Real-Time Energy Price-Aware Anycast RWA for Scheduled Lightpath Demands in Optical Data Center Networks

The energy consumption of the data center networks and the power consumption associated with transporting data to the users is considerably large, and it constitutes a significant portion of their costs. Hence, development of energy efficient schemes is very crucial to address this problem. Our research considers the fixed window traffic allocation model and the anycast routing scheme to select the best option for the destination node. Proper routing schemes and appropriate combination of the replicas can take care of the issue for energy utilization and at the same time help diminish costs for the data centers. We have also considered the real-time pricing model (which considers price changes every hour) to select routes for the lightpaths. Hence, we propose an ILP to handle the energyaware routing and wavelength assignment (RWA) problem for fixed window scheduled traffic model, with an objective to minimize the overall electricity costs of a datacenter network by reducing the actual power consumption, and using low-cost resources whenever possible

World-wide Networking for LHC Data Processing

CERN’s Large Hadron Collider is producing several Petabytes of physics data per year. We present the network environment used for LHC data processing, and provide outlook into evolution of computing models and networks supporting them

Disaster Resilient Optical Core Networks

During the past few years, the number of catastrophic disasters has increased and its impact sometimes incapacitates the infrastructures within a region. The communication network infrastructure is one of the affected systems during these events. Thus, building a resilient network backbone is essential due to the big role of networks during disaster recovery operations. In this thesis, the research efforts in building a disaster-resilient network are reviewed and open issues related to building disaster-resilient networks are discussed. Large size disasters not necessarily impact the communication networks, but instead it can stimulate events that cause network performance degradation. In this regard, two open challenges that arise after disasters are considered one is the short-term capacity exhaustion and the second is the power outage. First, the post-disaster traffic floods phenomena is considered. The impact of the traffic floods on the optical core network performance is studied. Five mitigation approaches are proposed to serve these floods and minimise the incurred blocking. The proposed approaches explore different technologies such as excess or overprovisioned capacity exploitation, traffic filtering, protection paths rerouting, rerouting all traffic and finally using the degrees of freedom offered by differentiated services. The mitigation approaches succeeded in reducing the disaster induced traffic blocking. Second, advance reservation provisioning in an energy-efficient approach is developed. Four scenarios are considered to minimise power consumption. The scenarios exploit the flexibility provided by the sliding-window advance reservation requests. This flexibility is studied through scheduling and rescheduling scenarios. The proposed scenarios succeeded in minimising the consumed power. Third, the sliding-window flexibility is exploited for the objective of minimising network blocking during post-disaster traffic floods. The scheduling and rescheduling scenarios are extended to overcome the capacity exhaustion and improve the network blocking. The proposed schemes minimised the incurred blocking during traffic floods by exploiting sliding window. Fourth, building blackout resilient networks is proposed. The network performance during power outages is evaluated. A remedy approach is suggested for maximising network lifetime during blackouts. The approach attempts to reduce the required backup power supply while minimising network outages due to limited energy production. The results show that the mitigation approach succeeds in keeping the network alive during a blackout while minimising the required backup power

Energy-Efficient Softwarized Networks: A Survey

With the dynamic demands and stringent requirements of various applications, networks need to be high-performance, scalable, and adaptive to changes. Researchers and industries view network softwarization as the best enabler for the evolution of networking to tackle current and prospective challenges. Network softwarization must provide programmability and flexibility to network infrastructures and allow agile management, along with higher control for operators. While satisfying the demands and requirements of network services, energy cannot be overlooked, considering the effects on the sustainability of the environment and business. This paper discusses energy efficiency in modern and future networks with three network softwarization technologies: SDN, NFV, and NS, introduced in an energy-oriented context. With that framework in mind, we review the literature based on network scenarios, control/MANO layers, and energy-efficiency strategies. Following that, we compare the references regarding approach, evaluation method, criterion, and metric attributes to demonstrate the state-of-the-art. Last, we analyze the classified literature, summarize lessons learned, and present ten essential concerns to open discussions about future research opportunities on energy-efficient softwarized networks.Comment: Accepted draft for publication in TNSM with minor updates and editin

