Luca G. Gianoli

Energy Management Through Optimized Routing and Device Powering for Greener Communication Networks

Recent data confirm that the power consumption of the information and communications technologies (ICT) and of the Internet itself can no longer be ignored, considering the increasing pervasiveness and the importance of the sector on productivity and economic growth. Although the traffic load of communication networks varies greatly over time and rarely reaches capacity limits, its energy consumption is almost constant. Based on this observation, energy management strategies are being considered with the goal of minimizing the energy consumption, so that consumption becomes proportional to the traffic load either at the individual-device level or for the whole network. The focus of this paper is to minimize the energy consumption of the network through a management strategy that selectively switches off devices according to the traffic level. We consider a set of traffic scenarios and jointly optimize their energy consumption assuming a per-flow routing. We propose a traffic engineering mathematical programming formulation based on integer linear programming that includes constraints on the changes of the device states and routing paths to limit the impact on quality of service and the signaling overhead. We show a set of numerical results obtained using the energy consumption of real routers and study the impact of the different parameters and constraints on the optimal energy management strategy. We also present heuristic results to compare the optimal operational planning with online energy management operation .

A MILP-based heuristic for energy-aware traffic engineering with shortest path routing

Internet energy consumption is rapidly becoming an issue due to the exponential traffic growth and the rapid expansion of communication infrastructures worldwide. We address the problem of energy-aware intra-domain traffic engineering in networks operated with a shortest path routing protocol. We consider the problem of switching off (putting in sleeping mode) network elements (links and routers) and of adjusting the link weights so as to minimize the energy consumption as well as maximizing a measure of effectiveness of the routing weight configuration. We propose a three-phase MILP-based heuristic for tackling this multi-objective problem with priority (first minimize the energy consumption and then the overall cost of link utilization), which exploits the IGPWO heuristic proposed for optimizing the link weights so as to minimize the total cost of link utilization. For comparison purposes, we also developed a greedy randomized search procedure with path-relinking. The computational results for four real network topologies and different types of traffic matrices show that it is possible to switch off a substantial number of core nodes during low and moderate traffic periods, while guaranteeing the same point-to-point service quality and moderately increasing the network total cost of link utilization.

Network Optimization Problems Subject to Max-Min Fair Flow Allocation

We propose a novel way to consider the max-min fairness (MMF) paradigm in traffic engineering. Since MMF appears as a reference model for a fair capacity allocation when the traffic flows are elastic and rates are adapted based on resource availability, we consider it as a requirement due to the way resources are shared by the distributed rate control scheme (like that of the transport protocol), rather than the routing objective. In particular, we define the traffic engineering problem where, given a network topology with link capacities and a set of elastic traffic demands to route, we must select a single path for each demand so as to maximize a network utility function, assuming an MMF bandwidth allocation. We propose a compact mixed-integer linear programming formulation as well as a restricted path formulation. We show with computational experiments that the exact formulation can be solved in a reasonable amount of computing time for medium-size networks and that the restricted path model provides solutions of comparable quality much faster.

A robust optimization approach for energy-aware routing in MPLS networks

A robust multi-period model is proposed to minimize the energy consumption of IP networks, while guaranteeing the satisfaction of uncertain traffic demands. Energy savings are achieved by putting into sleep mode cards and chassis. The study of the solution robustness shows that there is a trade-off between energy consumption and the solutions conservatism degree. The model allows this trade-off to be tuned by simply modifying a single parameter per link. The multi-period optimization is constrained by inter-period limitations necessary to guarantee network stability. Both, exact and heuristic methods are proposed. Results show that up to 60% of the energy savings can be achieved for realistic test scenarios in networks operated with flow-based routing protocols (i.e. MPLS) and with a good level of robustness to traffic variations.

Energy-aware multiperiod traffic engineering with flow-based routing

We propose a multi-period model to minimize the energy consumption of IP networks while guaranteeing the satisfaction of all the traffic demands. Energy savings are achieved by putting into sleep mode cards and chassis. The multi-period optimization is constrained by inter-period limitations necessary to guarantee the stability of the networks. Both exact and heuristic solutions are proposed. Results show that up to 50% of the energy savings can be achieved for realistic test scenarios in networks operated with flow-based routing protocols (i.e. MPLS).

On single-path network routing subject to max-min fair flow allocation

Abstract Fair allocation of flows in multicommodity networks has been attracting a growing attention. In Max-Min Fair (MMF) flow allocation, not only the flow of the commodity with the smallest allocation is maximized but also, in turn, the second smallest, the third smallest, and so on. Since the MMF paradigm allows to approximate the TCP flow allocation when the routing paths are given and the flows are elastic, we address the network routing problem where, given a graph with arc capacities and a set of origin-destination pairs with unknown demands, we must route each commodity over a single path so as to maximize the throughput, subject to the constraint that the flows are allocated according to the MMF principle. After discussing two properties of the problem, we describe a column generation based heuristic and report some computational results.

Energy-aware traffic engineering with elastic demands and MMF bandwidth allocation

In recent years, there has been a remarkable growth of the Internet energy consumption, which is expected to persist in the future at an even higher pace. At the same time the network access capacity of individual subscribers is rapidly reaching values high enough to move the traffic bottleneck from the access network to the core network in most scenarios. This will soon make the elastic nature of traffic an important aspect of network resource management and will require a redesign of the energy-aware traffic engineering techniques so far based on inelastic traffic demands. We propose a novel optimization approach to select a routing path for each elastic traffic demand and decide which routers and links to put to sleep so as to maximize a network utility measure depending on the traffic demand rates, while satisfying a constraint on the total energy consumption. Bandwidth is allocated to each elastic demand according to the Max-Min Fairness (MMF) paradigm, which approximates the resource allocation of the transport layer.

A Practicable Robust Counterpart Formulation for Decomposable Functions: A Network Congestion Case Study

Robust optimization is a powerful means to handle optimization problems where there is a set of parameters that are uncertain. The effectiveness of the method is especially noticeable when these parameters are only known to lie inside some uncertainty region. Unfortunately, there are important computational considerations that have prevented the methodology from being fully adopted in fields of practice where the cost function that needs to be robustified is nonlinear with respect to such parameters. In this paper, we propose a new robust optimization formulation that circumvent the computational burden in problems where the cost decomposes as the sum of costs that each involve a single decision variable. This is done by exploiting the fact that in this formulation the worst-case cost function can be expressed as a convex combination between a nominal and an upper-bounding cost function. One can still control the conservatism of the robust solution by adjusting how many terms of this total cost can simult...