Traffic in backbone networks is characterized by strong seasonality, with clear patterns visible in various services and applications based on their usage throughout the day. Data-driven networks can learn these patterns to manage resources more efficiently as they become increasingly saturated. In this paper, we explore the benefits of traffic prediction and grooming across different traffic patterns. To achieve this, we simulate network operations using uniform sets of time-varying connection requests, where all demands in a simulation share the same traffic pattern related to a specific network-based service or application. Our goal is to thoroughly evaluate the robustness of the proposed techniques across diverse scenarios. The results will facilitate the design of future application-aware algorithms for the most efficient handling of each traffic pattern.

Relentless competition among communications ser-vice providers, increasing expectations of users, and escalating variety of new applications and services trigger the need to develop advanced solutions that can support the optimization and management of communication networks. Prediction of network traffic is one of the possible solutions providing additional data analytics information. In this paper, we consider an application-aware optical network that transmits various types of time-varying traffic. We analyze traffic prediction under two scenarios. As a reference scenario, we assume that the system is aware of multiple traffic types, and various prediction models are developed and trained for each traffic type separately. The second scenario - agnostic prediction - assumes that a single prediction model agnostic of the traffic types is created and trained. We develop several models for each of the analyzed scenarios using various regression methods. Next, we run extensive numerical experiments on real and semi-synthetic datasets to verify the performance of the proposed regression methods and compare both analyzed scenarios. The obtained results demonstrate that the proposed prediction model agnostic to the forecasted type of traffic provides excellent results; in many cases, it outperforms the reference scenario with dedicated prediction models for each traffic type. Moreover, we evaluate the proposed model's adaptability to predict unseen-before traffic types, showing that the quality loss is negligible. Finally, we test the proposed framework in a multilayer network with time-varying traffic and show how using an aggregated model does not lead to bandwidth blocking increase compared to dedicated prediction models.

Real-world optimization problems may have a different underlying structure. In black-box optimization, the dependencies between decision variables remain unknown. However, some techniques can discover such interactions accurately. In Large Scale Global Optimization (LSGO), problems are high-dimensional. It was shown effective to decompose LSGO problems into subproblems and optimize them separately. The effectiveness of such approaches may be highly dependent on the accuracy of problem decomposition. Many state-of-the-art decomposition strategies are derived from Differential Grouping (DG). However, if a given problem consists of non-additively separable subproblems, DG-based strategies may discover many non-existing interactions. On the other hand, monotonicity checking strategies proposed so far do not report non-existing interactions for any separable subproblems but may miss discovering many of the existing ones. Therefore, we propose Incremental Recursive Ranking Grouping (IRRG) that suffers from none of these flaws. IRRG consumes more fitness function evaluations than the recent DG-based propositions, e.g., Recursive DG 3 (RDG3). Nevertheless, the effectiveness of the considered Cooperative Co-evolution frameworks after embedding IRRG or RDG3 was similar for problems with additively separable subproblems that are suitable for RDG3. After replacing the additive separability with non-additive, embedding IRRG leads to results of significantly higher quality.

With the ever-increasing traffic load, the prediction of future traffic patterns can bring significant benefits to network optimization. The appropriate choice of a forecasting model is crucial for the successful allocation of network resources, especially in application-aware networks, differentiating unique requirements of diverse traffic types. In this paper, we present a novel customizable metric called Allocation Outside Blocking Threshold (AOBT), linking the problem of network traffic prediction and bandwidth blocking probability in dynamic routing. Through extensive case studies, we show that the choice of a traffic prediction model is dependent on the metric, traffic type, and forecast horizon. We establish that the traffic prediction method should be selected individually for each unique scenario and traffic type in an application-aware network and how the AOBT metric enables it.

This work focuses on analysis of various sharing approaches in survivable spectrally-spatially flexible optical networks (SS-FONs), which use flexible optical signal regeneration based on transponders connected in back-to-back (B2B) configurations. Two sharing approaches, namely, spectrum sharing and transponder sharing are analyzed in the context of dynamic traffic. To tackle the considered optimization problem, we propose a new Adaptive Survivable Routing with B2B Regeneration with Spectrum and Transponder Sharing (ASRBR-STS) algorithm that allows to establish dynamic routing requests in translucent and survivable SS-FON with spectrum and transponder sharing. The simulations are conducted using two topologies: European and US. The obtained results show that the proposed algorithm outperforms other methods. Moreover, the results clearly demonstrate that spectrum and transponder sharing significantly improves the performance in terms of the network throughput (amount of served traffic), i.e., up to 55% more protected traffic can be served when compared to the non-sharing approach.

