and economic development in society. This article investigates a solution for rearchitecting a telecommunications company's (telco's) central office to offer services in a smart community, enabled by virtual network function elements running on a smart edge. At the same time, these elements manage a multiaccess underlying infrastructure and deploy telco services with minimal resource consumption. This solution has successfully been implemented in a living lab model the authors built within a Canadian university, providing various Internet of Things (IoT) services such as smart grid monitoring and public safety. The virtual smart edge flexibly routes traffic, aggregates, and splits flows across a heterogeneous network to achieve network operational efficiency, as well as optimizes computing resource allocation for last-mile IoT services hosted close to the end user. Instead of reducing resource consumption in a single home as in the traditional approach, this solution minimizes a community's global consumption.

In this paper, we consider a multi-user MEC wireless network in which multiple mobile devices can associate and perform computation offloading via wireless channels to MEC servers attached to the base stations. The decision whether the computation task is executed locally at the user device or to be offloaded for MEC server execution should be adaptive to the time-varying network dynamics. Taking into account the dynamic of the environment, we propose a deep reinforcement learning (DRL) based approach to solve the formulated nonconvex problem of minimizing computation cost in terms of total delay. However, real-world networks tend to have a large number of users and MEC servers involving large numbers of different actions, where evaluating the combination of every possible action becomes impractical. Therefore, conventional DRL methods may be difficult or even impossible to directly apply to the proposed model. Based on the recursive decomposition of the action space available to each state, we propose a DRL-based algorithm for joint server selection, cooperative offloading, and handover in a multi-access edge wireless network. Numerical results show that the proposed DRL based algorithm significantly outperforms the traditional Q-learning method and local computation in terms of task success rate and total delay.

Even though Internet of Things (IoT) applications are proliferating exponentially in recent years, there still exist several resource allocation problems in IoT application implementations with the traditional cloud computing. Because the number of IoT applications is increasing day by day with strict latency requirements. They become a burden to the cloud/datacenter platform in order to fulfill a huge number of real-time IoT services. As the emerging solution for latency requirements, the edges can bring processing power closer to datasource - the "Thing" in IoT. However, with the resource limitation at edges, the efficient resource allocation is a major concern to improve the performance of edge networks. In this work, we introduce the optimization model, named the Service-Oriented Resource Allocation (SORA) for IoT applications, which dynamically consolidates the system so as to reduce the system cost while improving the available resource at the edges. Unfortunately, SORA is unable to solve in polynomial time because it is NP-hard. Unlike the prior works that try to find solutions based on heuristic algorithms, we propose approximation algorithms to solve SORA with a near-optimal solution. Finally, we evaluate our model by providing several simulation cases, in which our proposed mechanisms show outstanding outcomes in terms of solving SORA and resource utilization.

Edge computing has recently emerged as a promising solution that provides services with stringent requirements, like extremely low latency and bandwidth costs, for a class of new 5G applications. However, due to their limited computing capacity, edge data centers are not able to afford a large number of user requests. A combined scheme of edge and cloud is therefore required to provide extended cloud computing service at the last-mile close to end-users. Key challenges faced by this complex system which includes edge, core, and public cloud is optimizing resource allocation to provide desired QoS levels and meet data center capacity constraints. In this paper, we present a completed formulation of the optimized resource allocation problem in the edge-cloud environment, and then propose an algorithmic solution to the problem. Experimental simulations show that our solution is cost-effective with an improvement in terms of efficiency compared to prior work

Closeness centrality is an substantial metric used in large-scale network analysis, in particular social networks. Determining closeness centrality from a vertex to all other vertices in the graph is a high complexity problem. Prior work has a strong focuses on the algorithmic aspect of the problem, and little attention has been paid to the definition of the data structure supporting the implementation of the algorithm. Thus, we present in this paper an efficient algorithm to compute the closeness centrality of all nodes in a social network. Our algorithm is based on (i) an appropriate data structure for increasing the cache hit rate, and then reducing amount of time accessing the main memory for the graph data, and (ii) an efficient and parallel complete BFS search to reduce the execution time. We tested performance of our algorithm, namely BigGraph, with five different real-world social networks and compare the performance to that of current approaches including TeexGraph and NetworKit. Experiment results show that BigGraph is faster than TeexGraph and NetworKit 1.27-2.12 and 14.78-68.21 times, respectively.

