Future Generation Computer Systems-The International Journal of Escience最新文献

Detecting vulnerabilities in source code using graph neural networks (GNN) has gained significant attention in recent years. However, the detection performance of these approaches relies highly on the graph structure, and constructing meaningful graphs is expensive. Moreover, they often operate at a coarse level of granularity (such as function-level), which limits their applicability to other scripting languages like Python and their effectiveness in identifying vulnerabilities. To address these limitations, we propose DetectVul, a new approach that accurately detects vulnerable patterns in Python source code at the statement level. DetectVul applies self-attention to directly learn patterns and interactions between statements in a raw Python function; thus, it eliminates the complicated graph extraction process without sacrificing model performance. In addition, the information about each type of statement is also leveraged to enhance the model’s detection accuracy. In our experiments, we used two datasets, CVEFixes and Vudenc, with 211,317 Python statements in 21,571 functions from real-world projects on GitHub, covering seven vulnerability types. Our experiments show that DetectVul outperforms GNN-based models using control flow graphs, achieving the best F1 score of 74.47%, which is 25.45% and 18.05% higher than the best GCN and GAT models, respectively.

Despite numerous prior attempts to boost transaction per second (TPS) of blockchain system, most of them were at a price of degraded decentralization and security. In this paper, we propose a bodyless block propagation (BBP) scheme for which the blockbody is not validated and transmitted during the block propagation process, to increase TPS without compromising security. Rather, the nodes in the blockchain network anticipate the transactions and their ordering in the next upcoming block so that these transactions can be pre-executed and pre-validated before the birth of the block. It is critical, however, that all nodes have a consensus on the transaction content of the next block.

This paper puts forth a transaction selection, ordering, and synchronization algorithm to drive the nodes to reach such a consensus. Yet, the Coinbase Address of the miner of the next block cannot be anticipated, and therefore transactions that depend on the Coinbase Address cannot be pre-executed and pre-validated. This paper further puts forth an algorithm to deal with such unresolvable transactions for an overall consistent and TPS-efficient scheme. With our scheme, most transactions do not need to be validated and transmitted during block propagation, ridding the dependence of propagation time on the number of transactions in the block, and making the system fully TPS scalable. Experimental results show that our protocol can reduce propagation time by 4$\times$ with respect to the current Ethereum blockchain, and its TPS performance is limited by the node hardware performance rather than block propagation.

Infectious viruses are spread during human-to-human contact and can cause worldwide pandemics. We have witnessed worldwide disasters during the COVID-19 pandemic because of infectious viruses, and these incidents often unfold in various phases and waves. During this pandemic, so many deaths have occurred worldwide that they cannot even be counted accurately. The biggest issue that comes to the forefront is that health workers going to treat patients suffering from COVID-19 also may get infected. Many health workers have lost their lives to COVID-19 and are still losing their lives. The situation can worsen further by coinciding with other natural disasters like cyclones, earthquakes, and tsunamis. In these situations, an intelligent automated model is needed to provide contactless medical services such as ambulance facilities and primary health tests. In this paper, we explore these types of services safely with the help of an intelligent automated transportation model using a vehicular delay-tolerant network. To solve the scenario, we propose an intelligent transportation system for automated medical services to prevent healthcare workers from becoming infected during testing and collecting health data by collaborating with a delay-tolerant network of vehicles in intelligent transport systems. The proposed model automatically categorizes and filters infected patients, providing medical facilities based on their illnesses. Our mathematical evaluation and simulation results affirm the effectiveness and feasibility of the proposed model, highlighting its strength compared to existing state-of-the-art protocols.

