The persistent memory (PM) requires maintaining the crash consistency and encrypting data, to ensure data recoverability and data confidentiality. The enforcement of these two goals does not only put more burden on pr...
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ISBN:
(数字)9783981926347
ISBN:
(纸本)9781728144689
The persistent memory (PM) requires maintaining the crash consistency and encrypting data, to ensure data recoverability and data confidentiality. The enforcement of these two goals does not only put more burden on programmers but also degrades performance. To address this issue, we propose a hardware-assisted encrypted persistent memory system. Specifically, logging and data encryption are assisted by hardware. Furthermore, we apply the counter-based encryption and the cipher feedback (CFB) mode encryption to data and log respectively, reducing the encryption overhead. Our primary experimental results show that the transaction throughput of the proposed design outperforms the baseline design by up to 34.4%.
Graph is one of the most important data structures to model social networks and becomes popular to find interesting relationships between individuals. Since graphs may contain sensitive information, data curators usua...
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Graph mining is becoming increasingly important due to the ever-increasing demands on analyzing complex structures in graphs. Existing graph accelerators typically hold most of the randomly-accessed data in an on-chip...
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ISBN:
(数字)9781728173832
ISBN:
(纸本)9781728173849
Graph mining is becoming increasingly important due to the ever-increasing demands on analyzing complex structures in graphs. Existing graph accelerators typically hold most of the randomly-accessed data in an on-chip memory to avoid off-chip communications. However, graph mining exhibits substantial random accesses from not only vertex dimension but also edge dimension (with the latter being excessively more complex than the former), leading to significant degradations in terms of both performance and energy *** observe that the most random memory requests arising in graph mining come from accessing a small fraction of valuable (vertex and edge) data when handling real-world graphs. To exploit this extension locality with maximum parallelism, we architect GRAMER, the first graph mining accelerator. GRAMER contains a specialized memory hierarchy, where the valuable data (precisely identified through a cost-efficient heuristic) is permanently resident in a high-priority memory while others are maintained in a cache-like memory under a lightweight replacement policy. The specific pipelined processing units are carefully designed to maximize computational parallelism. GRAMER is also equipped with a work-stealing mechanism to reduce load imbalance. We have implemented GRAMER on a Xilinx Alveo U250 accelerator card. Compared with two state-of-the-art CPU-based graph mining systems, Fractal and RStream, running on a 14-core Intel E5-2680 v4 processor, GRAMER achieves not only considerable speedups (1.11 × ~ 129.95 ) but also significant energy savings (5.79 × ~ 678.34×)
Android has become the most popular mobile operating system. Correspondingly, an increasing number of Android malware has been developed and spread to steal users' private information. There exists one type of mal...
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Android has become the most popular mobile operating system. Correspondingly, an increasing number of Android malware has been developed and spread to steal users' private information. There exists one type of malware whose benign behaviors are developed to camouflage malicious behaviors. The malicious component occupies a small part of the entire code of the application (app for short), and the malicious part is strongly coupled with the benign part. In this case, the malware may cause false negatives when malware detectors extract features from the entire apps to conduct classification because the malicious features of these apps may be hidden among benign features. Moreover, some previous work aims to divide the entire app into several parts to discover the malicious part. However, the premise of these methods to commence app partition is that the connections between the normal part and the malicious part are weak (e.g., repackaged malware). In this paper, we call this type of malware as Android covert malware and generate the first dataset of covert malware. To detect covert malware samples, we first conduct static analysis to extract the function call graphs. Through the deep analysis on call graphs, we observe that although the correlations between the normal part and the malicious part in these graphs are high, the degree of these correlations has a unique range of distribution. Based on the observation, we design a novel system, HomDroid, to detect covert malware by analyzing the homophily of call graphs. We identify the ideal threshold of correlation to distinguish the normal part and the malicious part based on the evaluation results on a dataset of 4,840 benign apps and 3,385 covert malicious apps. According to our evaluation results, HomDroid is capable of detecting 96.8% of covert malware while the False Negative Rates of another four stateof- the-art systems (i.e., PerDroid, Drebin, MaMaDroid, and IntDroid) are 30.7%, 16.3%, 15.2%, and 10.4%, respectiv
We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR...
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We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.
Java 8 has introduced lambda expressions, a core feature of functional programming. Since its introduction, there is an increasing trend of lambda adoptions in Java projects. Developers often adopt lambda expressions ...
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ISBN:
(纸本)9781665447843
Java 8 has introduced lambda expressions, a core feature of functional programming. Since its introduction, there is an increasing trend of lambda adoptions in Java projects. Developers often adopt lambda expressions to simplify code, avoid code duplication or simulate other functional features. However, we observe that lambda expressions can also incur different types of side effects (i.e., performance issues and memory leakages) or even severe bugs, and developers also frequently remove lambda expressions in their implementations. Consequently, the advantages of utilizing lambda expressions can be significantly compromised by the collateral side effects. In this study, we present the first large-scale, quantitative and qualitative empirical study to characterize and understand inappropriate usages of lambda expressions. Particularly, we summarized seven main reasons for the removal of lambdas as well as seven common migration patterns. For instance, we observe that lambdas using customized functional interfaces are more likely to be removed by developers. Moreover, from a complementary perspective, we performed a user study over 30 developers to seek the underlying reasons why they remove lambda expressions in practice. Finally, based on our empirical results, we made suggestions on scenarios to avoid lambda usages for Java developers and also pointed out future directions for researchers.
The volume of RDF data continues to grow over the past decade and many known RDF datasets have billions of triples. A grant challenge of managing this huge RDF data is how to access this big RDF data efficiently. A po...
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The brain tumor segmentation task aims to classify tissue into the whole tumor (WT), tumor core (TC) and enhancing tumor (ET) classes using multimodel MRI images. Quantitative analysis of brain tumors is critical for ...
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Automatically detecting software vulnerabilities in source code is an important problem that has attracted much attention. In particular, deep learning-based vulnerability detectors, or DL-based detectors, are attract...
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Modern large-scale cloud platforms require live migration technique on Docker containers with stateful workload to support load balancing, host maintenance, and Quality of Service (QoS) improvement. Efficient and scal...
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ISBN:
(数字)9781728187808
ISBN:
(纸本)9781728187815
Modern large-scale cloud platforms require live migration technique on Docker containers with stateful workload to support load balancing, host maintenance, and Quality of Service (QoS) improvement. Efficient and scalab.e Docker live migration is expected to guarantee the component-integrity (image, runtime, and management context) with negligible downtime. In this paper, we present a highly efficient live migration system called Sledge, which ensures the component-integrity by integrating both images and management context during runtime migration. The key insight is that the layered image can be leveraged to reduce the migration overhead, and appropriately selective migration of management context will effectively improve QoS with negligible downtime. To achieve good scalab.lity, a lightweight container registry mechanism for end-to-end image migration is designed to avoid the redundant layers transmission. In addition, a dynamic context loading scheme is proposed to precisely load the management context into the running daemon, which can significantly reduce downtime. Experiments show that, compared with the state-of-the-art, Sledge reduces 57% of total migration time, 55% of image migration time, and 70% downtime.
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