Environmental footprint resulting from datacenters activities can be reduced by both energy efficiency and renewable energy in a complementary fashion thanks to cloud computing paradigms. In a cloud hosting multi-tenant applications, virtual service providers can be provided with real-time recommendation techniques to allocate their virtual resources in edge, core, or access layers in an optimal way to minimize costs and footprint. Such a dynamic technique requires a flexible and optimized networking scheme to enable elastic virtual tenants spanning multiple physical nodes. In this paper, we investigate an environment-aware paradigm for virtual slices that allows improving energy efficiency and dealing with intermittent renewable power sources. A virtual slice consists of optimal flows assigned to virtual machines (VMs) in a virtual data center taking into account traffic requirements, VM locations, physical network capacity, and renewable energy availability. Considering various cloud consolidation schemes, we formulate and then propose an optimal solution for virtual slice assignment problem. Simulations on the GSN showed that the proposed model achieves better performance than the existing methods with respect to network footprint reductions.

In order to satisfy the high bandwidth and performance demands of applications, host servers are built with multiple network interfaces, and a data center network consists of multiple redundant links. It is important to make efficient use of all the available network capacity, using multiple physical paths whenever possible, but traditional forwarding mechanisms using a single path are not able to take advantages of available multiple physical paths. The state-of-the-art MPTCP (Multipath Transmission Control Protocol) solution uses multiple randomly selected paths, but cannot give total aggregated capacity. Moreover, it works as a TCP process, and so does not support other protocols like UDP. This paper presents an alternative solution using adaptive multipath routing in a Layer-2 network with static (capacity and latency) metrics, which adapts link and path failures. This solution provides in-network aggregated path capacity to individual flows, as well as scalability and multitenancy, by separating end-station services from the provider's network. The results of deploying a proof-of-concept prototype on a data center testbed, which show the aggregated path capacity per flow, demonstrate an improvement of 14% in the worst bisection bandwidth utilization, compared to the MPTCP with 5 subflows.

Although Internet of Things (IoT) has been growing rapidly in recent years, the deployment of large-scale IoT solutions are facing several challenges, in particular resource optimization. This is due to high requirements of IoT applications which are usually composed of numerous services each of which could be provided by multiple providers with various specifications. Since an IoT service is often micro-services, dynamically opting appropriate micro-services to guarantee the quality of delivered services to end-users is challenging because conventional network paradigm was not designed to support removal or insertion of modular services on the fly. In this paper, we investigate a cloud-based IoT service orchestration architecture to facilitate the IoT service deployment and propose algorithms to optimize service chaining process. Experimental results show our algorithms effectively orchestrating microservices by reducing total service cost up to 36% while maintaining real-time requirement.

Software-defined networking is an emerging method for providing flexible and scalable network connectivity in both intra- and inter-datacenter interconnects with regard to various requirements, including traffic-awareness, quality of service, energy efficiency, and renewable-power intermittency. This article investigates issues and solutions for the software-defined planning of a virtual slice that involves multiple virtual machines with interdependent constraints spanning a network of distributed datacenters. A flexible and optimized virtual-slice assignment that considers server consolidation and multipath forwarding can address large-scale cloud computing services. Simulations on large-scale testbeds, such as the GreenStar Network and the Green Telco Cloud, showed that the proposed model achieves good performance in terms of flexibility and energy efficiency.

Current generation routers are being replaced by scalable next generation routers in order to satisfy the growing demand of Internet traffic in core networks. The next generation routers, on the hardware side, are provided with a very large switching capacity, high speed interfaces and additional computing and memory resources. There is a need to re-design the router software in a distributed way in order to efficiently exploit advanced hardware features of the next generation routers. One of the key functions of a router is to manage routes obtained from different routing protocols. The current centralized architecture of the routing table manager will likely face difficulties to handle the growth of routing tables and path computation requests, particularly for high traffic-density networks. This paper proposes two distributed IGP route management schemes for routers. The first scheme enhances the scalability at the control card level. The second one uses the parallel processing of the switch fabric in order to increase the handled number of routes by adding more line cards. Scalability evaluation is done for the two distributed schemes and a centralized architecture in terms of CPU cycles used and required memory.

