Calculating the minimum bounds of energy consumption for cloud networks

This paper is aiming at facilitating the energy-efficient operation of an integrated optical network and IT infrastructure. In this context we propose an energy-efficient routing algorithm for provisioning of IT services that originate from specific source sites and which need to be executed by suitable IT resources (e. g. data centers). The routing approach followed is anycast, since the requirement for the IT services is the delivery of results, while the exact location of the execution of the job can be freely chosen. In this scenario, energy efficiency is achieved by identifying the least energy consuming IT and network resources required to support the services, enabling the switching off of any unused network and IT resources. Our results show significant energy savings that can reach up to 55% compared to energy-unaware schemes, depending on the granularity with which a data center is able to switch on/off servers

Field trial of a 15 Tb/s adaptive and gridless OXC supporting elastic 1000-fold all-optical bandwidth granularity

An adaptive gridless OXC is implemented using a 3D-MEMS optical backplane plus optical modules (sub-systems) that provide elastic spectrum and time switching functionality. The OXC adapts its architecture on demand to fulfill the switching requirements of incoming traffic. The system is implemented in a seven-node network linked by installed fiber and is shown to provide suitable architectures on demand for three scenarios with increasing traffic and switching complexity. In the most complex scenario, signals of mixed bit-rates and modulation formats are successfully switched with flexible per-channel allocation of spectrum, time and space, achieving over 1000-fold bandwidth granularity and 1.5 Tb/s throughput with good end-to-end performance

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: 1) chooses a given number of sites in a given network topology where to install server infrastructure; and 2) 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