We propose NVLog, an NVM-based acceleration framework for disk file systems, designed to transparently harness the high performance of NVM within the legacy storage stack. NVLog provides on-demand byte-granularity sync absorption, reserving the fast DRAM path for asynchronous operations, meanwhile occupying NVM space only temporarily. To accomplish this, we designed a highly efficient log structure, developed mechanisms to address heterogeneous crash consistency, optimized for small writes, and implemented robust crash recovery and garbage collection methods. Compared to previous solutions, NVLog is lighter, more stable, and delivers higher performance, all while leveraging the mature kernel software stack and avoiding data migration overhead. Experimental results demonstrate that NVLog can accelerate disk file systems by up to 15.09x and outperform NOVA and SPFS in various scenarios by up to 3.72x and 324.11x, respectively.

Quantum computers, leveraging superposition and entanglement, offer significant qubit efficiency for data processing compared to classical systems. However, encoding classical data into quantum states, given the current limitations of quantum hardware, often results in higher runtime complexity than classical methods, thus limiting the perceived quantum advantage. Previous quantum data compression methods, primarily based on Amplitude Encoding and mixed-state systems, result in lossy data recovery and necessitate extensive preprocessing. In this work, we propose Quantum Run-Length Encoding (QRLE), a novel lossless quantum data compression method that integrates Basic Encoding with Run-Length Encoding principles. By encoding repeated data sequences with their run lengths, QRLE achieves efficient and accurate data recovery on quantum computers, while exponentially reducing both qubit costs and runtime complexity compared to existing quantum data storage models. We further explore QRLE’s application in image processing, where it significantly optimizes quantum resource utilization over recent quantum image representation techniques. Experiments conducted on both quantum simulators and IBM’s superconducting quantum computer validate the efficiency of QRLE and confirm its compatibility with current quantum hardware.

Quantum computing offers parallel processing capabilities and resource-saving advantages, particularly useful for managing expansive datasets and complex image processing tasks. Grayscale images, being the simplest single-channel image mode, are frequently employed in artificial intelligence training. Before actual image applications, various image processing operations are typically required. However, the restoration of a grayscale image of dimensions 2n× 2nafter a series of linear transformations poses a challenge. Existing methods typically involve finding the inverse of the most recent linear transformation or re-encoding the image followed by repeated operations until the final transformation, resulting in excessive computational overhead and disconnection from subsequent quantum grayscale image applications. To address this issue, we propose a universal quantum linear restoration algorithm for grayscale image, denoted as QLR, which effectively bridges the stages of linear transformation and subsequent image applications. QLR reduces the time complexity from O(2n) to O(n) compared to classical counterpart. Building upon the QLR algorithm, we further propose two quantum resource-optimized compression methods for optional lossless image storage. Combining with other quantum algorithms and techniques, we design a framework (QGIP) aimed at bridging the processes of quantum grayscale image processing and applications. Experiments simulated on the IBM Quantum platform validate the correctness and efficiency of our proposal.

Various remote sensing applications have widely used domain adaptation (DA) methods. Since it does not need to add human interpretation in the target domain, it can be used in cross-region, multi-temporal, and multi-sensor application scenarios. In order to further optimize the design of the loss function and better address the challenges of DA in remote sensing, in this paper, we propose a new universal DA method named C3DA for scene recognition of remote sensing images. It has a comprehensive C3criterion for recognizing the "unknown" classes by innovatively fusing confidence, consistency, and certainty of samples to make our network training more efficient. We evaluate the performance of our proposed method based on six transfer tasks on three remote sensing datasets. The evaluation results show that our proposed method achieves an average H-score of 58.44%, significantly higher than other SOTA universal DA methods with an average improvement of 2.32~29.43%. Compared to the baseline ResNet-50, it achieves up to 19.92% improvement, demonstrating that the proposed method outperforms in the universal DA scenario. In the future, we also plan to expand the application of this method to more scenarios.

The Mobile Edge Network (MEN) has emerged as the basic infrastructure to support fifth-generation networks, mobile edge computing and fog computing. The characteristics of mobility must be addressed to guarantee the quality of service in MENs. As one of the critical problems in MENs, flexible deployment plays a part in exploiting edge networks. Despite the abundance of recently proposed strategies, most concentrate on the change in user demands and inevitably ignore the influence for the user mobility, which is common in future networks. We propose Provision for Mobile Edge Computing (PMEC), a prototype that takes advantage of storage devices with flexible placement. In PMEC, we accommodate various considerations and select different storage devices to cache, deploying the cache with the relationship of a two-tiered structure in the MEN. Thus, we construct a flexible overlay network with the objective of minimizing the cost in a two-tiered edge network. Based on the analysis of the problem, we solve the two-tiered placement from bottom to top using the dynamic minimal spanning tree (MST) algorithm and design two algorithms for each tier including the basic algorithm and the improved algorithms. The simulation is conducted on realistic data to demonstrate the performance of our algorithms.

Resource allocation and job scheduling are the core functions of grid computing. These functions are based on adequate information of available resources. Timely acquiring resource status information is of great importance in ensuring overall performance of grid computing. This work aims at building a distributed system for grid resource monitoring and prediction. In this paper, we present the design and evaluation of a system architecture for grid resource monitoring and prediction. We discuss the key issues for system implementation, including machine learning-based methodologies for modeling and optimization of resource prediction models. Evaluations are performed on a prototype system. Our experimental results indicate that the efficiency and accuracy of our system meet the demand of online system for grid resource monitoring and prediction.

Growing complexity in Grid environment makes bandwidth monitoring and prediction both increasingly difficult and increasingly important. The challenge is to design a low-cost and online prediction system to provide Grid users and developers with detailed bandwidth information. Bandwidth prediction is designed based on Grid Service Infrastructure, it is composed of a series of Grid services including monitoring service, prediction service, optimizing service and publication service. Support Vector Regression is used as prediction technology and optimizing strategy is proposed to enhance prediction accuracy. Bandwidth prediction is tested using benchmark data set and experimental results indicate that the prediction service is efficient in optimizing model hyperparameters and prediction error is low on both familiar and unfamiliar bandwidth samples.

To meet different applications' requirements, a preference-based fuzzy clustering method is used to pre-classify resources for selection. Meanwhile, a simple and direct algorithm with low complexity is introduced and then improved to compute transitive closure of fuzzy matrix when doing fuzzy clustering so as to realize resources' fast allocation. Experiments prove that resources which meet application requirements can be selected quickly by applying this method, thus grid applications' performance can be improved.

Recently, Epsilon-Insensitive Support Vector Regression (epsiv SVR) has been introduced to solve regression and prediction problems. However, the preprocessing of data set and the selection of parameters can become a real computational burden to developer and user. Improper parameters usually lead to prediction performance degradation. In this paper, by introducing Parallel Multidimensional Step Search (PMSS) method, standard epsiv-SVR method is extended to a systematic approach for user to finish model selection with high prediction accuracy. Experiments with both simulation data set and practical data set were performed on computing nodes in Grid environment. Experimental results were analyzed with statistical method to validate the effectiveness and accuracy of the proposed method.