Dynamic Consolidation Research Articles

Superposition‐based concurrent multiscale approaches for porodynamics

AbstractThe current study presents superposition‐based concurrent multiscale approaches for porodynamics, capable of capturing related physical phenomena, such as soil liquefaction and dynamic hydraulic fracture branching, across different spatial length scales. Two scenarios are considered: superposition of finite element discretizations with varying mesh densities, and superposition of peridynamics (PD) and finite element method (FEM) to handle discontinuities like strain localization and cracks. The approach decomposes the acceleration and the rate of change in pore water pressure into subdomain solutions approximated by different models, allowing high‐fidelity models to be used locally in regions of interest, such as crack tips or shear bands, without neglecting the far‐field influence represented by low‐fidelity models. The coupled stiffness, mass, compressibility, permeability, and damping matrices were derived based on the superposition‐based current multiscale framework. The proposed FEM‐FEM porodynamic coupling approach was validated against analytical or numerical solutions for one‐ and two‐dimensional dynamic consolidation problems. The PD‐FEM porodynamic coupling model was applied to scenarios like soil liquefaction‐induced shear strain accumulation near a low‐permeability interlayer in a layered deposit and dynamic hydraulic fracturing branching. It has been shown that the coupled porodynamic model offers modeling flexibility and efficiency by taking advantage of FEM in modeling complex domains and the PD ability to resolve discontinuities.

Energy-aware dynamic response and efficient consolidation strategies for disaster survivability of cloud microservices architecture

Energy-aware dynamic response and efficient consolidation strategies for disaster survivability of cloud microservices architecture

Matrix‐based implementation and GPU acceleration of hybrid FEM and peridynamic model for hydro‐mechanical coupled problems

AbstractThe hybrid finite element‐peridynamic (FEM‐PD) models have been evidenced for their exceptional ability to address hydro‐mechanical coupled problems involving cracks. Nevertheless, the non‐local characteristics of the PD equations and the required inversion operations when solving fluid equations result in prohibitively high computational costs. In this paper, a fast explicit solution scheme for FEM‐PD models based on matrix operation is introduced, where the graphics processing units (GPUs) are used to accelerate the computation. An in‐house software is developed in MATLAB in both CPU and GPU versions. We first solve a problem related to pore pressure distribution in a single crack, demonstrating the accuracy of the proposed method by a comparison of FEM‐PD solutions with those of PD‐only models and analytical solutions. Subsequently, several examples are solved, including a one‐dimensional dynamic consolidation problem and the fluid‐driven hydraulic fracture propagation problems in both 2D and 3D cases, to comprehensively validate the effectiveness of the proposed methods in simulating deformation and fracture in saturated porous media under the influence of hydro‐mechanical coupling. In the presented numerical results, some typical strong dynamic phenomena, such as stepwise crack advancement, crack branching, and pressure oscillations, are observed. In addition, we compare the wall times of all the cases executed on both the GPU and CPU, highlighting the substantial acceleration performance of the GPU, particularly when tackling problems with a significant computational workload.

Evaluating wave force and interface fluid velocity distribution at the vicinity of a breakwater by considering consolidation of porous media

Previous studies have successfully simulated the interaction between wave and porous media using volume-averaged Reynolds-averaged Navier-Stokes (VARANS) equations. However, the consolidation of porous media subject to wave loading affects the progressing wave, which is crucial to computation of wave force and wave-porous media interface fluid velocity. Research on this aspect is scarce. This study adopted the u -p approximation for Biot dynamic consolidation theory to quantify the consolidation of porous media. The continuity condition on the interfaces between the wave and porous seabed was considered to achieve their interaction. Coupling algorithms of wave and porous seabed were validated against experimental measurements in the literature. An engineering case of wave traveling over a breakwater was examined, and computation results of wave pressures and interface fluid velocities at the vicinity of a breakwater by VARANS equations and wave-porous seabed coupling algorithm of this study were compared to explore the influence of the consolidation effect on the progressing wave.

