Concurrency and Computation-Practice & Experience最新文献

Burst-buffering is a promising storage solution that introduces an intermediate high-throughput storage buffer layer to mitigate the I/O bottleneck problem that the current high-performance computing (HPC) platforms suffer. The existing Markov-Chain based probabilistic I/O scheduling utilizes the load state of burst-buffers and the periodic characteristics of applications to reduce I/O congestion due to the limited capacity of burst-buffers. However, this probabilistic approach requires consistent I/O characteristics of applications, including similar I/O duration and long application length, in order to obtain an accurate I/O load estimation. These consistency conditions do not often hold in realistic situations. In this paper, we propose a generic framework of dynamic probabilistic I/O scheduling based on application clustering (DPSAC) to make applications meet the consistency requirements. According to the I/O phase length of each application, our scheme first deploys a one-dimensional K-means clustering algorithm to cluster the applications into clusters. Next, it calculates the expected workload of each cluster through the probabilistic model of applications and then partitions the burst-buffers proportionally. Then, to handle dynamic changes (join and exit) of applications, it updates the clusters based on a heuristic strategy. Finally, it applies the probabilistic I/O scheduling, which is based on the distribution of application workload and the state of burst-buffers, to schedule I/O for all the concurrent applications to mitigate I/O congestion. The simulation results on synthetic data show that our DPSAC is effective and efficient.

To explore the complex stress mechanism of prestressed concrete U-beams in urban rail transit, in order to improve the safety of urban rail transit construction and the economy of beam structures. The study first analyzed the complex stress mechanism of U-beams and obtained a tension compression rod model through finite element analysis. Then, experimental research was conducted on the vertical three-dimensional finite element stress of U-beams, and strain cloud maps were obtained and compared with calculated values. The experimental data show that the beam can still recover to its original state after the second cycle, and the beam will not crack. This recovery mechanism means that U-beams have high crack resistance and stability under complex stress processes. In the vertical deformation cloud map of the U-beam, the deflection of the mid span section is the largest, with a maximum displacement of about 20.4 mm, which is very close to the measured value of 20.3 mm. In the measured data of concrete strain measuring points and the results of finite element calculation, the difference rate between measured values and calculated values of some measuring points is within 10%. The results indicate that the U-shaped beam tension and compression rod model combined with finite element analysis has a high degree of conformity with the actual situation, and can provide technical reference for the construction of urban rail transit. The stress mechanism and design method proposed in the study have high reliability and are suitable for the design and construction of prestressed concrete U-beams in urban rail transit construction.

Due to geographical factors, it is impossible to build large-scale communication network infrastructure in remote areas, which leads to poor network communication quality in these areas and a series of delay-sensitive tasks cannot be timely processed and responded. Aiming at the problem of limited coverage in remote areas, the space-air-ground integrated networks (SAGIN) combined with mobile edge computing (MEC) can provide low latency and high reliability transmission for offloading delay-sensitive tasks for users in remote areas. Considering the strong limitation of satellite resources in the space-ground integrated network and insufficient energy of local user equipment, firstly, a satellite-UAV cluster-ground three-layer edge computing network architecture is proposed in this paper. Under the condition that the delay requirements of various ground tasks are met, the task offloading problem is transformed into a Stackelberg game between ground user equipment and edge servers. In addition, it is proved that the existence of Nash equilibrium in non-cooperative game between ground user equipment by using potential game. Finally, a Nash equilibrium iterative offloading algorithm based on Stackelberg game (NEIO-SG) is proposed to find the optimal offloading strategy for tasks to minimize the system offloading cost and the optimal forwarding percentage strategy for offloading tasks to maximize the utility function of the edge server. Simulation results show that compared to other baseline algorithms, NEIO-SG reduces the total system latency during task offloading by about 13$��\%�$ and the energy consumption of the edge server by about 35$��\%�$.

Existing research on recommender systems primarily focuses on improving a single objective, such as prediction accuracy, often ignoring other crucial aspects of recommendation performance such as temporal factor, user satisfaction, and acceptance. To solve this problem, we proposed an explicable recommendation model using many-objective optimization and a time-assisted knowledge graph, which utilizes user interaction times within the graph to prioritize recommending recently frequently visited items and is further optimized using a many-objective optimization algorithm. In this model, the temporal weight of user actions at different times is first determined through a time decay function. Additionally, if a user clicks on the same item again, the current action's temporal weight is set to one. This strategy prioritizes recent user actions and frequently visited items, reflecting current interests and preferences better. Next, the created knowledge graph is used to create a list of potential recommendations. Embedding methods obtain the vectors for entities and relations in the path. These vectors, combined with the temporal weight of actions, quantify the explainability of user recommendations. Optimizing the rest of the recommendation performance with many objective algorithms while focusing on the user's recent frequent visits to the item. Finally, the outcomes of the research study indicate that, compared to other explicable recommended methods, our model, considering temporal factor, improved average accuracy by 11%, diversity by 1%, and explainability by 21% in the Useraction1 data set. Results in other data sets also indicate that the proposed model maintains accuracy, diversity, and novelty while enhancing explainability.

