Quantum Data Centers (QDCs) are needed to support large-scale quantum processing for both academic and commercial applications. While large-scale quantum computers are constrained by technological and financial barriers, a modular approach that clusters small quantum computers offers an alternative. This approach, however, introduces new challenges in network scalability, entanglement generation, and quantum memory management. In this paper, we propose a three-layer fat-tree network architecture for QDCs, designed to address these challenges. Our architecture features a unique leaf switch and an advanced swapping spine switch design, optimized to handle high volumes of entanglement requests as well as a queue scheduling mechanism that efficiently manages quantum memory to prevent decoherence. Through queuing-theoretical models and simulations in NetSquid, we demonstrate the proposed architecture's scalability and effectiveness in maintaining high entanglement fidelity, offering a practical path forward for modular QDC networks.

The rapid expansion of global cloud infrastructures and increasing network traffic volume and dynamicity have led to a rise in research on developing scalable and resilient Traffic Engineering (TE) solutions for Wide Area Networks (WANs). Despite recent advancements, striking the right balance between network availability, computational complexity, and resource utilization remains a significant challenge.This paper presents empirical findings that highlight the inherent traffic demand imbalance and link utilization heterogeneity that current TE solutions have overlooked. We then define two new performance metrics, namely critical link set and network criticality, that jointly represent these heterogeneities. We further introduce an efficient extension to the tunnel-based resilient TE algorithm to be adaptive to traffic profiles. An extensive simulation study on representative WAN topologies demonstrates the substantial performance enhancements achieved by our holistic solution approach.

Detecting and mitigating Denial-of-Service (DoS) attacks is crucial for ensuring the availability and security of online services. While various machine learning (ML) models have been utilized for DoS attack detection, there is a need for innovative approaches to improving their performance, especially for the more challenging multi-class detection problem. In this article, we propose adopting a cutting-edge approach called Combinatorial Fusion Analysis (CFA), which leverages a recently developed framework to combine multiple ML models for improved DoS attack detection. Our methodology involves advanced score combination, rank combination, weighted combination techniques, and the diversity strength of scoring systems. Through rigorous performance evaluations, we showcase the efficacy of the combinatorial fusion approach. Our evaluations encompass key metrics such as detection precision, recall, and F1-score, providing comprehensive insights into the interpretability and effectiveness of our approach. We highlight the challenge faced by individual models in classifying low-profiled attacks, while excelling in other attack types. To overcome this limitation, model fusion techniques were used to create a comprehensive model capable of addressing both low-profiled attacks and other traffic types. Furthermore, our findings highlight the potential of this approach for enhancing DoS attack detection capabilities and contributing to the development of more robust defense mechanisms.

Distributed Denial of Service (DDoS) attacks pose an increasingly substantial cybersecurity threat to organizations across the globe. In this paper, we introduce a new deep learning-based technique for detecting DDoS attacks, a paramount cyber-security challenge with evolving complexity and scale. Specifically, we propose a new dual-space prototypical network that leverages a unique dual-space loss function to enhance detection accuracy for various attack patterns through geometric and angular similarity measures. This approach capitalizes on the strengths of representation learning within the latent space (a lower-dimensional representation of data that captures complex patterns for machine learning analysis), improving the model’s adaptability and sensitivity towards varying DDoS attack vectors. Our comprehensive evaluation spans multiple training environments, including offline training, simulated online training, and prototypical network scenarios, to validate the model’s robustness under diverse data abundance and scarcity conditions. The Multilayer Perceptron (MLP) with Attention, trained with our dual-space prototypical design over a reduced training set, achieves an average accuracy of 94.85% and an F1-Score of 94.71% across our tests, showcasing its effectiveness in dynamic and constrained real-world scenarios.

