Today's laaS clouds allow dynamic scaling of VMs allocated to a user, according to real-time demand of the user. There are two types of scaling: horizontal scaling (scale-out) by allocating more VM instances to th...
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Today's laaS clouds allow dynamic scaling of VMs allocated to a user, according to real-time demand of the user. There are two types of scaling: horizontal scaling (scale-out) by allocating more VM instances to the user, and vertical scaling (scale-up) by boosting resources of VMs owned by the user. It has been a daunting issue how to efficiently allocate the resources on physical servers to meet the scaling demand of users on the go, which achieves the best server utilization and user utility. An accompanying critical challenge is how to effectively charge the incremental resources, such that the economic benefits of both the cloud provider and cloud users are guaranteed. There has been online auction design dealing with dynamic VM provisioning, where the resource bids are not related to each other, failing to handle VM scaling where later bids may rely on earlier bids of the same user. As the first in the literature, this paper designs an efficient, truthful online auction for resource provisioning and pricing in the practical cases of dynamic VM scaling, where: (i) users bid for customized VMs to use in future durations, and can bid again in the following time to increase resources, indicating both scale-up and scale-out options;(ii) the cloud provider packs the demanded VMs on heterogeneous servers for energy cost minimization on the go. We carefully design resource prices maintained for each type of resource on each server to achieve threshold-based online allocation and charging, as well as a novel competitive analysis technique based on submodularity of the offline objective, to show a good competitive ratio is achieved. The efficacy of the online auction is validated through solid theoretical analysis and trace-driven simulations.
Inductive pulsed power supply (IPPS) is a competitive type of power supply for electromagnetic launch (EML). Compared with the capacitive type, the structure and working process of IPPS are more complicated, which mak...
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Inductive pulsed power supply (IPPS) is a competitive type of power supply for electromagnetic launch (EML). Compared with the capacitive type, the structure and working process of IPPS are more complicated, which make the design of the IPPS module difficult. In this article, the method of calculating the module performance from structural and electrical parameters is proposed. Based on that, the biobjective optimization of a 100-kJ module is carried out. With the help of genetic algorithms, the optimization results are 2500 nondominated solutions on the Pareto front, and one of the representative solutions is verified by experiments. The performance of the built module is in good agreement with the calculated value. Its energy density reaches 3.98 MJ/m(3), the peak output current reaches 23 kA with 8.1-ms pulsewidth.
Conventional Internet of Things (IoT) applications involve data capture from various sensors in environments, and the captured data then is processed in remote clouds. However, some critical IoT applications (e.g., au...
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Conventional Internet of Things (IoT) applications involve data capture from various sensors in environments, and the captured data then is processed in remote clouds. However, some critical IoT applications (e.g., autonomous vehicles) require a much lower response latency and more secure guarantees than those offered by remote clouds today. Mobile edge clouds (MEC) supported by the network function virtualization (NFV) technique have been envisioned as an ideal platform for supporting such IoT applications. Specifically, MECs enable to handle IoT applications in edge networks to shorten network latency, and NFV enables agile and low-cost network functions to run in low-cost commodity servers as virtual machines (VMs). One fundamental problem for the provisioning of IoT applications in an NFV-enabled MEC is where to place virtualized network functions (VNFs) for IoT applications in the MEC, such that the operational cost of provisioning IoT applications is minimized. In this paper, we first address this fundamental problem, by considering a special case of the IoT application placement problem, where the IoT application and VNFs of each service request are consolidated into a single location (gateway or cloudlet), for which we propose an exact solution and an approximation algorithm with a provable approximation ratio. We then develop a heuristic algorithm that controls the resource violation ratios of edge clouds in the network. For the IoT application placement problem for IoT applications where their VNFs can be placed to multiple locations, we propose an efficient heuristic that jointly places the IoT application and its VNFs. We finally study the performance of the proposed algorithms by simulations and implementations in a real test-bed, Experimental results show that the performance of the proposed algorithms outperform their counterparts by at least 10 percent.
Following a general trend in artificial intelligence, Evolutionary Computation has, in recent years, witnessed substantial performance gains from landscape-aware selection and parameter tuning of algorithmic modules. ...
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ISBN:
(纸本)9781450367486
Following a general trend in artificial intelligence, Evolutionary Computation has, in recent years, witnessed substantial performance gains from landscape-aware selection and parameter tuning of algorithmic modules. Such approaches, however, are critically relying on suitable benchmarks, or training sets, that provide the appropriate blend of performance and generality. With this position paper we argue that, on a landscape analysis basis, the benchmark design problem will form a substantial part of the next-generation of automated, on-demand algorithm design principles.
Proper wear level information and early wear detection are crucial goals in many engineering applications and industrial components in order to improve efficiency and reduce production, maintenance, or replacements co...
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Proper wear level information and early wear detection are crucial goals in many engineering applications and industrial components in order to improve efficiency and reduce production, maintenance, or replacements costs. Furthermore, this should ideally be achieved with a user-friendly, low-cost, and easy to implement methodology for wear level monitoring and detection. In this work, we present the design of a new approach to accomplish early wear detection that is implemented by means of a stand-alone smartphone device and application providing real-time online metrology. The online monitoring is done by means of optical measurements and image processing based on the advanced smartphone vision system technology currently available in commercial devices. The developed mobile App works in continuous mode without interrupting the wear process. Specifically, it traces surface changes and monitors the progression of wear enabling just-in-time warning alarms for "significant wear" and "critical wear" detection. We demonstrate that critical wear of a surface prior to fatal rupture can be detected, which is the main objective in many industrial applications.
