With the development of data outsourcing technology, the data stored by cloud storage servers are exploding. Secure deduplication for encrypted data helps cloud servers reduce storage overhead in the scenario that clo...
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With the development of data outsourcing technology, the data stored by cloud storage servers are exploding. Secure deduplication for encrypted data helps cloud servers reduce storage overhead in the scenario that cloud users outsource their data in ciphertext. To satisfy client-side semantic security, most existing deduplication schemes for encrypted data need trusted third parties. However, trusted third parties are difficult to deploy and may cause potential risks. Therefore, we propose a secure cloud ciphertext deduplication scheme based on Intel SGX. The proposed scheme uses the Enclave security container provided by Intel SGX as the trusted execution environment on the cloud server to replace the trusted third party to perform sensitive operations. At the same time, our scheme simplifies the secure management of the file encryption keys so that the encryption key of the files with the same data can be securely distributed to other owners of the same file without the need for the original uploader online. We prove the security of the proposed scheme and the experiment shows the efficiency of the scheme.
This study proposes a collaborative design platform based on BIM and cloud computing, combined with an innovative real-time synchronization algorithm, to solve the problems of delay, data consistency and synchronizati...
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ISBN:
(数字)9798350389579
ISBN:
(纸本)9798350389586
This study proposes a collaborative design platform based on BIM and cloud computing, combined with an innovative real-time synchronization algorithm, to solve the problems of delay, data consistency and synchronization conflict in cloud collaborative design. The proposed real-time synchronization algorithm is based on a distributed computing model. By introducing a distributed lock and timestamp mechanism, it effectively guarantees the data consistency and real-time performance in large-scale collaborative design, and can reduce data conflicts and improve collaborative efficiency when multiple design teams operate in parallel. In order to verify the effectiveness of the platform, this study conducted experiments in multiple architectural design projects, and the platform runs in mainstream cloud computing environments (such as AWS and Azure). The experimental results show that the collaborative design platform based on the innovative algorithm has significant advantages over traditional collaborative design tools in terms of synchronization delay, data transmission speed and number of concurrent users. When multiple design teams collaborate, the design cycle of the platform is shortened by 15 %, the overall work efficiency is improved by more than 20 %, and it can maintain stability and low response time in a high concurrent user environment. In addition, the platform's real-time rendering and version control mechanism also significantly improve the design quality and accuracy.
With the continuous emergence of large-scale low-Earth orbit satellite constellations and the service-oriented functionality of satellites, we have designed a dynamic service substitution approach to enhance space-bas...
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ISBN:
(数字)9798331509712
ISBN:
(纸本)9798331509729
With the continuous emergence of large-scale low-Earth orbit satellite constellations and the service-oriented functionality of satellites, we have designed a dynamic service substitution approach to enhance space-based missions’ reliability and completion rate. Based on our formal description model for satellite services, this approach generates one-to-one direct substitution or one-to-many composite substitution schemes when satellite service failures are detected, enabling task migration between satellites through real-time monitoring of satellite status. Additionally, when it is impossible to generate service substitution schemes that strictly meet function and resource requirements, this approach produces suboptimal solutions with relaxed constraints. These solutions are evaluated using our proposed evaluation model. Testing the prototype system demonstrated that our dynamic service substitution approach improves satellite missions’ reliability and completion rate compared to existing methods.
Multi-tenancy is a crucial criterion for cloud service providers since it enables them to share resources among tenants, thus reducing costs. As all tenants share the same buffer space in the cloud server, a tenant’s...
