China ADS front-end demo linac (CAFe) uses a distributed control system based on experimental physics and industrial control system (EPICS), and the EPICS software system Alarms is used to monitor the alarm states of ...
China ADS front-end demo linac (CAFe) uses a distributed control system based on experimental physics and industrial control system (EPICS), and the EPICS software system Alarms is used to monitor the alarm states of process variable(PV) in real time. Alarms stores a large amount of alarm data of time series representing alarm events, and the cause of failure can be determined by analyzing the correlation between alarm events. The traditional association rule algorithm is limited by the minimum support and can only get the association rules among frequent alarm events. Therefore, this paper proposes a parallel association rules algorithm, called CApriori, based on Spark, the big data computing engine, for processing the large amount of time series alarm data to find the association rules between low-support alarm events. In the second stage of the CApriori, distance correlation is introduced to remove candidate sets that of high frequency but low correlation. The proposed algorithm is applied to the data generated by the CAFe alarm system, and the results show that CApriori can find the association rules between the alarm events with high correlation and low support, which provides a basis for the intelligent fault diagnosis of the accelerator.
In order to solve the challenges brought by the operation and maintenance of power system in the era of big data, APM (Application Performance Management) system is introduced, which can monitor the operation of softw...
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ISBN:
(纸本)9781728170022
In order to solve the challenges brought by the operation and maintenance of power system in the era of big data, APM (Application Performance Management) system is introduced, which can monitor the operation of software and hardware system, show the health of system operation, and find the performance bottleneck. On the Hadoop platform, a big data deep mining and analysis platform based on map / reduce mode is built, integrating regression analysis, association analysis, data classification, data clustering, text mining, web mining and other data mining algorithms. It can complete 100TB level data retrieval in 30s, and then analyze;the system monitoring server can run stably in a cluster of 256 nodes. The use of APM system can prevent performance bottlenecks, greatly reduce the response time of performance problem processing, and quickly locate the location of performance problems, so as to ensure higher availability and stability of information system.
On the one hand, the application of microgrid can effectively cut down the effect of distributed generation on distribution network, on the other hand, it helps to improve the power quality of distribution network. Ho...
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The paper is devoted to certain aspects of the current functioning and development of National Research Computer Network (NIKS) in the status of National Research and Education Network (NREN) of Russia. The main empha...
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Pairwise sequence alignment is an important application to identify regions of similarity that may indicate the relationship between two biological sequences. This is a computationally intensive task that usually requ...
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ISBN:
(纸本)9783030856656;9783030856649
Pairwise sequence alignment is an important application to identify regions of similarity that may indicate the relationship between two biological sequences. This is a computationally intensive task that usually requires parallel processing to provide realistic execution times. This work introduces a new framework for a deadline constrained application of sequence alignment, called MASA-CUDAlign, that exploits cloud computing with Spot GPU instances. Although much cheaper than On-Demand instances, Spot GPUs can be revoked at any time, so the framework is also able to restart MASA-CUDAlign from a checkpoint in a new instance when a revocation occurs. We evaluate the proposed framework considering five pairs of DNA sequences and different AWS instances. Our results show that the framework reduces financial costs when compared to On-Demand GPU instances while meeting the deadlines even in scenarios with several instances revocations.
Numerous businesses now adopt cloud computing, putting their server, storage, and application resources on a network in the cloud somewhere online. Data from the user is stored on cloud at a low price, making cloud co...
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Numerous businesses now adopt cloud computing, putting their server, storage, and application resources on a network in the cloud somewhere online. Data from the user is stored on cloud at a low price, making cloud computing the most rapidly developing technologies. In a nutshell, cloud technology provides users with the access to massive infrastructures for low latency via a distinct middleware that is comparable to grid and HPC computing, both of which are currently in use. Over the years, these kinds of systems have become more and more popular since they offer a setting in which to host scalable applications. But rather than offering customers a common platform, the cloud network is vulnerable to several security issues, including malware infections, DDoS assaults, SQL injection, and even more complex attacks like zombie botnets. We describe our research on security challenges and virus spread in cloud networks in this paper. However, the detection capabilities of conventional host-based antivirus software are constrained, and theseprograms frequently miss threats; recent assaults are becoming more complicated and evasive of conventional security measures. To understand cloud security and use cutting-edge malware detection techniques to safeguard cloud, malware detection and research on these approaches are described in this article. Because the resources in a cloud are delivered to the user in the form of VM’s, they are susceptible to distributed denial of service attacks, malware exploits,and VM Escape-based attacks. This paper discusses the various detection techniques and the most effectiveones. The discussion of each method will be covered from a broad perspective that also focuses on reliable solutions, future directions, etc. The details are outlined in our article
Although major cloud providers have captured and published workload executions in the form of traces, it is not clear how to use them for workload generation on a wide range of existing platforms. A methodological cha...
