With the growing complexity of modern micro-architectures, processor design must accommodate a wide array of parameters, resulting in vast design spaces. Identifying the optimal trade-offs among various design metrics poses a significant challenge. Performance is inherently difficult to model analytically, while area can be represented by analytical models. Performance evaluation relies on expensive and time-consuming cycle-accurate simulators (CAS), which puts forward strict requirements on the convergence speed and data dependence of the optimization methods. In this paper, based on the characteristics of the design metrics, the processor design problem is modeled as a hybrid black-box and white-box multi-objective discrete optimization problem (BWMO-DOP). In engineering applications, parallel simulation is a common acceleration technology. Therefore, white-box objective, area, is formulated as parallel constraints, while black-box objective, performance, is approximated by order-preserving surrogate objective. And then, BWMO-DOP is simplified into parallel single-objective expensive black-box optimization problems, which are solved by an efficient SOP-MOOA. SOP-MOOA iteratively explores more design points, enhancing the accuracy of the surrogate model while simultaneously updating the Pareto set. Experimental results demonstrate that the proposed algorithm outperforms baseline algorithm in an engineering case and three general numerical cases. In the engineering experiment for performance-area optimization, the proposed algorithm outperforms the baseline algorithm by a factor of more than two when considering the combined effect of performance improvement percentage and area reduction percentage. In numerical tests conducted on three 40-dimensional black-box functions under the same evaluation overhead, the proposed algorithm consistently identified Pareto set of superior quality.

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.

With the popularity of electric vehicles (EVs) and renewable energy sources (RES), the flexibility of charging and discharging of EVs and the intermittency of RES have brought challenges to building operations. Considering the mobility of EVs as commuting tools between buildings and the uncertainty of RES, it is of great practical significance to coordinate multiple buildings with RES and EVs on the premise of meeting the state of energy (SOE) requirements of the future trip. We formulate this coordination problem as a stochastic centralized mixed integer linear programming problem. A polyhedral convex set is constructed to describe the SOE uncertainty of EVs. New nonanticipative constraints (NCs) are derived through forward recursion based on constructed scenarios to guarantee the all-scenario-feasibility (ASF) and nonanticipativity of the decision. A Lagrangian relaxation-based decentralized all-scenario-feasible (LR-DASF) algorithm is developed to solve the centralized optimization problem in a decomposition and coordination way. In this method, the optimal ASF solution can be obtained with a fast convergence rate by updating Lagrangian multipliers without solving all subproblems with NCs. The performance of the LR-DASF algorithm is verified by numerical results, which shows that the algorithm can guarantee the ASF of the solution, as well as promote computational efficiency. Note to Practitioners—EVs as energy storage devices bring energy exchanges between buildings accompanying the mobility of EVs which is an opportunity to improve the energy efficiency of multiple buildings. However, as commuting tools, the SOE of EVs must be guaranteed to be larger than the trip requirement over the randomness of RES generation. Furthermore, solving the coordinated optimization problem of multiple buildings with RES and EVs still faces computational complexity challenge due to the spatio-temperal coupling between EVs and buildings, which may lead to costly computational effort in the premise of guaranteeing the feasibility and nonanticipativity of the decision over the uncertainties in practice. Therefore, in order to overcome the above challenges, an LR-DASF algorithm is developed in this paper to solve the coordinated optimization problem of multiple buildings with RES and EVs. Based on the algorithm, for the system operator, it updates and broadcasts the Lagrangian multipliers information to the local coordinators of buildings. For each building, the local coordinator can make ASF decisions based on new NCs with the information obtained from the system operator independently to guarantee the SOE requirement. The method developed in this paper can make faster optimal decisions without perceivable degradation in accuracy and guarantee the SOE requirement of EVs simultaneously, to meet the requirements of feasibility and computational efficiency of decision making in practice. It is conducive to the future application of LR-DASF in the coordinated optimization of buildings and EVs at the city or regional scale.

Point cloud semantic segmentation (PCSS) shows great potential in generating accurate 3D semantic maps for digital twin railways. Deep learning-based methods have seen substantial advancements, driven by numerous PCSS datasets. Nevertheless, existing datasets tend to neglect railway scenes, with limitations in scale, categories, and scene diversity. This motivated us to establish WHU-Railway3D, a diverse PCSS dataset specifically designed for railway scenes. WHU-Railway3D is categorized into urban, rural, and plateau railways based on scene complexity and semantic class distribution. The dataset spans approximately 30 km with 4.6 billion points labeled into 11 classes, such as rails, masts, overhead lines, and fences. In addition to 3D coordinates, WHU-Railway3D provides rich attribute information such as reflected intensity, scanning angle, and number of returns. Cutting-edge methods are extensively evaluated on the dataset, followed by in-depth analysis. Lastly, key challenges and potential future work are identified to stimulate further innovative research. The dataset is accessible athttps://github.com/WHU-USI3DV/WHU-Railway3D.