An efficient dynamic migration and consolidation method of VMs based on improved K-nearest neighbor algorithm and attribute weighting

Virtual machine (VM) consolidation and migration that only consider current workload can result in excessive unnecessary migrations. To address this issue, a VM consolidation algorithm based on resource utilization prediction is proposed. An improved K-nearest neighbor (KNN) classification algorithm weighted by attribute inconsistency is proposed to predict the workload of both the host and the VMs. Firstly, two distributions are partitioned according to the neighboring relationship for comparing consistency. Then, an inconsistency evaluation function based on earth mover’s distance (EMD) is designed to measure the inconsistency between the neighboring sample set of each sample under each attribute and the equivalent partition refined by the decision attribute. Finally, the inconsistency level of the neighboring samples is transformed into the importance of the corresponding attribute to implement the attribute weighting KNN classifier. When selecting the source host and target host for VM migration, both current and predicted overloads are considered to avoid unnecessary VM migrations. Simulation tests were performed with random and realistic workloads, and the results show that the proposed method can reduce the overall energy consumption of the host, while also reducing service level agreement (SLA) violations and VM migration.

Dynamic Feature Pruning and Consolidation for Occluded Person Re-identification

Occluded person re-identification (ReID) is a challenging problem due to contamination from occluders. Existing approaches address the issue with prior knowledge cues, such as human body key points and semantic segmentations, which easily fail in the presence of heavy occlusion and other humans as occluders. In this paper, we propose a feature pruning and consolidation (FPC) framework to circumvent explicit human structure parsing. The framework mainly consists of a sparse encoder, a multi-view feature mathcing module, and a feature consolidation decoder. Specifically, the sparse encoder drops less important image tokens, mostly related to background noise and occluders, solely based on correlation within the class token attention. Subsequently, the matching stage relies on the preserved tokens produced by the sparse encoder to identify k-nearest neighbors in the gallery by measuring the image and patch-level combined similarity. Finally, we use the feature consolidation module to compensate pruned features using identified neighbors for recovering essential information while disregarding disturbance from noise and occlusion. Experimental results demonstrate the effectiveness of our proposed framework on occluded, partial, and holistic Re-ID datasets. In particular, our method outperforms state-of-the-art results by at least 8.6% mAP and 6.0% Rank-1 accuracy on the challenging Occluded-Duke dataset.

Rheological consolidation analysis of saturated clay ground under cyclic loading based on the fractional order Kelvin model

AbstractBased on Biot porous medium theory, considering the coupled reaction of soil skeleton rheology and pore pressure dissipation, the present work investigates the dynamic consolidation characteristics of saturated clay ground under cyclic loading. First, the rheological behavior of the soil skeleton was described by the fractional order Kelvin model. The dynamic consolidation governing equations for the saturated clay were established theoretically in a three‐dimensional axisymmetric coordinate system. Second, the transform domain analytical solution of the dynamic consolidation of saturated clay was obtained using the Hankel–Laplace coupled transform method, and the solution in the time–space domain was further obtained through numerical inversion. Finally, the rheological consolidation behavior of saturated clay under cyclic loading and the influences of parameters were analyzed. The results show that the rheology of the soil skeleton had an inhibitory effect on pore water permeability; compared with elastic skeleton soil, the rheological clay had a slower settlement rate in the primary consolidation stage, a faster rate in the secondary consolidation stage, and greater long‐term settlement. In addition, under cyclic loading, the pore pressure response in saturated clay lags behind the effective stress, and a larger viscous order is associated with faster development of cumulative settlement.