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ISBN:
(纸本)9783030856656;9783030856649
Although major cloud providers have captured and published workload executions in the form of traces, it is not clear how to use them for workload generation on a wide range of existing platforms. A methodological challenge that remains is to generate and execute realistic datacenter workloads on any infrastructure, using information from available traces. In this paper, we propose Tracie, a methodology addressing this challenge, and introduce the tool supporting its implementation. We present all the necessary steps starting from a trace up to workload execution: analysis of datacenter traces, extraction of parameters, application selection, and scaling of a workload to match the capabilities of the underlying infrastructure. Our evaluation validates that Tracie can generate executable workloads that closely resemble their trace-based counterparts. For validation, we correlate the recorded system metrics of a trace against the actual execution. We find that the average system metrics of synthetic workloads differ at most 5% compared to the trace and that they are highly correlated at 70% on average.
This paper presents OTM-MPI, an extension of the Open Traffic Models platform (OTM) for running macroscopic traffic simulations in high-performance computing environments. OTM-MPI represents the first open-source, dis...
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ISBN:
(纸本)9781728141497
This paper presents OTM-MPI, an extension of the Open Traffic Models platform (OTM) for running macroscopic traffic simulations in high-performance computing environments. OTM-MPI represents the first open-source, distributed-memory, macroscopic simulation model developed for modern high performance parallel machines and large networks. Macroscopic simulations are appropriate for studying regional traffic scenarios when aggregate trends are of interest, rather than individual vehicle traces. They are also appropriate for studying the routing behavior of classes of vehicles, such as app-informed vehicles. The network partitioning was performed with METIS. Inter-process communication was done with MPI (message-passing interface). Results are provided for two networks: one realistic network which was obtained from Open Street Maps for Chattanooga, TN, and another larger synthetic grid network. The software recorded a speedups of 198x using 256 cores for Chattanooga, and 475x with 1,024 cores for the synthetic network.
Federated and continual learning are training paradigms addressing data distribution shift in space and time. More specifically, federated learning tackles non-i.i.d data in space as information is distributed in mult...
Federated and continual learning are training paradigms addressing data distribution shift in space and time. More specifically, federated learning tackles non-i.i.d data in space as information is distributed in multiple nodes, while continual learning faces with temporal aspect of training as it deals with continuous streams of data. Distribution shifts over space and time is what it happens in real federated learning scenarios that show multiple challenges. First, the federated model needs to learn sequentially while retaining knowledge from the past training rounds. Second, the model has also to deal with concept drift from the distributed data distributions. To address these complexities, we attempt to combine continual and federated learning strategies by proposing a solution inspired by experience replay and generative adversarial concepts for supporting decentralized distributed training. In particular, our approach relies on using limited memory buffers of synthetic privacy-preserving samples and interleaving training on local data and on buffer data. By translating the CL formulation into the task of integrating distributed knowledge with local knowledge, our method enables models to effectively integrate learned representation from local nodes, providing models the capability to generalize across multiple *** test our integrated strategy on two realistic medical image analysis tasks — tuberculosis and melanoma classification — using multiple datasets in order to simulate realistic non-i.i.d. medical data scenarios. Results show that our approach achieves performance comparable to standard (non-federated) learning and significantly outperforms state-of-the-art federated methods in their centralized (thus, more favourable) formulation.
With diversified demands for location-based services (LBS), smartphone-based indoor pedestrian positioning becomes a research hotspot in the academic and industrial society. Due to the complexity of the indoor environ...
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