High Time Overhead Research Articles

Flexi-BOPI: Flexible granularity pipeline inference with Bayesian optimization for deep learning models on HMPSoC

To achieve high-throughput deep learning (DL) model inference on heterogeneous multiprocessor systems-on-chip (HMPSoC) platforms, the use of pipelining for the simultaneous utilization of multiple resources has emerged as a promising solution. Nevertheless, current research faces two primary challenges: determining the optimal pipeline partitioning granularity, which directly influences the inference performance, and addressing the high time overhead of the search algorithms. To address these challenges, we propose Flexi-BOPI, a pipeline inference method for DL models of HMPSoCs. Flexi-BOPI offers flexible pipeline partitioning granularity down to a minimum size of a single core, enhancing the performance by better adapting to the diverse computational demands of different layers in DL models. Flexi-BOPI employs a Bayesian optimization-based search algorithm to significantly reduce the search overhead. In addition, we propose a surrogate model based on the heteroscedastic Gaussian process (HGP) to address the challenge of sample noise during the evaluation process. This approach can further reduce search overhead. Our experimental results demonstrate that the proposed method achieves significant improvements in inference performance and search overhead compared to existing methods.

Exploiting DBSCAN and Combination Strategy to Prioritize the Test Suite in Regression Testing

Test case prioritization techniques improve the fault detection rate by adjusting the execution sequence of test cases. For static black-box test case prioritization techniques, existing methods generally improve the fault detection rate by increasing the early diversity of execution sequences based on string distance differences. However, such methods have a high time overhead and are less stable. This paper proposes a novel test case prioritization method (DC-TCP) based on density-based spatial clustering of applications with noise (DBSCAN) and combination policies. By introducing a combination strategy to model the inputs to generate a mapping model, the test inputs are mapped to consistent types to improve generality. The DBSCAN method is then used to refine the classification of test cases further, and finally, the Firefly search strategy is introduced to improve the effectiveness of sequence merging. Extensive experimental results demonstrate that the proposed DC-TCP method outperforms other methods in terms of the average percentage of faults detected and exhibits advantages in terms of time efficiency when compared to several existing static black-box sorting methods.

Optimizing VLIW Instruction Scheduling via a Two-Dimensional Constrained Dynamic Programming

Typical embedded processors, such as Digital Signal Processors (DSPs), usually adopt Very Long Instruction Word (VLIW) architecture to improve computing efficiency. The performance of VLIW processors heavily relies on Instruction-Level Parallelism (ILP). Therefore, it is crucial to develop an efficient instruction scheduling algorithm to explore more ILP. While heuristic algorithms are widely used in modern compilers due to simple implementation and low computational cost, they have limitations in providing accurate solutions and are prone to local optima. On the other hand, exact algorithms can usually find the optimal solution, but their high time overhead makes them less suitable for large-scale problems. This paper proposes a two-dimensional constrained dynamic programming (TDCDP) approach and a quantitative model for instruction scheduling. The TDCDP approach achieves near-optimal solutions within an acceptable time overhead. Furthermore, we integrate our TDCDP approach into mainstream compiler architecture, encompassing Pre- and Post-RA (register allocation) scheduling. We conduct a quantitative evaluation of TDCDP compared to four heuristic algorithms on a typical VLIW processor. Our approach achieves an efficiency improvement of up to 58.34% in final solutions compared to the heuristic algorithms. Additionally, the Post-RA Scheduling enhances programs with an average speedup of 14.04% than solely applying the Pre-RA Scheduling.

