Mobile Edge Computing (MEC) enables both to prolong the battery life of mobile devices and support the execution of computationally intensive applications at the edge. This can be achieved by offloading these tasks to a server deployed near the base station and/or by directly caching them. Previous works focus on only one of these two strategies or formulate optimization problems that are hard to solve and propose a suboptimal solution. In this paper, we propose a linear model for the joint task caching and offloading optimization problem. Moreover, we present two efficient heuristics which provide close-to-optimal results in terms of energy efficiency with a low execution time. We further prove that the offloading subproblem can be solved with an optimal algorithm. Finally, we demonstrate the performance and scalability of our propositions by extensive simulations on a large number of $10^{5}$ mobile devices

Video streaming dominates the Internet traffic. Assessing the carbon footprint of video streaming has received recently a significant attention with a number of models proposed to associate a CO2cost to one hour of streaming. In this work, we compare the modeling assumptions and computation methods used by five recent works to inform the debate. Indeed, initial results can be at odds, with up to one order of magnitude difference in the estimates. Our contributions are: (i) we relate the difference in the results primarily to the perimeter of the study, e.g. including production cost or not, (ii) we question some of the modeling assumptions made using a real deployment of a streaming server in a controlled environment with up to 2000 clients and (iii) we propose a technique to reconcile the models and obtain a CO2estimate in between 60 and $\mathbf{1 4 0}$ grams when considering the average worldwide carbon intensity of electricity.

Abstract Modern 5G networks promise more bandwidth, less delay and more flexibility for an ever increasing number of users and applications, with Software Defined Networking, Network Function Virtualization and Network Slicing as key enablers. Within that context, efficiently provisioning the network and cloud resources of a wide variety of applications with dynamic user demand is a real challenge. We study here the network slice reconfiguration problem. Reconfiguring network slices from time to time reduces network operational costs and increases the number of slices that can be managed within the network. However, this affect the Quality of Service of users during the reconfiguration step. To solve this issue, we study solutions implementing a make-before-break scheme. We propose new models and scalable algorithms (relying on column generation techniques) that solve large data instances in few seconds.

Modern 5G networks promise more bandwidth, less delay, and more flexibility for an ever increasing number of users and applications, with Software Defined Networking, Network Function Virtualization, and Network Slicing as key enablers. Within that context, efficiently provisioning network and cloud resources of a wide variety of applications with dynamic users' demands is a real challenge. In this work, we consider the problem of network slice reconfiguration. Reconfiguring from time to time network slices allows to reduce the network operational costs and to increase the number of slices that can be managed within the network. However, it impacts users' Quality of Service during the reconfiguration step. To solve this issue, we study solutions implementing a make-before-break scheme. We propose new models and scalable algorithms (relying on column generation techniques) that solve large data instances in few seconds.

Network Functions Virtualization (NFV) enables the complete decoupling of network functions from proprietary appliances and runs them as software applications on general- purpose servers. Service Function Chains (SFC) are paths with an ordered sequence of network functions that have to be processed. In this paper, we consider the problem of reconfiguring SFCs with the goal of bringing the network from a sub-optimal to an optimal operational state. We propose optimization models based on the make-before-break mechanism, in which a new SFC is set up before the old one is torn down. Our method takes into consideration the chaining requirements of the flows and scales well with the number of nodes in the network. We show that, with our approach, the network operational cost defined in terms of both bandwidth and installed network function costs can be reduced and a higher acceptance rate can be achieved.

Software-defined Network (SDN) is a new networking paradigm enabling innovation through network programmability. Over past few years, many applications have been built using SDN such as server load balancing, virtual-machine migration, traffic engineering and access control. In this paper, we focus on using SDN for energy-aware routing (EAR). Since traffic load has a small influence on the power consumption of routers, EAR allows putting unused links into sleep mode to save energy. SDN can collect traffic matrix and then computes routing solutions satisfying QoS while being minimal in energy consumption. However, prior works on EAR have assumed that the SDN forwarding table switch can hold an infinite number of rules. In practice, this assumption does not hold since such flow tables are implemented in Ternary Content Addressable Memory (TCAM) which is expensive and power hungry. We consider the use of wildcard rules to compress the forwarding tables. In this paper, we propose optimization methods to minimize energy consumption for a backbone network while respecting capacity constraints on links and rule space constraints on routers. In details, we present two exact formulations using Integer Linear Program (ILP) and introduce efficient heuristic algorithms. Based on simulations on realistic network topologies, we show that using this smart rule space allocation, it is possible to save almost as much power consumption as the classical EAR approach.

Energy aware routing aims at reducing the energy consumption of ISP networks. The idea is to adapt routing to the traffic load in order to turn off some hardware. However, it implies to make dynamic changes to routing configurations which is almost impossible with legacy protocols. The Software Defined Network (SDN) paradigm bears the promise of allowing a dynamic optimization with its centralized controller. In this work, we propose SENAtoR, an algorithm to enable energy aware routing in a scenario of progressive migration from legacy to SDN hardware. Since in real life, turning off network equipments is a delicate task as it can lead to packet losses, SENAtoR provides also several features to safely enable energy saving services: tunneling for fast rerouting, smooth node disabling and detection of both traffic spikes and link failures. We validate our solution by extensive simulations and by experimentation. We show that SENAtoR can be progressively deployed in a network using the SDN paradigm. It allows to reduce the energy consumption of ISP networks by 5 to 35% depending on the penetration of SDN hardware, while diminishing the packet loss rate compared to legacy protocols.