Energy and Performance Centric Resource Allocation Framework in Virtual Machine Consolidation Using Reinforcement Learning Approach

Abstract:: Virtual machines are used to reduce cloud platform application performance, management costs, and access irregularities. Virtual machines are frequently vulnerable to delays, overburdening workloads, and other obstacles while consolidating and migrating servers. To significantly disperse loads among virtual machines, dynamic consolidation techniques are implemented to control energy dissipation, monitor overloading, and address underloading problems. Background:: The process of consolidation involves more calculations and resources in order to transfer services between virtual machines, provided that Service Level Agreements are observed. Methods:: The suggested approach promotes the use of cutting-edge architecture to combine virtual machines, and, therefore, strike a balance between performance and energy requirements. The main design considerations for the suggested Dynamic Weightage algorithm, which includes the clustering approach in relation to reinforcement learning approaches, are overall resource needs and Performance to Power Ratio (PPR). A cluster of ideal virtual machines is created, and resources are distributed according to performance and energy requirements. Virtual machine resource requests are converted into a matching relationship factor, which represents the individual hosts while taking PPR into account. The overall workload associated with virtual machine consolidation is also provided by these estimations. It is noted that there is little energy trade-off and that performance is maintained at a nominal level across the cluster. The architecture is put into practice throughout offline platforms, which are dispersed ecosystems that allow for increased system performance and scaling. Results:: The CloudSim simulator is used to validate the system using datasets that are obtained from PlanetLab. According to the data, energy saving has produced yields of up to 47% and promising quality of service attributes. Conclusion:: The validation of the system is performed using the CloudSim simulator with datasets from PlanetLab. The results indicate significant energy conservation, up to 47%, along with promising quality of service parameters. The proposed architecture is compared with other state-of-the-art algorithms for distributed architectures and heterogeneous environments, showcasing its efficiency. The conclusion emphasizes the prioritization of VM consolidation and energy efficiency in the proposed architecture, which has been tested on a Proliant G7- based data center using a variety of hosts. Notably, the CloudSim Toolkit is highlighted as outperforming OpenStack-based techniques in simulation results.

Memory consolidation of sequence learning and dynamic adaptation during wakefulness.

Motor learning involves acquiring new movement sequences and adapting motor commands to novel conditions. Labile motor memories, acquired through sequence learning and dynamic adaptation, undergo a consolidation process during wakefulness after initial training. This process stabilizes the new memories, leading to long-term memory formation. However, it remains unclear if the consolidation processes underlying sequence learning and dynamic adaptation are independent and if distinct neural regions underpin memory consolidation associated with sequence learning and dynamic adaptation. Here, we first demonstrated that the initially labile memories formed during sequence learning and dynamic adaptation were stabilized against interference through time-dependent consolidation processes occurring during wakefulness. Furthermore, we found that sequence learning memory was not disrupted when immediately followed by dynamic adaptation and vice versa, indicating distinct mechanisms for sequence learning and dynamic adaptation consolidation. Finally, by applying patterned transcranial magnetic stimulation to selectively disrupt the activity in the primary motor (M1) or sensory (S1) cortices immediately after sequence learning or dynamic adaptation, we found that sequence learning consolidation depended on M1 but not S1, while dynamic adaptation consolidation relied on S1 but not M1. For the first time in a single experimental framework, this study revealed distinct neural underpinnings for sequence learning and dynamic adaptation consolidation during wakefulness, with significant implications for motor skill enhancement and rehabilitation.

Towards virtual machine scheduling research based on multi-decision AHP method in the cloud computing platform

Virtual machine scheduling and resource allocation mechanism in the process of dynamic virtual machine consolidation is a promising access to alleviate the cloud data centers of prominent energy consumption and service level agreement violations with improvement in quality of service (QoS). In this article, we propose an efficient algorithm (AESVMP) based on the Analytic Hierarchy Process (AHP) for the virtual machine scheduling in accordance with the measure. Firstly, we take into consideration three key criteria including the host of power consumption, available resource and resource allocation balance ratio, in which the ratio can be calculated by the balance value between overall three-dimensional resource (CPU, RAM, BW) flat surface and resource allocation flat surface (when new migrated virtual machine (VM) consumed the targeted host’s resource). Then, virtual machine placement decision is determined by the application of multi-criteria decision making techniques AHP embedded with the above-mentioned three criteria. Extensive experimental results based on the CloudSim emulator using 10 PlanetLab workloads demonstrate that the proposed approach can reduce the cloud data center of number of migration, service level agreement violation (SLAV), aggregate indicators of energy comsumption (ESV) by an average of 51.76%, 67.4%, 67.6% compared with the cutting-edge method LBVMP, which validates the effectiveness.