Today's information and communications technology (ICT) services emit an increasing amount of greenhouse gases. Carbon footprint models can enable research into ICT energy efficiency and carbon reduction. The GreenStar Network (GSN) testbed is a prototype wide-area network of data centers powered by renewable energy sources. Through their work developing the GSN, the authors have researched fundamental aspects of green ICT such as virtual infrastructure, unified management of compute, network, power, and climate resources, smart power control, and a carbon assessment protocol.

Infrastructure as a Service (IaaS) is considered a prominent model for IP based service delivery. As grid and cloud computing have become a stringent demand for today's Internet services, IaaS is required for providing services, particularly "private cloud," regardless of physical infrastructure locations. However, enabling IaaS on traditional Internet Service Provider (ISP) network infrastructures is challenging because IaaS requires a high abstraction level of network architectures, protocols, and devices. Network control plane architecture plays therefore an essential role in this transition, particularly with respect to new requirements of scalability, reliability, and flexibility. In this article we review the evolutionary trend of network element control planes from monolithic to distributed architectures according to network growth, and then present a new virtualization oriented architecture that allows infrastructure providers and service providers to achieve service delivery independently and transparently to end users based on virtualized network control planes. As a result, current ISP infrastructures will be able to support new services, such as heavy resource consuming data center applications. We also show how to use network virtualization for providing cloud computing and data center services in a flexible manner on the nationwide CANARIE network infrastructure.

Network Function Virtualization (NFV) technology has emerged as a promising solution to optimize the deployment of network elements in cloud computing environment, both in terms of user Quality of Service (QoS) and resource allocation. To deliver IP Multimedia Subsystem (IMS) advanced services across multiple access networks, a cloud-based model likely improves not only flexibility in network management but also in service invocation. This model is particularly suitable for the inter-network between the IMS and Web Real-Time Communication (WebRTC) domain, which is a natural combination to significantly expand potential end-points of multimedia sessions. However, optimally allocating resource for each component in the system to provide QoS is challenging, and has not been fully considered in prior work. In this paper, we investigate an NFV architecture to enable effective communication between IMS and WebRTC users supporting smart community services, and propose an optimization model to design and allocate resources for such system while ensuring desired QoS level. Experimental results show our algorithms effectively allocate required amounts of resources for virtual components according to real-time increase or decrease in user demand.

The falling trend in the revenue of traditional telephony services has attracted attention to new IP based services. The IP Multimedia System (IMS) is a key architecture which provides the necessary platform for delivery of new multimedia services. However, current implementations of IMS do not offer automatic scalability or elastisity for the growing number of customers. Although the cloud computing paradigm has shown many promising characteristics for web applications, it is still failing to meet the requirements for telecommunication applications. In this paper, we present some related cloud computing patterns and discuss their adaptations for implementation of IMS or other telecommunication systems.

The IP Multimedia Subsystem (IMS) defined by the 3GPP has been mainly developed and deployed by telephony vendors on vendor-specific hardware. Recent advances in Network Function Virtualisation (NFV) technology paved the way for virtualized hardware and telephony function elasticity. As such, Telecom vendors have started to embrace the cloud as a deployment platform, usually selecting a privileged virtualization platform. Operators would like to deploy telecom functionality on their already existing IT cloud platforms. Achieving such flexibility would require the telecom vendors to adopt a software architecture allowing deployment on many cloud platforms or even heterogeneous cloud platforms. We propose a distributed software architecture enabling the deployment of a single software version on multiple cloud platforms thus allowing for a solution-based deployment. We also present a prototype we developed to study the characteristics of this architecture.

