Migration Technique Research Articles

Data Migration Techniques across DBMS by Using Metadata

Abstract: The data migration process is usually needed when there is a change in system, format, or storage type. Currently, there are several techniques and tools for migrating data, for example, CSV files, ODBC, SQL Dump, and so on. Inappropriately not all of these techniques can be implemented for migrating data between two different DBMS. This research describes a data migration technique that can be used to migrate data across DBMS. The data migration technique described makes use of existing metadata in each DBMS. The data migration process described here goes through three stages, namely capture, convert and construct. A prototype was built to test this data migration technique. By using the HR schema, cross-DBMS data migration trials were carried out between Oracle and MySQL. Using this technique, full-schema data migration takes an average of 20.43 seconds from Oracle DBMS to MySQL and 12.96 seconds for the reverse scenario. As for partial data migration, it takes an average of 5.95 seconds from Oracle DBMS to MySQL and 2.19 seconds for the reverse scenario

Reflection seismic imaging across a greenstone belt, Abitibi (Ontario), Canada

AbstractEstablished 2D seismic data processing methods such as Kirchhoff pre‐stack time migration and Kirchhoff pre‐stack depth migration function relatively well for regular acquisition geometries and well‐constrained velocity models. Recently developed focusing pre‐stack depth migration methods have the potential to enhance image quality in the case of sparse and non‐regular source–receiver distribution. We have tested the performance of the coherency migration method as one of these focusing migration approaches in comparison to standard dip‐moveout and Kirchhoff pre‐stack time migration techniques by applying them to the Swayze East seismic profile acquired in the Abitibi greenstone belt of Canada. This seismic profile represents a crooked‐line survey that intersects several metal‐bearing deformation zones, providing good target geometries to examine various pre‐stack migration methods. Analysis of the seismic data indicates reflectivity associated with shallowly dipping reflections appears relatively well preserved over the entire 0–6 km offset range for the frequency range between 20 to 90 Hz. Although most reflections are visible already in either the dip‐moveout or the Kirchhoff pre‐stack time migration results, the coherency migration method delivers the most improved image showing all reflective structures inferred for this area. The comparisons suggest the coherency migration method can be considered as superior in terms of resulting seismic image quality compared with conventional approaches for this type of crooked‐line seismic survey in such a complex geological setting.

Energy-Aware Live VM Migration Using Ballooning in Cloud Data Center

The demand for digitization has inspired organizations to move towards cloud computing, which has increased the challenge for cloud service providers to provide quality service. One of the challenges is energy consumption, which can shoot up the cost of using computing resources and has raised the carbon footprint in the atmosphere; therefore, it is an issue that it is imperative to address. Virtualization, bin-packing, and live VM migration techniques are the key resolvers that have been found to be efficacious in presenting sound solutions. Thus, in this paper, a new live VM migration algorithm, live migration with efficient ballooning (LMEB), is proposed; LMEB focuses on decreasing the size of the data that need to be shifted from the source to the destination server so that the total energy consumption of migration can be reduced. A simulation was performed with a specific configuration of virtual machines and servers, and the results proved that the proposed algorithm could trim down energy usage by 18%, migration time by 20%, and downtime by 20% in comparison with the existing approach of live migration with ballooning (LMB).

Elastic anisotropic finite-difference full-wave modeling and imaging of 2D tilted transversely isotropic (TTI) media

Imaging below the tilted transversely isotropic (TTI) media pose serious problems and distortions of the subsurface geological targets of interest for hydrocarbon exploration, which is very common in fold-thrust belts or active tectonic areas like the Rocky Mountain foothills of the Canada or foothills of the Himalaya. To model and image below the TTI media having symmetry axis orthogonal to dipping anisotropic layers, a 2D TTI thrust sheet is considered for elastic anisotropic finite-difference full-wave modeling and generation of synthetic seismic data using the robust staggered-grid scheme of wave propagation. Free-surface boundary conditions at the top and absorbing boundary conditions for the other three sides of the model have been imposed to reduce undesirable edge effects and suppress dispersions. The synthetic seismic data generated for the 2D TTI thrust model are migrated using the anisotropic Kirchhoff pre-stack time (PSTM) and depth migration (PSDM) technique followed by the migration velocity analysis (MVA) algorithm. The traveltime computations are made using robust eikonal equations with fourth-order elastic anisotropic finite-difference solutions to handle large tilts (ν) along with anisotropic ray-tracing to image below the TTI thrust sheet. The MVA algorithm adopted also estimates the anisotropic parameters ε and δ accurately within the acceptable error limit through successive iterations of inversion and parameter update, which results in sufficient flattening of the reflection events in the common image gathers (CIGs). This is a powerful and stable methodology to handle large tilt (ν = 60°) of the steeply dipping reflectors in complex geological structure like the TTI thrust model without much distortion of CIGs for which the symmetry axis of each TTI block is set orthogonal to the bottom of the reflectors having different tilts.

