Jens Buysse

Energy Efficiency in integrated IT and optical network infrastructures: The GEYSERS approach

In this paper we propose energy efficient design and operation of infrastructures incorporating integrated optical network and IT resources. For the first time we quantify significant energy savings of a complete solution jointly optimizing the allocation and provisioning of both network and IT resources. Our approach involves virtualization of the infrastructure resources and it is proposed and developed in the framework of the European project GEYSERS - Generalised Architecture for Dynamic Infrastructure Services.

Energy-efficient resource-provisioning algorithms for optical clouds

Rising energy costs and climate change have led to an increased concern for energy efficiency (EE). As information and communication technology is responsible for about 4% of total energy consumption worldwide, it is essential to devise policies aimed at reducing it. In this paper, we propose a routing and scheduling algorithm for a cloud architecture that targets minimal total energy consumption by enabling switching off unused network and/or information technology (IT) resources, exploiting the cloud-specific anycast principle. A detailed energy model for the entire cloud infrastructure comprising a wide-area optical network and IT resources is provided. This model is used to make a single-step decision on which IT end points to use for a given request, including the routing of the network connection toward these end points. Our simulations quantitatively assess the EE algorithms potential energy savings but also assess the influence this may have on traditional quality-of-service parameters such as service blocking. Furthermore, we compare the one-step scheduling with traditional scheduling and routing schemes, which calculate the resource provisioning in a two-step approach (selecting first the destination IT end point and subsequently using unicast routing toward it). We show that depending on the offered infrastructure load, our proposed one-step calculation considerably lowers the total energy consumption (reduction up to 50%) compared to the traditional iterative scheduling and routing, especially in low- to medium-load scenarios, without any significant increase in the service blocking.

Anycast Routing for Survivable Optical Grids: Scalable Solution Methods and the Impact of Relocation

In this paper, we address the issue of resiliency against single link network failures in optical grids and show how the anycast routing principle, which is typical of grids, can be exploited in providing efficient shared path protection. We investigate two different integer linear program models for the full anycast routing problem, deciding on the primary and backup server locations as well as on the lightpaths toward them. The first model is a classical integer linear programming (ILP) model, which lacks scalability. The second model is a large-scale optimization model which can be efficiently solved using column generation techniques. We also design two new heuristics: the first one is an improvement of a previously proposed one which, although providing near optimal solutions, lacks scalability, while the second one is highly scalable, at the expense of reduced accuracy. Numerical results are presented for three mesh networks with varying node degrees. They allow an illustration of the scalability of the newly proposed approaches. Apart from highlighting the difference in performance (i.e., scalability and optimality) among the algorithms, our case studies demonstrate the bandwidth savings that can be achieved by exploiting relocation rather than using a backup path to the original (failure-free) destination site. Numerical results for varying network topologies, as well as different numbers of server sites show that relocation allows bandwidth savings in the range of 7-21%.

Survivable Optical Grid Dimensioning: Anycast Routing with Server and Network Failure Protection

Grids can efficiently deal with challenging computational and data processing tasks which cutting edge science is generating today. So-called e-Science grids cope with these complex task by deploying geographically distributed server infrastructure, interconnected by high speed networks. The latter benefit from optical technology, offering low latencies and high bandwidths, thus giving rise to so-called optical grids or lambda grids. In this paper, we address the dimensioning problem of such grids: how to decide how much server infrastructure to deploy, at which locations in a given topology, the amount of network capacity to provide and which routes to follow along them. Compared to earlier work, we propose an integrated solution solving these questions in an integrated way, i.e., we jointly optimize network and server capacity, and incorporate resiliency against both network and server failures. Assuming we are given the amount of resource reservation requests arriving at each network node (where a resource reservation implies to reserve both processing capacity at a server site, and a network connection towards it), we solve the problem of first choosing a predetermined number of server locations to use, and subsequently determine the routes to follow while minimizing resource requirements. In a case study on a meshed European network comprising 28 nodes and 41 links, we show that compared to classical (i.e. without relocation) shared path protection against link failures only, we can offer resilience against both single link and network failures by adding about 55% extra server capacity, and 26% extra wavelengths.

Resilient network dimensioning for optical grid/clouds using relocation

In this paper we address the problem of dimensioning infrastructure, comprising both network and server resources, for large-scale decentralized distributed systems such as grids or clouds. We will provide an overview of our work in this area, and in particular focus on how to design the resulting grid/cloud to be resilient against network link and/or server site failures. To this end, we will exploit relocation: under failure conditions, a request may be sent to an alternate destination than the one under failure-free conditions. We will provide a comprehensive overview of related work in this area, and focus in some detail on our own most recent work. The latter comprises a case study where traffic has a known origin, but we assume a degree of freedom as to where its end up being processed, which is typically the case for e.g., grid applications of the bag-of-tasks (BoT) type or for providing cloud services. In particular, we will provide in this paper a new integer linear programming (ILP) formulation to solve the resilient grid/cloud dimensioning problem using failure-dependent backup routes. Our algorithm will simultaneously decide on server and network capacity. We find that in the anycast routing problem we address, the benefit of using failure-dependent (FD) rerouting is limited compared to failure-independent (FID) backup routing. We confirm our earlier findings in terms of network capacity savings achieved by relocation compared to not exploiting relocation (order of 6-10% in the current case studies).