Reducing greenhouse gas (GHG) emissions is one of the most challenging research topics in ICT because of people's overwhelming use of electronic devices. Current solutions focus mainly on efficient power consumption at the micro level; few consider large-scale energy-management strategies. The low-carbon, nationwide GreenStar Network in Canada uses network and server virtualization techniques to migrate data center services among network nodes according to renewable energy availability. The network deploys a "follow the sun, follow the wind" optimization policy as a virtual infrastructure-management technique.

One of the main concerns of network operators is that current routers are not scalable in order to meet future traffic requirements on the core Internet taking into account new applications. The next generation routers with petabit switching capacity are being built to serve higher demands. Their processing capability is enhanced by additional memory and computing resources on control and line cards with a very large number of high speed interfaces. However, the current routing software architecture is not able to fully exploit such an advanced hardware platform. This paper proposes a first distributed software architecture of MPLS/LDP targeting the next generation routers. We investigate the ability of offloading components of the current centralized architecture of MPLS/LDP on to line cards in order to share the load between the control and line cards. This allows the signaling to be achieved entirely at the line card level, hence, improving the robustness, scalability and resiliency of the system. Performance evaluation, considering the CPU utilization and the number of exchanged messages, is also presented.

Cloud computing services have recently become a ubiquitous service delivery model, covering a wide range of applications from personal file sharing to being an enterprise data warehouse. Building green data center networks providing cloud computing services is an emerging trend in the Information and Communication Technology (ICT) industry, because of Global Warming and the potential GHG emissions resulting from cloud services. As one of the first worldwide initiatives provisioning ICT services entirely based on renewable energy such as solar, wind and hydroelectricity across Canada and around the world, the GreenStar Network (GSN) was developed to dynamically transport user services to be processed in data centers built in proximity to green energy sources, reducing Greenhouse Gas (GHG) emissions of ICT equipments. Regarding the current approach, which focuses mainly in reducing energy consumption at the micro-level through energy efficiency improvements, the overall energy consumption will eventually increase due to the growing demand from new services and users, resulting in an increase in GHG emissions. Based on the cooperation between Mantychore FP7 and the GSN, our approach is, therefore, much broader and more appropriate because it focuses on GHG emission reductions at the macro-level. This article presents some outcomes of our implementation of such a network model, which spans multiple green nodes in Canada, Europe and the USA. The network provides cloud computing services based on dynamic provision of network slices through relocation of virtual data centers.

In recent years, the exponential growth of Internet users with increased bandwidth requirements has led to the emergence of the next generation of IP routers. Distributed architecture is one of the promising trends providing petabit routers with a large switching capacity and high-speed interfaces. Distributed routers are designed with an optical switch fabric interconnecting line and control cards. Computing and memory resources are available on both control and line cards to perform routing and forwarding tasks. This new hardware architecture is not efficiently utilized by the traditional software models where a single control card is responsible for all routing and management operations. The routing table manager plays an extremely critical role by managing routing information and in particular, a forwarding information table. This article presents a distributed architecture set up around a distributed and scalable routing table manager. This architecture also comes provides improvements in robustness and resiliency. The proposed architecture is based on a sharing mechanism between control and line cards and is able to meet the scalability requirements for route computations, notifications, and advertisements. A comparative scalability evaluation is made between distributed and centralized architectures in terms of required memory and computing resources.

We investigate an intrusion attack called automated intrusion which persists the control of victim system for long time. This attack combines intrusion techniques, encodings schemes and social engineering methods to enhance hacking capability. Experiments in local and wide-area networks show how this type of attack can penetrate a computer system and take control over multiple OSes. We then propose few countermeasures in order to protect networks and keep users informed of the attacks.

As one of the first worldwide initiatives provisioning ICT (Information and Communication Technologies) services entirely based on renewable energy such as solar, wind and hydroelectricity across Canada and around the world, the GreenStar Network (GSN) is developed to dynamically transport user services to be processed in data centers built in proximity to green energy sources, reducing GHG (Greenhouse Gas) emissions of ICT equipments. Regarding the current approach, which focuses mainly in reducing energy consumption at the micro-level through energy efficiency improvements, the overall energy consumption will eventually increase due to the growing demand from new services and users, resulting in an increase in GHG emissions. Based on the cooperation between Mantychore FP7 and the GSN, our approach is, therefore, much broader and more appropriate because it focuses on GHG emission reductions at the macro-level. Whilst energy efficiency techniques are still encouraged at low-end client equipments, the heaviest computing services are dedicated to virtual data centers powered completely by green energy from a large abundant reserve of natural resources, particularly in northern countries.