Optimization enabled deep learning method in container-based architecture of hybrid cloud for portability and interoperability-based application migration

ABSTRACT Virtualisation is a major part of the cloud as it permits the deployment of several virtual servers over the same physical layer. Due to the adaption of cloud services, the count of the application running on repositories increases, resulting in overload. However, the application migration in the cloud with optimal resource allocation is still a challenging task. The application migration is employed to reduce the dilemma of resource allocation. Hence, this paper proposes a technique for portability and interoperability-based application migration in the cloud platform. The cloud simulation is done with the Physical Machine (PM), Virtual Machine (VM), and container. The interoperable application migration is provided using the newly devised Lion-based shuffled shepherd (Lion-SS) optimisation algorithm. The Lion-SS algorithm combines the shuffled shepherd optimisation algorithm (SSOA) and the Lion optimisation algorithm (LOA). The new objective function is devised based on predicted load, demand, transmission cost, and resource capacity. Besides, the prediction of the load is performed using Deep long short-term memory (Deep LSTM). The proposed technique obtained the minimal load of 0.007 and resource capacity of 0.342.

Accurate Range Migration for Fast Quantitative Fourier-Based Image Reconstruction With Monostatic Radar

Range migration (or range focusing) techniques are widely used in optical, acoustic, and microwave real-time image reconstruction methods. They have been successfully applied to far-field 3-D imaging where they rely on plane-wave assumptions, which ignore the data amplitude variation over the acquisition aperture. The accuracy of the plane-wave assumption, however, quickly degrades in close-range imaging, where amplitude variations are significant and where the range to the target is on the order of the range sampling step. Here, we present a range-focusing method of improved accuracy, which is applicable to both far-zone and close-range monostatic radar. It refocuses the system point-spread function (PSF) to any range location, taking into account both magnitude and phase changes. The approach can be applied with any Fourier-based imaging algorithm utilizing the Lippmann–Schwinger equation as the underlying scattering model. Here, it is validated through examples based on simulated and measured data where the images are reconstructed with quantitative microwave holography (QMH). QMH employs measured PSFs to achieve quantitative imaging in real-time. The proposed range-migration method is applicable with measured PSFs, too, leading to reduced system-calibration effort and the ability to focus an image at any desired range.

Self-supervised learning for modal transfer of brain imaging.

Today's brain imaging modality migration techniques are transformed from one modality data in one domain to another. In the specific clinical diagnosis, multiple modal data can be obtained in the same scanning field, and it is more beneficial to synthesize missing modal data by using the diversity characteristics of multiple modal data. Therefore, we introduce a self-supervised learning cycle-consistent generative adversarial network (BSL-GAN) for brain imaging modality transfer. The framework constructs multi-branch input, which enables the framework to learn the diversity characteristics of multimodal data. In addition, their supervision information is mined from large-scale unsupervised data by establishing auxiliary tasks, and the network is trained by constructing supervision information, which not only ensures the similarity between the input and output of modal images, but can also learn valuable representations for downstream tasks.

Application of Elastic Reverse Time Migration to Ultrasonic Echo Data in Civil Engineering

The ultrasonic echo technique is widely used in non-destructive testing for investigation and damage analysis of concrete constructions. To improve the ultrasonic imaging of complicated structures in concrete, we transferred a seismic migration technique, the Reverse Time Migration (RTM), to non-destructive testing. In a preliminary study, we tested a 2D acoustic RTM algorithm on measured ultrasonic echo data acquired at a concrete foundation slab. Compared to the conventional used synthetic aperture focusing technique (SAFT) algorithms for ultrasonic data reconstruction, our acoustic RTM results showed a significant improvement in imaging the interior structure of the concrete slab. In contrast to SAFT, RTM is a wavefield-continuation method in time and uses the full wave equation. RTM is, thus, able to include multiple reflections and to handle multi-pathing as well as many other complex situations. As a drawback RTM requires extensive computing power and memory capacity. An RTM algorithm that uses the full elastic wave equation instead of the full acoustic one (as applied in our preliminary work) has the potential to optimize the imaging results even further. This is due to the fact, that our ultrasonic data are generated by exciting elastic waves. In a first step, we tested two 2D elastic RTM algorithms on synthetic ultrasonic echo data generated with a concrete model. Our synthetic elastic RTM results showed an enhancement in imaging the features inside the test model compared to acoustic RTM and SAFT. In a second step, we acquired ultrasonic measurement data at a concrete specimen consisting of three steps and four air-filled tendon ducts. Processing the real ultrasonic data with our elastic RTM codes was successful and improved the reconstruction of the geometries of the steps and tendon ducts. With our study we have shown that elastic RTM is a step forward for ultrasonic testing in civil engineering.

