SDN data plane generally utilizes TCAM to accommodate flow tables for fast packet classification, which also leads to serious problem of high energy consumption. Existing techniques are difficult to stably achieve satisfactory energy-saving effect especially under network traffic jitters. To address this problem, this paper first designs an elastic energy-saving cache to always keep sufficient number of active exact flows for stably high cache hit rates even under network traffic jitters. Particularly, we adaptively adjust the number of cache segments, in terms of the relationship between current cache hit rate and its preset expected range, to maintain high cache hit rate. Meanwhile, we regulate the threshold of packet inter-arrival time for identifying active exact flows, in accordance with current cache occupancy rate, to match the number of active exact flows with cache capacity. Furthermore, we theoretically derive cache occupancy rate based on randomly mapping assumption of each active exact flow and cache hit rate based on the assumption of flow activity degree model. Subsequently, we build an elastically energy-saving flow table storage architecture, by applying the elastic energy-saving cache to always enable a majority of incoming packets to bypass energy-hungry TCAM flow table lookups. Finally, we set up an experimental SDN platform to evaluate its performance on real network traffic traces. Experimental results indicate that our built flow table storage architecture steadily achieves high energy saving rates around 82.43% even under network traffic jitters, with the increase of 6.32˜7.55% compared to the state-of-the-art one.

In the paradigm of Software-Defined Networking (SDN), its data plane generally perform packet forwarding based on flow table lookup on TCAM with high energy consumption. Popular energy-saving methods employ caching techniques for most packets to bypass energy-intensive TCAM lookups. However, existing energy-saving caches cannot adapt to network traffic fluctuation with sufficient utilization of cache space due to non-negligible hash conflicts. To overcome this issue, we design a traffic-aware energy-saving cache with optimal utilization for TCAM flow tables in SDN data plane. In particular, we first devise a nearly conflict-free hashing algorithm for the cache called FelisCatus, which provides three candidate locations for each incoming flow by adjacent hopping, and searches for an empty or replaceable entry for each conflicting flow by co-directional kicking. Then, we propose an adaptive adjustment mechanism of flow activity criterion, i.e., packet inter-arrival time threshold, for enabling the cache to consistently accommodate the most active exact flows in network traffic. Furthermore, we build an energy-efficient SDN flow table storage architecture by applying the above cache and exploiting the accessing features of different memories. Finally, we verify the performance of our designed energy-saving cache and flow table storage architecture by experiments with backbone network traffic traces. Experimental results indicate that, our designed energy-saving cache obtains stable and high hit rates around 75% even under network traffic fluctuation, and our proposed flow table storage architecture achieve high energy saving rates around 71%, with the increase of 7.89% compared to state-of-the-art ones.

It is well-established that terrestrial communication systems may fail during emergencies such as earthquakes, tsunamis, and floods. Fortunately, with the rapid advancement of unmanned aerial vehicle (UAV) network technology, deploying UAV nodes as aerial base stations (BSs) is assuming an increasingly crucial role in facilitating downlink transmissions and restoring ground communication capabilities. However, a single UAV node is not sufficient to meet the requirements. Inspired by distributed communication, we introduce a performance analysis framework based on stochastic geometry to analyze the distributed coordinated multi-point (CoMP) UAV-assisted communication network. Specifically, we assume that all UAV nodes follow a homogeneous Poisson point process (PPP) and maintain a constant altitude. The entire space is tessellated by multiple hexagons, with multiple UAV nodes within each hexagonal region working together to serve terrestrial user equipments (UEs). For this region-centric cooperative model, we derive an exact expression for the coverage probability to quantify the performance improvement enabled by UAVs, analyze the upper bound of the coverage probability, and provide a simplified approximation. We then compare this model to a user-centric model. Our numerical findings demonstrate that the cooperation of UAV nodes can significantly enhance the coverage probability and save spectrum resources.

Security and forensics represent two key components for network management, especially to guarantee the trusted operation of massive access networks such as the Internet of Things (IoT). As a core technology to provide low latency and high communication for IoT, Mobile Edge Computing (MEC) pulls computing resources from remote cloud centers to devices. The process of MEC service involves three types of entities: devices, data generated by devices and digital evidence generated after the data interaction. These entities are fully distributed and difficult to protect through traditional, highly centralized security and authentication mechanisms. As a decentralized shared ledger and database, the emerging blockchain is considered to provide cooperative trust and collaborative action among multiple subjects while ensuring the integrity and confidentiality of data. Because of its anonymity, non-tampering and traceability, the blockchain arouses research on the combination of blockchain and edge computing for device security, data security and forensics in IoT. This survey analyzes the application of blockchain in MEC-IoT systems and mainly focuses on approaches and technologies to manage the security and forensics issues for IoT. Finally, we present open issues and prospects for future work and research directions.