The routing engine is the essential part of a router. As a software component, the routing engine is used to control the router activities and to build the data forwarding table. Along with the hardware evolution, several software generations for routing engines have been experienced. In this paper, we first review the different routing engine architectures over the time and their main components. In some previous papers, we have investigated new directions for routing engine development for next generation routers, where specific distributed architectures have been proposed for several routing protocols. Taking into account the increasing traffic in the core networks, we next propose a framework integrating all specific distributed routing protocol architectures in order to significantly improve the scalability of the next generation routers. Bottlenecks are reduced, resulting in improving both the overall performance and the resiliency in the presence of faults. The scalability of the proposed distributed framework is estimated with respect to each routing protocol.

Routers, whose main tasks include forwarding and best route computing, have undergone several design architectures in the last years in order to deal with the explosion of traffic in the Internet. Next generation routers are made with enhanced memory capacity and computing resources, distributed across a very high speed switching fabric. However, the current routing software products, particularly those built by thirdparty developers, are not able to fully exploit the new hardware platform of these routers due to their centralized architectures with a solely control card being responsible for all routing and management tasks. This paper proposes a first fully distributed software architecture of the RSVP-TE module for next generation routers in order to scale with the stringent requirements of the MPLS signaling. The components of the current centralized architecture of RSVP-TE module are offloaded onto line cards in order to share the load between the control card and the line cards. Such a distributed architecture meets the scalability requirements, e.g., high volume of traffic of next generation routers. The robustness and resiliency are also improved. We propose an estimation of the scalability of the distributed RSVP-TE architecture based on the CPU utilization.

Current Internet is operated based on interdomain routers executing interdomain routing protocols interconnecting nodes of various autonomous systems. Due to new requirements of traffic in the core Internet, next generation routers with enhanced memory capacity and computing resources, distributed across a very high speed switching fabric are developed in order to replace the currently used interdomain routers. An essential requirement for these routers is to redesign the current routing and signaling software modules, traditionally with centralized architectures, which do not scale in order to fully exploit such an advanced router hardware architecture. This paper discusses a first distributed BGP architecture for next generation routers, aiming at increasing the scalability and resiliency. The proposed architecture is based on a set of BGP processes running on a set of control cards of a router. The distributed processes cooperate in a manner that internally exploits the distributed hardware architecture of next generation router, while maintaining the behavior of a single routing protocol process communicating with its peers in the network. The proposed architecture improves both the overall performance and the resiliency of routers in the presence of faults.

In this paper, we present our research aiming at building a petabit router model for next generation networks. Considering the increasing traffic requirements on the Internet, current gigabit and terabit speed routers will soon not be able to meet user demand. One of the promising trends of router evolution is to build next generation routers with enhanced memory capacity and computing resources, distributed across a very high speed switching fabric. The main limitation of the current routing and signaling software modules, traditionally designed in a centralized manner, is that they do not scale in order to fully exploit such an advanced distributed hardware architecture. This paper discusses an implementation for an control plane for next generation routers integrating several protocol dedicated distributed architectures, aiming at increasing the scalability and resiliency. The proposed architecture distributes the processing functions on router cards, i.e., on both control and line cards. Therefore, it reduces the bottlenecks and improves both the overall performance and the resiliency in the presence of faults. Scalability is estimated with respect to the CPU utilization and memory requirements.