Denial of Service (DoS) attacks pose a significant threat to AI systems security, causing substantial financial losses and downtime. However, AI systems’ high computational demands, dynamic behavior, and data variability make monitoring and detecting DoS attacks challenging. Nowadays, statistical and machine learning (ML)-based DoS classification and detection approaches utilize a broad range of feature selection mechanisms to select a feature subset from networking traffic datasets. Feature selection is critical in enhancing the overall model performance and attack detection accuracy while reducing the training time. In this paper, we investigate the importance of feature selection in improving ML-based detection of DoS attacks. Specifically, we explore feature contribution to the overall components in DoS traffic datasets by utilizing statistical analysis and feature engineering approaches. Our experimental findings demonstrate the usefulness of the thorough statistical analysis of DoS traffic and feature engineering in understanding the behavior of the attack and identifying the best feature selection for ML-based DoS classification and detection.

Most recent network failure diagnosis systems focused on data center networks where complex measurement systems can be deployed to derive routing information and ensure network coverage in order to achieve accurate and fast fault localization. In this paper, we target wide-area networks that support data-intensive distributed applications. We first present a new multi-output prediction model that directly maps the application level observations to localize the system component failures. In reality, this application-centric approach may face the missing data challenge as some input (feature) data to the inference models may be missing due to incomplete or lost measurements in wide area networks. We show that the presented prediction model naturally allows the multivariate imputation to recover the missing data. We evaluate multiple imputation algorithms and show that the prediction performance can be improved significantly in a large-scale network. As far as we know, this is the first study on the missing data issue and applying imputation techniques in network failure localization.

Developing a distributed controller system for pro-duction software defined networks (SDN) requires substantial en-gineering effort to satisfy the stringent performance, adaptability, availability and security requirements that are well beyond the basic function development. The softwarization nature of SDN provides the opportunity to leverage the recent advancements in software engineering and system automation. In this paper we present our recent work to develop and deploy a wide-area SDN control framework prototype towards production deployment and operation. We primarily focus on three critical areas (1) enhancement of the software functions and the substrate virtualization configurations to fully support advanced connection services and fault tolerance in both data plane and control plane (2) a high-fidelity testing pipeline con-sisting of unit tests emulation and a testbed and (3) a continuous integration and continuous deployment (CI/CD) pipeline. Our experience proved that the presented environment and process greatly increased the software quality and development efficiency which would ultimately lead to a reliable and continuous deploy-ment and automation of the targeted production SDN network.

To support latency-sensitive applications (e.g., emergency response) in Software-Defined Networking (SDN) environments, reliable Quality of Service (QoS) mechanisms are needed to ensure the minimization of end-to-end (E2E) latency and control response time. In this research, we design a double-queue system with feedback, named QoSP, to improve data-control communication performance and impose efficient queueing control in SDN. We further study a priority-based queueing scheme to achieve differentiated QoS provisioning via maintaining multiple OpenFlow queues with different priorities at the switch ports of a data plane. The prototype of QoSP is implemented and evaluated on the NSF-sponsored GENI testbed.

Software-defined networking (SDN) is an emerging networking technology and allows for a separation of data and control planes traffic in order to enhance the Quality of Service (QoS) for traffic and services delivery. Latency metric is regarded as a critical parameter by service providers and end users alike, where inter-link delays can be measured using end-to-end (E2E) probing packets. Moreover, to support latency-sensitive applications in SDN such as emergency response, we need a comprehensive QoS mechanism to ensure the minimization of E2E latency, the efficacious calculation of forwarding paths, and the minimization of control response time. We first distinguish between a flow’s admission latency and a flow’s packet forwarding latency. The former is decided by its controller’s response time, and the latter by its data plane queue delay. We then design a two-queue system with feedback, named QoSP, that aims to control overall latency performance in SDN through improving data-control communication performance and imposing efficient queueing control. We specifically introduce a priority-based queueing discipline in the data plane to achieve differentiated QoS provisioning via maintaining multiple OpenFlow queues with different priorities at each switch port. We implement the proposed QoSP using a Floodlight SDN controller and conduct emulation studies on the Global Environment for Networking Innovations. Our evaluation shows that QoSP can optimize the E2E delay and significantly reduce the control response time for priority traffic.

