Publish/subscribe systems on the traditional Internet suffer from poor scalability and high delay in the face of the Internet of Things (IoT) environment. Being customizable, the paradigm of software-defined networking (SDN) provides a chance to establish an IoT-specific network. In this paper, we propose an SDN-based publish/subscribe system named SDNPS, which can construct and fine-tune topic-connected overlays for the sake of disseminating events efficiently and non-redundantly based on a global topology overview. It organizes topics as a Huffman-like topic tree and codes them into binary strings so that filtering and forwarding events can be operated directly on SDN-configurable switches, which helps to reduce end-to-end latency. This hierarchical organization form of topic tree makes it possible to incrementally construct and store overlays, which contribute to reducing the time and space complexity of routing computation. More specifically, it achieves a better tradeoff between load-balancing of the overall optimization objective and the minimal forwarding cost of per-topic overlay.

Natural answer generation (NAG) is more and more popular in real-world knowledge base question answering (KBQA) systems for being able to automatically generate natural language answers with structured KB. Large-scale community QA-pairs crawled from the Internet could be directly used to train NAG models. However, it is pervasive in these datasets that one question may contain multiple answers of varied quality, and NAG models suffer from the simple principle of equal treatment of these answers. To address this problem, we propose two kinds of attention-based algorithms to handle all answers to a question at a time. Selective attention and self-attention mechanisms are used to dynamically weight the answers to one question during the training process. Specifically, selective attention methods weight the answers using the relationships between all the KB objects the question needs and the generated answers, and self-attention methods weight them according to the generating difficulty. The experiments on the public open-domain community QA dataset demonstrate that Selective-ATT outperforms the state-of-the-art by 10.53% in the entity accuracy, 9.34% in the BLEU score, and 1.19% in the Rouge score.

At present, most publish/subscribe middlewares suppose that there are equal Quality of Service (QoS) requirements for all users. However, in many real-world Internet of Things (IoT) service scenarios, different users may have different delay requirements. How to provide reliable differentiated services has become an urgent problem. The rise of Software-Defined Networking (SDN) provides endless possibilities to improve the QoS of publish/subscribe middlewares due to its greater programmability. We can encode event topics and priorities into flow entries of SDN switches directly to meet customized requirements. In this paper, we first propose an SDN-like publish/subscribe middleware architecture and describe how to use this architecture and priority queues supported by OpenFlow switches to realize differentiated services. Then we present a machine learning method using the eXtreme Gradient Boosting (XGBoost) model to solve the difficult issue of getting the queuing delay of switches accurately. Finally, we propose a reliable differentiated services guarantee mechanism according to the queuing delay and the programmability of SDN to improve QoS, namely, a two-layer queue management mechanism. Experimental evaluations show that the delay predicted by the XGBoost method is closer to the real value; our mechanism can save end-to-end delay, reduce packet loss rate, and allocate bandwidth more reasonably.

Virtualization is a key technology in cloud computing; it can accommodate numerous guest VMs to provide transparent services, such as live migration, high availability, and rapid checkpointing. Cloud computing using virtualization allows workloads to be deployed and scaled quickly through the rapid provisioning of virtual machines on physical machines. However, I/O virtualization, particularly for networking, suffers from significant performance degradation in the presence of high-speed networking connections. In this paper, we first analyze performance challenges in network I/O virtualization and identify two problems-conventional network I/O virtualization suffers from excessive virtual interrupts to guest VMs, and the back-end driver does not efficiently use the computing resources of underlying multicore processors. To address these challenges, we propose optimization methods for enhancing the networking performance: 1) Efficient interrupt coalescing for network I/O virtualization and 2) virtual receive-side scaling to effectively leverage multicore processors. These methods are implemented and evaluated with extensive performance tests on a Xen virtualization platform. Our experimental results confirm that the proposed optimizations can significantly improve network I/O virtualization performance and effectively solve the performance challenges.

Complex Event Processing (CEP) systems extract interest situations from event flows based on event detection patterns. However, local event processing for distributed Internet of Things (IoT) has not been discussed yet. Besides, it is complex or impossible to discover such patterns in some applications of IoT. In this article, we design a complex event service to process event flows based on IoT resource models, which does not depend on existing patterns, and deals with both discrete events and continuous variables. To improve the CEP performance, local IoT resources are used for local event processing, and a lazy exchange method is designed to realize the collaborated event processing between network edges and a data center. Our evaluation shows that our solution is feasible and effective.