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.

The efficiency and profitability of cloud data centers are significantly influenced by virtual machine (VM) placement. However, the Non-Uniform Memory Access (NUMA), which has been practically applied to reduce the memory bandwidth competition, is often neglected in the existing research. Actually, the incorporation of NUMA may change the traditional resource allocation mechanism, and demands for a new VM placement model. Hence, considering the multi-NUMA architecture, this paper studies the NUMA-aware VM placement (NAVMP) problem in a cloud computing system, where the resource pool is composed of enormous number of heterogeneous servers with diverse multi-resource remains. The NAVMP problem is analytically formulated as an integer program (IP). Also, for the first time, the incarnations of VM types are introduced to simplify the VM deployment rules originated from complex NUMA architecture. We aim to maximize the VM provision ability (VPA) of the resource pool, and thus propose a novel Value Function to describe servers’ VPA. The resulting formulation, which is a new variant of the multiple-choice multiple multi-dimensional knapsack (MMMK) problem, is of significant computational challenges. So we customize a decomposition approach based on Column Generation (CG) to support the offline optimization. Numerical experiments on a practical dataset demonstrate the validity and scalability of the customized CG-based approach. Our approach outperforms a professional IP solver, i.e., Cbc, and a popular meta-heuristic algorithm, i.e., genetic algorithm (GA), and can efficiently address large-scale NAVMP instances with ten thousands of VM demands and servers.Note to Practitioners—This paper proposes a novel IP model for NAVMP. To cope with the complicated deployment logic associated with the complex multi-NUMA architecture of modern multi-core systems, we present an NAVMP formulation from the perspective of incarnations of VM types. Different from the traditional VM placement problem that aims to minimize the number of activated servers, i.e., the vector bin packing (VBP)-based model, we adopt the objective that maximizes the VPA of a resource pool for further improving the resource utilization. The resulting formulation is an MMMK problem, which is computational very challenging for a practical scale resource pool. Hence, to mitigate the computation burden, we design and implement a CG-based decomposition approach to support the offline optimization for NAVMP. Parallelization scheme and nontrivial heuristic strategies are applied to promote the computation efficiency. According to our numerical experiments, the proposed decomposition approach demonstrates a much superior solution capacity to the Cbc solver and GA. In particular, to achieve a comparable solution precision with Cbc, the computing time can be reduced by orders of magnitude. Also the CG-based approach outperforms GA in both the solution quality and computation time for large-scale instances. Besides, compared to the VBP model, our MMMK-based NAVMP model has improved the VPA up to 44.39%. Practically, the proposed offline approach can be leveraged to guide online VM allocation decisions, and perform efficient results evaluation.

Battery swapping station (BSS) can provide fast battery swapping and flexible battery charging in off-peak hours, it is thus beneficial for electric vehicles (EVs) and power grid in terms of battery life extension and power grid regulation. However, this increases the charging scheduling complexity in BSS since the batteries are not necessarily required to be charged immediately as they arrived. This problem becomes challenging in the presence of nonlinear battery charging characteristics and demand/supply uncertainties. Since it is difficult for the traditional learning-based methods to deal with the constraints caused by nonlinear charging characteristics, low sampling efficiency and unstable training issues can occur. In order to solve these issues, we present a novel deep reinforcement learning (DRL) approach. In contrast to the traditional approaches where the battery charging characteristic is simplified to a constant-current or constant-power process, we propose an equivalent circuit model (ECM) to capture the nonlinear charging characteristics. In ECM, the battery’s open-circuit voltage (OCV) is a function of its state of charge (SoC), as a result, the upper bound of the charging/discharging power of battery is influenced by its SoC. Then we construct a constrained Markov decision process (CMDP) model and propose a Beta distribution-based DRL approach with a continuous action mask (AM) to improve the sampling efficiency and consistency of the training process. Numerical experiments show that our new approach can provide better results in terms of operation cost and quality of service (QoS) in comparison with other state-of-the-art DRL methods.

