Fractional vegetation cover (FVC) retrieval results of high spatial resolution satellite remote sensing images are usually upscaled as training and validation data (FVCUIH) for low spatial resolution satellite remote sensing images. However, few studies have focused on the impact of the spatial scale conversion on the evaluation of FVC retrieval accuracy. In this study, we first investigated the influence of spatial scale conversion on FVC retrieval accuracy based on FVC measured by unmanned aerial vehicle (FVCUAV) at three scales (Sentinel-2 MSI, Landsat-8 OLI, and MODIS). Then, the NDVI threshold method is proposed to further analyze the uncertainty caused by the underlying surface heterogeneity. The results showed that the use of FVCUIHas training and validation data in the process of spatial scale conversion led to overestimation of FVC accuracy, and its influence on FVC retrieval cannot be ignored. In addition, the uncertainty of the underlying surface heterogeneity at the measured sites increased the uncertainty of the FVC retrieval, while these results could be optimized by detecting the underlying surface heterogeneity. Our results suggested that both spatial scale conversion and underlying surface heterogeneity would cause the inaccurate FVC retrieval, while the latter could be optimized by detecting the underlying surface heterogeneity. This study provided a reference for the improvement of multiscale FVC retrieval accuracy based on single-scale FVC-measured data.

Software-based virtual switches are indispensable in multi-tenant cloud networks. They either work as bridges between virtual machines and the underlying networks, or act as the virtual network function carriers for flexible service chaining and orchestration. Therefore, high-accuracy monitoring of virtual switches is significant for ease of data center network management. The recently proposed In-band Network Telemetry, which relies on the protocol-independent switch architecture (PISA), can achieve the monitoring requirements. However, not all the virtual switches with production quality are P4-based or built under the PISA. In this work, we provide the design and implementation of label-based INT and probe-based INT on top of OVS and VPP, the two mainstream software-based virtual switches with non-PISA architecture. Extensive evaluation shows that our implementation has low performance overhead in terms of forwarding latency, packet loss ratio and CPU consumption. Under 100Mbps traffic pressure, the CPU overhead of INT on OVS and VPP are less than 0.1% and 0.5%, and the switch latency are added by less than$4 \mu\mathrm{s}$and$3\mu\mathrm{s}$, respectively.

Visibility is essential for operating and troubleshooting intricate networks. In-band Network Telemetry (INT) has been embedded in the latest merchant silicons to offer high-precision device and traffic state visibility. INT is actually an underlying technique and each INT instance covers only one monitoring path. The network-wide measurement coverage therefore requires a high-level orchestration to provision multiple INT paths. An optimal path planning is expected to produce a minimum number of paths with a minimum number of overlapping links. Eulerian trail has been used to solve the general problem. However, in production networks, the vantage points where one can deploy probes to start and terminate INT paths are constrained. In this work, we propose an optimal path planning algorithm, INT-probe, which achieves the network-wide telemetry coverage under the constraint of stationary probes. INT-probe formulates the constrained path planning into an extended multi-depot k-Chinese postman problem (MDCPP-set) and then reduces it to a solvable minimum weight perfect matching problem. We analyze algorithm's theoretical bound and the complexity. Extensive evaluation on both wide area networks and data center networks with different scales and topologies are conducted. We show INT-probe is efficient, high-performance, and practical for real-world deployment. For a large-scale data center networks with 1125 switches, INT-probe can generate 112 monitoring paths (reduced by 50.4 %) by allowing only 1.79% increase of the total path length, promptly resolving link failures within 744.71ms.

Introducing the words of texts, entities, and relations of a knowledge graph (KG) into the same semantic space has great significance in KG complement and knowledge computing. Current methods mainly utilize the "alignment constraint" of words and entities to construct uniform objective functions. However, the "alignment constraint" limits the joint representation space to specific KGs and texts. Meanwhile, the representation effect still suffers from the scale of the "alignment constraint". This paper propose a novel transference framework, the method firstly learns the text representation space and KG representation space independently, and then transfers the word representation in the text space to the knowledge space with projection models, and finally constructs a joint representation space. Our approach can decrease the dependency on "alignment constraint", and allow two spaces to be optimized and extended independently. Hence, it has better flexibility, general applicability and helps improve the capability of the joint representation space. Further more, to enhance the word transference performance, we incorporate the relation constraint into the mapping models. To the best of our knowledge, this is the first study using transference method to construct the joint semantic space. The experimental results show that linear mapping models are more suitable than nonlinear models during the projection process. The results of word analogy and relation extraction tasks illustrate the effectiveness of our method compared with state-of-the-art methods.

