scispace - formally typeset
Search or ask a question
Author

Krzysztof Walkowiak

Bio: Krzysztof Walkowiak is an academic researcher from Wrocław University of Technology. The author has contributed to research in topics: Unicast & Anycast. The author has an hindex of 25, co-authored 239 publications receiving 2727 citations. Previous affiliations of Krzysztof Walkowiak include Maastricht University & University of Wrocław.


Papers
More filters
Journal ArticleDOI
TL;DR: This letter formulate RSA as an Integer Linear Programming (ILP) problem and propose an effective heuristic to be used if the solution of ILP is not attainable.
Abstract: A spectrum-sliced elastic optical path network (SLICE) architecture has been recently proposed as an efficient solution for a flexible bandwidth allocation in optical networks In SLICE, the problem of Routing and Spectrum Assignment (RSA) emerges In this letter, we both formulate RSA as an Integer Linear Programming (ILP) problem and propose an effective heuristic to be used if the solution of ILP is not attainable

317 citations

Journal ArticleDOI
TL;DR: This study overviews the state-of-the-art solutions in the scope of planning and operating SDM optical networks in a systematic way as well as to identify some open issues that lack solutions and need to be addressed.

166 citations

Journal ArticleDOI
TL;DR: This paper addresses an offline problem of routing and spectrum allocation (RSA) with dedicated path protection (DPP) in EON with a Tabu Search-based algorithm (TS), and a hybrid Adaptive Frequency Assignment-TS (AFA/TS) algorithm that outperform other reference algorithms.

104 citations

Journal ArticleDOI
TL;DR: In this article, an elastic optical network (EON) approach is proposed for provisioning cloud computing traffic, which allows for both scalable bandwidth provisioning and flexible resource allocation, and the deployment cost, energy consumption, and bandwidth usage for both EON and classical WSON transport networks are compared in pan-European and U.S. backbone networks for 2012-2020 using Cisco traffic predictions.
Abstract: This article provides motivation for the elastic optical network (EON) approach, an efficient and cost-effective solution for provisioning of cloud computing traffic. As opposed to wavelength switched optical networks (WSONs), the capabilities of which are limited by the use of rigid frequency grids, EON architectures allow for both scalable bandwidth provisioning and flexible resource allocation. The deployment cost, energy consumption, and bandwidth usage for both EON and classical WSON transport networks are compared in pan-European and U.S. backbone networks for 2012-2020 using Cisco traffic predictions. Results show that the EON concept significantly outperforms WSON in all examined criteria, and the gap between the two architectures increases in subsequent years. Moreover, potential advantages of anycast routing in transport networks with data center traffic are demonstrated.

93 citations

Journal ArticleDOI
TL;DR: Three new approaches to optimizing multicast traffic in elastic optical networks that implement distance-adaptive transmission based on candidate tree modeling of multicasting outperform other methods for larger problem instances in terms of spectrum usage and applicability.
Abstract: In this letter, we propose three new approaches to optimizing multicast traffic in elastic optical networks that implement distance-adaptive transmission. Two of the approaches are based on integer linear programming modeling, whereas the last one is an effective heuristic. We also compare our approaches with reference methods proposed in the literature. The comparison is based on numerical experiments supported by a discussion on the effectiveness and applicability of the considered methods. The algorithms based on candidate tree modeling of multicasting outperform other methods for larger problem instances in terms of spectrum usage and applicability.

61 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions.

1,267 citations

Journal ArticleDOI
TL;DR: An up-to-date survey on multiple classifier system (MCS) from the point of view of Hybrid Intelligent Systems is presented, providing a vision of the spectrum of applications that are currently being developed.

856 citations

Journal ArticleDOI
TL;DR: This paper surveys research on ensembles for data stream classification as well as regression tasks and discusses advanced learning concepts such as imbalanced data streams, novelty detection, active and semi-supervised learning, complex data representations and structured outputs.

757 citations

Journal ArticleDOI
TL;DR: The results of this study show that the technologies of cloud and big data can be used to enhance the performance of the healthcare system so that humans can then enjoy various smart healthcare applications and services.
Abstract: The advances in information technology have witnessed great progress on healthcare technologies in various domains nowadays. However, these new technologies have also made healthcare data not only much bigger but also much more difficult to handle and process. Moreover, because the data are created from a variety of devices within a short time span, the characteristics of these data are that they are stored in different formats and created quickly, which can, to a large extent, be regarded as a big data problem. To provide a more convenient service and environment of healthcare, this paper proposes a cyber-physical system for patient-centric healthcare applications and services, called Health-CPS, built on cloud and big data analytics technologies. This system consists of a data collection layer with a unified standard, a data management layer for distributed storage and parallel computing, and a data-oriented service layer. The results of this study show that the technologies of cloud and big data can be used to enhance the performance of the healthcare system so that humans can then enjoy various smart healthcare applications and services.

682 citations