AgileDCN:An Agile Reconfigurable Optical Data Center Network Architecture

This paper presents a detailed examination of a novel data center network (DCN) that can satisfy the high capacity and low latency requirements of modern cloud computing applications. This reconfigurable architecture called AgileDCN uses fast-switching optical components with a centralized control function and workload scheduler. By providing a highly flexible optical network fabric between server racks, very high network efficiencies can be achieved even under imbalanced loading patterns. Our simulation results show that, at high (70%) loads, TCP flow completion times in the AgileDCN are significantly lower than in an equivalent electronic leaf-spine network

Virtualisation and resource allocation in MECEnabled metro optical networks

The appearance of new network services and the ever-increasing network traffic and number of connected devices will push the evolution of current communication networks towards the Future Internet. In the area of optical networks, wavelength routed optical networks (WRONs) are evolving to elastic optical networks (EONs) in which, thanks to the use of OFDM or Nyquist WDM, it is possible to create super-channels with custom-size bandwidth. The basic element in these networks is the lightpath, i.e., all-optical circuits between two network nodes. The establishment of lightpaths requires the selection of the route that they will follow and the portion of the spectrum to be used in order to carry the requested traffic from the source to the destination node. That problem is known as the routing and spectrum assignment (RSA) problem, and new algorithms must be proposed to address this design problem. Some early studies on elastic optical networks studied gridless scenarios, in which a slice of spectrum of variable size is assigned to a request. However, the most common approach to the spectrum allocation is to divide the spectrum into slots of fixed width and allocate multiple, consecutive spectrum slots to each lightpath, depending on the requested bandwidth. Moreover, EONs also allow the proposal of more flexible routing and spectrum assignment techniques, like the split-spectrum approach in which the request is divided into multiple "sub-lightpaths". In this thesis, four RSA algorithms are proposed combining two different levels of flexibility with the well-known k-shortest paths and first fit heuristics. After comparing the performance of those methods, a novel spectrum assignment technique, Best Gap, is proposed to overcome the inefficiencies emerged when combining the first fit heuristic with highly flexible networks. A simulation study is presented to demonstrate that, thanks to the use of Best Gap, EONs can exploit the network flexibility and reduce the blocking ratio. On the other hand, operators must face profound architectural changes to increase the adaptability and flexibility of networks and ease their management. Thanks to the use of network function virtualisation (NFV), the necessary network functions that must be applied to offer a service can be deployed as virtual appliances hosted by commodity servers, which can be located in data centres, network nodes or even end-user premises. The appearance of new computation and networking paradigms, like multi-access edge computing (MEC), may facilitate the adaptation of communication networks to the new demands. Furthermore, the use of MEC technology will enable the possibility of installing those virtual network functions (VNFs) not only at data centres (DCs) and central offices (COs), traditional hosts of VFNs, but also at the edge nodes of the network. Since data processing is performed closer to the enduser, the latency associated to each service connection request can be reduced. MEC nodes will be usually connected between them and with the DCs and COs by optical networks. In such a scenario, deploying a network service requires completing two phases: the VNF-placement, i.e., deciding the number and location of VNFs, and the VNF-chaining, i.e., connecting the VNFs that the traffic associated to a service must transverse in order to establish the connection. In the chaining process, not only the existence of VNFs with available processing capacity, but the availability of network resources must be taken into account to avoid the rejection of the connection request. Taking into consideration that the backhaul of this scenario will be usually based on WRONs or EONs, it is necessary to design the virtual topology (i.e., the set of lightpaths established in the networks) in order to transport the tra c from one node to another. The process of designing the virtual topology includes deciding the number of connections or lightpaths, allocating them a route and spectral resources, and finally grooming the traffic into the created lightpaths. Lastly, a failure in the equipment of a node in an NFV environment can cause the disruption of the SCs traversing the node. This can cause the loss of huge amounts of data and affect thousands of end-users. In consequence, it is key to provide the network with faultmanagement techniques able to guarantee the resilience of the established connections when a node fails. For the mentioned reasons, it is necessary to design orchestration algorithms which solve the VNF-placement, chaining and network resource allocation problems in 5G networks with optical backhaul. Moreover, some versions of those algorithms must also implements protection techniques to guarantee the resilience system in case of failure. This thesis makes contribution in that line. Firstly, a genetic algorithm is proposed to solve the VNF-placement and VNF-chaining problems in a 5G network with optical backhaul based on star topology: GASM (genetic algorithm for effective service mapping). Then, we propose a modification of that algorithm in order to be applied to dynamic scenarios in which the reconfiguration of the planning is allowed. Furthermore, we enhanced the modified algorithm to include a learning step, with the objective of improving the performance of the algorithm. In this thesis, we also propose an algorithm to solve not only the VNF-placement and VNF-chaining problems but also the design of the virtual topology, considering that a WRON is deployed as the backhaul network connecting MEC nodes and CO. Moreover, a version including individual VNF protection against node failure has been also proposed and the effect of using shared/dedicated and end-to-end SC/individual VNF protection schemes are also analysed. Finally, a new algorithm that solves the VNF-placement and chaining problems and the virtual topology design implementing a new chaining technique is also proposed. Its corresponding versions implementing individual VNF protection are also presented. Furthermore, since the method works with any type of WDM mesh topologies, a technoeconomic study is presented to compare the effect of using different network topologies in both the network performance and cost.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

