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Pervasive Container Computing–––a Fine-Grained Loosely Coupled Cloud Cluster

Pervasive Container Computing–––a Fine-Grained Loosely Co...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xinhong Hei Dan Wu YiFan Liu Wenjiang Ji Yichuan Wang Lei Zhu School of computer science and engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

With the emergence of CaaS, a cloud service model that can quickly build a production environment, this model has been widely used in the industry. However, the degree of loose coupling of various containers in the cluster and the fine-grained level of function division are often difficult to achieve people’s expectations. In addition, it is difficult to achieve the desired effect in terms of cloud cluster security. Therefore, this paper takes container-based cloud computing clusters as the main research object, and proposes a fine-grained, loosely coupled cloud cluster model based on containers, is called Pervasive-container computing. The experimental results show that Pervasive-container computing not only meets the fine-grained loose coupling characteristics required by microservices, but also adopts the strategy of “created with time” and “destroy after use”, which provides guarantees in terms of endogenous security. In addition, the proposed scheduling algorithm based on Pervasive-container calculation reduces the time consumption of file transfer by 20% compared to the time consumption without the algorithm.

关键词： Couplings Industries Cloud computing Scheduling algorithms Computational modeling Microservice architectures Production

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An adaptive Memory Interface Controller for improving bandwidth utilization of hybrid and reconfigurable systems 14

An adaptive Memory Interface Controller for improving bandwi...

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Design, Automation and Test in Europe conference and Exhibition

作者： Vito Giovanni Castellana Antonino Tumeo Fabrizio Ferrandi DEIB Politccnico di Milano Milano Italy Pacific Northwest National Laboratory Richland WA USA

ISBN: (纸本)9783981537024

Data mining, bioinformatics, knowledge discovery, social network analysis, are emerging irregular applications that exploits data structures based on pointers or linked lists, such as graphs, unbalanced trees or unstructured grids. These applications are characterized by unpredictable memory accesses and generally are memory bandwidth bound, but also presents large amounts of inherent dynamic parallelism because they can potentially spawn concurrent activities for each one of the element they are exploring. Hybrid architectures, which integrate general purpose processors with reconfigurable devices, appears promising target platforms for accelerating irregular applications. These systems often connect to distributed and multi-ported memories, potentially enabling parallel memory operations. However, these memory architectures introduce several challenges, such as the necessity to manage concurrency and synchronization to avoid structural conflicts on shared memory locations and to guarantee consistency. In this paper we present an adaptive Memory Interface Controller (MIC) that addresses these issues. The MIC is a general and customizable solution that can target several different memory structures, and is suitable for High Level Synthesis frameworks. It implements a dynamic arbitration scheme, which avoids conflicts on memory resources at runtime, and supports atomic memory operations, commonly exploited for synchronization directives in parallel programming paradigms. The MIC simultaneously maps multiple accesses to different memory ports, allowing fine grained parallelism exploitation and ensuring correctness also in the presence of irregular and statically unpredictable memory access patterns. We evaluated the effectiveness of our approach on a typical irregular kernel, graph Breadth First Search (BFS), exploring different design alternatives.

关键词： Microwave integrated circuits Kernel Synchronization Hardware parallel processing Memory management Concurrent computing

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The Impact of Application Mapping on Non-Random and Random network Topologies

The Impact of Application Mapping on Non-Random and Random N...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Yao Hu Michihiro Koibuchi National Institute of Informatics 2-1-2 Hitotsubashi Chiyoda-ku Tokyo Japan

