In recent years, since edge computing has improved the performance of transportation systems, research on edge computing-enabled transportation systems has received widespread attention. However, most previous studies overlooked that task requests in transportation systems are unevenly distributed in time and space, which easily causes the overloading of edge servers, resulting in high response latency. To this end, we present a novel task offloading scheme based on Graph Neural Network (GNN) and Deep Reinforcement Learning (DRL) in Edge computing-enabled Transportation systems (TransEdge). Specifically, we first propose an adaptive node placement algorithm to assign IoT sensors to appropriate edge servers, thereby minimizing transmission latency. Then, an improved DRL scheme based on GNN is designed to capture the spatial features between sensors, aiming to improve the accuracy of task offloading decisions. Finally, we introduce a task forwarding strategy based on the greedy algorithm to achieve collaborative task offloading between different edge servers and overcome the system instability caused by a sudden surge in task requests. We conduct extensive experiments on two real-world traffic datasets. The results show that TransEdge reduces the response latency by at least 3.7% compared to four baselines while achieving a success rate of 99%.

Server consolidation is critical for energy efficiency of cloud-scale computing. In production environments like Aliyun, which is one of the largest public cloud platforms in the world, server consolidation has several challenges. First, the widespread use of local storage remarkably increases the migration cost (time). Second, the resource utilization of service instances varies over time, which may result in migration oscillation. Third, server consolidation must follow practical constraints. E.g., instances can only be migrated within a maintenance window, and both the resource utilization and the number of instances on a server are bounded. This paper designs and implements C4, a Cost-Efficient Consolidating Service for Aliyun's Cloud-Scale Computing, which Aliyun uses to consolidate servers. We analyze user pattern, resource utilization, and migration cost in Aliyun, showing that traditional utilization-based consolidation approaches cannot meet the needs in production environments, especially for the local-storage-based computing. This motivates us to propose the migration cost model, by which to select servers with the minimum migration time to release. We use the Worst-Fit heuristic to migrate instances to balance the load. Evaluation shows that C4 achieves cost-efficient, load-balanced, and oscillation-free consolidating service. We describe experience with over one year of C4 production deployment, lessons learned, and areas for future work.

In recent years, distributed denial of service (DDoS) attacks have increasingly shown the trend of multiattack vector composites, which has significantly improved the concealment and success rate of DDoS attacks. Therefore, improving the ubiquitous detection capability of DDoS attacks and accurately and quickly identifying DDoS attack traffic play an important role in later attack mitigation. This paper proposes a method to efficiently detect and identify multivector DDoS attacks. The detection algorithm is applicable to known and unknown DDoS attacks.

The accelerated integration of edge computing and artificial intelligence has promoted the rise of edge intelligence, which is regarded as the key to solving the last-mile delivery problem of artificial intelligence technology. Although previous research has made many efforts in this area, they have struggled to serve the scenario containing multiple heterogeneous task requests. This challenge is further exacerbated when the number of terminal devices increases and multiple edge servers are required to collaborate to handle task requests. To this end, this paper proposes a fine-grained Collaborative DNN Model Selection scheme for heterogeneous edge computing systems (CoMS), aiming to promote cooperation between edge servers and achieve more effective model selection. Specifically, we first design a reinforcement learning scheme with real-time dynamic normalization strategy, aiming to accelerate model convergence and improve the efficiency of model selection. Next, we introduce a model selection strategy based on greedy algorithm and an efficient fine-grained collaborative model selection strategy respectively to promote cooperation between edge servers, thereby further achieving a balance between inference accuracy and overhead. Extensive experimental results show that compared with baselines, our CoMS reduces the average trade-off overhead by 4.2% to 12.8% and improves the success ratio by 3% to 16%.