We focus on dynamic crosstalk-aware lightpath provisioning in translucent space-division multiplexing (SDM) elastic optical networks, in which spectral super-channels are carried over multi-core fibers (MCFs), distance-adaptive transmission is considered, and back-to-back (B2B) signal regeneration is applied. We aim at establishing lightpaths for connection requests in such a way that the inter-core crosstalk (XT) impairment affecting the quality of lightpaths in the network does not exceed allowable threshold levels. To this end, we propose and analyze a method that is based on per-core static/worst-case XT estimation. This method is implemented in a dynamic XT-aware routing, spatial mode, and spectrum allocation (RSSA) algorithm, which is used to solve the lightpath provisioning problem in an efficient way, such that the bandwidth blocking probability is minimized. The performed extensive simulation experiments in realistic network scenarios show a significant impact of this method on network performance and motivate for its application instead of commonly used per-fiber worst-case XT estimation.

We focus on the routing, spatial mode, and spectrum allocation (RSSA) problem-a basic optimization problem in spectrally and spatially flexible optical networks (SS-FON). RSSA is a very challenging problem since it introduces a new dimension, related to the selection of spatial resources, to the already complex (NP-hard) routing and spectrum allocation (RSA) problem. To allow solving large RSSA problem instances, in particular, in optical backbone networks with tens of nodes and hundreds of demands as well as with optical fibers supporting a numerous number of spatial modes, specialized optimization algorithms are required. In this paper, we propose and study several dedicated optimization procedures and some enhancements in algorithm processing, including parallel processing, which aim at both speeding up and increasing the effectiveness of the RSSA process and at estimating the quality of generated solutions. We combine the proposed procedures into an efficient optimization framework which, as presented numerical results show, is capable of providing high-quality solutions to large instances of the RSSA optimization problem in reasonable computation times. As a case study scenario, we consider an SS-FON with spectral super-channels (SChs) transmitted over bundles of single-mode fibers (SMFB) without spatial mode conversion. Nonetheless, the proposed optimization framework is generic and can be straightforwardly adapted to other SS-FON scenarios.

In this paper, we analyze the problem of transceiver sharing in survivable spectrally-spatially flexible optical networks (SS-FONs) that realize transmission of spectral super-channels and where flexible signal regeneration is provided by transceivers operating in back-to-back (B2B) configurations. Namely, to improve network throughput, we propose to use a backup transceiver sharing approach for protection of lightpaths provisioned in the network. To address the considered problem, we develop an Adaptive Survivable Routing with Back-to-Back Regeneration with Transceiver Sharing (ASRBR-TS) algorithm that serves dynamic routing requests protected by backup paths. The ASRBR-TS algorithm accounts for limited spectrum and transceiver resources and makes use of the flexibility of the B2B regeneration. With the use of ASRBR-TS, we examine potential performance gains of transceiver sharing in terms of bandwidth blocking probability (BBP) and the traffic load that can be provisioned in the network with 1% BBP threshold. The numerical experiments are run for two representative network topologies with realistic assumptions concerning applied physical model. In the experiments, we also analyze a squeezed protection approach, in which the backup path supports only a part of the bit-rate realized on the working path. The results show that the transceiver sharing approach can significantly improve the network performance, i.e., it allows to provision up to 49% more traffic when compared with a dedicated transceiver approach.

The electronic health record (EHR) groups all digital documents related to a given patient as anamnesis, results of the laboratory tests, prescriptions, recorded medical signals as ECG or images etc. Dealing with such data representation we face with plethora of problems as different form of data, unstructured data (as doctor's notes), huge and fast growing volume, etc. It causes that EHR should be considered as the complex data representation. Accordingly, taking into consideration its complexity, hetorogenousity, fast growing and size we need special tools to analyse such medical big data. Such tools should be able to analyse datasets characterized by so-called 4Vs (volume, velocity, variety, and veracity). Notwithstandingly, we should also add the fifth V-value, because the only analytics tool deployment makes sense if it leads to healthcare improvement (as personalised patient's care, unnecessary hospitalization decreasing or reducing the patient's readmissions). In this paper we focus on the selected aspects EHR analysis from the big data perspective.