Private Set Intersection Based on Lightweight Oblivious Key-Value Storage Structure

At this stage, the application of Private Set Intersection (PSI) protocols is essential for smart homes. Oblivious Key-Value Stores (OKVS) can be used to design efficient PSI protocols. Constructing OKVS with a cuckoo hashing graph is a common approach. It increases the number of hash functions while reducing the possibility of collisions into rings. However, the existing OKVS construction scheme requires a high time overhead, and such an OKVS applied to PSI protocols would also have a high communication overhead. In this paper, we propose a method called 3-Hash Garbled Cuckoo Graph (3H-GCG) for constructing cuckoo hash graphs. Specifically, this method handles hash collisions between different keys more efficiently than existing methods, and it can also be used to construct an OKVS structure with less storage space. Based on the 3H-GCG, we design a PSI protocol using the Vector Oblivious Linear Evaluation (VOLE) and OKVS paradigm, which achieves semi-honest security and malicious security. Extensive experiments demonstrate the effectiveness of our method. When the set size is 218–220, our PSI protocol is less computationally intensive than other existing protocols. The experiments also show an increase in the ratio of raw to constructed data of about 7.5%. With the semi-honest security setting, our protocol achieves the fastest runtime with the set size of 218. With malicious security settings, our protocol has about 10% improvement in communication compared with other existing protocols.

Research on Lightweight Authentication and Key Agreement Protocol in Power Grid Based on the SM9 Cryptographic Algorithm

With the wide application of power grids, the massive heterogeneous terminals become important data collection nodes and data relay nodes. However, the openness of the power system exposes the power grid to various security threats. Authentication key agreement protocol can provide authentication and session key sharing services and is the most important technical means to protect the communication security of power grids. However, the design of authentication key agreement protocols for power grids faces many challenges. Firstly, the members of the power grid change frequently, and each new member needs to be authenticated with the edge side equipment every time it joins, so the existing authentication scheme has a large computational overhead. Furthermore, the network communication bandwidth is limited, and the existing schemes have a high time overhead due to the number of communication interactions when performing terminal authentication with the edge side equipment. Finally, the existing schemes all use non-national secret algorithms, which cannot achieve autonomous control. This paper designs a lightweight power grid authentication key agreement protocol based on the state-secure SM9 algorithm to address the above technical challenges. Also, corresponding security proofs based on the threat model of power grids and the existing attack patterns are offered, and code implementation is carried out. It is experimentally verified that the computation time overhead and communication overhead of this scheme are smaller than those of the existing authentication key agreement protocols based on identification cryptosystems.

Data Protection of Accounting Information Based on Big Data and Cloud Computing

With the rapid development of Internet technology, mankind has entered the era of big data. The Internet records all kinds of information, and the amount of information generated in the future is in a state of explosive growth. With the development of enterprises, the data of accounting information have grown, and its security has also been threatened. Once the accounting information is leaked, it will have a great impact on the company. Therefore, it is very necessary to protect accounting information data. This paper aims to study how to protect accounting information data based on big data and cloud computing. Based on this, this paper proposes an attribute-based encryption method based on big data and cloud computing and analyzes the ABE scheme of a single authority and multiple authorities. The multiauthority ABE scheme can effectively solve the problems of low computational efficiency, poor security performance, and high time overhead in the single-authority ABE scheme. The experimental results of this paper show that, under the single-authority ABE scheme in the information data protection of enterprise A, the confidentiality of data is 54% at the highest and 46% at the lowest, which is generally not very high. In the information data protection of the multiauthority ABE scheme in enterprise A, the highest data confidentiality is 86% and the lowest is 79%. The highest is 32% more than the single-authority ABE scheme. The lowest is also 33% more than that. Generally speaking, the confidentiality of its data is very high. In terms of reliability, integrity, and effectiveness, the single-authority ABE scheme is not as good as the multiauthority ABE scheme. It shows that the multiauthority ABE scheme proposed in this paper has a powerful information data security protection function and can be effectively applied to accounting information data protection.