Mechanical response of shield tunnel lining under nonlinear wave and current action in sloping seabed

AbstractWith the increasing demand for transportation construction in coastal areas, tunnel safety research is becoming increasingly important. Previous studies take less account of the nonlinear wave and current action under the sloping seabed terrain. Furthermore, there are few researches on the additional deformation of shield tunnel lining caused by wave‐current dynamic pressure. In this paper, according to the interaction theory of Stokes third‐order nonlinear wave and uniform current and Biot's dynamic consolidation equation, the excess pore pressure response of sloping pure seabed under the wave‐current action is obtained. The excess pore pressure reflected by the tunnel is acquired using the seepage continuity condition on the permeable lining surface. Considering the residual excess pore pressure caused by the cumulative effect under the cyclic action of seepage force. Then the distribution law of seepage pore pressure around the shield tunnel in the sloping seabed under the wave‐current action is obtained based on the superposition principle. The displacement response of tunnel lining during the long‐term service period is obtained by combining the exponential decay model. Moreover, the accuracy of theoretical analysis is verified by experimental monitoring data and numerical simulation, and the hydraulic parameters, seabed parameters and tunnel parameters are analyzed.

Energy and Performance-Efficient Dynamic Consolidate VMs Using Deep-Q Neural Network

With cloud computing facing higher levels of big data than ever, the processor scale is rapidly expanding. Large clusters place a heavy burden on cloud service providers and the environment. High energy consumption decreases the economic benefits of cloud service providers, while enormous power demands pressure on the environment. The dynamic consolidation of virtual machines (VMs), which uses live migration technology to optimize resource usage and reduce energy consumption, is sufficient for saving energy while ensuring high performance with the desired level of quality of service (QoS) between cloud providers and users. In this paper, we propose a novel machine-learning algorithm called deep-Q neural network VM consolidation (DQNVMC) that combines the Q-leaning approach with deep learning neural network to find an approximately optimal solution. Furthermore, based on the real workload trace in the cloud environment, the experiments show that DQNVMC effectively reduces energy consumption while meeting the high performance of QoS requirements.

Design of an energy efficient dynamic virtual machine consolidation model for smart cities in urban areas

The growing smart cities in urban areas are becoming more intelligent day by day. Massive storage and high computational resources are required to provide smart services in urban areas. It can be provided through intelligence cloud computing. The establishment of large-scale cloud data centres is rapidly increasing to provide utility-based services in urban areas. Enormous energy consumption of data centres has a destructive effect on the environment. Due to the enormous energy consumption of data centres, a massive amount of greenhouse gases (GHG) are emitted into the environment. Virtual Machine (VM) consolidation can enable energy efficiency to reduce energy consumption of cloud data centres. The reduce energy consumption can increase the Service Level Agreement (SLA) violation. Therefore, in this research, an energy-efficient dynamic VM consolidation model has been proposed to reduce the energy consumption of cloud data centres and curb SLA violations. Novel algorithms have been proposed to accomplish the VM consolidation. A new status of any host called an almost overload host has been introduce, and determined by a novel algorithm based on the Naive Bayes Classifier Machine Learning (ML) model. A new algorithm based on the exponential binary search is proposed to perform the VM selection. Finally, a new Modified Power-Aware Best Fit Decreasing (MPABFD) VM allocation policy is proposed to allocate all VMs. The proposed model has been compared with certain well-known baseline algorithms. The comparison exhibits that the proposed model improves the energy consumption by 25% and SLA violation by 87%.