Elastic optical network (EON) is a promising technology that provides flexible modulation format conversion, which allows more efficient use of spectrum resources comparing to classical fix-grid in WDM networks. In addition, a significant issue observed currently is the predominant role of cloud computing and content-oriented services provided by geographically dispersed data centers. Consequently, an anycast transmission - defined as one-to-one-of-many - gains much attention due to the need for cost-effective and scalable data delivery in inter-data center networks. In this paper, we address the Routing, Modulation and Spectrum Allocation (RMSA) problem in the context of dynamic routing for anycast and unicast traffic in EONs. We propose a new adaptive and regenerator-aware algorithm for dynamic RMSA that facilitates the possibility of modulation change along the lightpath. Numerical experiments clearly show advantage of our approach comparing to previous methods in terms of blocking probability. Moreover, our results show an interesting trade-off between blocking probability and the usage of regenerators.

Nowadays, multicasting is increasingly popular, due to the ability to provision in efficient way such desirable services like streaming, IP television, software distribution, etc. At the same time, the research in the field of optical networks concentrates on the very promising elastic optical networks (EONs) approach. In this paper, we focus on the joint optimization of multicast and unicast flows in EONs. We propose to model multicast flows in EONs using pre-generated candidate trees. We present two new ILP models and a heuristic method, dedicated to solve the optimization problem with joint multicast and unicast flows. We report results of the numerical experiments carried out to compare proposed ILP models and the heuristic method as well as to evaluate potential benefits of multicasting in EONs. We show that the proposed modeling approach and heuristic method are highly elastic in terms of their applicability to different network scenarios, whilst multicasting can bring significant spectrum savings compared to unicasting.

Elastic optical network (EON) is a novel optical technology introduced recently to provide flexible and multibitrate data transmission in the optical layer. Since many new network services including cloud computing and content delivery networks are provisioned with the use of specialized data centers located in different network nodes, in place of one‐to‐one unicast transmission, the anycast transmission defined as one‐to‐one‐of‐many gains much popularity as a quite simple way to improve network performance. Therefore, this article focuses on modeling and static optimization of anycast flows in EONs. In particular, a ‐hard Routing and Spectrum Allocation for Restoration of Anycast Flows (RSA/RAF) problem is formulated. Next, various optimization approaches are proposed to solve this problem, namely, integer linear programming (ILP) using branch and bound algorithm, constraint programming (CP), and various heuristic approaches. Extensive numerical experiments are run to evaluate and compare all proposed methods. The main conclusion is that in some cases the CP approach is more efficient than the ILP modeling. Moreover, the results show that the SA algorithm significantly outperforms other heuristic methods. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 66(4), 253–266 2015

The continuous use of Internet services and applications triggers a sustainable growth of data transfer, therefore generating a necessity to develop an efficient and scalable transport platform. A novel-networking concept - Elastic Optical Networks (EONs) - can support bandwidth-demanding traffic from an advent of new services including cloud computing and content delivery networks. In this paper, we focus on dynamic routing of anycast and unicast traffic; anycast refers to cloud computing or content traffic served by data centers. We assume that EONs support various distance-adaptive modulation formats, namely BPSK, QPSK, and m-QAM, where m belongs to {8, 16, 32 and 64}. A higher modulation format consumes less spectrum, which can lead to a smaller blocking probability (BP) in dynamic routing. However, at the same time, a higher modulation format provides a smaller transmission distance, which triggers the need to use regenerators and increases CAPEX/OPEX network costs as well as power consumption. Therefore, the main goal of our research is to find a trade-off between the BP and using extra regenerators in the context of dynamic routing of anycast and unicast traffic in EONs.

In this paper, we focus on issues related to dynamic routing of anycast and unicast traffic demands in Elastic Optical Networks (EONs). The growing interest in cloud computing and content-oriented services triggers the need to examine anycast traffic in the context of the EON concept, which is perceived as a promising approach for future optical transport networks. Anycasting - defined as one-to-one of many transmission - ideally corresponds to concepts of cloud computing and content-oriented services, where the same service/content is available in many, geographically spread data centers. In the paper, we propose two types of algorithms for dynamic routing of anycast and unicast requests. The former algorithm is based on the shortest path first (SPF) method, while the latter algorithm is based on the selection of a routing path from a set of candidate paths (CP). To examine performance of the algorithms, we run simulations using a real pan-European topology and traffic patterns created according to Cisco predictions. The results show a tradeoff between SPF and CP methods in terms of the blocking probability and execution time. Moreover, we observe that the blocking probability depends on the number of data centers placed in the network.

