Migration Algorithm Research Articles

RESEARCH OF METHODS TO SUPPORT DATA MIGRATION BETWEEN RELATIONAL AND DOCUMENT DATA STORAGE MODELS

The subject matter of the article is heterogeneous model-inhomogeneous data migration between relational and document-oriented data storage models, existing strategies and methods to support such migrations, the use of relational algebra and set theory in the context of databases in building a new data migration algorithm. The goal of the work is to consider the features and procedure of data migration, explore methods to support data migration between relational and documentary data models, build a mathematical model and algorithm for data migration. The following methods were used: analysis and comparison of existing approaches to data migration, choice of strategy for further use in compiling the migration algorithm, mathematical modeling of the algorithm of heterogeneous model-inhomogeneous data migration, formalization of the data migration algorithm. The following tasks were solved in the article: consideration of the concept and types of data migration, justification for choosing a document-oriented data model as a target for data migration, analysis of existing literature sources on methods and strategies of heterogeneous model data migration from relational to document-oriented data model, highlighting advantages and disadvantages existing methods, choosing an approach to the formation of the data migration algorithm, compiling and describing a mathematical model of data migration using relational algebra and set theory, presentation of the data migration algorithm, which is based on the focus on data queries. The following results were obtained: the possibilities of relational algebra and set theory in the context of data models and queries are used, as well as in model redesign, the strategy of migration of data models is chosen, which provides relational and document-oriented data models, the algorithm of application of this method is described. Conclusions: because of the work, the main methods of migration support for different data storage models are analyzed, with the help of relational algebra, set theory a mathematical model is built, and an algorithm for transforming a relational data model into a document-oriented data model is taken into account. The obtained algorithm is suitable for use in real examples, and is the subject of further research and possible improvements, analysis of efficiency in comparison with other methods.

Internet of Things Task Migration Algorithm under Edge Computing in the Design of English Translation Theory and Teaching Practice Courses

This study aims to improve the quality of English teaching in contemporary colleges and universities, so as to cultivate more English translation talents. Taking corpus, English translation, and teaching practice as the main research horizons, this study analyzes the application of master of translation and interpreting (MTI) course design, English translation, and task migration by combining the task migration algorithm under Internet of Things (IoT) with the self-built corpus, so as to realize the cultivation of translation talents and design of teaching practice. A translation corpus is constructed, a two-way interactive online course is designed, and the experimental results of the complete local migration algorithm (CLM algorithm), random migration algorithm (RM algorithm), and greedy heuristic migration algorithm (GHM algorithm) used in English teaching practice courses are analyzed and compared. The experimental simulation results reveal that the GHM algorithm proposed in this study shows good system stability, its duration is 60% better than that of the CLM algorithm, and its system throughput is increased by 50% compared with the CLM algorithm. In addition, the maximum delay time has little effect on the system throughput. When the system time slot length is fixed at 20 ms, the user migration rate of the genetic algorithm is the highest under the different total numbers of users. In addition, in view of the wide application of neural network in English translation teaching, this study establishes an English translation evaluation model based on the combination of particle swarm optimization (PSO) algorithm and neural network and compares it with the traditional neural network model in simulation experiments. The results show that the addition of PSO algorithm can effectively improve the convergence speed of artificial neural network (ANN), reduce the training time of the model, and improve the accuracy of the ANN network. Using the PSO algorithm to train the neural network, the optimal solutions of different particle swarms can be obtained, and the error is small. The PSO-ANNs model can promote the quality of English translation teaching and improve the English translation ability of the students. Therefore, applying the task transfer algorithm and PSO algorithm to the practice of English translation teaching has greatly improved the efficiency of the English classroom. To sum up, this study provides new ideas for the curriculum design of contemporary college English teaching and has reference value for the cultivation of college English translation talents.

Near Field 3-D Millimeter-Wave SAR Image Enhancement and Detection with Application of Antenna Pattern Compensation

In this paper, a novel near-field high-resolution image focusing technique is proposed. With the emergence of Millimeter-wave (mmWave) devices, near-field synthetic aperture radar (SAR) imaging is widely used in automotive-mounted SAR imaging, UAV imaging, concealed threat detection, etc. Current research is mainly confined to the laboratory environment, thus ignoring the adverse effects of the non-ideal experimental environment on imaging and subsequent detection in real scenarios. To address this problem, we propose an optimized Back-Projection Algorithm (BPA) that considers the loss path of signal propagation among space by converting the amplitude factor in the echo model into a beam-weighting. The proposed algorithm is an image focusing algorithm for arbitrary and irregular arrays, and effectively mitigates sparse array imaging ghosts. We apply the 3DRIED dataset to construct image datasets for target detection, comparing the kappa coefficients of the proposed scheme with those obtained from classic BPA and Range Migration Algorithm (RMA) with amplitude loss compensation. The results show that the proposed algorithm attains a high-fidelity image reconstruction focus.