Traditional research on vehicular edge computing often assumes that the requested and processed task types are the same or that the edge servers have identical computing resources, ignoring the heterogeneity of task types in mobile vehicles and the services provided by edge servers. Meanwhile, the complexity of the vehicular edge environment and the large amount of real-time data required by DRL are often ignored when using Deep Reinforcement Learning (DRL) to process the vehicular edge tasks; Furthermore, traditional offloading and scheduling models are usually based on idealized models with deterministic task quantities and a single objective (such as latency or energy consumption). This paper proposes a Digital Twin(DT)-based multi-objective optimized task offloading and scheduling scheme for vehicular edge networks to address these issues. To address the complexity of vehicular edge environments and the need for a large amount of real-time data for DRL, this paper designs a DT-assisted vehicular edge environment; To tackle the problem of task heterogeneity in mobile vehicles and edge server service differentiation, a computation model based on Deep Neural Networks (DNN) partitioning and an early exit mechanism is proposed, which leverages the resources of mobile vehicles and edge servers to reduce the time and energy consumption of DNN tasks during the computation process. For the uncertain task quantity of DNN tasks, a schedule model based on the pointer network and Asynchronous Advantage Actor-Critic (A3C) is proposed, which utilizes the characteristics of the pointer network in handling variable-length sequence problems to solve it and trains the pointer network with the A3C algorithm for improved performance. Moreover, this paper introduces the joint optimization of multiple metrics, including energy consumption and latency. Experimental comparative analysis demonstrates that the proposed scheme outperforms other schemes and can reduce time and energy consumption.

With the adoption of new storage technologies like NVMs, tiered storage has gained popularity in large-scale, hyper-converged clusters. The storage back-end of hyper-converged systems supports data storage on devices such as SSDs and HDDs, yet lacks fine-grained tiered storage solutions. For example, Ceph selects storage nodes based primarily on limited criteria, such as node storage capacity, disregarding the diverse performance characteristics of various storage media. In this study, we introduce Olsync, an object-level tiering and coordination system designed to enhance storage resource utilization and data access performance. Specifically, Olsync employs PIPO (Packet-In-Packet-Out), an innovative network communication framework based on Software-defined Networking (SDN), to collaboratively optimize both the network control plane and underlying data plane. Additionally, Olsync can offer efficient object-level tiering and coordination services using the global views obtained by PIPO (e.g., data access patterns and interfering object requests) to make tiered storage and performance optimization decisions. We incorporated the Olsync prototype into Ceph and performed a thorough comparison with contemporary state-of-the-art systems. The evaluation results demonstrate that Olsync significantly enhances system response time (up to 68%), I/O throughput (up to 24%), and 99th percentile latency (up to 16%) in various environments.

The Internet of Things (IoT) networks are poised to play a critical role in providing ultra-low latency and high bandwidth communications in various real-world IoT scenarios. Assuring end-to-end secure, energy-aware, reliable, real-time IoT communication is hard due to the heterogeneity and transient behavior of IoT networks. Additionally, the lack of integrated approaches to efficiently schedule IoT tasks and holistically offload computing resources, and computational limits in IoT systems to achieve effective resource utilization. This paper makes three contributions to research on overcoming these problems in the context of distributed IoT systems that use the Software Defined Networking (SDN) programmable control plane in symbiosis with blockchain to benefit from the dispersed or decentralized, and efficient environment of distributed IoT transactions over Wide Area Networks (WANs). First, it introduces a Blockchain-SDN architectural component to reinforce flexibility and trustworthiness and improve the Quality of Service (QoS) of IoT networks. Second, it describes the design of an IoT-focused smart contract that implements the control logic to manage IoT data, detect and report suspected IoT nodes, and mitigate malicious traffic. Third, we introduce a novel consensus algorithm based on the Proof-of-Authority (PoA) to achieve agreements between blockchain-enabled IoT nodes, improve the reliability of IoT edge devices, and establish absolute trust among all smart IoT systems. Experimental results show that integrating SDN with blockchain outperforms traditional Proof-of-Work (PoW) and Practical Byzantine Fault Tolerance (PBFT) algorithms, delivering up to 68% lower latency, 87% higher transaction throughput, and 45% better energy savings.

