This article primarily investigates the tracking control problem of high-order uncertain nonlinear systems with odd-rational-power under false data injection (FDI) attacks and malicious attacks, based on the fully actuated system (FAS) theory. Due to the corruption of the state information by an additional attack signal, the true state information cannot be directly used for controller design. To mitigate the impact of unknown FDI attacks, a coordinate transformation is applied using the attacked state. In addition, using a piecewise smooth function approaching a saturation function, a new lemma is proposed to deal with the unknown control gain of the prescribed-time control input saturation and malicious attacks problem. Theoretical analysis demonstrates that the tracking errors converge in the prescribed time and all closed-loop system signals remain bounded. Finally, a numerical example is provided, along with a practical case study based on a single-link robotic manipulator, to validate the effectiveness of the proposed method.

The virtual machine (VM) placement considering the topology constraints is difficult because the unpredictable topological VMs raise additional structural requirements (including the affinity, anti-affinity and fault-domain) on the resource pool. Thus, the service level agreement (SLA) can be violated even when the occupancy of the resource pool is quite modest. In order to solve this problem, we propose an efficient buffer-migration-based heuristic online algorithm. First, we build an integer programming model for the topology-aware VM placement problem. Second, we propose a hierarchical resource-preserving online approach, where the Rack and physical machine (PM) nodes are selected in the upper and lower layers respectively. Finally, we utilize the buffer to place and migrate the unfitted VMs to enhance the capacity of the resource pool. The proposed approach is tested with high proportional topological VM requests (nearly 60%) in the resource pool with the scale of 500, 1000 and 1500 PMs. The results show that our online approach (with unknown upcoming VM information) can achieve more than 85% of the performance for the offline approach (with complete upcoming VM information). The latency is lower than 5ms per VM.

In serverless computing, an idle container is not recycled directly, in order to mitigate time-consuming cold container startup. These idle containers still occupy the memory, exasperating the memory shortage of today's data centers. By offloading their cold memory to remote memory pool could potentially resolve this problem. However, existing offloading policies either hurt the Quality of Service (QoS) or are too coarse-grained in serverless computing scenarios.

This article focuses on the adaptive stabilization control of high-order strict-feedback systems (SFSs) with unknown control directions. It addresses the challenge of dealing with unknown control directions in the fully actuated theory. To overcome this challenge, the Nussbaum gain technique and tuning functions directly based on the high-order fully actuated system (FAS) method are proposed to deal with the unknown control directions and system uncertainties, while avoiding the phenomenon of overparameterization. Additionally, a relative threshold event-triggered strategy is utilized to effectively avoid continuous controller updates, which helps conserve network resources used for signal transmission. The proposed controller guarantees the convergence of all system state variables to zero and ensures that all signals within the closed-loop system remain bounded. To validate the effectiveness of this approach, a numerical simulation and a practical example using the Nomoto ship model are conducted. Overall, this study contributes to the FAS field by providing a solution for adaptive stabilization control in high-order SFSs with unknown control directions.