Smartphones have become the most important devices for users to communicate and interact with different forms of media, and at the same time stored a large amount of sensitive and private data. The security and protection of such data has become increasingly critical. As the sensor technology rapid developed, the diversity of sensors on smartphones has greatly increased (e.g., motion sensor and touchscreen sensor), empowering the smartphones to provide continuous and implicit user authentication by capturing behavioral biometrics. Unfortunately, it remains a challenge to make full use of the data from the multimodality sensors to provide accurate authentication. Toward this end, in this article, we develop an implicit authentication (IA) framework AttAuth which explores organic integration of such multimodality data to authenticate smartphone users through the usage session. Specifically, AttAuth first develops a series of data processing techniques to process the multichannel motion sensor data and the discrete touchscreen data. Then, a temporal and channelwise attention-based temporal and channelwise attention recurrent neural network (TCA-RNN) is developed to build authentication model, which allows to jointly model continuously monitored motion sensor data and irregularly recorded discrete touchscreen data effectively. By generating a guidance vector based on touch events, TCA-RNN guides the temporalwise attention mechanism on the processed multichannel motion sensor data and outputs authentication results. We evaluate AttAuth on a real-world multimodality smartphone usage data set of 100 users. Extensive experiments demonstrate that AttAuth achieves the state-of-the-art authentication accuracy. Additional experiments are provided to examine the applicability of AttAuth in terms of running overheads and sensitivity to various application scenarios.

Convergence acceleration is always a critical issue in distributed multi-area scheduling. The critical region projection (CRP) algorithm based on multi-parametric quadratic programming (MPQP) shows better convergence performance for multi-area static economic dispatch. However, its imperfect decomposition framework cannot be directly applied to dynamic economic dispatch. Therefore, this paper modifies the CRP algorithm to achieve fast distributed multi-area intra-day economic dispatch (MAIDED). First, we introduce slack variables to each area and add the corresponding penalty term into their objective functions to present a primal decomposition framework with penalty relaxation. It is more realistic than the decomposition framework of traditional CRP (TCRP). Then, an iterative algorithm of double spatial scale search (DSSS) is developed to improve the convergence rate of distributed solving based on the similar optimal value function in adjacent critical regions (CRs). Moreover, we design an initial value selection method based on data fitting to further reduce the number of iterations. Finally, three interconnected power systems of different sizes are used for numerical testing to demonstrate that the proposed modified CRP (MCRP) algorithm can meet the practical application and has higher convergence efficiency.Note to Practitioners—This paper is motivated by the problem of distributed MAIDED for power systems but it also applies to other multi-agent networks with a coordinator. The convergence speed of the existing distributed optimization algorithm is slow, which increases the risk of communication failure and attack. Meanwhile, more iterations will result in increased communication and computing costs. This paper modified the TCRP algorithm for high-efficient distributed MAIDED. The penalty relaxation is used to ensure that the decomposition framework is consistent with the actual system operation. The DSSS algorithm and the initial value selection method based on data fitting are designed to effectively reduce the number of iterations. In this paper, the DSSS is that the coordinator enlarges the CRs uploaded from each area to perform a rough optimization for approaching the global optimal solution fast, and then in the next iteration, conducts a precise optimization like the TCRP algorithm to determine the precise optimal solution. The numerical testing of the interconnected power systems of different sizes indicates that the MCRP algorithm has fewer iterations and calculation times than the TCRP algorithm. Future work will extend the MCRP algorithm to solve the distributed multi-area unit commitment problem.