The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents.

Deep learning is a branch of machine learning that uses neural networks to mimic the behaviour of the human brain. Various types of models are used in deep learning technology. This article will look at two important models and especially concentrate on unsupervised learning methodology. The two important models are as follows: the supervised and unsupervised models. The main difference is the method of training that they undergo. Supervised models are provided with training on a particular dataset and its outcome. In the case of unsupervised models, only input data is given, and there is no set outcome from which they can learn. The predicting/forecasting column is not present in an unsupervised model, unlike in the supervised model. Supervised models use regression to predict continuous quantities and classification to predict discrete class labels; unsupervised models use clustering to group similar models and association learning to find associations between items. Unsupervised migration is a combination of the unsupervised learning method and migration. In unsupervised learning, there is no need to supervise the models. Migration is an effective tool in processing and imaging data. Unsupervised learning allows the model to work independently to discover patterns and information that were previously undetected. It mainly works on unlabeled data. Unsupervised learning can achieve more complex processing tasks when compared to supervised learning. The unsupervised learning method is more unpredictable when compared with other types of learning methods. Some of the popular unsupervised learning algorithms include k-means clustering, hierarchal clustering, Apriori algorithm, clustering, anomaly detection, association mining, neural networks, etc. In this research article, we implement this particular deep learning model in the marketing oriented asset allocation of high level accounting talents. When the proposed unsupervised migration algorithm was compared to the existing Fractional Hausdorff Grey Model, it was discovered that the proposed system provided 99.12% accuracy by the high level accounting talented candidate in market-oriented asset allocation.

Fourier Transform of SAR Data Cube and 3-D Range Migration Algorithm

To develop a 3-D imaging synthetic aperture radar(SAR) algorithm, a theoretical expression for a Fourier transform of data cube is required. In this article, we introduce a function representing the Fourier transform of 3-D SAR data cube. The function is derived using the method of stationary phase and similar to the one used for 2-D data matrix. For verification and evaluation, a 3-D range migration algorithm using this function for bulk compression is examined. The simulation results based on the parameters of an experimental terahertz indoor SAR testbed show that the algorithm can well focus the 3-D image and, hence, verify the derived function. The image quality assessments such as spatial resolutions and peak sidelobe ratio are used to further evaluate the function and the algorithm. The measured results are compared with the ones provided by a backprojection algorithm.

Distribution Grid Resource Business Center Integrating Dynamic Migration Algorithm and Lightweight System

The existing power system does not have a unified grid resource management model. The basic data of equipment assets cannot be stored, managed, and maintained in a unified manner. The production systems of different departments are developed by different manufacturers at different times, which leads to the problem of production data silos and high maintenance costs. To solve this problem, a grid resource business middle platform based on State grid-corporation public data model (SG-CIM, CIM) model is designed in this paper. Firstly, the concept and core technology of the middle platform in the power grid resource business are introduced. Then a dynamic data migration strategy based on the FA algorithm is proposed. Finally, the model based on SG-CIM and the application scenario of power transmission and distribution are built and its effectiveness is analyzed.

GAJEL-DSDN: an intelligent hybrid genetic-Jaya-based switch migration algorithm for efficient load balancing in distributed SDNs

GAJEL-DSDN: an intelligent hybrid genetic-Jaya-based switch migration algorithm for efficient load balancing in distributed SDNs

Hybrid boundary full-wave-equation depth migration based on dual-sensor acquisition system and its application

The dual-sensor acquisition system arranges two-layer geophones on the surface and their corresponding underground positions, and receives the wavefield simultaneously for accurately solving the full-wave equation. Theoretically, using the two-layer wavefield data obtained by the acquisition system, the two-way wavefield extrapolation can be realized in the depth domain without approximate processing of the wave equation, and the migration algorithm based on the dual-sensor framework has better amplitude preservation. However, limited to the acquisition expenses and difficulties, the spacing of the two-layer sensors is hard to match the actual migration step in real data acquisition. Meanwhile, poor geophone coupling and nonstandard data processing procedures would also lead to unsatisfied imaging results. Thus, some schemes suitable to dual-sensor data, including hybrid boundary wavefield extrapolation strategy, data processing, quality monitor, are proposed to handle these issues, using the delay time difference as a main analyzing object. The numerical simulation and actual data verify the feasibility and effectiveness of the proposed method.

