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)

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

Optical Grid Network Dimensioning, Provisioning, and Job Scheduling

An optical grid network reliably provides high speed communications. It consists of grid resources (e.g., computing and data servers) and huge-data paths that are connected to geographically dispersed resources and users. One of the important issues is dimensioning optical grid networks, i.e., to determine the link bandwidth utilization and amount of server resources, and finding the location of servers. Another issue is the provisioning of the job requests (maximization of services) on the capacitated networks, also referred to as Grade of Service (GoS). Additionally, job scheduling on the servers has also an important impact on the utilization of computing and network resources. Dimensioning optical grid network is based on Anycast Routing and Wavelength Assignment (ACRWA) with the objective of minimizing (min-ACRWA) the resources. The objective of GoS is maximizing the number of job requests (max-ACRWA) under the limited resources. Given that users of such optical grid networks in general do not care about the exact physical locations of the server resources, a degree of freedom arises in choosing for each of their requests the most appropriate server location. We will exploit this anycast routing principle -- i.e., the source of the traffic is given, but the destination can be chosen rather freely. To provide resilience, traffic may be relocated to alternate destinations in case of network/server failures. This thesis investigates dimensioning optical grids networks and task scheduling. In the first part, we present the link capacity dimensioning through scalable exact Integer Linear Programming (ILP) optimization models (min-ACRWA) with survivability. These models take step by step transition from the classical RWA (fixed destination) to anycast routing principle including shared path protection scheme. In the second part, we present scalable optimization models for maximizing the IT services (max-ACRWA) subject to survivability mechanism under limited link transport capacities. We also propose the link capacity formulations based on the distance from the servers and the traffic data set. In the third part, we jointly investigate the link dimensioning and the location of servers in an optical grid, where the anycast routing principle is applied for resiliency under different levels of protection schemes. We propose three different decomposition schemes for joint optimization of link dimensioning and finding the location of servers. In the last part of this research, we propose the exact task scheduling ILP formulations for optical grids (data centers). These formulations can also be used in advance reservation systems to allocate the grid resources. The purpose of this study is to design efficient tools for planning and management of the optical grid networks

Resource Allocation for Periodic Traffic Demands in WDM Networks

Recent research has clearly established that holding-time-aware routing and wavelength assignment (RWA) schemes lead to significant improvements in resource utilization for scheduled traffic. By exploiting the knowledge of the demand holding times, this thesis proposes new traffic grooming techniques to achieve more efficient resource utilization with the goal of minimizing resources such as bandwidth, wavelength channels, transceivers, and energy consumption. This thesis also introduces a new model, the segmented sliding window model, where a demand may be decomposed into two or more components and each component can be sent separately. This technique is suitable for applications where continuous data transmission is not strictly required such as large file transfers for grid computing. Integer linear program (ILP) formulations and an efficient heuristic are put forward for resource allocation under the proposed segmented sliding window model. It is shown that the proposed model can lead to significantly higher throughput, even over existing holding-time-aware models