GC-Loc

Indoor localization techniques play a fundamental role in empowering plenty of indoor location-based services (LBS) and exhibit great social and commercial values. The widespread fingerprint-based indoor localization methods usually suffer from the low feature discriminability with discrete signal fingerprint or high time overhead for continuous signal fingerprint collection. To address this, we introduce the collaboration mechanism and propose a graph attention based collaborative indoor localization framework, termed GC-Loc, which provides another perspective for efficient indoor localization. GC-Loc utilizes multiple discrete signal fingerprints collected by several users as input for collaborative localization. Specifically, we first construct an adaptive graph representation to efficiently model the relationships among the collaborative fingerprints. Then taking state-of-the-art GAT model as basic unit, we design a deep network with the residual structure and the hierarchical attention mechanism to extract and aggregate the features from the constructed graph for collaborative localization. Finally, we further employ ensemble learning mechanism in GC-Loc and devise a location refinement strategy based on model consensus for enhancing the robustness of GC-Loc. We have conducted extensive experiments in three different trial sites, and the experimental results demonstrate the superiority of GC-Loc, outperforming the comparison schemes by a wide margin (reducing the mean localization error by more than 42%).

Privacy information protection algorithm of ultra dense network nodes based on edge computing

In order to solve the problems of high node loss rate, high time overhead and high risk of privacy disclosure in network node privacy information protection, an ultra dense network node privacy information protection algorithm based on edge computing is proposed. The weight update algorithm is used to detect the security vulnerabilities of ultra dense network nodes. According to the detection results, the characteristics of node vulnerabilities are obtained, the sensitive label information of node vulnerabilities is protected through the weighted graph, and the k-anonymity technology is used to anonymize the privacy information of ultra dense network nodes; Finally, edge computing is used to protect the privacy information of nodes. The experimental results show that the node loss rate of the proposed method is always less than 2%, the time overhead is small, and the risk coefficient of privacy disclosure is small.

Improved YOLO v5 with balanced feature pyramid and attention module for traffic sign detection

With the development of automatic driving technology, traffic sign detection has become a very important task. However, it is a challenging task because of the complex traffic sign scene and the small size of the target. In recent years, a number of convolutional neural network (CNN) based object detection methods have brought great progress to traffic sign detection. Considering the still high false detection rate, as well as the high time overhead and computational overhead, the effect is not satisfactory. Therefore, we employ lightweight network model YOLO v5 (You Only Look Once) as our work foundation. In this paper, we propose an improved YOLO v5 method by using balances feature pyramid structure and global context block to enhance the ability of feature fusion and feature extraction. To verify our proposed method, we have conducted a lot of comparative experiments on the challenging dataset Tsinghua-Tencent-100K (TT100K). The experimental results demonstrate that the mAP@.5 and mAP@.5:0.95 are improved by 1.9% and 2.1%, respectively.

Cooperative Detection Method for DDoS Attacks Based on Blockchain

Distributed Denial of Service (DDoS) attacks is always one of the major problems for service providers. Using blockchain to detect DDoS attacks is one of the current popular methods. However, the problems of high time overhead and cost exist in the most of the blockchain methods for detecting DDoS attacks. This paper proposes a blockchain-based collaborative detection method for DDoS attacks. First, the trained DDoS attack detection model is encrypted by the Intel Software Guard Extensions (SGX), which provides high security for uploading the DDoS attack detection model to the blockchain. Secondly, the service provider uploads the encrypted model to Inter Planetary File System (IPFS) and then a corresponding Content-ID (CID) is generated by IPFS which greatly saves the cost of uploading encrypted models to the blockchain. In addition, due to the small amount of model data, the time cost of uploading the DDoS attack detection model is greatly reduced. Finally, through the blockchain and smart contracts, the CID is distributed to other service providers, who can use the CID to download the corresponding DDoS attack detection model from IPFS. Blockchain provides a decentralized, trusted and tamper-proof environment for service providers. Besides, smart contracts and IPFS greatly improve the distribution efficiency of the model, while the distribution of CID greatly improves the efficiency of the transmission on the blockchain. In this way, the purpose of collaborative detection can be achieved, and the time cost of transmission on blockchain and IPFS can be considerably saved. We designed a blockchain-based DDoS attack collaborative detection framework to improve the data transmission efficiency on the blockchain, and use IPFS to greatly reduce the cost of the distribution model. In the experiment, compared with most blockchain-based method for DDoS attack detection, the proposed model using blockchain distribution shows the advantages of low cost and latency. The remote authentication mechanism of Intel SGX provides high security and integrity, and ensures the availability of distributed models.