Container placement and migration strategies for cloud, fog, and edge data centers: A survey

SummaryThe last decade has witnessed important development of network softwarization that has revolutionized the practice of networks. Virtualized networks bring novel and specific requirements for the control and orchestration of containerized network functions that are scattered across the network. In this regard, the migration of virtualized network functions plays a pivotal role to best meet the requirements of optimal resource utilization, load balancing and fault tolerance. The purpose of this survey is to offer a detailed overview of the progress on container migration so as to provide a better understanding of the trade‐off between the benefits associated with the migration and the practical challenges. The paper includes a classification of the placement algorithms that map the containerized network functions on the virtualized infrastructure. Following, a taxonomy of the migration techniques that perform the transfer of the containerized microservices is proposed.

Zeta potential as a tool for functional materials development

The importance of the isoelectric point for the preparation and characterization of functional materials to understand their behavior and optimize their performance is highlighted, with especial focus on industrial catalysts. In different liquid-phase steps involved in materials synthesis, the knowledge of the surface charge as a function of the pH of the medium is fundamental, since it is the dynamizing agent of processes such as gelation, peptization, coagulation and agglomeration to form solid particles. Therefore, zeta potential (ζ) measurements, often based on electrophoretic migration techniques, are a powerful source of information. The basic principles are introduced to explain through relevant examples the possibilities for materials design. Regarding materials preparation, the measurement of the zeta potential to control the synthesis and coating of supports is reviewed, with special attention to shaped supports. Aspects as the influence of the binders on the supports properties depending on their mutual interaction are analyzed, with example systems based on TiO2 or Al2O3 and natural silicates such as sepiolite. Also, how the knowledge of ζ as a function of pH allows, by choosing the appropriate pH, the preferential impregnation of noble metals on porous supports, as well as the washcoating of dense ceramic monoliths with porous supports. Special attention has been paid to surfaces characterization based on the use of zeta potential measurements to estimate the formation and relative location of species, the apparent surface coverage, or the adsorption and reactivity, to establish synthesis strategies to achieve specific properties.

Memory/Disk Operation Aware Lightweight VM Live Migration

Live virtual machine migration technique allows migrating an entire OS with running applications from one physical host to another, while keeping all services available without interruption. It provides a flexible and powerful way to balance system load, save power, and tolerate faults in data centers. Meanwhile, with the stringent requirements of latency, scalability, and availability, an increasing number of applications are deployed across distributed data-centers. However, existing live migration approaches still suffer from long downtime and serious performance degradation in cross data-center scenes due to the mass of dirty retransmission, which limits the ability of cross data-center scheduling. In this paper, we propose a system named Memory/disk operation aware Lightweight VM Live Migration across data-centers with low performance impact (MLLM). It significantly improves the cross data-center migration performance by reducing the amount of dirty data in the migration process. In MLLM, we predict disk read workingset (i.e., more frequently read contents) and memory write workingset (i.e., more frequently write contents) based on the access sequence traces. And then we adjust the migration models and data transfer sequence by the workingset information. We further proposed an improved algorithm for workingset estimation. Moreover, we discussed the potential use of machine learning (ML) to enhance the performance of the VM migration and also propose a two-level hierarchical network model to make the ML-based prediction more efficient. We implement MLLM and its improved versions on the QEMU/KVM platform and conduct several experiments. The experimental results show that 1) MLLM averagely reduces 62.9% of total migration time and 36.0% service downtime over existing methods; 2) The improved workingset estimation algorithm reduces 9.32% memory pre-copy time on average over the original algorithm.