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ISBN:
(数字)9798331509712
ISBN:
(纸本)9798331509729
Multi-tenancy is a crucial criterion for cloud service providers since it enables them to share resources among tenants, thus reducing costs. As all tenants share the same buffer space in the cloud server, a tenant’s overall performance will be affected by other tenants. Thus, developing an efficient buffering scheme to ensure tenant isolation becomes an urgent need. Aiming to solve this problem, this paper proposes a novel buffering scheme for managing the shared buffer in a multi-tenant cloud database. We first present an SLA (Service Level Agreement)-based model to quantify the buffering performance of each tenant. Then, we propose MTRP (Multi-Tenant Replacement Policy) for multi-tenant scenarios. The novelty of MTRP lies in three aspects: (1) it partitions the buffer into logical zones to implement tenant isolation. Each zone manages the pages one tenant uses, but the zone space can be dynamically adjusted by buffer replacements; (2) it proposes a two-step replacement algorithm, including global replacement and local replacement, which can improve space efficiency and reduce the SLA cost; (3) it adopts a reinforcement learning model to select the most appropriate zone for page replacement. The experimental results on various multi-tenant workloads show that MTRP achieves a higher hit ratio and lower SLA costs than LRU, LFU, LRU-2, and LeCaR.
It has become a great challenge to balance accuracy and diversity in recommendation systems. Graph Neural Networks (GNNs), while powerful, can lead to node representation homogeneity and information redundancy due to ...
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ISBN:
(数字)9798331509712
ISBN:
(纸本)9798331509729
It has become a great challenge to balance accuracy and diversity in recommendation systems. Graph Neural Networks (GNNs), while powerful, can lead to node representation homogeneity and information redundancy due to the indiscriminate aggregation of neighbor information. Therefore we propose a novel method that integrates maximum entropy neighbor selection with graph contrast learning to enhance the diversity of recommendations. The method introduces a strategy for neighbor selection based on maximum entropy to ensure a diverse subset of neighbors is chosen during the aggregation phase. A layer attention mechanism is implemented to address the over-smoothing issue, directing greater focus on higher-order neighbors. Furthermore, a loss re-weighting technique is applied to emphasize the learning of long-tail items. The overarching objective is to significantly improve recommendation diversity while maintaining system accuracy, underpinned by graph contrast learning method. Experimental results on the Beauty and MIND-small datasets demonstrate significant enhancements in the accuracy and diversity metrics of the proposed method. In particular, regarding the Recall@300 metric, a substantial improvement of up to 30.56% is observed. Conversely, the method experiences a mere 4.63% reduction in accuracy compared to the optimal baseline. This indicates that the proposed method markedly amplifies the diversity of recommendations without significantly compromising recommendation accuracy.
We study the problem of efficiently scheduling a computational DAG on multiple processors. While previous works have mostly studied this problem in rather simple models, we instead consider the well-established BSP mo...
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ISBN:
(数字)9798350364606
ISBN:
(纸本)9798350364613
We study the problem of efficiently scheduling a computational DAG on multiple processors. While previous works have mostly studied this problem in rather simple models, we instead consider the well-established BSP model, and extend it with non-uniform memory access (NUMA) effects. This results in a notably more realistic model that captures communication costs, synchronization costs, and the hierarchical structure of modern computing architectures. We develop a range of algorithms to minimize the scheduling cost in this more complex setting: several initialization heuristics, a hill-climbing local search, and different approaches to solve the problem as an Integer Linear Program (ILP). We analyze our scheduling algorithms on a diverse set of real-world computational DAGs to show that they outperforms both academic and practical baselines: without NUMA effects, our scheduler finds solutions of 44%-51% smaller cost on average, and with NUMA effects, it achieves up to a factor 3× improvement. Finally, we develop a multilevel scheduling algorithm for the special case when the problem is dominated by very high communication costs.
Optimizing General Matrix Multiplication (GEMM) on GPU platforms has become increasingly important due to the scaling demands of modern deep neural network research. While substantial progress has been made in acceler...
