Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Software systems must satisfy rapidly increasing demands imposed by emerging...
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Software systems must satisfy rapidly increasing demands imposed by emerging applications. For example, new AI applications, such as autonomous driving, require quick responses to an environment that is changing continuously. At the same time, software systems must be fault-tolerant in order to ensure a high degree of availability. As it stands, however, developing these new distributed software systems is extremely challenging even for expert software engineers due to the interplay of concurrency, asynchronicity, and failure of components. The objective of our research is to develop reusable solutions to the above challenges by means of novel programming models and frameworks that can be used to build a wide range of applications. This talk reports on our work on the design, implementation, and foundations of programming models and languages that enable the robust construction of large-scale concurrent and distributed software systems.
Aiming at the high complexity of parameter optimization for portfolio models, this paper designs a distributed high-performance portfolio optimization platform(HPPO) based on parallelcomputing framework and event dri...
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ISBN:
(纸本)9780738111162
Aiming at the high complexity of parameter optimization for portfolio models, this paper designs a distributed high-performance portfolio optimization platform(HPPO) based on parallelcomputing framework and event driven architecture. The platform consists of the data layer, the model layer, and the excursion layer, which is built in a component, pluggable, and loosely coupled way. The platform adopts parallelization acceleration for backtesting and optimizing parameters of portfolio models in a certain historical interval. The platform is able to docking portfolio model with real-time market. Based on the HPPO platform, a parallel program is designed to optimize the parameters of the value at risk(VAR) model. The performance of the platform are summarized by analyzing the experimental results and comparing with the open source framework Zipline and Rqalpha.
Detection and analysis framework of anomalous Internet crime data based on edge computing is designed in this paper. The edge server is both the edge data processing center and the data storage center. The edge server...
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ISBN:
(纸本)9781728146850
Detection and analysis framework of anomalous Internet crime data based on edge computing is designed in this paper. The edge server is both the edge data processing center and the data storage center. The edge server receives the edge device data for processing and returns the processing result to the edge device. It complements cloud computing and cloud services, is close to users and data sources, and provides a new computing model for intelligent computing. Therefore, edge computing is another new computing model after distributedcomputing, gridcomputing, and cloud computing. Inspired by the features of this technology, this paper proposes the novel data crime analytic framework. The numerical experiment has proven the satisfactory performance of the proposed method.
With the rapid development of the 5G and Internet of Things (IoT), mobile edge computing has gained considerable popularity in academic and industrial field, which provides physical resources closer to end users. Serv...
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Container has emerged as a new technology in clouds to replace virtual machines (VM) for distributed applications deployment and operation. With the increasing number of new cloud-focused applications, such as deep le...
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ISBN:
(纸本)9781450370523
Container has emerged as a new technology in clouds to replace virtual machines (VM) for distributed applications deployment and operation. With the increasing number of new cloud-focused applications, such as deep learning and high performance applications, started to reply on the high computing throughput of GPUs, efficiently supporting GPU in container cloud becomes essential. While GPU virtualization has been extensively studied for VM, limited work has been done for containers. One of the key challenges is the lack of support for GPU sharing between multiple concurrent containers. This limitation leads to low resource utilization when a GPU device cannot be fully utilized by a single application due to the burstiness of GPU workload and the limited memory bandwidth. To overcome this issue, we designed and implemented KubeShare, which extends Kubernetes to enable GPU sharing with fine-grained allocation. KubeShare is the first solution for Kubernetes to make GPU device as a first class resources for scheduling and allocations. Using real deep learning workloads, we demonstrated KubeShare can significantly increase GPU utilization and overall system throughput around 2x with less than 10% performance overhead during container initialization and execution.
With the rapid development and popularization of internet technologies such as AI and 5G, more and more organizations and individuals have suffered a dramatic increase in the number of ransomware attacks, which has br...
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With the rapid development and popularization of internet technologies such as AI and 5G, more and more organizations and individuals have suffered a dramatic increase in the number of ransomware attacks, which has brought substantial economic losses to them. Ransomware is an illegal act that blackmails victims into paying a ransom by locking their devices or encrypting files. Achieving fast and effective ransomware classification, attack intent and pattern analysis can improve the efficiency of security analysts and discover ransomware variants earlier. Therefore, we propose a new ransomware classification method, which uses the entropy map extracted from the ransomware binary file for classification. The entropy map retains more fine-grained features in the ransomware family, which can improve the classification result. Aiming at the problem of data imbalance among ransomware families, we propose a data augmentation method based on the Cycle-GAN network, which combines the fine-tuning technology in transfer learning to improve the classification result of the framework further. At the same time, the attention mechanism is introduced into VGG-16. It is used to enhance the ability of feature extraction of the network. The experimental results show that the proposed method achieves the best performance on 14 ransomware families, and the accuracy rate can reach 97.16%, which is better than other traditional ransomware visualization classification methods. Our proposed method still has the best classification performance compared with other neural networks.
Massive deployment of distributed energy resources (DER) through zero-inertia power electronic converters has made the power grid vulnerable to frequency instabilities and in particular inter-area oscillations. Low-fr...
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ISBN:
(纸本)9781728161273
Massive deployment of distributed energy resources (DER) through zero-inertia power electronic converters has made the power grid vulnerable to frequency instabilities and in particular inter-area oscillations. Low-frequency oscillations are of major concerns as they have the potential to limit maximum power transfer, and even cause blackouts. This paper presents a novel distributed control algorithm called distributed frequency deviation control (DFDC) based on local frequency deviation from the estimate of the average frequency of the network. The efficacy of the control unit is demonstrated via modal analyses and time-domain simulations. The results show that all the inter-area oscillation modes are damped for all the test cases without affecting the other dynamic modes of the system.
distributed-generation (DG) facilities widely employ gas-turbine (GTU) and gas-piston (GPU) generator sets (GS) capable of islanded operation or running in parallel with the grid. This paper considers various operatin...
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ISBN:
(纸本)9781728145907
distributed-generation (DG) facilities widely employ gas-turbine (GTU) and gas-piston (GPU) generator sets (GS) capable of islanded operation or running in parallel with the grid. This paper considers various operating conditions that may cause significant GS load throw and load-shedding that entails disconnecting and subsequent consumer power outage. Analysis reveals which GTU/GPU design features may result in damage and/or outage if the unit is exposed to external disturbances in the adjacent grid. It also demonstrates the efficiency of comprehensive solutions that use energy storage and variable-frequency motor-based drives to prevent GS damage and/or outage. Besides, the paper substantiates the need of, and proposes DG GS maneuverability requirements for more efficient use.
Edge computing is a promising cloud computing paradigm that reduces computing latency by deploying edge servers near data sources and users, which is of great importance to implement delay-sensitive applications like ...
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Feedback-based optimization algorithms use real-time measurements to update the optimal control for the underlying system which may not be fully identified. Recently, we have developed a distributed feedback-based alg...
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ISBN:
(纸本)9781728161273
Feedback-based optimization algorithms use real-time measurements to update the optimal control for the underlying system which may not be fully identified. Recently, we have developed a distributed feedback-based algorithm [1] that avoids the requirement of fast communication between central computing and local actuator/sensor agents. This paper extends the work by greatly reducing the number of copies of variables involved in the distributed feedback-based algorithm, which results in faster convergence and lower communication requirement. The main idea is to leverage the specific structural properties of the admittance matrix for distribution systems with tree network topology. We also show the effectiveness of the proposed algorithm in simulations.
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