In order to optimize energy efficiency, network operators try to switch off as many network devices as possible. Recently, there is a trend to introduce content caches as an inherent capacity of network equipment, with the objective of improving the efficiency of content distribution and reducing network congestion. In this work, we study the impact of using in-network caches and content delivery network (CDN) cooperation on an energy efficient routing. We formulate this problem as Energy Efficient Content Distribution; we propose an integer linear program and a heuristic algorithm to solve it. The objective of this problem is to find a feasible routing, so that the total energy consumption of the network is minimized while the constraints given by the demands and the link capacity are satisfied. We exhibit for which range of parameters (size of caches, popularity of content, demand intensity, etc.) it is useful to use caches. Experimental results show that by placing a cache on each backbone router to store the most popular content, along with choosing well the best content provider server for each demand to a CDN, we can save about 20% of power on average in all the backbone networks considered.

Software Defined Networking (SDN) is gaining momentum with the support of major manufacturers. While it brings flexibility in the management of flows within the data center fabric, this flexibility comes at the cost of smaller routing table capacities. In this paper, we investigate compression techniques to reduce the forwarding information base (FIB) of SDN switches. We validate our algorithm, called MINNIE, on a real testbed able to emulate a 20 switches fat tree architecture. We demonstrate that even with a small number of clients, the limit in terms of number of rules is reached if no compression is performed, increasing the delay of all new incoming flows. MINNIE, on the other hand, reduces drastically the number of rules that need to be stored with a limited impact on the packet loss rate. We also evaluate the actual switching and reconfiguration times and the delay introduced by the communications with the controller.

Software-defined Networks (SDN), in particular OpenFlow, is a new networking paradigm enabling innovation through network programmability. Over past few years, many applications have been built using SDN such as server load balancing, virtual-machine migration, traffic engineering and access control. In this paper, we focus on using SDN for energy-aware routing (EAR). Since traffic load has a small influence on power consumption of routers, EAR allows to put unused links into sleep mode to save energy. SDN can collect traffic matrix and then computes routing solutions satisfying QoS while being minimal in energy consumption. However, prior works on EAR have assumed that the table of OpenFlow switch can hold an infinite number of rules. In practice, this assumption does not hold since the flow table is implemented with Ternary Content Addressable Memory (TCAM) which is expensive and power-hungry. In this paper, we propose an optimization method to minimize energy consumption for a backbone network while respecting capacity constraints on links and rule space constraints on routers. In details, we present an exact formulation using Integer Linear Program (ILP) and introduce efficient greedy heuristic algorithm. Based on simulations, we show that using this smart rule space allocation, it is possible to save almost as much power consumption as the classical EAR approach.

To optimize energy efficiency in network, operators try to switch off as many network devices as possible. Recently, there is a trend to introduce content caches as an inherent capacity of network equipment, with the objective of improving the efficiency of content distribution and reducing network congestion. In this work, we study the impact of using in-network caches and content delivery network (CDN) cooperation on an energy-efficient routing. We formulate this problem as Energy Efficient Content Distribution. The objective is to find a feasible routing, so that the total energy consumption of the network is minimized subject to satisfying all the demands and link capacity. We exhibit the range of parameters (size of caches, popularity of content, demand intensity, etc.) for which caches are useful. Experimental results show that by placing a cache on each backbone router to store the most popular content, along with well choosing the best content provider server for each demand to a CDN, we can save a total up to 23% of power in the backbone, while 16% can be gained solely thanks to caches.

Traditional IP multicast has been proposed in order to manage group communications over the Internet in a bandwidth efficient manner. Although this proposition has been well studied, there are still some problems for its deployment. In this paper, we propose a new algorithm mQMA that deals with two important problems of traditional IP multicast, i.e., multicast forwarding state scalability and multi-constrained QoS routing. The algorithm mQMA builds few trees and maintains few forwarding states for the groups thanks to the technique of multicast tree aggregation, which allows several groups to share the same delivery tree. Moreover, the algorithm mQMA builds trees satisfying multiple QoS constraints. We show, trough extensive simulations, that mQMA leverages the same QoS performances as Mamcra which is the main multi-constrained multicast routing algorithm. Moreover, mQMA reduces dramatically the number of trees to be maintained.

Multicast tree aggregation is an efficient proposition that can solve the multicast forwarding state scalability problem. Existing works on tree aggregation have focused on developing and simulating protocols that build trees dynamically. However, the underlying problem of the impact of the tree construction algorithm on the performance of the protocols remains untouched. In this paper, we propose a study on the number of trees that need to be configured in a domain depending on the tree construction algorithm. We ran extensive simulations on several real domains and with different tree construction algorithms. Our results show that for a given set of multicast groups, even when this set includes all the possible groups, the number of trees that need to be configured is small. This allows a network administrator to configure off-line all these trees in order to maintain a stable set of trees and to have knowledge of the routes used by the multicast packets. Knowing the set of all the possible trees is also useful to determine the best subset to configure and to give an upper bound of the number of different trees.

This paper gives us several perspectives of research. Indeed, we can think of configuring a set of multicast trees for a given domain. The number of multicast forwarding states needed to be stored is rather small as shown during the simulations. This set of trees can be rather stable and the routers do not need to configure others forwarding states for new multicast groups except in case of failures where a reconfiguration may be done. This allows to think of a distributed protocol where the entities responsible of the aggregation will not need to exchange messages in order to aggregate new groups.