Fuzzy correlational analysis for dynamic consolidation of virtual machines in cloud computing environment

This theoretical research in fuzzy correlation analysis integrates data uncertainty analysis by measuring the strength of the linear relationship restricted to two fuzzy sets. As the relevant contribution to this research area, this paper presents the axiomatic definition of the n-dimensional generalized fuzzy correlation coefficient (nGCC), assigning to n input fuzzy sets an output value in the interval [−1,1]. Thus, the properties of general overlap functions and fuzzy negations are studied, discussing binary non-normed restricted dissimilarity functions, and the n-dimensional non-normed conjunctive functions. This study provides new methods for the n-dimensional generalized fuzzy correlation coefficient analysis, regarding their applications in solving multi-criteria and decision-making problems founded on fuzzy logic extensions. The n-dimensional non-normed conjunctive aggregation functions are also introduced, as range domain extensions from [0,1] to [−1.1], covering the interpretation of negative to positive variable associations. The proposal correlation analysis promotes a better evaluation and data selection even when more than one algorithm is applied to evaluate the reduction methods in the defuzzification process based on Interval-valued Fuzzy Logic. We also investigate the relevance to determine a reliable result from the fuzzy inference system based on the nGCC methodology. This correlation methodology applied to the Interval Fuzzy Load Balancing for Cloud Computing (Int-FLBCC) model contributes as a flexible approach for virtual machines dynamic consolidation enabling improvements in resource usage and power efficiency, improving the computational system’s energy efficiency. So, the nGCC methodology extends the Int-FLBCC model by adding other degrees of reliability to the results obtained with diverse evaluations through n-dimensional generalized fuzzy correlation coefficient expressions, exploring average aggregations as arithmetic and exponential means and the median operator.

Influence of DOC of vacuum preloading on dynamic consolidation for dredged slurry

Generally, marine reclamation land needs to be improved by a large margin before the construction of civil structures in coastland due to poor geotechnical properties of the dredged slurry. The combination of vacuum preloading (VP) and dynamic consolidation (DC) has been developed for application in such projects. However, the effect of the combined VP and DC method is mainly controlled by the start-up time of DC (time of first tamping); in this study, the start-up time was determined by the degree of consolidation (DOC), and the DOC under VP was estimated by pore water pressure (PWP). According to a series of previously studied laboratory test results, the DC can be applied when DOC is greater than or equal to 60%. Thus, DOCs of 60%, 65%, 70%, and 75% were considered as the judgment index of start-up time. During the model tests, the settlement and PWP were monitored and measured. The water content and vane shear strength of the dredged slurry were also measured after the completion of tests. The start-up time of DC was determined when an optimal DOC was 70%.

Dynamic shakedown limits of the pavement on saturated subgrade subjected to traffic rolling-sliding loading

This paper establishes a semi-analytical lower-bound dynamic shakedown method to investigate the shakedown of the pavement on saturated subgrade based on dynamic Biot's consolidation theory, considering a rolling-sliding contact between vehicle tire and pavement. This analysis has demonstrated that properties of the saturated subgrade generally have negative effects on the shakedown limit of the pavement system under high-speed traffic loadings. In general, the classical single-phased shakedown theorem overestimates the shakedown limit of the pavement on the soft ground of low permeability, particularly under high-speed traffic loadings. The shakedown limit of the pavement on a saturated subgrade gradually reduces with decreasing permeability of the subgrade. The sliding-induced friction significantly affects the shakedown limit and the location of critical shakedown failure. The permeability of the saturated subgrade has considerable effects on the shakedown limit for the case of low tire-pavement friction. The shakedown limit of the pavement on a saturated subgrade and a single-phased subgrade shows a considerable difference at the optimum modulus ratio, at which the critical shakedown failure tends to shift from the saturated subgrade to the pavement layer. The proposed method will be more desirable to evaluate the performance of the pavement in the saturated soft ground.

