Panagiotis C. Kokkinos

Experimental Demonstration of an Impairment Aware Network Planning and Operation Tool for Transparent/Translucent Optical Networks

Core optical networks using reconfigurable optical switches and tunable lasers appear to be on the road towards widespread deployment and could evolve to all-optical mesh networks in the coming future. Considering the impact of physical layer impairments in the planning and operation of all-optical (and translucent) networks is the main focus of the Dynamic Impairment Constraint Optical Networking (DICONET) project. The impairment aware network planning and operation tool (NPOT) is the main outcome of DICONET project, which is explained in detail in this paper. The key building blocks of the NPOT, consisting of network description repositories, the physical layer performance evaluator, the impairment aware routing and wavelength assignment engines, the component placement modules, failure handling, and the integration of NPOT in the control plane are the main contributions of this study. Besides, the experimental result of DICONET proposal for centralized and distributed control plane integration schemes and the performance of the failure handling in terms of restoration time is presented in this study.

Joint Online Routing, Wavelength Assignment and Regenerator Allocation in Translucent Optical Networks

In translucent (or managed reach) WDM optical networks, regenerators are employed at specific nodes. Some of the connections in such networks are routed transparently, while others have to go through a sequence of 3R regenerators that serve as ?refueling stations? to restore their quality of transmission (QoT). We extend an online multicost algorithm for transparent networks presented in our previous study, to obtain an IA-RWA algorithm that works in translucent networks and makes use, when required, of the regenerators present at certain locations of the network. To characterize a path, the algorithm uses a multicost formulation with several cost parameters, including the set of available wavelengths, the length of the path, the number of regenerators used, and noise variance parameters that account for the physical layer impairments. Given a new connection request and the current utilization state of the network, the algorithm calculates a set of non dominated candidate paths, meaning that any path in this set is not inferior with respect to all cost parameters than any other path. This set consists of all the cost-effective (in terms of the domination relation) and feasible (in terms of QoT) lightpaths for the given source-destination pair, including all the possible combinations for the utilization of available regenerators of the network. An optimization function or policy is then applied to this set in order to select the optimal lightpath. Different optimization policies correspond to different IA-RWA algorithms. We propose and evaluate several optimization policies, such as the most used wavelength, the best quality of transmission, the least regeneration usage, or a combination of these rules. Our results indicate that in a translucent network the employed IA-RWA algorithm has to consider all problem parameters, namely, the QoT of the lightpaths, the utilization of wavelengths and the availability of regenerators, to efficiently serve the online traffic.

Experimental demonstration of centralized and distributed impairment-aware control plane schemes for dynamic transparent optical networks

We demonstrate and compare distributed and centralized impairment-aware control plane schemes for transparent optical networks with dynamic traffic. Experimental results show that distributed scheme yields one fifth of setup time required by previously reported alternatives.

Multi-cost job routing and scheduling in Grid networks

A key problem in Grid networks is how to efficiently manage the available infrastructure, in order to satisfy user requirements and maximize resource utilization. This is in large part influenced by the algorithms responsible for the routing of data and the scheduling of tasks. In this paper, we present several multi-cost algorithms for the joint scheduling of the communication and computation resources that will be used by a Grid task. We propose a multi-cost scheme of polynomial complexity that performs immediate reservations and selects the computation resource to execute the task and determines the path to route the input data. Furthermore, we introduce multi-cost algorithms that perform advance reservations and thus also find the starting times for the data transmission and the task execution. We initially present an optimal scheme of non-polynomial complexity and by appropriately pruning the set of candidate paths we also give a heuristic algorithm of polynomial complexity. Our performance results indicate that in a Grid network in which tasks are either CPU- or data-intensive (or both), it is beneficial for the scheduling algorithm to jointly consider the computational and communication problems. A comparison between immediate and advance reservation schemes shows the trade-offs with respect to task blocking probability, end-to-end delay and the complexity of the algorithms.

Data Consolidation: A Task Scheduling and Data Migration Technique for Grid Networks

In this work we examine a task scheduling and data migration problem for grid networks, which we refer to as the data consolidation (DC) problem. DC arises when a task needs for its execution two or more pieces of data, possibly scattered throughout the grid network. In such a case, the scheduler and the data manager must select the data replicas to be used and the site where these will accumulate for the task to be executed. The policies for selecting the data replicas and the data consolidating site comprise the data consolidation problem. We propose and experimentally evaluate a number of DC techniques. Our simulation results brace our belief that DC is an important technique for data grids since it can substantially improve task delay, network load and other performance related parameters.