The centralized control capability of Software Defined Networking (SDN) presents a unique opportunity for enabling Quality of Service (QoS) routing. For delay sensitive traffic flows, a QoS mechanism requires efficiently computing path latency and minimizing controller's response time. At the core of the challenges is how to handle short term network state fluctuations in terms of congestion and latency while guaranteeing the end-to-end latency performance of networking services. In this paper, we present LatencySmasher, a systematic framework that considers active link latency measurements, efficient statistic estimate of network states, and fast adaptive path computation. We first implement LatencySmasher as an SDN controller application and then conduct extensive experimental studies on the Global Environment for Network Innovations (GENI), a real-world distributed network testbed. Our performance evaluation shows that the proposed framework can find optimal end-to-end paths with minimum latency and significantly reduce the control overhead.

Auction-based resource allocation and pricing mechanisms have attracted substantial research interests to enhance the utility gain and fairness of cloud platforms. A fundamental problem in cloud resource auction design that has not been fully addressed is how to ensure the timely execution of applications while allocation decisions have to be made online. Adding to the complexity of designing a truthful and efficient mechanism is that application requests are normally heterogeneous in resource demand and execution requirement. In this paper, we present a novel online combinatorial auction mechanism for deadline- aware multi-resource allocation for cloud platforms, which achieves both strategy-proofness and approximate efficiency on social welfare.

In a distributed computing environment, guaranteeing the hard deadline for real-time messages is essential to ensure schedulability of real-time tasks. Since capabilities of the shared resources for transmission are limited, e.g., the buffer size is limited on network devices, it becomes a challenge to design an effective and feasible resource sharing policy based on both the demand of real-time packet transmissions and the limitation of resource capabilities. We address this challenge in two cooperative mechanisms. First, we design a static routing algorithm to find forwarding paths for packets to guarantee their hard deadlines. The routing algorithm employs a validation-based backtracking procedure capable of deriving the demand of a set of real-time packets on each shared network device, and it checks whether this demand can be met on the device. Second, we design a packet scheduler that runs on network devices to transmit messages according to our routing requirements. We implement these mechanisms on virtual software-defined network (SDN) switches and evaluate them on real hardware in a local cluster to demonstrate the feasibility and effectiveness of our routing algorithm and packet scheduler.

When multiple computational resource elements collaborate to handle events in a cyber-physical system, scheduling algorithms on these resource elements and the communication delay between them contribute to the overall system utilization and schedulability. Employing earliest deadline first (EDF) scheduling in real-time cyber-physical systems has many challenges. First, the network layer of a resource has to interrupt and notify the scheduler about the deadlines of arrived messages. The randomness of interruption makes context switch costs unpredictable. Second, lack of globally synchronized clocks across resources renders event deadlines derived from local clocks and piggybacked in messages meaningless. Third, communication delay variances in a network increase the unpredictability of the system, e.g., When multiple resources transmit message bursts simultaneously. We address these challenges in this work. First, we combine EDF scheduling with periodic message transmission tasks. Then, we implement an EDF-based packet scheduler, which transmits packets considering event deadlines. Third, we employ bandwidth limitations on the transmission links of resources to decrease network contention and network delay variance. We have implemented our hybrid EDF scheduler in a real-time distributed storage system. We evaluate it on a cluster of nodes in a switched network environment resembling a distributed cyber-physical system to demonstrate the real-time capability of our scheduler.

In this paper we address the problem of implementing wide-area oscillation monitoring algorithms for large power system networks using distributed processing of Synchrophasor measurements. We consider two computational approaches, namely decentralized least squares (DLS) and its recursive implementation (RLS). Both algorithms are executed using multiple phasor data concentrators (PDC), deployed as virtual computing machines communicating over a fiber-optic communication network. Results are demonstrated using the US-Wide ExoGENI communication network connected to a PMU test bed at NC State University, and analyze the end-to-end computational and communication delays for both algorithms.

Currently, the North American power grid uses a centralized system to monitor and control wide-area power grid states. This centralized architecture is becoming a bottleneck as large numbers of wind and photo-voltaic (PV) generation sources require real-time monitoring and actuation to ensure sustained reliability. We have designed and implemented a distributed storage system, a real-time distributed hash table (DHT), to store and retrieve this monitoring data as a real-time service to an upper layer decentralized control system. Our real-time DHT utilizes the DHT algorithm Chord in a cyclic executive to schedule data-lookup jobs on distributed storage nodes. We formally define the pattern of the workload on our real-time DHT and use queuing theory to stochastically derive the time bound for response times of these lookup requests. We also define the quality of service (QoS) metrics of our real-time DHT as the probability that deadlines of requests can be met. We use the stochastic model to derive the QoS. An experimental evaluation on distributed nodes shows that our model is well suited to provide time bounds for requests following typical workload patterns and that a prioritized extension can increase the probability of meeting deadlines for subsequent requests.

