Truong Khoa Phan

Optimizing rule placement in software-defined networks for energy-aware routing

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.

Minimization of network power consumption with redundancy elimination

Recently, energy-aware routing has gained increasing popularity in the networking research community. The idea is that traffic demands are aggregated over a subset of the network links, allowing other links to be turned off to save energy. In this paper, we propose GreenRE - a new energy-aware routing model with the support of the new technique of data redundancy elimination (RE) . This technique, enabled within the routers, can identify and remove repeated content from network transfers. Hence, capacity of network links are virtually increased and more traffic demands can be aggregated. Based on our real experiments on Orange Labs platform, we show that performing RE consumes some energy. Thus, while preserving connectivity and QoS, it is important to identify at which routers to enable RE and which links to turn off so that the power consumption of the network is minimized. We model the problem as an Integer Linear Program and propose a greedy heuristic algorithm. Simulations on several network topologies show that GreenRE can gain further 30% of energy savings in comparison with the traditional energy-aware routing model.

Robust Redundancy Elimination for Energy-Aware Routing

Many studies have shown that energy-aware routing (EAR) can significantly reduce energy consumption of a backbone network. Redundancy Elimination (RE) techniques provide a complementary approach to reduce the amount of traffic in the network. In particular, the GreenRE model combines both techniques, offering potentially significant energy savings. We propose a concept for respecting uncertain rates of redundant traffic within the GreenRE model, closing the gap between theoretical modeling and drawn-from life data. To model redundancy rate uncertainty, the robust optimization approach of Bertsimas and Sim (2004) is adapted and the problem is formally defined as mixed integer linear program. An exemplary evaluation of this concept with real-life traffic traces and estimated fluctuations of data redundancy shows that this closer-to-reality model potentially offers significant energy savings in comparison to GreenRE and EAR.

Robust energy-aware routing with redundancy elimination

Many studies in literature have shown that energy-aware routing (EAR) can significantly reduce energy consumption for backbone networks. Also, as an arising concern in networking research area, the protocol-independent traffic redundancy elimination (RE) technique helps to reduce (a.k.a compress) traffic load on backbone network. Motivation from a formulation perspective, we first present an extended model of the classical multi-commodity flow problem with compressible flows. Moreover, our model is robust with fluctuation of traffic demand and compression rate. In details, we allow any set of a predefined size of traffic flows to deviate simultaneously from their nominal volumes or compression rates. As an applicable example, we use this model to combine redundancy elimination and energy-aware routing to increase energy efficiency for a backbone network. Using this extra knowledge on the dynamics of the traffic pattern, we are able to significantly increase energy efficiency for the network. We formally define the problem and model it as a Mixed Integer Linear Program (MILP). We then propose an efficient heuristic algorithm that is suitable for large networks. Simulation results with real traffic traces on Abilene, Geant and Germany50 networks show that our approach allows for 16-28% extra energy savings with respect to the classical EAR model. HighlightsWe present an extended multi-commodity flow problem with compressible traffic flows where uncertainties in both traffic volumes and compression rates are taken into consideration.We apply this extended model into energy-aware routing and formally define the Robust-GreenRE problem using Mixed Integer Linear Program (MILP).We propose a heuristic algorithm that is effective for large instances.By simulation, we show the energy savings offered by our methods on backbone networks with real-life data traffic traces and compression rate fluctuations.

Bandwidth Fair Application Layer Multicast for Multi-Party Video Conference Application

In this paper we propose bandwidth fair N-Tree algorithm for ALM distribution tree construction and a new protocol for ALM packet replication and distribution, namely Almcast. Both the tree construction algorithm and packet replication/distribution protocol were implemented as proof-of concept by modifying an existing multi-party video conference application. The results show that N-Tree algorithm takes less than 3ms to construct ALM distribution tree for 12 nodes. Almcast implementation enables the intermediate relay node to lookup for next destination, replicate and forward packets as fast as its receiving rate at application layer.

Extended Cutset Inequalities for the Network Power Consumption Problem

Abstract In this paper, we enhance the MIP formulation for the Network Power Consumption problem, proposed by Giroire et al. We derive cutting planes, extending the wellknown cutset inequalities, and report on preliminary computations.

On Uncoordinated Service Placement in Edge-Clouds

Edge computing has emerged as a new paradigm to bring cloud applications closer to users for increased performance. ISPs have the opportunity to deploy private edge-clouds in their infrastructure to generate additional revenue by providing ultra-low latency applications to local users. We envision a rapid increase in the number of such applications for “edge” networks in the near future with virtual/augmented reality (VR/AR), networked gaming, wearable cognitive assistance, autonomous driving and IoT analytics having already been proposed for edge- clouds instead of the central clouds to improve performance. This raises new challenges as the complexity of the resource allocation problem for multiple services with latency deadlines (i.e., which service to place at which node of the edge-cloud in order to satisfy the latency constraints) becomes significant. In this paper, we propose a set of practical, uncoordinated strategies for service placement in edge-clouds. Through extensive simulations using both synthetic and real-world trace data, we demonstrate that uncoordinated strategies can perform comparatively well with the optimal placement solution, which satisfies the maximum amount of user requests.

Optimizing IGP link weights for energy-efficiency in multi-period traffic matrices

Recently, due to the increasing power consumption and worldwide gases emissions in ICT (Information and Communication Technology), energy efficient ways to design and operate backbone networks are becoming a new concern for network operators. Since these networks are usually overprovisioned and since traffic load has a small influence on power consumption of network equipments, the most common approach to save energy is to put unused line cards that drive links between neighboring routers into sleep mode. To guarantee QoS, all traffic demands should be routed without violating capacity constraints and the network should keep its connectivity. From the perspective of traffic engineering, we argue that stability in routing configuration also plays an important role in QoS. In details, frequent changes in network configuration (link weights, slept and activated links) to adapt with traffic fluctuation in daily time cause network oscillations. In this work, we propose a novel optimization method to adjust the link weights of Open Shortest Path First (OSPF) protocol while limiting the changes in network configurations when multi-period traffic matrices are considered. We formally define the problem and model it as Mixed Integer Linear Program (MILP). We then propose an efficient heuristic algorithm that is suitable for large networks. Simulation results with real traffic traces on three different networks show that our approach achieves high energy saving while keeping the networks in stable state (less changes in network configuration).

Utility-maximizing server selection

This paper presents a new method for selection between replicated servers distributed over a wide area, allowing application and network providers to trade-off costs with quality-of-service for their users. First, we create a novel utility framework that factors in quality of service metrics. Then we design a polynomial optimization algorithm to allocate user service requests to servers based on the utility while satisfying transit cost constraint. We then describe an efficient - low overhead distributed model with the need to only know a small subset of the data required by a global optimization formulation. Extensive simulations show that our method is scalable and leads to higher user utility compared with mapping user requests to the closest service replica.

Energy-Aware Routing in Software-Defined Network using Compression

Software-defined Networks (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, trac engineering and access control. In this paper, we focus on using SDN for energy-aware routing (EAR). Since trac 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 trac 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 ecient 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.