The virtual memory subsystem (VMS) is a long-standing and integral part of an operating system (OS). It plays a vital role in enabling remote memory systems over fast data center networks and is promising in terms of ...
The virtual memory subsystem (VMS) is a long-standing and integral part of an operating system (OS). It plays a vital role in enabling remote memory systems over fast data center networks and is promising in terms of transparency and generality. Specifically, these systems use three VMS mechanisms: demand paging, page swapping, and page prefetching. However, the VMS inherent data path is costly, which takes a huge toll on performance. Despite prior efforts to propose page swapping and prefetching algorithms to minimize the occurrences of the data path, they still fall short due to the semantic gap between the OS and applications - the VMS has limited knowledge of its running applications' memory access behaviors. In this paper, orthogonal to prior efforts, we take a fundamen-tally different approach by building an efficient framework to collect full memory access traces at the local bus, and make them availab.e to the OS through CPU cache. Consequently, the page swapping and page prefetching can use this trace to make better decisions, thereby improving the overall performance of systems. We implement a proof-of-concept prototype on commodity x86 servers using a hardware-based memory tracking tool. To show-case our framework's benefits, we integrate it with a state-of-the-art remote memory system and the default kernel page eviction subsystem. Our evaluation shows promising improvements.
With advancements in AI infrastructure and Trusted Execution Environment (TEE) technology, Federated Learning as a Service (FLaaS) through Jointcloudcomputing (JCC) is promising to break through the resource constrai...
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To solve the problem of low accuracy of rolling bearing fault diagnosis under complex noise and variable load conditions, this paper proposes a neural network based solution SSRNet. First, the rolling bearing signal i...
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Accurate fault diagnosis is the key to ensure the safe and operation of rotating machines. Accuracy of bearing fault diagnosis will be reduced in complex noise environments and variable load conditions. Therefore, thi...
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With the emergence of more and more Web services, finding suitable services becomes a difficult problem. Service link prediction is employed to disclose relationships among services, which facilitates the further deve...
With the emergence of more and more Web services, finding suitable services becomes a difficult problem. Service link prediction is employed to disclose relationships among services, which facilitates the further development of service composition, selection, and recommendation. But the existing link prediction approaches simply utilize the structural features of the service network. In reality, the rich text content in service node description documents also carries latent but fine-grained semantics generated by multifaceted topic-aware factors, yet few efforts are committed to mining them. In this paper, we propose a Web service link prediction method based on a topic-aware heterogeneous graph neural network. Specifically, the method consists of two main layers, including the meta-path intra-decomposition and the meta-path inter-mergence. Meta-path intra-decomposition aims to mine the topic distribution of the meta-paths-based context while capturing fine-grained topic-aware semantics. Meta-path inter-mergence uniquely aggregates topic-aware factors according to the mined distribution and adopts a multifaceted attention mechanism to aggregate different meta-paths, enabling service nodes to generate multifaceted topic-aware embeddings that preserve not only the structure and but also the topic-aware semantics. In addition, a topic prior guidance regularization item is set up for quality assurance of multifaceted topic-aware embedding that depends on global knowledge of the unstructured text content in description documents. Experimental results on real datasets show that our proposed model outperforms other existing baselines methods in the link prediction task, successfully validating the effectiveness of our proposed method.
In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implica...
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作者：
Xiaofan BaiChaoxiang HeXiaojing MaBin Benjamin ZhuHai JinSchool of Cyber Science and Engineering
Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security MicrosoftSchool of Computer Science and Technology
Huazhong University of Science and Technology and National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab.
cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerpr...
cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 lab.ls. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 lab.ls. The IBSF code is availab.e at: https://***/CGCL-codes/IBSF.
The number of Web services on the Internet has been steadily increasing in recent years due to their growing popularity. Under the big data environment, how to effectively manage Web services is of significance for se...
The number of Web services on the Internet has been steadily increasing in recent years due to their growing popularity. Under the big data environment, how to effectively manage Web services is of significance for service discovery, service recommendation, etc. It is widely studied that Web services clustering is an effective way for service management. However, most of the current Web service clustering only extracts the information of Web services for clustering from one view, such as Web service content descriptions, networks in which Web services participate, and so on. Extracting information from Web services only unilaterally will not be able to provide a three-dimensional and comprehensive description of Web services, which may diminish the effect of Web service clustering. In addition, some Web service resources will be wasted if other information of Web services is not used at the same time. We find that multi-view clustering can simultaneously consider multiple information of a data at the same time, and multiple information can complement and enhance each other according to the characteristics of multiview clustering. Therefore, in this paper, we apply Web services to graph-based multi-view clustering in multi-view clustering to improve the performance of Web service clustering by simultaneously considering multiple feature information about Web services and distributing different weights to different information in the clustering process.
With the wide adoption of Web APIs released on Internet, users tend to reuse them for business requirements or software development. Mashup is a useful technology for composing Web APIs into a new and value-added appl...
With the wide adoption of Web APIs released on Internet, users tend to reuse them for business requirements or software development. Mashup is a useful technology for composing Web APIs into a new and value-added application. With the increasing number of Web APIs and Mashups, the API-Mashup ecosystem has emerged based on the invocation relationship between Mashups and Web APIs. In this paper, we take ProgrammableWeb, a typical API-Mashup ecosystem, as an example to investigate its dynamic evolutionary analysis. Although there have been some works on the API-Mashup ecosystem, they mainly focus on static analysis, i.e., the static characteristics of the API- Mashup ecosystem on a fixed time point. This paper conducts a comprehensive study on the dynamic evolutionary analysis of the API-Mashup ecosystem with a long time range from 2005 to 2021. First, we conduct a dynamic statistical analysis based on the API-Mashup ecosystem dataset. Next, we construct two cooperation networks, one between Web APIs, and the other between their categories. And the general characteristics of the two cooperation networks are presented. Finally, we investigate the derived cooperation networks from four perspectives: dynamic characteristics, degree distribution, betweenness centrality, and assortative mixing. Meanwhile, the corresponding insights are uncovered. Our work provides a foundation for visualization and understanding of the API-Mashup ecosystem from the timeline.
With the rapid development of cloudcomputing, virtual machine scheduling has become one of the most important but challenging issues for the cloudcomputing community, especially for practical heterogeneous request s...
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ISBN:
(纸本)9781665480468
With the rapid development of cloudcomputing, virtual machine scheduling has become one of the most important but challenging issues for the cloudcomputing community, especially for practical heterogeneous request sequences. By analyzing the impact of request heterogeneity on some popular heuristic schedulers, it can be found that existing scheduling algorithms can not handle the request heterogeneity properly and efficiently. In this paper, a plug-and-play virtual machine scheduling intensifier, called Resource Assigner (ReAssigner), is proposed to enhance the scheduling efficiency of any given scheduler for heterogeneous requests. The key idea of ReAssigner is to pre-assign roles to physical resources and let resources of the same role form a virtual cluster to handle homogeneous requests. ReAssigner can cooperate with arbitrary schedulers by restricting their scheduling space to virtual clusters. With evaluations on the real dataset from Huawei cloud, the proposed ReAssigner achieves significant scheduling performance improvement compared with some state-of-the-art scheduling methods.
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