Advanced networks are an essential element of data-driven science enabled by next generation cyberinfrastructure environments. Computational activities increasingly incorporate widely dispersed resources with linkages among software components spanning multiple sites and administrative domains. We have seen recent advances in enabling on-demand network circuits in the national and international backbones coupled with Software Defined Networking (SDN) advances like OpenFlow and programmable edge technologies like OpenStack. These advances have created an unprecedented opportunity to enable complex scientific applications to run on specially tailored, dynamic infrastructure that include compute, storage and network resources, combining the performance advantages of purpose-built infrastructures, but without the costs of a permanent infrastructure. This work presents an experience deploying scientific workflows on the ExoGENI national test bed that dynamically allocates computational resources with high-speed circuits from backbone providers. Dynamically allocated bandwidth-provisioned high-speed circuits increase the ability of scientific applications to access and stage large data sets from remote data repositories or to move computation to remote sites and access data stored locally. The remainder of this extended abstract is a brief description of the test bed and several scientific workflow applications that were deployed using bandwidth-provisioned high-speed circuits.

This paper presents the design, implementation, and evaluation of a new system for on-demand provisioning of Hadoop clusters across multiple cloud domains. The Hadoop clusters are created "on-demand" and are composed of virtual machines from multiple cloud sites linked with bandwidth-provisioned network pipes. The prototype uses an existing federated cloud control framework called Open Resource Control Architecture (ORCA), which orchestrates the leasing and configuration of virtual infrastructure from multiple autonomous cloud sites and network providers. ORCA enables computational and network resources from multiple clouds and network substrates to be aggregated into a single virtual "slice" of resources, built to order for the needs of the application. The experiments examine various provisioning alternatives by evaluating the performance of representative Hadoop benchmarks and applications on resource topologies with varying bandwidths. The evaluations examine conditions in which multi-cloud Hadoop deployments pose significant advantages or disadvantages during Map/Reduce/Shuffle operations. Further, the experiments compare multi-cloud Hadoop deployments with single-cloud deployments and investigate Hadoop Distributed File System (HDFS) performance under varying network configurations. The results show that networked clouds make cross-cloud Hadoop deployment feasible with high bandwidth network links between clouds. As expected, performance for some benchmarks degrades rapidly with constrained inter-cloud bandwidth. MapReduce shuffle patterns and certain Hadoop Distributed File System (HDFS) operations that span the constrained links are particularly sensitive to network performance. Hadoop's topology-awareness feature can mitigate these penalties to a modest degree in these hybrid bandwidth scenarios. Additional observations show that contention among co-located virtual machines is a source of irregular performance for Hadoop applications on virtual cloud infrastructure.

Emerging high-end applications require a rich set of network provisioning services that go beyond the traditional source-destination, end-to-end path service. They also require high-bandwidth and high-quality circuit services that only optical networks could offer. In this paper, we first analyze some representative high-end Grid applications and abstract their needed generic set of network provisioning services. Then we provide some preliminary analysis on the routing, resource allocation, and survivability mechanisms of these generic network services. The focus is on the temporal and spatial extensions over the traditional network service definitions. We also introduce our implementation and experimental activities within the Enlightened Computing project and present ongoing research work.

This article describes a distributed classroom experiment carried out by five universities in the US and Europe at the beginning of 2007. This experiment was motivated by the emergence of new digital media technology supporting uncompressed high-definition video capture, transport and display as well as the networking services required for its deployment across wide distances. The participating institutes have designed a distributed collaborative environment centered around the new technology and applied it to join the five sites into a single virtual classroom where a real course has been offered to the registered students. Here we are presenting the technologies utilized in the experiment, the results of a technology evaluation done with the help of the participating students and we identify areas of future improvements of the system. While there are a few hurdles in the path of successfully deploying this technology on a large scale, our experiment shows that the new technology is sustainable and the significant quality improvements brought by it can help build an effective distributed and collaborative classroom environment.