Indoor Localization With Adaptive Signal Sequence Representations

Indoor location-based services (LBS) have exhibited large commercial and social values in smart cities, and urgent demands of which have spurred many localization techniques. Existing indoor localization approaches mostly rely on fingerprint techniques, leveraging either spatially discrete fingerprints or temporally consecutive ones for localization. However, these approaches often suffer from large errors or high time overhead in practice due to signal ambiguities or long input sequences. To overcome these drawbacks, this paper proposes a framework utilizing multiple adaptive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">representations</i> of signal sequences for localization, where each representation indicates a corresponding signal structure with underlying location clues. As an example, the proposed approach takes geomagnetic signal sequences as input and infers location features from two intuitive representations, e.g., spatial and temporal ones. With adaptive signal representations, the proposed approach takes specifically optimized neural networks to extract corresponding location clues respectively and fuses them to generate more distinguishing features for more accurate localization. Furthermore, the ensemble learning mechanism is adopted in the approach and a weighted k-NN based location estimation algorithm is devised to enhance the robustness. Extensive experiments in three different trial sites demonstrate that the proposed approach outperforms state-of-the-art competing schemes by a wide margin, reducing mean localization error by more than 46%.

A priority based path searching method for improving hybrid fuzzing

Hybrid fuzzing which combines classical fuzzing with concolic execution to produce effective test suites is an advanced software vulnerability detection technique. Because fuzzing and concolic execution are complementary in nature, some researchers propose “optimal strategy” and “discriminative dispatch strategy” to improve the performance of hybrid fuzzing. Although the ideas are interesting and useful, they have some limitations, such as high time overhead and difficulties in implementation. In this paper, we propose a Priority Based Path Searching method (PBPS) to utilize the capability of concolic execution better. PBPS evaluates each path's solving cost and solving demand, and prioritizes them based on two path characteristics, which are path lengths and sample-hits for concolic execution. The rationale is to keep the pipeline full by readily feeding the concolic engine with paths whose constraints are simpler to solve and are less likely to be explored by fuzz testing. We implement PBPS in Driller, which is a popular hybrid fuzzer and we evaluate our system “QuickFuzz” with the CQE dataset. Experimental results show that compared with DigFuzz and the original Driller, “QuickFuzz” discovers more vulnerabilities and achieves higher code coverage on the CQE dataset.

A Hierarchical Provable Massive Data Migration Method under Multicloud Storage

Now, many users have stored files on multiple clouds, and sometime, a large number of files are migrated between clouds. Because cloud providers and cloud servers are not entirely trusted, the corruption of user’s files event occur from time to time during the processes of storage and migration. Therefore, integrity verification must be performed, and the time verification overhead should be as low as possible. The existing provable data migrate methods still have the issue of high time overhead when a large number of files are migrated. Aiming at this problem, this paper proposed a hierarchical provable data migration method, which can provide the efficiency of data transfer integrity verification when moving large number of continuous files between clouds. In this paper, the proposed method is described in detail as well as the security analysis performance evaluation. The results proved that the proposed method can significantly decrease the detection latency of files transfer between clouds.

OKCM: improving parallel task scheduling in high-performance computing systems using online learning

Task scheduling is becoming increasingly important in large-scale high-performance computing real-time systems as the parallel scale, number and types of task continue to increase. The prioritizing policies and backfilling mechanisms are the most useful practice for improving scheduling performance. In particular, these methods highly depend on the task running time prediction. Previous studies focused on improving the running time prediction accuracy, resulting in higher time overhead and deployment difficulties in real-time scheduling system. In this paper, an efficient running time prediction model, referred to as online learning and K-nearest neighbors (KNN)-based predictor with correction mechanism (OKCM), is proposed. OKCM updates in real time through online algorithm and is friendly to users with a small data accumulation by KNN-based predictor. To evaluate our model, a trace-driven simulator, named HPCsim, is designed and implemented. The experimental results demonstrated that OKCM can achieve higher prediction accuracy with a low overhead. Furthermore, OKCM can achieve significant scheduling performance improvement and can be used to enhance primary prioritizing and backfilling methods without being restricted by specific scheduling method.