Efficient Smart Grid Load Balancing via Fog and Cloud Computing

As the cloud data centers size increases, the number of virtual machines (VMs) grows speedily. Application requests are served by VMs be located in the physical machine (PM). The rapid growth of Internet services has created an imbalance of network resources. Some hosts have high bandwidth usage and can cause network congestion. Network congestion affects overall network performance. Cloud computing load balancing is an important feature that needs to be optimized. Therefore, this research proposes a 3-tier architecture, which consists of Cloud layer, Fog layer, and Consumer layer. The Cloud serves the world, and Fog analyzes the services at the local edge of network. Fog stores data temporarily, and the data is transmitted to the cloud. The world is classified into 6 regions on the basis of 6 continents in consumer layer. Consider Area 0 as North America, for which two fogs and two cluster buildings are considered. Microgrids (MG) are used to supply energy to consumers. In this research, a real-time VM migration algorithm for balancing fog load has been proposed. Load balancing algorithms focus on effective resource utilization, maximum throughput, and optimal response time. Compared to the closest data center (CDC), the real-time VM migration algorithm achieves 18% better cost results and optimized response time (ORT). Realtime VM migration and ORT increase response time by 11% compared to dynamic reconFigure with load (DRL) with load. Realtime VM migration always seeks the best solution to minimize cost and increase processing time.

A two-stage container management in the cloud for optimizing the load balancing and migration cost

Container technology is driving the transformation of Container as a Service (CaaS), which calls for an optimal solution of container management. In this paper, we present a two-stage framework of container management in the cloud, which includes container placement and container migration. Specifically, we investigate the container placement strategy of cloud servers for load balancing in a long time scale. In a short time scale, we investigate the container migration strategy among the cloud servers for minimizing the migration cost while maintaining the degree of load balance. Both of the strategies are formulated as constrained optimization problems. We propose Balance Aware Container Placement (BACP) algorithm and Adaptive Threshold Container Migration (ATCM) algorithm to solve the optimization problems in different time scales. Furthermore, workload prediction of the containers is proposed to enhance the solution. We show by experiments that we can achieve the load balance while reducing the migration cost. Moreover, the proposed algorithm outperforms the meta-heuristic algorithms, i.e., PSO (Particle Swarm Optimization) and CRO (Coral Reef Optimization).

Algorithm of ESXi cluster migration between different vCenter servers with the ability to rollback

Introduction: Hypervisor managers may become deprecated or outdated or may fail. In addition, customers may want to rebalance hypervisor load. All these can cause problems related to the incompatibility of software with different manager versions or to the level of application integration. An effective solution to such problems is to automatically migrate the configuration and resources between hypervisor manager instances. Purpose: To develop an algorithm of the automatic migration of configurations and resources of an ESXi cluster between different vCenter Server instances with the ability to rollback in the event of a failure. Results: An automatic migration algorithm has been proposed. On its basis a program module for enterprise storage has been developed, which allows migrating an ESXi cluster on which a storage system is deployed from one vCenter Server to another. The module handles errors, and in case an exception or error occurs the module initiates a rollback operation in order to revert to the original vCenter Server. The study presents an experimental evaluation based on the comparison of the manual migration and the execution of the developed module. The time needed for the migration procedure has been reduced from 55 minutes to about 3 minutes, i.e., by 15–18 times. The amount of information to be known or taken into consideration by a user has also been reduced. Practical relevance: The study results have been used for enterprise storage. The developed module makes migrations to a new vCenter Server easier and faster.

Optimal Open-Loop Control of Discrete Deterministic Systems by Application of the Perch School Metaheuristic Optimization Algorithm

A new hybrid metaheuristic method for optimizing the objective function on a parallelepiped set of admissible solutions is proposed. It mimics the behavior of a school of river perch when looking for food. The algorithm uses the ideas of several methods: a frog-leaping method, migration algorithms, a cuckoo algorithm and a path-relinking procedure. As an application, a wide class of problems of finding the optimal control of deterministic discrete dynamical systems with a nonseparable performance criterion is chosen. For this class of optimization problems, it is difficult to apply the discrete maximum principle and its generalizations as a necessary optimality condition and the Bellman equation as a sufficient optimality condition. The desire to extend the class of problems to be solved to control problems of trajectory bundles and stochastic problems leads to the need to use not only classical adaptive random search procedures, but also new approaches combining the ideas of migration algorithms and swarm intelligence methods. The efficiency of this method is demonstrated and an analysis is performed by solving several optimal deterministic discrete control problems: two nonseparable problems (Luus–Tassone and LiHaimes) and five classic linear systems control problems with known exact solutions.