Grid allows creation of abundant services by sharing widely distributed heterogeneous resources. As the Grid technology evolves, the network is elevated as the first-class resource like other Grid resources, i.e. CPU and storages, which can be managed and scheduled altogether. In this paper, we first propose a Grid internetworking architecture based on PCE (Path Computation Element) model. Within this architecture, a virtual service domain framework is presented in a multiple-domain environment. Furthermore, a PCE-based network element and a signaling mechanism are designed to address the inter-domain routing and resource reservation. Within our knowledge, this is the first work ever for Grid internetworking based on IETF PCE protocol.

rdquoWe present a novel group-based mechanism to reconfigure the virtual topology of survivable MPLS/WDM networks by using the existing shared protection backup resource. With this mechanism, lightpaths are divided into groups and those in the same group can be reconfigured simultaneously at one step. Ideally, this mechanism won't incur any service disruption during the reconfiguration process. The optimal reconfiguration policy is obtained through solving following two problems: the Grouping problem which minimizes the reconfiguration steps, and the Sequencing problem which minimizes the network resource used during the reconfiguration process. We prove these two problems to be NP-hard and present efficient heuristic algorithms. A general mathematical method of rollout is applied to the heuristics to improve the solution quality. Numerical results are presented to show the optimal tradeoff between the reconfiguration duration and the required redundant capacity.

In a sensor network, usually a large number of sensors transport data messages to a limited number of sinks. Due to this multipoint-to-point communications pattern in general homogeneous sensor networks, the closer a sensor to the sink, the quicker it will deplete its battery. This unbalanced energy depletion phenomenon has become the bottleneck problem to elongate the lifetime of sensor networks. In this paper, we consider the effects of joint relay node deployment and transmission power control on network lifetime. Contrary to the intuition the relay nodes considered are even simpler devices than the sensor nodes with limited capabilities. We show that the network lifetime can be extended significantly with the addition of relay nodes to the network. In addition, for the same expected network lifetime goal, the number of relay nodes required can be reduced by employing efficient transmission power control while leaving the network connectivity level unchanged. The solution suggests that it is sufficient to deploy relay nodes only with a specific probabilistic distribution rather than the specifying the exact places. Furthermore, the solution does not require any change on the protocols (such as routing) used in the network.

This paper studies the important fault management issue with focus on the fast restoration mechanisms for optical burst switched (OBS) networks. In order to reduce the burst losses during the restoration process, effective fast restoration schemes are necessary. This is illustrated via two basic fast restoration schemes, the distributed deflection scheme and the local deflection scheme, compared with the slow global routing update mechanism. A novel priority-based QoS restoration scheme is also proposed to provide differentiated restoration services. Through detailed descriptive analysis and a comprehensive simulation study, these fast restoration schemes demonstrate fast restoration process, low fault management overheads, and excellent burst loss performance. As far as we know, this is the first comprehensive study on the restoration mechanisms for OBS networks.

It has been widely recognized that physical layer impairments, including power losses, must be taken into account when routing optical connections in transparent networks. In this paper we study the problem of constructing light-trees under optical layer power budget constraints, with a focus on algorithms which can guarantee a certain level of quality for the signals received by the destination nodes. We define a new constrained light-tree routing problem by introducing a set of constraints on the source-destination paths to account for the power losses at the optical layer. We investigate a number of variants of this problem, we characterize their complexity, and we develop a suite of corresponding routing algorithms; one of the algorithms is appropriate for networks with sparse light splitting and/or limited splitting fanout. We find that, in order to guarantee an adequate signal quality and to scale to large destination sets, light-trees must be as balanced as possible. Numerical results demonstrate that existing algorithms tend to construct highly unbalanced trees, and are thus expected to perform poorly in an optical network setting. Our algorithms, on the other hand, are designed to construct balanced trees which, in addition to having good performance in terms of signal quality, they also ensure a certain degree of fairness among destination nodes. While we only consider power loss in this work, the algorithms we develop could be appropriately modified to account for other physical layer impairments, such as dispersion.