Erasure Coding (EC) in cloud storage minimizes data replication by reconstructing data from parity fragments. This method enhances data redundancy and efficiency while reducing storage costs and improving fault tolerance. It is more advantageous than replication in Object Storage Systems. EC guarantees data integrity by ensuring lossless transmission of all coded pieces. As data volumes continue to increase rapidly, the time efficiency of the EC method becomes crucial in ensuring optimal system performance. Various variables, including the algorithm employed, data size, number of storage nodes, hardware resources, and network conditions, can influence the speed of EC operations. Although some literature covers various aspects, there is still a research gap in understanding the I/O activities, time efficiency, and fault tolerance of EC in object storage systems. Hence, our research aims to address these challenges in cloud-based object storage systems. We analyze and benchmark the data storage I/O performance of OpenStack Swift, focusing on the time efficiency of the Reed–Solomon (RS) algorithm across two datasets. Additionally, our contributions include benchmarking EC performance in both local and remote testbeds, utilizing the SimEDC simulator for comprehensive efficiency and fault tolerance assessments. Moreover, we create a comprehensive dataset (MCSD-100) for benchmarking and conduct a systematic literature review. Finally, we identify and discuss future opportunities for enhancing EC in cloud-based object storage systems.

Rapid scientific and technological development has brought many innovations to electronic devices, which has greatly improved our daily lives. Nowadays, many apps require the permission to access user location information, causing the concern on user privacy and making it an important task to protect user trajectory information. This paper proposes a novel model called LSTM-DCGAN by integrating LSTM (Long Short-Term Memory Network) with DCGAN (Deep Convolution Generative Adversarial Network). LSTM-DCGAN takes the advantages of LSTM to remember attributes in the trajectory data and the generator and the discriminator in DCGAN to generate and discriminate the trajectories. The proposed model is trained using real user trajectory data and the experimental results are validated from the perspectives of both effectiveness and practicality. Results show that the proposed LSTM-DCGAN model outperforms similar methods in generating synthesized trajectories that are similar to real trajectories in terms of the temporal and the spatial characteristics. In addition, various influencing factors are evaluated to investigate ways of further improving and optimizing the model. Overall, the proposed LSTM-DCGAN model can achieve the balance between the effectiveness of privacy protection and the practicality of user trajectory data and can thus be applied to safeguarding user trajectory information.

The multi-objective evolutionary algorithm (MOEA) has been widely applied to solve various optimization problems. Existing search models based on dominance and decomposition are extensively used in MOEAs to balance convergence and diversity during the search process. In this paper, we propose for the first time a two-stage MOEA based on a knowledge-driven approach (TMOK). The first stage aims to find a rough Pareto front through an improved nondominated sorting algorithm, whereas the second stage incorporates a dynamic learning mechanism into a decomposition-based search model to reasonably allocate computational resources. To further speed up the convergence of TMOK, we present a Markov chain-based TMOK (MTMOK), which can potentially capture variable dependencies. In particular, MTMOK employs a marginal probability distribution of single variables and an N-state Markov chain of two adjacent variables to extract valuable knowledge about the problem solved. Moreover, a simple yet effective local search is embedded into MTMOK to improve solutions through variable neighborhood search procedures. To illustrate the potential of the proposed algorithms, we apply them to solve a distributed production and transportation-integrated problem encountered in many industries. Numerical results and comparisons on 54 test instances with different sizes verify the effectiveness of TMOK and MTMOK. We have made the 54 instances and the source code of our algorithms publicly available to support future research and real-life applications.

We provide an overview of the software engineering efforts and their impact in QMCPACK, a production-level ab-initio Quantum Monte Carlo open-source code targeting high-performance computing (HPC) systems. Aspects included are: (i) strategic expansion of continuous integration (CI) targeting CPUs, using GitHub Actions own runners, and NVIDIA and AMD GPUs used in pre-exascale systems, (ii) incremental reduction of memory leaks using sanitizers, (iii) incorporation of Docker containers for CI and reproducibility, and (iv) refactoring efforts to improve maintainability, testing coverage, and memory lifetime management. We quantify the value of these improvements by providing metrics to illustrate the shift towards a predictive, rather than reactive, maintenance approach. Our goal, in documenting the impact of these efforts on QMCPACK, is to contribute to the body of knowledge on the importance of research software engineering (RSE) for the stewardship and advancement of community HPC codes to enable scientific discovery at scale.