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ISBN:
(数字)9798331509712
ISBN:
(纸本)9798331509729
Optimizing General Matrix Multiplication (GEMM) on GPU platforms has become increasingly important due to the scaling demands of modern deep neural network research. While substantial progress has been made in accelerating high-precision GEMM, optimizing lower-bit GEMM remains an open problem. The CUTLASS library offers highly optimized low-bit GEMM based on tensor cores, but performance varies significantly with tile and pipeline settings across different GPUs. We introduce a novel auto-tuning framework for low-bit CUTLASS GEMM that employs a neural network model to predict optimal GEMM template parameters for target GPUs. This model was trained on a synthetic dataset featuring various matrix sizes from different Ampere GPUs and evaluated on these GPUs. In the test dataset, our method achieved an accuracy of up to 92.9%. Real-time evaluations of low-bit data types on the A100 GPU demonstrated speedups of up to 2.03× for GEMM and 1.44× for the linear layer compared to the default templates.
Data sharing among ground rescue vehicles is essential for safe and efficient operations in disaster-stricken areas. When disasters disrupt communication infrastructure, unmanned aerial vehicles (UAVs) can be deployed...
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ISBN:
(数字)9798331509712
ISBN:
(纸本)9798331509729
Data sharing among ground rescue vehicles is essential for safe and efficient operations in disaster-stricken areas. When disasters disrupt communication infrastructure, unmanned aerial vehicles (UAVs) can be deployed to facilitate data sharing among vehicles. However, maintaining reliable inter-vehicle communication is challenging due to potential UAV and link failures. To enhance communication reliability, we propose a fault tolerance mechanism for UAV-assisted vehicular networks. We introduce the novel concept of K-fault tolerance, which marks the first study of fault tolerance in this context. Moreover, we develop a UAV deployment algorithm that minimizes UAV requirements, controlling costs while maintaining K-fault tolerance. Furthermore, we propose a fault tolerance topology control algorithm (FTTCA) that adjusts deployed UAV communication ranges to further reduce costs while ensuring K-fault tolerance, supported by corresponding theorem proofs. Extensive simulations validate the effectiveness of our proposed UAV deployment algorithm and FTTCA and demonstrate the superiority of our fault tolerance mechanism in managing UAV and link failures.
With the rapid development of renewable energy generation and the distribution network, the dc network has become a hotspot in research and engineering. In the dc network, the power electronic transformer (PET, also k...
With the rapid development of renewable energy generation and the distribution network, the dc network has become a hotspot in research and engineering. In the dc network, the power electronic transformer (PET, also known as solid-state transformer, SST) is the crucial interface device. To achieve better characteristics, the SST based on modular-multilevel-converter (MMC) is proposed. The cascade modulization and high switch efficiency of MMC-SST bring challenges to its electromagnetic (EMT) simulation. The numerous submodules increase the order of the node admittance matrix, requiring more computation resources; the high switch frequency requires a smaller time step, decreasing the calculation speed. To deal with these problems, this paper proposes a parallel EMT simulation method based on the diakoptics. This method achieves parallelized calculation at the system level and submodule level by tearing the whole system into subsystems and applying the compact EMTP method. The proposed method is verified in MATLAB and an FPGA-based real-time simulation platform.
The concept of memory disaggregation has recently been gaining traction in research. With memory disaggregation, data center compute nodes can directly access memory on adjacent nodes and are therefore able to overcom...
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ISBN:
(数字)9781665497473
ISBN:
(纸本)9781665497480
The concept of memory disaggregation has recently been gaining traction in research. With memory disaggregation, data center compute nodes can directly access memory on adjacent nodes and are therefore able to overcome local memory restrictions, introducing a new data management paradigm for distributed computing. This paper proposes and demonstrates a memory disaggregated in-memory object store framework for big data applications by leveraging the newly introduced Thymes-isFlow memory disaggregation system. The framework extends the functionality of the pre-existing Apache Arrow Plasma object store framework to distributedsystems by enabling clients to easily and efficiently produce and consume data objects across multiple compute nodes. This allows big data applications to increasingly leverage parallel processing at reduced development costs. In addition, the paper includes latency and throughput measurements that indicate only a modest performance penalty is incurred for remote disaggregated memory access as opposed to local (~6.5 vs ~5.75 GiB/s). The results can be used to guide the design of future systems that leverage memory disaggregation as well as the newly presented framework. This work is open-source and publicly accessible at https://***/10.5281/zenodo.6368998.
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