This paper proposes generalized mathematical model of different passive interferences and develops an effective algorithm of digital signal processing for detection on the background of them. Models of interferences a...
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ISBN:
(数字)9781665464390
ISBN:
(纸本)9781665464390
This paper proposes generalized mathematical model of different passive interferences and develops an effective algorithm of digital signal processing for detection on the background of them. Models of interferences as random process of K-distribution is used with parametrization for the unwanted reflections from atmosphere, land, and sea. Robust algorithm for signal detection on the background of such interferences, in particular in case of non-gaussian distribution, is developed. Its effectiveness is researched and confirmed.
Accurate and reliable measurement of real-world walking activity is clinically relevant, particularly for people with mobility difficulties. Insights on walking can help understand mobility function, disease progressi...
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Accurate and reliable measurement of real-world walking activity is clinically relevant, particularly for people with mobility difficulties. Insights on walking can help understand mobility function, disease progression, and fall risks. People living in long-term residential care environments have heterogeneous and often pathological walking patterns, making it difficult for conventional algorithms paired with wearable sensors to detect their walking activity. We designed two walking bout detection algorithms for people living in long-term residential care. Both algorithms used thresholds on the magnitude of acceleration from a 3-axis accelerometer on the lower back to classify data as "walking" or "non-walking". One algorithm had generic thresholds, whereas the other used personalized thresholds. To validate and evaluate the algorithms, we compared the classifications of walking/non-walking from our algorithms to the real-time research assistant annotated labels and the classification output from an algorithm validated on a healthy population. Both the generic and personalized algorithms had acceptable accuracy (0.83 and 0.82, respectively). The personalized algorithm showed the highest specificity (0.84) of all tested algorithms, meaning it was the best suited to determine input data for gait characteristic extraction. The developed algorithms were almost 60% quicker than the previously developed algorithms, suggesting they are adaptable for real-time processing.
For many systems of linear equations that arise from the discretization of partial differential equations, the construction of an efficient multigrid solver is challenging. Here we present EvoStencils, a novel approac...
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For many systems of linear equations that arise from the discretization of partial differential equations, the construction of an efficient multigrid solver is challenging. Here we present EvoStencils, a novel approach for optimizing geometric multigrid methods with grammar-guided genetic programming, a stochastic program optimization technique inspired by the principle of natural evolution. A multigrid solver is represented as a tree of mathematical expressions that we generate based on a formal grammar. The quality of each solver is evaluated in terms of convergence and compute performance by automatically generating an optimized implementation using code generation that is then executed on the target platform to measure all relevant performance metrics. Based on this, a multi-objective optimization is performed using a non-dominated sorting-based selection. To evaluate a large number of solvers in parallel, they are distributed to multiple compute nodes. We demonstrate the effectiveness of our implementation by constructing geometric multigrid solvers that are able to outperform hand-crafted methods for Poisson's equation and a linear elastic boundary value problem with up to 16 million unknowns on multi-core processors with Ivy Bridge and Broadwell microarchitecture.
The complete elliptic integral of the first kind (CEI-1) plays a significant role in mathematics, physics and engineering. There is no simple formula for its computation, thus numerical algorithms are essential for co...
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The complete elliptic integral of the first kind (CEI-1) plays a significant role in mathematics, physics and engineering. There is no simple formula for its computation, thus numerical algorithms are essential for coping with the practical problems involved. The commercial implementations for the numerical solutions, such as the functions ellipticK and EllipticK provided by MATLAB and Mathematica respectively, are based on K-cs(m) instead of the usual form K(k) such that K-cs(k(2)) =K(k) and m = k( 2 ). It is necessary to develop open source implementations for the computation of the CEI-1 in order to avoid potential risks of using commercial software and possible limitations due to the unknown factors. In this paper, the infinite series method, arithmetic-geometric mean (AGM) method, Gauss-Chebyshev method and Gauss-Legendre methods are discussed in details with a top-down strategy. The four key algorithms for computing the CEI-1 are designed, verified, validated and tested, which can be utilized in R& D and be reused properly. Numerical results show that our open source implementations based on K(k) are equivalent to the commercial implementation based on K-cs(m) . The general algorithms for computing orthogonal polynomials developed are valuable for the STEM education and scientific computation.
It is difficult to mine online buying behavior data by ignoring the classification of online buying behavior data, and the precision and recall are both on the low side. The training set of online buying behavior data...
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It is difficult to mine online buying behavior data by ignoring the classification of online buying behavior data, and the precision and recall are both on the low side. The training set of online buying behavior data is processed by top-down recursion, and a single decision tree is created recursively, and a decision tree classification model is constructed. Based on the classification results of behavior data, the regular estimation of online shopping features is calculated by preprocessing customer behavior features, and the deep mining algorithm is designed. Experimental results show that the decision tree model has good data clustering effect. Based on this, the precision and recall of online shopping behavior data mining are high, and the application performance is ideal.
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