Driving under influence: Robust controller migration for MEC-enabled platooning

Connected cars are becoming more common. With the development of multi-access edge computing (MEC) for low-latency applications, it will be possible to manage the cooperative adaptive cruise control (CACC, also known as platooning) of such vehicles from the edge of cellular networks. In this paper, we present a controller that manages platooning from the network edge by adapting to varying network conditions. We incorporate a mechanism in the controller that allows vehicles to switch to automated cruise control when delays exceed safety thresholds, and back to platooning when the delays are sufficiently low to support it. We also formulate the problem of maintaining a low-latency connection in the presence of high mobility through migration, and propose a Q-Learning algorithm to solve this problem. We finally propose an Asynchronous Shared Learning scheme that enables multiple migration agents to cooperate, in order to expedite the convergence of migration policies. Compared to state-of-the-art migration techniques, our scheme exhibits better compliance of vehicle speed and spacing values to preset targets, and ameliorates statistical dispersion.

Container-based Service Relocation for Beyond 5G Networks

<p>With the advent of 5G networks, various research on Multi-access Edge Computing (MEC) to provide high-reliability and ultra-low latency services are being actively conducted. MEC is an intelligent service distributed cloud technology that provides a high level of personal services by deploying cloud servers to edge networks physically closed to users. However, there is a technical issue to be solved, e.g., the service being used by a user does not exist in the new edge network, and there may even be situations in which the service cannot be provided in the new edge network. To address this, the service application must be relocated according to the location of the user’s movement. Various research works are underway to solve this service relocation issue, e.g., cold/live migration studies have been carried in legacy cloud environments. In this paper, we propose a container migration technique that guarantees a smooth service application relocation for mobile users. We design scenarios for adaptive handoff and describe the detailed operation process. In addition, we present our MEC testbed, which has been used to experiment our container migration technique.</p> <p> </p>

Design and Simulation of Content-Aware Hybrid DRAM-PCM Memory System

Phase Change Memory (PCM) can directly connect persistent memory to main memory bus, while it achieves high read throughput and low standby power, the critical concerns are its poor write performance and limited durability. A naturally in-spired design is the hybrid memory architecture that fuses DRAM and PCM, so as to exploit the positive aspects of both types of memory. Unfortunately, existing solutions are seriously challenged by the limited main memory size, which is the primary bottleneck of in-memory computing. In this paper, we introduce a novel Content Aware Hybrid DRAM-PCM memory system framework—CAHRAM, which exploits deduplication to improve line sharing with high memory efficiency. It reduces write traffic to hybrid memory by removing unnecessary duplicate line writes, thereby further enhancing the write endurance of PCM. And it also substantially extends available free memory space by coalescing redundant lines in hybrid memory. We also design a reference-based page migration technique to minimize the access overheads caused by the performance gap between DRAM and PCM. Compared with the state-of-the-art in a hybrid memory simulator, our experiment results show that CAHRAM can achieve the highest I/O performance and the longest PCM lifetime with the competitive efficiencies in space and energy.

Analysis of Data and Virtualization Security in Cloud

Cloud computing is getting attention of the researchers these days. Large number of users are attached with cloud computing. But as there are several merits of cloud computing some demerits are also there. The one of the greatest threat in cloud computing is security. This security is concerned with data of the users that is present in cloud and is transferred across the clouds and the virtualization concept. In this paper several attacks on cloud are studied and solutions to those threats are compiled. In this paper a secure technique to handle data in cloud and secure virtualization is proposed in this paper. A secure VM migration technique which gives VM isolation guarantee and VM escape protection is proposed here. And it is shown that this security algorithm possess no considerable overhead.

Hybrid Optimization Algorithm for VM Migration in Cloud Computing

The number of cloud users and their respective workload increases everyday with the inherent benefits of cloud computing. On the other hand, it becomes critical for service providers to maintain Quality of Service (QoS) even under heavy workload conditions. In order to provide better computing services, cloud utilizes Virtual Machine (VM) migration techniques, which eases the process of providing the services to the user without any delay and with minimum energy consumption. The existing cloud computing services mainly rely on migration techniques; nevertheless, handling large VM migrations consumes more energy, which directly affects the VM performances. This necessitates the need to develop an effective VM migration strategy to perform the necessary migrations and avoid unnecessary migrations. Conventional migration techniques perform migration based on static parameters which attains less efficient results and lags in performance while handling the resource utilization. In this research work, a hybrid optimization algorithm is presented to handle VM migration in a cloud environment. Cuckoo search optimization algorithm and particle swarm optimization algorithm are combined to obtain the proposed hybrid optimization model. The major objective of this research work is to reduce energy consumption, computation time, and migration cost. Maximizing resource utilization is another objective of this research work. To validate the research objective, the performance of hybrid optimization model is verified through simulation analysis and compared with conventional algorithms like firefly optimization, whale optimization, hybrid whale optimization, and hybrid bee colony optimization in terms of energy consumption, migration cost, resource availability, and computation time.