Anycast communication is gaining much interest due to the deployment and the growing importance of new network services, including Content Delivery Networks (CDNs) and grid/cloud computing, among others. Concurrently, elastic optical networks (EONs), being a very promising solution for future optical transport networks, are considered as effective and cost-efficient approach for supporting such bandwidth-demanding applications. In this article, we focus on anycast communication in EON. Specifically, we formulate a joint anycast and unicast Routing and Spectrum Allocation (RSA/JAU) off-line optimization problem and solve it by means of both Integer Linear Programming (ILP) and dedicated heuristic algorithms. We evaluate the performance of algorithms and assess the effectiveness of anycast communication under various network scenarios. The main conclusion is that anycast routing brings significant spectrum savings in EON.

Overlay multicasting providing live streaming services is a crucial service for the contemporary Internet. In this article, we focus on applying overlay multicasting for delivery of critical data that must be transmitted safely, intact, and with as little delay as possible; for example, financial data, software security patches, antivirus signature database updates, etc. To improve survivability of overlay multicasting, we propose using a dual homing approach; that is, each peer is connected to the overlay by two separate access links. We consider the following network failures: overlay link failure, uploading node failure, and Internet server provider (ISP) interconnection failure. The objective is to minimize the maximum delay in the network. The optimization problem is formulated in the form of an integer linear programming (ILP) model. We introduce a simulated annealing (SA) algorithm and a tabu search (TS) algorithm for the considered problem and compare them with optimal results provided by CPLEX solver. Our studies demonstrate that both SA and TS methods yield results close to optimal and provide better scalability compared to CPLEX, because they can solve much larger problem instances than CPLEX in reasonable time. Furthermore, our studies show that the additional survivability requirements do not have a substantial impact on the overlay multicasting system expressed as the maximum delay.

We are living in a digital world consisting of a large number of computers and other equipment creating ubiquitous computer networks. Because most human activities depend on computer networks, secu...

In this paper we address the problem of shared backup path protection (SBPP) against random failures of single links in connection-oriented communication networks. Most of recent works has been concentrated on protecting the unicast traffic. Our paper is also focused on shared protection of anycast (i.e. one-to-one-out-of-many) flows. Anycasting is frequently met in practice (e.g. in Content Delivery Networks, peer-to-peer systems, or DNS service). A new ILP model for joint optimization of dedicated working and shared backup paths of anycast and unicast demands is introduced and is followed by extensive simulation research. The model is the first one that provides the optimal solutions to the problem of anycast traffic shared protection by using the Node-Link notation. The results show that our approach reduces the network cost and capacity utilization ratio by 36% and 42% accordingly, compared to the case when capacity sharing is not used.

Concepts of network virtualization and overlays are nowadays gaining much popularity and are perceived as key approaches for future Internet. One of possible applications used in overlay networks is live streaming based on Peer-to-Peer (P2P) multicasting. This paper deals with the problem of overlay networks dimensioning. In the problem-formulated as an Integer Program - access link capacities are to be selected with the objective to minimize the network cost expressed as the cost of access links. We solve the optimization problem using both CPLEX solver and our own heuristic algorithm that provides results close to optimal but with significantly lower execution time. Based on the proposed framework we examine the problem of how to design the overlay network to provide sufficient capacity for P2P multicasting. Using a simulator of a real P2P multicasting system we run extensive experiments on large networks (up to 100000 nodes) showing the dependency between the overlay network capacity and the performance of P2P multicasting streaming. Our results can be helpful in the process of overlay networks design and management.

In this paper we focus on issues of restorable dynamic routing in MPLS networks. We assume that the considered MPLS network must provide reliable service including restorable routing. Thus, for each arriving request two paths are required: active path and link-disjoint backup path. We apply the no-sharing information model, in which the only information available for routing is the total bandwidth that has been allocated on each link. Previous works use the idea of maximum 2-route flow. We formulate a new approach called adjacent 2-route flow that offers comparable performance to maximum 2-route flow and much lower time complexity. We propose a framework, in which adjacent 2-route flow can be applied as a scaling factor for a wide number of existing algorithms. Obtained results of extensive simulations show that adjacent 2-route flow can improve the performance of restorable routing for each tested algorithm.