Power-aware traffic engineering via coordinated sleeping is usually formulated into Integer Programming problems, which are generally NP-hard with unbounded computation time for large-scale networks. This results in delayed control decision making in dynamic network environments. Motivated by advances in deep Reinforcement Learning, we consider building intelligent systems that learn to adaptively change router/switch’s power state according to changing network conditions. Neural network’s forward propagation can greatly speed up power on/off decision making. Generally, conducting RL requires a learning agent to iteratively explore and perform the "good" actions based on the feedback from the environment. By coupling Software-Defined Networking for performing centrally calculated actions to the environment and In-band Network Telemetry for collecting feedback from the environment, we develop GreenTE.ai, a closed-loop control/training system to automate power-aware traffic engineering. Furthermore, we propose novel techniques to enhance the learning ability and reduce the learning complexity. With both energy efficiency and traffic load balancing considered, GreenTE.ai can generate reasonable power saving actions within 276ms under a network testbed of 11 software P4 switches.

Information-Centric Networking (ICN) provides scalable and efficient content distribution at the Internet scale due to in-network caching and native multicast. To support these features, a content router needs high performance at its data plane, which consists of three forwarding steps: checking the Content Store (CS), then the Pending Interest Table (PIT), and finally the Forwarding Information Base (FIB). In this work, we build an analytical model of the router and identify that CS is the actual bottleneck. Then, we propose a novel mechanism called “NB-Cache” to address CS’s performance issue from a network-wide point of view. In NB-Cache, when packets arrive at a router whose CS is fully loaded, instead of being blocked and waiting for the CS, these packets are forwarded to the next-hop router, whose CS may not be fully loaded. This approach essentially utilizes Content Stores of all the routers along the forwarding path in parallel rather than checking each CS sequentially. NB-Cache follows a design pattern of on-demand load balancing and can be formulated into a non-trivial N-queue bypass model. We use the Markov chain to establish its theoretical base and find an algorithm for automated transition rate matrix generation. Experiments show significant improvement of data plane performance: 70% reduction in round-trip time (RTT) and 130% increase in throughput. NB-Cache decouples the fast packet forwarding from the slower content retrieval thus substantially reducing CS’s heavy dependency on fast but expensive memory.

Network reliability becomes increasingly important in modern data center networks (DCNs). The DCNs are expected to work sustainably under internal failures and assist network operators in troubleshooting them rapidly. However, some network failures will happen silently with packets discarded without producing any explicit notification before causing tremendous damage to the network. To troubleshoot these "gray failures", in this work, we present a rapid gray failure detection and localization mechanism based on the recently proposed In-band Network Telemetry (INT). Specifically, we leverage simplified INT probe packets to conduct network-wide telemetry to help the servers under ToR switches obtain all the feasible paths between sources and destinations. Once a network failure occurs, the affected thus unavailable paths will immediately be detected and flushed out of the path information table at each server by a timeout mechanism. Hence, servers can proactively perform source routing-based fast traffic reroute to avoid massive packet loss and retain uninterrupted quality of experience. At the meantime, all the aged path entries will be uploaded to a remote controller for centralized failure localization by identifying common path elements. To verify the feasibility of our design, we build a virtual network testbed with software P4 switches and a Redis database. Evaluation shows that our system can successfully detect network gray failures and reroute the affected traffic in no time while complete failure localization within only a few seconds.

With the ever-increasing complexity of networks, fine-grained network monitoring enables better network reliability and timely feedback control. The In-band Network Telemetry (INT) allows cost-effective network monitoring by encapsulating device-internal states into probe packets. However, INT only specifies an underlying device-level primitive while how to achieve network-wide traffic monitoring remains undefined. In this work, we propose INT-path, a network-wide telemetry framework, by decoupling the system into a routing mechanism and a routing path generation policy. Specifically, we embed source routing into INT probes to allow specifying the route the probe packet takes through the network. Above the mechanism, we develop an Euler trail-based path planning policy to generate non-overlapped INT paths that cover the entire network with a minimum path number. Besides, an exhaustive analysis of algorithm's run-time complexity is also provided. INT-path can “encode” the network-wide traffic status into a series of “bitmap images”, transforming network troubleshooting into pattern recognition problems. INT-path is very suitable for deployment in data center networks thanks to their symmetric network topologies.

Information-Centric Networking (ICN) provides scalable and efficient content distribution at the Internet scale due to its in-network caching and native multicast capabilities. To support these features, a content router needs high performance at its data plane, which consists of three forwarding steps: checking the Content Store (CS), then the Pending Interest Table (PIT), and finally the Forwarding Information Base (FIB). While prior works focus on performance optimization of a single step, we build an analytical model of content router's entire data plane and identify that CS is the actual bottleneck in the pipeline. Compared with PIT and FIB, CS is more challenging because it has more data to read/write, may have more entries in its table to store and lookup, and needs to organize content objects to sustain frequent cache replacement. Then, we propose a novel mechanism called "NB-Cache" to address CS's performance issue from a network-wide point of view rather than a single router's. In NB-Cache, when packets arrive at a router whose CS is fully loaded, instead of being blocked and waiting for the CS, these packets are forwarded to the next-hop router, whose CS may not be fully loaded. This approach essentially utilizes Content Stores of all the routers along the forwarding path in parallel rather than checking each CS sequentially. Our experiments show significant improvement of data plane performance: 70% reduction in round-trip time (RTT) and 130% increase in throughput.