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Due to recent artificial intelligence (AI) technology progress, more and more applications present all-to-all, irregular or unpredictable communication patterns among compute nodes in high-performance computing (HPC) systems. Traditional communication infrastructures, e.g., torus or fat-tree interconnection networks, may not handle well their matchmaking problems with these newly emerging applications. For these typical non-random network topologies, there are already many communication-efficient application mapping algorithms. However, for the above unpredictable communication patterns, it is difficult to efficiently map their applications onto the non-random network topologies. In this case, a simple optimization is to map their applications with small diameter or average shortest path length (ASPL) among the assigned compute nodes. In this context, we recommend to use random network topologies as the communication infrastructures, which have drawn increasing attention for the use of HPC interconnects. In this study, we make a comparative study to analyze the performance impact of application mapping on non-random and random network topologies. We list several application mapping policies, and compare their job scheduling performances assuming that the communication patterns are unpredictable to the computing system. Evaluations with a large compound application workload show that, when compared to non-random topologies, random topologies can reduce the average turnaround time up to 39.3% by a random connected mapping method and up to 72.1% by a diameter/ASPL-based mapping method.

关键词： network topology Topology Supercomputers Mesh networks Multiprocessor interconnection Processor scheduling Scheduling

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Implicit Neural Alignment network for Arbitrary-scale Space-Time Video Super-Resolution

Implicit Neural Alignment Network for Arbitrary-scale Space-...

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International Joint conference on Neural networks (IJCNN)

作者： Qin Jiang Qinglin Wang Lihua Chi Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing Software for Frontier Equipment Hunan GuoKe Computility Technology Co. Ltd

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Space-time video super-resolution (STVSR) is a comprehensive task comprising two subtasks: video super resolution in space dimension and video frame interpolation in time dimension. Conventional decoupled two-stage approaches tend to overlook the intrinsic correlation between the two tasks. Overcoming this challenge requires the development of a unified model capable of simultaneously implementing space-time super-resolution across arbitrary scales. Most existing models are confined to training on fixed space upsampling scales or specific frame-rate videos, resulting in limited generalization capabilities for flexible space-time super-resolution scenarios. In response to this limitation, our approach draws inspiration from continuous implicit neural representation. We propose an enhanced Implicit Neural Alignment network (INAN) based on the VideoINR framework, encompassing feature refinement, precise motion flow estimation, and multi-scale feature fusion to optimize the final implicit neural decoding. Our extensive experimental evaluations on multiple benchmarks underscore the efficacy of the INAN model, indicate its superior performance compared to prior STVSR methods.

关键词： Training Interpolation Correlation Superresolution Neural networks Estimation Benchmark testing

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Big Data Classification Model and Algorithm based on Double Quantum Particle Swarm Optimization

Big Data Classification Model and Algorithm Based on Double ...

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Control, Electronics and Computer Technology (ICCECT), IEEE International conference on

作者： Duan Jingbo Guangdong Business and Technology University Guangdong China Philippine Christian University Center for International Education Manila Philippine

For the traditional big data classification models and algorithms that have the drawback of long processing time, the use of intelligent algorithms to model identification of multivariable systems has become a mainstream research direction. Discussion This paper proposes an efficient solution for data-parallel optimized computing. The problems in the identification of multi-variable systems that integrate historical big data and intelligent algorithms are analyzed, and the impact of input variables on output is quantified. Build a big data classification model, mine valid data points, arrange data combination methods, divide data local connection methods, obtain local node micro-cluster data, calculate redundant data and invalid data increment values in node data groups, and reconstruct central node sample algorithm, adjust the integrated data classification strategy, optimize and update the data integration classification method. Design the experiment, simulate the experimental environment, and verify that the proposed model calculation method can shorten the data processing time in practical application, which has practical research value. Through the design of the overall process of the algorithm, data classification to form a data set, and then describe the steps of the algorithm, and finally verify the feasibility of the algorithm through simulation experiments, which can not only greatly reduce the communication cost between network nodes, but also greatly improve the global mining accuracy and improve the query efficiency in distributed data, It has good utilization value in the analysis of massive data information.