A Fuzzy Approach to Fault Tolerant in Cloud using the Checkpoint Migration Technique

Fault tolerance is one of the most important issues in cloud computing to provide reliable services. It is difficult to implement due to dynamic service infrastructures, complex configurations and different dependencies. Extensive research efforts have been made to implement fault tolerance in the cloud environment. Many studies focus only on fault detection and do not consider fault tolerance. For this reason, in this paper, in addition to recognizing the nature of the fault, a fuzzy logic-based approach is proposed to provide an appropriate response and increase the fault tolerance in the cloud environment. Checkpoint-based migration technique is used to increase fault tolerance. Using a checkpoint during migration can reduce time and processing costs and balance the load between virtual machines in the event of a fault. The simulation is performed according to the data center of Vietnam Telecommunications Company (VDC). The results of the proposed method in a period of 60 minutes show 98.03% fault detection accuracy, which is 4.5% and 4.1% superior to FLPT and PLBFT algorithms, respectively.

Live Migration of Virtualized Carrier Grade SIP Server

The concept of network virtualization, such as network functions virtualization, has attracted considerable attention from telecom carriers and a live migration technique is a key feature of virtualization technology. However, there are some challenges associated with applying server virtualization technology including live migration to a SIP server. Previous work has not dealt with the performance or behavior of a SIP server during live migration. Neither has it targeted a carrier grade SIP server for live migration. In this paper we present a virtualized carrier grade SIP server running on a virtual machine, which is configured with Carrier Grade Linux, HA middleware and SIP-AS application. We also assess its performance to investigate the impact of throughput degradation and suspension on a SIP layer and HA cluster configuration.

Novel Approach for Midface Depression.

Background:Although Le Fort type II, prosthesis detainment, and orthodontic treatment are considered for the management of midface retraction, they may be limited by their high cost, infection risk, and excessive amount of tooth movement. Therefore, the Point A-Koji method was devised as a novel treatment in patients with midface retraction.Methods:This is a case report of a 26-year-old woman who presented with a feeling of depressed midface and protrusion of the mouth. Preoperatively, the position of the lip and line connecting the nasal apex and mental muscles (E-line) were normal, but the subnasale was located posteriorly. The patient had a narrow nasolabial angle of 74 degrees and the subnasale-Pog’ to the upper lip of 6.5 mm. After insertion of a metallic-plate implant under the periosteum, the plate was screwed and fixed to the bone. The Point A-Koji method was used for treatment in this patient. This is characterized by the A-point anterior migration technique in which the periosteum of hard tissue A-point circumferential attachment was shifted anteriorly, thereby preventing the return of soft tissue.Results:The following changes with respect to preoperative findings were noted 5 months postsurgery: facial convexity from 3.3 degrees to 7.6 degrees; nasolabial angle from 74 degrees to 90.2 degrees; true horizontal line from 50 degrees to 73 degrees; and subnasale-Pog’ to the upper lip from 6.5 mm to 4.7 mm. This resulted in an improved midface retraction.Conclusion:The Point A-Koji method may be an ideal method to improve the midface retraction in patients.

Energy-Aware Real-Time Tasks Processing for FPGA-Based Heterogeneous Cloud

Cloud computing is becoming a popular model of computing. Due to the increasing complexity of the cloud service request, it often exploits heterogeneous architecture. Moreover, some service requests (SRs)/tasks exhibit real-time features, which are required to be handled within a specified duration. Along with the stipulated temporal management, the strategy should also be energy efficient, as energy consumption in cloud computing is challenging. In this paper, we have proposed a strategy, called “Efficient Resource Allocation of Service Request” (ERASER) for energy efficient allocation and scheduling of periodic real-time SRs on cloud platform. The cloud platform is consists of Field Programmable Gate Arrays (FPGAs) as Processing Elements (PEs) along with the General Purpose Processors (GPP). We have further proposed, an SR migration technique to reduce the tasks rejection by serving maximum SRs. Simulation based experimental results demonstrate that the proposed methodology is capable to achieve upto 90 percent resource utilization with only 26 percent SR rejection rate over different experimental scenarios. Comparison results with other state-of-the-art techniques reveal that the proposed strategy outperforms the existing technique with 17 percent reduction in SR rejection rate and 21 percent reduction in energy consumption. Further, the simulation outcomes have been validated on real FPGA test-bed based on Xilinx Zynq SoC with standard benchmark tasks.