Improving energy efficiency in optical cloud networks by exploiting anycast routing

Exploiting anycast routing significantly reduces optical network and server energy usage. In this work we present a case study showing that intelligently selecting destinations and routes thereto, while switching off unused (network) elements, cuts power consumption by around 20% and saves network resources by 29%.

Joint dimensioning of server and network infrastructure for resilient optical grids/clouds

We address the dimensioning of infrastructure, comprising both network and server resources, for large-scale decentralized distributed systems such as grids or clouds. We design the resulting grid/cloud to be resilient against network link or server failures. To this end, we exploit relocation: Under failure conditions, a grid job or cloud virtual machine may be served at an alternate destination (i.e., different from the one under failure-free conditions). We thus consider grid/cloud requests to have a known origin, but assume a degree of freedom as to where they end up being served, which is the case for grid applications of the bag-of-tasks (BoT) type or hosted virtual machines in the cloud case. We present a generic methodology based on integer linear programming (ILP) that: chooses a given number of sites in a given network topology where to install server infrastructure; and determines the amount of both network and server capacity to cater for both the failure-free scenario and failures of links or nodes. For the latter, we consider either failure-independent (FID) or failure-dependent (FD) recovery. Case studies on European-scale networks show that relocation allows considerable reduction of the total amount of network and server resources, especially in sparse topologies and for higher numbers of server sites. Adopting a failure-dependent backup routing strategy does lead to lower resource dimensions, but only when we adopt relocation (especially for a high number of server sites): Without exploiting relocation, potential savings of FD versus FID are not meaningful.

Exploiting relocation to reduce network dimensions of resilient optical grids

Optical Grids are widely deployed to solve complex problems we are facing today. An important aspect of the supporting network is resiliency i.e. the ability to overcome network failures. In contrast to classical network protection schemes, we will not necessarily provide a back-up path between the source and the original destination. Instead, we will try to relocate the job to another server location if this means that we can provide a backup path which comprises less wavelengths than the one the traditional scheme would suggest. This relocation can be backed up by the Grid specific anycast principle: a user generally does not care where his job is executed and is only interested in its results. We present ILP formulations for both resilience schemes and we evaluate them in a case study on an European network topology.

Providing resiliency for optical grids by exploiting relocation: A dimensioning study based on ILP

Grids use a form of distributed computing to tackle complex computational and data processing problems scientists are presented with today. When designing an (optical) network supporting grids, it is essential that it can overcome single network failures, for which several protection schemes have been devised in the past. In this work, we extend the existing Shared Path protection scheme by incorporating the anycast principle typical of grids: a user typically does not care on what specific server this job gets executed and is merely interested in its timely delivery of results. Therefore, in contrast with Classical Shared Path protection (CSP), we will not necessarily provide a backup path between the source and the original destination. Instead, we allow to relocate the job to another server location if we can thus provide a backup path which comprises less wavelengths than the one CSP would suggest. We assess the bandwidth savings enabled by relocation in a quantitative dimensioning case study on an European and an American network topology, exhibiting substantial savings of the number of required wavelengths (in the order of 11-50%, depending on network topology and server locations). We also investigate how relocation affects the computational load on the execution servers. The case study is based on solving a grid network dimensioning problem: we present Integer Linear Programming (ILP) formulations for both the traditional CSP and the new resilience scheme exploiting relocation (SPR). We also outline a strategy to deal with the anycast principle: assuming we are given just the origins and intensity of job arrivals, we derive a static (source,destination)-based demand matrix. The latter is then used as input to solve the network dimensioning ILP for an optical circuit-switched WDM network.

Bringing optical networks to the cloud: an architecture for a sustainable future internet

Over the years, the Internet has become a central tool for society. The extent of its growth and usage raises critical issues associated with its design principles that need to be addressed before it reaches its limits. Many emerging applications have increasing requirements in terms of bandwidth, QoS and manageability. Moreover, applications such as Cloud computing and 3D-video streaming require optimization and combined provisioning of different infrastructure resources and services that include both network and IT resources. Demands become more and more sporadic and variable, making dynamic provisioning highly needed. As a huge energy consumer, the Internet also needs to be energyconscious. Applications critical for society and business (e.g., health, finance) or for real-time communication demand a highly reliable, robust and secure Internet. Finally, the future Internet needs to support sustainable business models, in order to drive innovation, competition, and research. Combining optical network technology with Cloud technology is key to addressing the future Internet/Cloud challenges. In this context, we propose an integrated approach: realizing the convergence of the IT- and optical-network-provisioning models will help bring revenues to all the actors involved in the value chain. Premium advanced network and IT managed services integrated with the vanilla Internet will ensure a sustainable future Internet/Cloud enabling demanding and ubiquitous applications to coexist.