De‐migration‐based supervised learning for interpolation and regularization of 3D offset classes

ABSTRACTRegularization and interpolation of 3D offset classes prior to imaging are an important and challenging step in the marine seismic data processing flow. Here we describe how to perform this task using a deep neural network, and we explain how to overcome the challenge of creating a suitable training data set. The training data set is generated by de‐migrating stacked pre‐stack depth migration images. For each offset class volume, we de‐migrate the pre‐stack depth migrated stacked image into two configurations: (i) the original survey configuration consisting of the recorded source/receiver positions and (ii) an ‘Ideal’ survey configuration with constant offset and azimuth for each 3D offset class. The training creates a 3D convolutional encoder–decoder model that will regularize and interpolate seismic data. The convolutional encoder–decoder is trained on 3D sliding windows in each 3D offset cube to map from (i) to (ii), i.e. to map the original survey configuration with irregular and sparse sampling into the fully sampled regular offset cubes suitable for offset‐based migration, such as Kirchhoff migration. Such migration algorithms rely on regular and sufficiently dense sampling to achieve constructive interference to image the structures and destructive interference to suppress migration noise. We test the new method on one synthetic and one field data example and show that it performs better than a standard regularization/interpolation method based on anti‐leakage Fourier transform, especially for the smallest offset classes. On the synthetic data, we also demonstrate that the convolutional encoder–decoder method preserves the amplitude versus offset as well as the standard method.

Energy-latency tradeoffs for edge caching and dynamic service migration based on DQN in mobile edge computing

Mobile edge computing sinks computing and storage capabilities to the edge of the network to provide reliable and low-latency services. However, the mobility of users and the limited coverage of edge servers can cause service interruptions and reduce service quality. A cooperative edge caching strategy based on energy-latency balance is proposed to solve high power consumption and latency caused by processing computationally intensive applications. In the cache selection phase, the request prediction method based on a deep neural network improves the cache hit rate. In the cache placement stage, the objective function is established by comprehensively considering power consumption and latency, and We use the branch-and-bound algorithm to get the optimal value. We propose an improved service migration method to solve the problem of service interruption caused by user movement. The service migration problem is modeled using a Markov decision process (MDP). The optimization goal is to reduce service latency and improve user experience under the premise of specified cost and computing resources. Finally, the optimal solution of the model is solved by the deep Q-Network (DQN) algorithm. Experiments show that our edge caching algorithm has lower latency and energy consumption than other algorithms in the same conditions. The service migration algorithm proposed in this paper is superior to different service migration algorithms in migration cost and success rate.

Improved Migratory Bird Migration Algorithm for Flexible Job Shop Scheduling

Abstract For flexible job shop scheduling, an improved migratory bird migration algorithm is proposed to minimize the maximum completion time. Firstly, global, local and random selection methods were used to initialize the population, which improved the quality of the initial solution; The structure of migratory bird group was changed by increasing leading birds, and the crossover and mutation operators were introduced to enhance the global optimization ability. And different variable neighborhood search algorithms on critical path were introduced among different bird groups, which improved the local optimization ability of and the diversity of migratory bird groups; The jumping and competition mechanisms were proposed to improve the ability of the algorithm to jump out of the local optimal solution and convergence. Finally, the simulation results of the benchmarks verified the algorithm’s searching ability and stability of the algorithm.

The offloading algorithm of mobile edge computing considering mobility in the intelligent inspection scenario

AbstractIn industrial scenarios, it is very common to replace manual detection with new intelligent applications such as image recognition based on deep learning. However, these applications often have the characteristics of high computational energy consumption and high computational density. It is impossible to deal with these tasks only by relying on intelligent detection equipment. Mobile edge computing (MEC) is an effective method to solve the problems of high energy consumption and insufficient computing power of detection equipment. A reasonable offloading strategy can reduce equipment energy consumption and reduce system maintenance costs. This article studies the intelligent inspection scenarios of multi‐user and multi‐MEC servers, and considers the resource competition and mobility among users. We divided the entire inspection process into several computing cycles, and designed a mobile migration model based on the traditional computing offloading model. Finally, a distributed algorithm based on game theory is proposed to solve the problem of minimizing energy consumption. The proposed intelligent inspection and offloading migration algorithm is evaluated through simulation experiments. Theoretical and experimental results show that our method can better adapt to the scenario proposed in this article, with low algorithm complexity and can effectively reduce the energy consumption of the whole inspection system. Compared with random and greedy algorithms, the energy consumption is reduced by 60% and 50% respectively.