Seismic Response of Earth-Rock Dams with Innovative Antiseepage Walls on the Effect of Microscopic Fluid-Solid Coupling

In dykes and dam projects, the microscopic fluid-solid coupling effect from the interaction between soil skeleton and pore water during an earthquake is crucial to consider, as it can lead to dam safety problems. To control seepage in medium- and small-sized dams, polymer antiseepage walls have emerged as effective measures. In recent years, this method has been increasingly utilized in projects worldwide as it is essential for preventing potential dam safety issues. To address concerns related to seismic safety, this study conducts theoretical analysis, model tests, and numerical simulations to investigate the seismic response of earth-rock dams with polymer antiseepage walls, with a specific focus on the microscopic fluid-solid coupling effect. The dynamic viscoelastic constitutive model used in this study incorporates Biot’s theory of dynamic consolidation and the results of dynamic mechanical analysis (DMA) of polymer materials. To validate the model, a centrifuge test is performed, and it is then utilized to study the seismic response of earth-rock dams with polymer antiseepage walls. Furthermore, the influence of factors such as fluid-solid coupling, water level, polymer material density, and wall thickness on the seismic response of dams with antiseepage walls is analyzed. Finally, the seismic safety of the earth-rock dam with the polymer antiseepage wall is thoroughly examined. The results emphasize the need to consider the fluid-solid coupling effect, as factors like water level and design parameters of the antiseepage wall significantly impact the seismic response of earth-rock dams with polymer antiseepage walls.

Modeling and Simulation of Real-Time Virtual Machine Allocation in a Cloud Data Center

For dynamic resource scheduling in cloud data centers, a novel lightweight simulation system is proposed; two existing simulation systems at the application level for cloud computing are reviewed; and results gained using the suggested simulation system are examined and discussed. The usage of resources and energy efficiency in cloud data centers can be improved by load balancing and the consolidation of virtual machines. An aspect of dynamic virtual machine consolidation that directly affects resource usage and the quality of service the system is delivering is the timing of when it is ideal to reallocate Virtual Machines from an overloaded host [1]. Because server overloads result in a lack of resources and a decline in application performance, they have an impact on quality of service. In order to determine the best answer, existing approaches to the problem of host overload detection typically rely on statistical analysis inspired by nature. These strategies' drawbacks include the fact that they provide less-than-ideal outcomes and prevent the explicit articulation of a Quality-of-Service target. By optimizing the mean inter-migration time under the defined Quality of Service target ideally, we present a novel method for detecting host overload for any stationary workload that is known and a particular state configuration [2]. We demonstrate that our technique exceeds the best benchmark algorithm and offers over 88%of the performance of the ideal offline algorithm through simulations with real-world workload traces from more than a thousand Virtual Machines.

Energy-aware QoS-based dynamic virtual machine consolidation approach based on RL and ANN

Energy-aware QoS-based dynamic virtual machine consolidation approach based on RL and ANN

Real-Time Virtual Machine Scheduling in Industry IoT Network: A Reinforcement Learning Method

The widespread adoption of Industrial Internet of Things (IIoT)-based applications has driven the emergence and development of cloud-related computing paradigms with the ability to seamlessly leverage cloud resources. Heterogeneous resources, mobility factors in IoT, and dynamic behavior make it challenging for the corresponding virtual machine (VM) scheduling problem to address the processing effectiveness of application requests in these kinds of cloud environments. Based on reinforcement learning theory, this article proposes an online VM scheduling scheme (OSEC) for joint energy consumption and cost optimization that divides the scheduling process into two parts: VM allocation and VM migration. First, all the VMs and the physical machines (PMs) are regarded as a set of states and actions in the cloud environment, and the Q-learning feedback is used to achieve the iterative computation of Q-values to obtain the optimal parallel allocation sequence for multiple VMs. Then, VMs are migrated among the active PMs according to a grouping policy and the best-fit principle to achieve dynamic consolidation of the resources in the data center. Finally, experimental results show that compared with state-of-the-art algorithms under different conditions, the proposed method reduces energy consumption by approximately 18.25%, VM execution costs by approximately 21.34%, and service level agreement (SLA) violations by approximately 90.51%.