Energy Efficient Big Data Networks

The continuous increase of big data applications in number and types creates new challenges that should be tackled by the green ICT community. Data scientists classify big data into four main categories (4Vs): Volume (with direct implications on power needs), Velocity (with impact on delay requirements), Variety (with varying CPU requirements and reduction ratios after processing) and Veracity (with cleansing and backup constraints). Each V poses many challenges that confront the energy efficiency of the underlying networks carrying big data traffic. In this work, we investigated the impact of the big data 4Vs on energy efficient bypass IP over WDM networks. The investigation is carried out by developing Mixed Integer Linear Programming (MILP) models that encapsulate the distinctive features of each V. In our analyses, the big data network is greened by progressively processing big data raw traffic at strategic locations, dubbed as processing nodes (PNs), built in the network along the path from big data sources to the data centres. At each PN, raw data is processed and lower rate useful information is extracted progressively, eventually reducing the network power consumption. For each V, we conducted an in-depth analysis and evaluated the network power saving that can be achieved by the energy efficient big data network compared to the classical approach. Along the volume dimension of big data, the work dealt with optimally handling and processing an enormous amount of big data Chunks and extracting the corresponding knowledge carried by those Chunks, transmitting knowledge instead of data, thus reducing the data volume and saving power. Variety means that there are different types of big data such as CPU intensive, memory intensive, Input/output (IO) intensive, CPU-Memory intensive, CPU/IO intensive, and memory-IO intensive applications. Each type requires a different amount of processing, memory, storage, and networking resources. The processing of different varieties of big data was optimised with the goal of minimising power consumption. In the velocity dimension, we classified the processing velocity of big data into two modes: expedited-data processing mode and relaxed-data processing mode. Expedited-data demanded higher amount of computational resources to reduce the execution time compared to the relaxed-data. The big data processing and transmission were optimised given the velocity dimension to reduce power consumption. Veracity specifies trustworthiness, data protection, data backup, and data cleansing constraints. We considered the implementation of data cleansing and backup operations prior to big data processing so that big data is cleansed and readied for entering big data analytics stage. The analysis was carried out through dedicated scenarios considering the influence of each V’s characteristic parameters. For the set of network parameters we considered, our results for network energy efficiency under the impact of volume, variety, velocity and veracity scenarios revealed that up to 52%, 47%, 60%, 58%, network power savings can be achieved by the energy efficient big data networks approach compared to the classical approach, respectively