Data aggregation, in its basic form, has been widely used, and several solutions have been proposed for IoT environments. However, to calculate statistical metrics, detect anomalies, and predict future trends, we need to perform various data analysis functions on the aggregated data. Recently, multifunctional data aggregation (MFDA) has been proposed to calculate various statistical functions such as sum, mean, variance, covariance, and analyze of variance (ANOVA). The purpose of MFDA is to enable the improvement of decision making, resource allocation and system performance by providing diverse and varied statistical data. However, the existing solutions involving MFDA generate significant communication and calculation costs. Furthermore, they cannot prevent malicious aggregators from sending fake data. Recently, the Fog computing paradigm has been adopted in IoT environments to address various challenges and enhance the efficiency of data processing and storage. The blockchain technology has been integrated in various IoT applications to enhance the security, increase transparency, and facilitate decentralized data exchange and transactions. In this article, we propose BMDA, a blockchain-based secure multifunctional data aggregation method for IoT-Fog environments. BMDA employs an encoding function to structure the data before their transmission. Furthermore, to ensure privacy preservation, authentication, data integrity and to resist malicious aggregators, we employ Paillier homomorphic encryption, BLS signature, and blockchain technology. The security analysis demonstrates the robustness of our proposal, and the performance analysis in terms of computations and communications shows the effectiveness of BMDA compared to existing solutions.

Although some many-objective optimization algorithms (MaOEAs) have been proposed recently, Pareto dominance-based MaOEAs still cannot effectively balance convergence and diversity in solving many objective optimization problems (MaOPs) due to insufficient selection pressure. To address this problem, a bi-directional fusion niche domination is proposed. This method merges the strengths of cone and parallel decomposition directions in comparing dominations for nondominance stratification within the candidate population, augmenting the selection pressure of population. Subsequently, the crowding distance is introduced as an additional selection criterion to further refine the selection of nondominated individuals within the critical layer. Lastly, a MaOEA based on bi-directional fusion niche dominance (MaOEA/BnD) is proposed, utilizing bi-directional fusion niche dominance and crowding distance as important components of environmental selection. The performance of MaOEA/BnD was compared with five representative MaOEAs in 20 benchmark problems. Experimental results demonstrate that MaOEA/BnD effectively balances convergence and diversity when handling MaOPs with complex Pareto fronts.

The exponential growth of data poses a critical challenge for cloud storage systems. Redundant data consumes valuable storage space and increases infrastructure costs. Data deduplication, a technique for eliminating duplicate data copies, offers a promising solution. However, existing deduplication techniques often compromise data security, especially when dealing with encrypted data. This paper proposes a novel approach that merges convergent encryption (CE) with data deduplication. CE leverages user data itself to generate unique encryption keys, enabling secure deduplication on encrypted data. We analyze existing literature on secure data deduplication and categorize various techniques using UML activity diagrams. We then present our proposed CE-based deduplication system, outlining its functionalities through UML diagrams. This research contributes to the field of secure data storage by proposing a novel and secure deduplication approach. By demonstrating its efficiency and security benefits, this work paves the way for more efficient and secure cloud storage solutions. Finally, we demonstrate the system's effectiveness through a comparative analysis, highlighting its potential to significantly improve storage efficiency while maintaining data security.

Matrix factorization (MF)-based recommender systems (RSs) as black-box models fail to provide explanations for the recommended items. While some models attain a degree of explainability by integrating neighborhood algorithms, which compute explainability based on the preferences of proximate users, they overlook the contribution of the subjective preferences of the target user to enhancing model explainability, resulting in suboptimal model explainability. To address this problem, an explainable RS directed by reconstructed explanatory factors and multi-modal matrix factorization (ERS-REFMMF) is proposed. By integrating users' subjective sentiment and preference features into the rating matrix to form a multi-modal matrix, ERS-REFMMF utilizes the Funk-singular value decomposition method at the foundational layer to decompose the multi-modal matrix and generate a candidate item set. At the upper layer, explainability is constructed based on the target user's subjective preferences and latent features derived from MF, and the final recommended list is optimized for accuracy, diversity, novelty, and explainability through multi-objective optimization algorithms. ERS-REFMMF models around users' explicit preferences and latent associations, reconstructs explainability with hybrid factors, and enhances overall performance through a many-objective optimization algorithm. Experimental results on real datasets demonstrate that the proposed model is competitive in both phases compared to existing recommendation methods.