Error Diluting: Exploiting 3-D nand Flash Process Variation for Efficient Read on LDPC-Based SSDs

3-D NAND flash has become the mainstream in modern SSD designs because it offers superior bit storage density. However, while enjoying the large capacity, 3-D NAND flash is highly prone to bit errors due to its cylindrical cell structure. Modern SSDs employ the low-density parity-check (LDPC) error-correcting code to manage bit errors in 3-D NAND flash. Strong LDPC error correction is subject to a high time overhead, because it may require many sensing levels on read to obtain sufficiently confident bit input information. By exploiting the bit-error rate variation among vertical layers of 3-D NAND flash, we propose diluting bit errors of cells at error-prone, lower layers by mixing them with bit data of cells from reliable, upper layers. Cells at reliable layers provide highly confident bit input information that helps reduce the number of sensing levels on cell at error-prone layers. Our experimental results showed that the proposed approach improved the read throughput by 29% and reduced the read latency by 43% compared with a conventional multichip SSD design.

A Multi-Floor Location Method Based on Multi-Sensor and WiFi Fingerprint Fusion

Aiming at the problems of high time overhead, low positioning accuracy, and inability to meet the requirements of indoor positioning applications in WiFi and Pedestrian Dead Rockoning (PDR) positioning technologies, a cross-layer positioning method based on multi-sensor and WiFi fingerprint fusion is proposed. Firstly, Multi-dimensional scaling technology (MDS) is used in WiFi location, which reduces the overhead of offline stage of large area WiFi fingerprint location and improves the responsiveness of online location. Without limiting the holding mode, the high and low threshold detection method is used to reduce the error in gait detection of the PDR method. Unscented Kalman Filter (UKF) is used to fusion WiFi and PDR positioning results, output the optimal two-dimensional estimation, and finally use WiFi signal and multi-sensor information to determine the height of pedestrian position and output three-dimensional coordinates. The experiment results show that the proposed method have higher accuracy and better stability compared with WiFi and PDR positioning method.

A Software Behavior Automaton Model Based on System Call and Context

According to the problems of high time overhead of capturing the system call context by walking the stack and inaccuracy of system call argument policies for traditional software behavior models, a software behavior automaton model based on system call and context is proposed. First, data flow information containing system call argument policies is combined with software control flow and is used to anomaly detection of software behavior. Second, a new approach of context value for capturing system call context with accuracy and low time overhead is proposed. Third, system call argument context based on system call context is introduced and system call argument policies based on context including system call context and system call argument context are presented. The experimental results show that the software behavior automaton model based on system call and context can capture the system call context accurately with low time overhead, can describe system call argument policies precisely, and can well detect the anomaly of software behavior based on control flow and data flow.

Alternative majority-voting methods for real-time computing systems

Two techniques that provide a compromise between the high time overhead in maintaining synchronous voting and the difficulty of combining results in asynchronous voting are proposed. These techniques are specifically suited for real-time applications with a single-source/single-sink structure that need instantaneous error masking. They provide a compromise between a tightly synchronized system in which the synchronization overhead can be quite high, and an asynchronous system which lacks suitable algorithms for combining the output data. Both quorum-majority voting (QMV) and compare-majority voting (CMV) are most applicable to distributed real-time systems with single-source/single-sink tasks. All real-time systems eventually have to resolve their outputs into a single action at some stage. The development of the advanced information processing system (AIPS) and other similar systems serve to emphasize the importance of these techniques. Time bounds suggest that it is possible to reduce the overhead for quorum-majority voting to below that for synchronous voting. All the bounds assume that the computation phase is nonpreemptive and that there is no multitasking.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>