SDN-based traffic engineering in data centers, Interconnects, and Carrier Networks

Server virtualization and cloud computing have escalated the bandwidth and performance demands on the DCN (data center network). The main challenges in DCN are maximizing network utilization and ensuring fault tolerance to address multiple node-and-link failures. A multitenant and highly dynamic virtualized environment consists of a large number of endstations, leading to a very large number of flows that challenge the scalability of a solution to network throughput maximization. The challenges are scalability, in terms of address learning, forwarding decision convergence, and forwarding state size, as well as flexibility for offloading with VM migration. Geographically distributed data centers are inter-connected through service providers’ carrier network. Service providers offer wide-area network (WAN) connection such as private lines and MPLS circuits between edges of data centers. DC sides of network operators try to maximize the utilization of such defined overlay WAN connection i.e. data center interconnection (DCI), which applies to edges of DC networks. Service provider sides of network operators try to optimize the core of carrier network. Along with the increasing adoption of ROADM, OTN, and packet switching technologies, traditional two-layer IP/MPLS-over-WDM network has evolved into three-layer IP/MPLS-over-OTN-over-DWDM network and once defined overlay topology is now transitioning to dynamic topologies based on on-demand traffic demands. Network operations are thus divided into three physical sub-networks: DCN, overlay DCI, and multi-layer carrier network. Server virtualization, cloud computing and evolving multilayer carrier network challenge traffic engineering to maximize utilization on all physical subnetworks. The emerging software-defined networking (SDN) architecture moves path computation towards a centralized controller, which has global visibility. Carriers indicate a strong preference for SDN to be interoperable between multiple vendors in heterogeneous transport networks. SDN is a natural way to create a unified control plane across multiple administrative divisions. This thesis contributes SDN-based traffic engineering techniques for maximizing network utilization of DCN, DCI, and carrier network. The first part of the thesis focuses on DCN traffic engineering. Traditional forwarding mechanisms using a single path are not able to take advantages of available multiple physical paths. The state-of-the-art MPTCP (Multipath Transmission Control Protocol) solution uses multiple randomly selected paths, but cannot give total aggregated capacity. Moreover, it works as a TCP process, and so does not support other protocols like UDP. To address these issues, this thesis presents a solution using adaptive multipath routing in a Layer-2 network with static (capacity and latency) metrics, which adapts link and path failures. This solution provides innetwork aggregated path capacity to individual flows, as well as scalability and multitenancy, by separating end-station services from the provider’s network. The results demonstrate an improvement of 14% in the worst bisection bandwidth utilization, compared to the MPTCP with 5 sub-flows. The second part of the thesis focuses on DCI traffic engineering. The existing approaches to reservation services provide limited reservation capabilities, e.g. limited connections over links returned by the traceroute over traditional IP-based networks. Moreover, most existing approaches do not address fault tolerance in the event of node or link failures. To address these issues, this thesis presents ECMP-like multipath routing algorithm and forwarding assignment scheme that increase reservation acceptance rate compared to state-of-art reservation frameworks in the WAN-links between data centers, and such reservations can be configured with a limited number of static forwarding rules on switches. Our prototype provides the RESTful web service interface for link-fail event management and re-routes paths for all the affected reservations. In the final part of the thesis, we focused on multi-layer carrier network traffic engineering. New dynamic traffic trends in upper layers (e.g. IP routing) require dynamic configuration of the optical transport to re-direct the traffic, and this in turn requires an integration of multiple administrative control layers. When multiple bandwidth path requests come from different nodes in different layers, a distributed sequential computation cannot optimize the entire network. Most prior research has focused on the two-layer problem, and recent three-layer research studies are limited to the capacity dimensioning problem. In this thesis, we present an optimization model with MILP formulation for dynamic traffic in a three-layer network, especially taking into account the unique technological constraints of the distinct OTN layer. Our experimental results show how unit cost values of different layers affect network cost and parameters in the presence of multiple sets of traffic loads. We also demonstrate the effectiveness of our proposed heuristic approach