The growing traffic on the core Internet entails new requirements related to scalability and resiliency of the routers. One of the promising trends of router evolution is to build next generation routers with enhanced memory capacity and computing resources, distributed across a very high speed switching fabric. The main limitation of the current routing and signaling software modules, traditionally designed in a centralized manner, is that they do not scale in order to fully exploit such an advanced distributed hardware architecture. This paper discusses an implementation for an OSPF architecture for next generation routers, aiming at increasing the scalability and resiliency. The proposed architecture distributes the OSPF processing functions on router cards, i.e., on both control and line cards. Therefore, it reduces the bottlenecks and improves both the overall performance and the resiliency in the presence of faults. Scalability is estimated with respect to the CPU utilization and memory requirements.

In order to deal with the growing traffic on the Internet while minimizing the router management cost, the next generation routers are gradually replacing the current core routers, which are no longer scalable. They are built with enhanced memory capacity and computing resources, distributed across a very high speed switching fabric. However, the current routing software products, particularly those developed by third-party developers, do not fully exploit the distributed hardware platform of these routers, as they are designed on a centralized software architecture. This paper proposes a distributed software architecture of the MPLS module for the next generation routers. In particular, we investigate the ability to transfer components of the current centralized MPLS architectures on the line cards in order to balance the load between the control card and the line cards. This will improve the robustness, scalability and resiliency of the router. Performance evaluation, in terms of the CPU utilization, is also presented.

Next generation (NG) routers, characterized by high speed interfaces, large switching capacity and petabit packet processing speed have recently been deployed in core networks of world class operators. Based on distributed architectures, these routers are designed with control cards and line cards interconnected by a very high-speed switch fabric, where line cards contain processing and memory resource allowing the sharing of some route processing tasks with control cards. The traditional implementation model of router software, where control cards assume all the processing tasks, is therefore no longer appropriate. In this paper, we propose a distributed model for implementing router software in order to fully exploit the hardware platform of the next router generation, taking into account the additional capacity of line cards. The model corresponds to a distributed architecture with control cards acting as super nodes and line cards acting as peers. It also provides "direct" communication between line cards, allowing them to cooperate in some task processing without going through control cards. Such a model significantly increases the robustness, scalability and availability of routers. We also investigate the proposed distributed model in the context of different protocols supported by a router, such as signaling and routing protocols. Two case studies are presented where we discuss the advantages of the distributed model for OSPF and LDP protocols.

Although many QoS management architectures have been recently introduced with a lot of advanced features, they have never been widely used in the existing applications due to the lack of interoperation between providers and users, or between network operators. One of the main issues is the heterogeneity of QoS information coming from different sources: clients, communication networks, servers, data. etc. In the context of Quality-Driven Delivery (QDD) referring to the ability of a system to deliver data objects while considering the end-users expectations, all components of a distributed multimedia system have to contribute to satisfy users requirements. The mapping activity is therefore essential for dealing with the variety of QoS information of these components. In this paper, we propose an approach aimed at creating QoS mapping rules using statistical data analysis and data mining techniques combined with monitoring tools. The automatic generation of QoS mapping rules allows adapting the QoS management architectures to different environments as well as different classes of users.

The information and communications technologies (ICT) sector is seeking to reduce the electricity consumption of data processing centres. Among the initiatives to improve energy efficiency is the shift to cloud computing technology. Thanks to very favourable geographical conditions, the Canadian energy mix is highly suited to the implementation of data centres, especially in light of the significant potential of renewable energy, which can help to curb greenhouse gas emissions. In the green sustainable Telco cloud (GSTC) project, an efficient cloud computing network would be set up to optimize renewable energy use based on several data centres. This study aimed to develop a temporally differentiated life cycle assessment (LCA) model, adapted to short-term predictions, to provide a regionalized inventory to model electricity generation. Purpose of this model is (i) to calculate more accurately the carbon emissions of ICT systems and (ii) to minimize the daily carbon emissions of the GSTC servers. This paper focuses mainly on the electricity generation modelling during the use phase in the context of the life cycle assessment methodology. Considering the time scale of the model, the difference between the annual fixed average and a shorter period may be highly relevant, in particular when assessing the green house gases (GHG) emissions of a process such as an ICT system, which mainly operates during peak load hours. The time dependent grid mix modelling makes it possible to manage the server load migrations between data centres on an hourly basis. Index Terms—Life cycle assessment, data centre, carbon footprint, dynamic power mix.