Renewable energy sources and electric vehicles provide an effective way to reduce the energy cost of an enterprise microgrid. However, the uncertainties of renewable energy sources and the time coupling characteristic of electric vehicles bring great challenges of non-anticipativity and feasibility for supply-demand coordination. To satisfy the non-anticipativity, we develop a supply-demand coordination optimal model using pre-scheduling method with virtual re-scheduling. In this model, the current decision only depends on the current and past realizations of random variables. Furthermore, we enhance the model with time-coupled robust constraints to guarantee the feasibility of the strategy under all possible realizations of the random variables. These time-coupled robust constraints bring high computational complexity to solve this model. So, we develop the method of combining forward recursion and backward recursion to decouple these time-coupled robust constraints in time. In this way, the coordination model is transformed to a mixed integer linear programming (MILP) model which can be efficiently solved. Finally, numerical test based on a real case is analysed and the results show that the energy cost of the enterprise is about 136129$\$ $if the flexible load is about 20% and load shifting and generators rescheduling can reduce the energy cost more than 6%. Note to Practitioners—This study is encouraged by the challenging problem caused by the multi-distributed energy introduced into an enterprise microgrid. In enterprises, as the large-area flat workshop roof assists in convenience for photovoltaics’ development and the EVs are widely used, the issue to best utilize renewable energy and EVs shows vital significance in reducing the energy cost. However, there exist the following three main challenges: (1) the non-anticipativity of the model, (2) the solution’s feasibility under all possible realizations, and (3) the effectiveness of the solution method. For the concerns of non-anticipativity, we develop the model using a pre-scheduling model with virtual re-scheduling in which the current decision only depends on the current and past realizations of random variables. To handle the second challenge, an ideal of scenario model with robust constraints is developed considering both feasibility and economy. In order to solve the model with robust constraints, the all-scenario-feasible method and a combination of the forward recursion and backward recursion method are used to deal with time-independent and the time-coupled robust constraints, respectively. The numeric results demonstrate that load shifting and generators rescheduling can reduce the energy cost more than 6%, and using the method with the forward and backward recursion process can reduce the energy cost more than 9%.

Current point-based trackers are usually implemented by the following two branches: a classification branch for predicting the target candidate locations and a regression branch for regressing the tracking box, which may lead to a spatial misalignment between the two tasks. Meanwhile, they ignore a meaningful exploration on how to define positive and negative samples during training and explicit border information for accurate box prediction. In this research, we investigate the key issues of point-based trackers and unlock their key limitations. First, we design a novel task-aligned component and a new loss function, named task-aligned loss, to learn the alignment of the classification and regression tasks. Second, we introduce a border alignment (BorderAlign) component in both the classification and regression branches to effectively exploit the border features of a tracking target. Third, we develop an adaptive training sample assignment (ATSA) to adaptively divide the positive and negative samples based on the statistical characteristics of the tracking object. Finally, a deformable transformer is developed to enhance the representations of search features and explore rich temporal contexts among video frames. Extensive experimental results demonstrate that the proposed tracker achieves state-of-the-art performance on six tracking benchmark datasets.

Machines consume intensive energy in some production systems. Although substantial efforts have been devoted to performance analysis, continuous improvement, and design of production systems, the research on reducing the energy consumption in these systems is limited. In this article, the problem of minimizing the energy consumed by machines in the two-machine Bernoulli serial line, which has been formulated as nonlinear programming with production rate constraint, is investigated. Specifically, structural characteristics and optimality conditions of the problem are analyzed, and two nonlinear algebraic optimality equations are established. To solve the optimality equations, their properties are explored, and an effective algorithm based on the binary search method is developed. Furthermore, the sensitivity of the optimal solution with respect to system parameters is quantitatively analyzed. Based on the sensitivity analysis, some useful insights on the optimal objective value are extracted.Note to Practitioners—In energy-intensive manufacturing enterprises, reducing the energy consumption in their production systems is urgent and challenging. In this article, an energy consumption optimization problem in a simple model of production systems (i.e., in the two-machine Bernoulli line) is investigated. Machine efficiencies are optimized so that the total energy consumption of machines is minimized, while a required production rate is ensured. The reported results enable a novel managerial paradigm for production systems, especially for energy-intensive systems. Although the model of the two-machine Bernoulli line is simple, as a cornerstone, this research will be extended, in the future, to long Bernoulli lines and more practical production systems, e.g., lines with geometric, exponential, and non-exponential machine reliability models.