Towards cost-aware VM migration to maximize the profit in federated clouds

Federated cloud has emerged as a cost-effective and scalable platform for multiple cloud providers to collaborate in serving the growing demand for cloud resources. In a federation, the major concern for each cloud provider is to maximize its own profit while meeting the quality of service requirements of end-users. Virtual machine (VM) migration allows the cloud provider to rent resources across the other cloud providers in a federation to minimize the cost. However, VM migration comes with an additional penalty in terms of time and cost, and it might degrade the performance of the system during migration.We address the problem of cost-aware VM migration in federated clouds to maximize the profit of the cloud provider by minimizing the cost. We analyze the impact of VM migration across the data centers in a federation by modeling migration cost, migration time, and downtime. We formulate an optimization problem to minimize the total cost of operation (TCO) of the cloud provider as well as the migration time. We model the TCO to include the cost of hosted VMs and their migration cost. We propose efficient heuristics to select the source and destination data centers for migration, as well as the VM to be migrated. Based on these heuristics, we design a polynomial-time VM migration algorithm to minimize the TCO and migration time. The proposed algorithm considers the characteristics of the VMs, the bandwidth and the data transfer price across data centers to give the highest benefit in migration. We evaluate the proposed algorithm using CloudSim across different scenarios. Our results demonstrate the advantages of VM migration in a federated cloud for both the cloud provider and the end-user, to lower the TCO and the migration downtime. The proposed algorithm is shown to outperform other baseline approaches, with up to 48% reduction in the TCO along with reducing the migration time, downtime, and number of migrations. We also evaluate the impact of network bandwidth and the page dirty rate of VMs on migration performance.

Dynamic Migration Algorithm of Virtual Network Aware Data Based on Machine Learning

Dynamic Migration Algorithm of Virtual Network Aware Data Based on Machine Learning

Priority-awareness VNF migration method based on deep reinforcement learning

By decoupling the software function on hardware devices, Network Function Virtualization (NFV) provides a new service architecture named Service Function Chain (SFC), which combines multiple Virtual Network Functions (VNFs) in a specific order. In order to improve the reliability and Quality of Service (QoS) of network services, VNF migration provides an effective solution for this requirement. However, traditional migration methods lack an appropriate VNF selection mechanism and consider a single network state. Moreover, how to determine the accurate migration location dynamically with machine learning is challenging. This paper proposed a novel PAVM algorithm for reliable and optimal VNF migration, which distinguishes two kinds of VNF migration schemes for network congestion and node failure states respectively, and a node and VNF priority-awareness mechanism is designed, which can select appropriate VNF on suitable node to migrate. Moreover, PAVM utilizes deep reinforcement learning algorithm to choose target node location of VNF migration, which jointly utilizes delay, load balancing of network as feedback factors to optimize the QoS. The experimental results indicate that compared with other three benchmark algorithms, PAVM can effectively reduce the transmission delay and improve node and link load balancing after the VNF migration.

A discrete animal migration algorithm for dual-resource constrained energy-saving flexible job shop scheduling problem

This paper presents a discrete animal migration optimization (DAMO) to solve the dual-resource constrained energy-saving flexible job shop scheduling problem (DRCESFJSP), with the aim of minimizing the total energy consumption in the workshop. A job-resource-based two-vector encoding method is designed to represent the scheduling solution, and an energy-saving decoding approach is given based on the left-shift rule. To ensure the quality and diversity of initial scheduling solutions, a heuristic approach is employed for the resource assignment, and some dispatching rules are applied to acquire the operation permutation. In the proposed DAMO, based on the characteristics of the DRCESFJSP problem, the search operators of the basic AMO are discretized to adapt to the problem under study. An animal migration operator is presented based on six problem-based neighborhood structures, which dynamically changes the search scale of each animal according to its solution quality. An individual updating operator based on crossover operation is designed to obtain new individuals through the crossover operation between the current individual and the best individual or a random individual. To evaluate the performance of the proposed algorithm, the Taguchi design of experiment method is first applied to obtain the best combination of parameters. Numerical experiments are carried out based on 32 instances in the existing literature. Computational data and statistical comparisons indicate that both the left-shift decoding rule and population initialization strategy are effective in enhancing the quality of the scheduling solutions. It also demonstrate that the proposed DAMO has advantages against other compared algorithms in terms of the solving accuracy for solving the DRCESFJSP.