Developing Scheduling Policies in gLite Middleware

We describe our experiences from implementing and integrating a new job scheduling algorithm in the gLite Grid middleware and present experimental results that compare it to the existing gLite scheduling algorithms. It is the first time that gLite scheduling algorithms are put under test and compared with a new algorithm under the same conditions. We describe the problems that were encountered and solved, going from theory and simulations to practice and the actual implementation of our scheduling algorithm. In this work we also describe the steps one needs to follow in order to develop and test a new scheduling algorithm in gLite. We present the methodology followed and the testbed that was set up for the comparisons. Our research sheds light on some of the problems of the existing gLite scheduling algorithms and makes clear the need for the development of new.

Implementing multiplayer pervasive installations based on mobile sensing devices: Field experience and user evaluation from a public showcase

In this work we discuss Fun in Numbers, a software platform for implementing multiplayer games and interactive installations, that are based on the use of ad hoc mobile sensing devices. We utilize a detailed log of a three-day long public showcase as a basis to discuss the implementation issues related to a set of games and installations, which are examples of this unique category of applications, utilizing a blend of technologies. We discuss their fundamental concepts and features, also arguing that they have many aspects and potential uses. The architecture of the platform and implementation details are highlighted in this work, along with detailed descriptions of the protocols used. Our experiments shed light on a number of key issues, such as network scaling and real-time performance, and we provide experiments regarding cross-layer software issues. We additionally provide data showing that such games and installations can be efficiently supported by our platform, with as many as 50 concurrent players in the same physical space. These results are backed up by a user evaluation study from a large sample of 136 visitors, which shows that such applications can be seriously fun.

Multi-Parametric Online RWA Based on Impairment Generating Sources

We propose and evaluate an impairment-aware multi-parametric routing and wavelength assignment algorithm for online traffic in transparent optical networks. In such networks the signal quality of transmission degrades due to physical layer impairments. In the multiparametric approach, a vector of cost parameters is assigned to each link, from which the cost vectors of candidate lightpaths are calculated. In the proposed scheme the cost vector includes impairment generating source parameters, such as the path length, the number of hops, the number of crosstalk sources and other inter-lightpath interfering parameters, so as to indirectly account for the physical layer effects. For a requested connection the algorithm calculates a set of candidate lightpaths, whose quality of transmission is validated using a function that combines the impairment generating parameters. For selecting the lightpath we propose and evaluate various optimization functions that correspond to different IA-RWA algorithms. Our performance results indicate that the proposed algorithms utilize efficiently the available resources and minimize the total accumulated signal degradation on the selected lightpaths, while having low execution times.

Scheduling efficiency of resource information aggregation in grid networks

We consider information aggregation as a method for reducing the information exchanged in a Grid network and used by the resource manager in order to make scheduling decisions. In this way, information is summarized across nodes and sensitive or detailed information can be kept private, while resources are still publicly available for use. We present a general framework for information aggregation, trying to identify issues that relate to aggregation in Grids. In this context, we describe a number of techniques, including single point and intra-domain aggregation, define appropriate grid-specific domination relations and operators for aggregating static and dynamic resource information, and discuss resource selection optimization functions. The quality of an aggregation scheme is measured both by its effects on the efficiency of the schedulers decisions and also by the reduction it brings on the amount of resource information recorded, a tradeoff that we examine in detail. Simulation experiments demonstrate that the proposed schemes achieve significant information reduction, either in the amount of information exchanged, or in the frequency of the updates, while at the same time maintaining most of the value of the original information as expressed by a stretch factor metric we introduce.

A framework for providing hard delay guarantees and user fairness in Grid computing

We present and theoretically and experimentally analyze a Quality of Service (QoS) framework for Grids that provides (i) deterministic delay bounds to Guaranteed Service (GS) users and (ii) fair sharing of resources to Best Effort (BE) users. The framework adopts concepts from Data Networks and applies them in the Grid environment. We initially describe the proposed framework assuming that task computational workloads are known (or can be estimated), and then provide extensions for the more realistic case where we have no a-priori knowledge of the task workloads. Task migration across multiple resources is also examined in this context. We also look at a specific implementation of the proposed QoS scheme, where we distinguish computational resources, based on the type of users (GS or BE) they serve and the priority they give to each type. We validate experimentally the proposed QoS framework for Grids, verifying that it satisfies the delay guarantees promised to GS users and provides fairness among BE users, while simultaneously improving performance in terms of deadlines missed and resource utilization. In our simulations, data from a real Grid Network are used.