The penetration of plug-in electric vehicles (PEVs) has greatly increased over the past few years. By using vehicle-to-grid (V2G) technology, PEVs can be used as “mobile batteries” in a microgrid. Here, we aim to coordinate the V2G dispatch with traditional energy management in a corporate energy system (CES). To do so, a two-stage robust optimization (RO) model is built with respect to uncertainties in the CES, e.g., photovoltaic (PV) power. Particularly, relationships between the working time schedule and PEVs are investigated and analyzed for the first time, and a novel PEV aggregator model, i.e., shift-working V2G, is presented. The shift-working V2G model provides beneficial characteristics, like weakened randomness and stable storage capacity. A quantitative method to evaluate the V2G capacity is then presented. An analytical solution methodology is also proposed, which can equivalently convert the robust “min-max-min” model to a single-level mixed-integer linear programming (MILP) model. Case studies are conducted for an iron and steel company in Shanghai, China, with almost 40 000 PEVs. The results show that V2G integration can significantly improve the load-tracking ability of CES and help reduce the energy cost, although the V2G cost is considered. The computational efficiency is also improved compared with the existing methods. Note to Practitioners-This article is motivated by the problem of using the battery storage capacities of plug-in electric vehicles (PEVs) in a corporate energy system (CES) such as an iron and steel plant, aiming at minimizing the energy cost. In existing studies, behaviors of PEVs (e.g., arriving or leaving time) have usually been elusive since they are directly decided by drivers, and thus, it is difficult to determine how much storage capacity PEVs can provide. However, in a CES where a shift-work regulation is implemented, employees should arrive or leave punctually during each shift, and the same is true of their PEVs. Based on this fact, PEVs within one shift could show weakened randomness and stable storage capacities. In this article, the influences of the shift-work regulation on the PEVs are fully analyzed, and the shift-working V2G provides an easy way to integrate PEVs into the CES. In practice, the shift-working V2G model can be applied to any energy system that also implements a shift-work regulation, such as a fire station.

Many optimization problems for heating, ventilation, and air conditioning (HVAC) control systems usually refer to multiple timescales. This paper studies a two-timescale decision problem for indoor temperature regulation by HVAC with the objective to improve user's comfort under limited energy consumption. A slow timescale is divided into several fast timescales, so the two timescales have inherent association. Using states of fast timescale to represent the state of its slow timescale properly is challenging, thereby the weighted mean type is proposed in this paper. We find that the realizability and consistency in two-timescale cannot be guaranteed in existing empirical models, which are the bases for physical application. Therefore, this paper proposes a method that building the fast timescale model first and then inducing the slow timescale model from the fast timescale model. In addition, such a problem in the high-order system is more complex, whose solution is also discussed in this paper. The results of case studies show that the induced model can guarantee the consistency and realizability and meet the user desired temperature for comfort. This paper was motivated by the multi-timescale problem that making a decision of controlling the indoor temperature for the user's comfort with energy constraint. Existing approaches to build a two-timescale model by experience would cause the inconsistent deviation. If the HVAC works in a closed environment system like a space station, besides crew comfort, the temperature for experiments in the space station should be accurate. Therefore, the deviation from the actual temperature and the desired temperature will cause adverse effects. In this paper, we mathematically derive the thermal state transfer equations and analyze some problems. Then, this paper suggests a new approach to build the two-timescale model for various conditions, in which the models of two timescales are consistent with each other. Preliminary experiments suggest that this approach is feasible and effective. In future research, we will jointly make optimization and decision of multiple appliances in home energy management (HEM) system.

In this paper, we present a system to detect and count the number of vehicles in traffic surveillance videos based on Fast Region-based Convolutional Network (Fast R-CNN). Fast R-CNN is a state-of-the-art object detection network, which takes an entire image and a set of object proposals as input, produces bounding-box positions with probability estimates over object classes as output. First, we fine-tune a pre-trained Fast R-CNN net with images captured from traffic videos for accuracy improvement. Second, we define a series of rules of bounding boxes screening for vehicle counting. The proposed system takes around 3 seconds per image to count vehicles on a GTX970 GPU, and then records the corresponding number of vehicles into a database for traffic flow analysis. Experimental results demonstrated that the proposed system can provide significant improvements on the detection accuracy. In addition, experiments on challenging videos with occlusions or full of vehicles show that the proposed system works effectively.