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An Identity Privacy Protection Strategy in Vehicle Named Data network

An Identity Privacy Protection Strategy in Vehicle Named Dat...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xuejie Liu Qi Bing Xinhua Lu Lihong Zhong Da Wei Guannan Qu College of Computer Science and Technology Jilin University Changchun China Jilin University Changchun China

ISBN: (纸本)9781728152103

Although Vehicle Named Data network (VNDN) possess the communication benefits of Named Data network and Vehicle Opportunity network, it also introduces some new privacy problems, including the identity security of Data Requesters and Data Providers. Data providers in VNDN need to sign data packets directly, which will leak the identity information of the providers, while the vicinity malicious nodes can access the sensitive information of Data Requesters by analyzing the relationship between Data Requesters and the data names in Interest Packages that are sent directly in plaintext. In order to solve the above privacy problems, this paper presents an identity privacy protection strategy for Data Requesters and Data Providers in VNDN. A ring signature scheme is used to hide the correlation between the signature and the data provider and the anonymous proxy idea is used to protect the real identity of the data requester in the proposed strategy. Security Analysis and experiments in the ONE-based VNDN platform indicate that the proposed strategy is effective and practical.

关键词： computer network security data privacy digital signatures Internet vehicular ad hoc networks DADANET network security digital signatures Data privacy vehicle V2V Internets Privacy personal identity cleartext Sensitive data

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Symmetric Non-negative Matrix Factorization based Link Partition Method for Overlapping Community Detection

Symmetric Non-negative Matrix Factorization Based Link Parti...

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IEEE International conference on Systems, Man and Cybernetics

作者： Xiang Zhang Naiyang Guan Wenju Zhang Xuhui Huang Shuyi Wu Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Hunan P.R. China Institute of Software College of Computer National University of Defense Technology Hunan P.R. China Department of Computer Science and Technology College of Computer National University of Defense Technology Hunan P.R. China

ISBN: (纸本)9781479986989

Partitioning links rather than nodes is effective in overlapping community detection (OCD) on complex networks. However, it consumes high CPU and memory overheads because the volume of links is huge especially when the network is rather complex. In this paper, we proposes a symmetric non-negative matrix factorization (SNMF) based link partition method called SNMF-Link to overcome this deficiency. In particular, SNMF-Link represents data in a lower-dimensional space spanned by the node-link incidence matrix. By solving a lighter SNMF problem, SNMF-Link learns the clustering indicators of each links. Since traditional multiplicative update rule (MUR) based optimization algorithm for SNMF suffers from slow convergence, we applied the augmented Lagrangian method (ALM) to efficiently optimize SNMF. Experimental results show that SNMF-Link is much more efficient than the representative clustering algorithms without reducing the OCD performance.

关键词： Symmetric matrices Convergence Optimization Partitioning algorithms Chlorine Complex networks Image edge detection

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Server-uncertain spectrum trading in cognitive radio networks: A queueing-theoretic modeling approach

Server-uncertain spectrum trading in cognitive radio network...

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International conference on Cognitive Radio Oriented Wireless networks and Communications

作者： Lixia Liu Gang Hu Ming Xu Yuxing Peng National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Department of Network Engineering College of Computer National University of Defense Technology Changsha China

Spectrum trading is the promising method to improve spectrum efficiency from the perspective of economics. In this paper we propose a queueing-theory based spectrum trading model, where the primary user plays the server role providing spectrum to the secondary user who acts as the customer. The most significant challenge is how to optimize the spectrum trading model considering the server uncertainty which includes service state, service time, service area, service content and service price. We design a STACP queueing model according to the server attributes, so that the secondary user can choose the right queue quickly and reasonably according to its demand. Moreover, we further analyze the optimizing strategies for STACP model which can maximize the profit of the primary user and minimize the service cost of the secondary user. The simulation results demonstrate the analysis results.

关键词： Servers Uncertainty Loss measurement Queueing analysis Economics Analytical models Business

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Density-Attentive Head Detector for Crowd Counting

Density-Attentive Head Detector for Crowd Counting

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International conference on Data Science and Business Analytics (ICDSBA)

作者： Ziyu Zhao Xingyu Shen Long Lan Xiaoyao Yin Xiang Zhang Yonggang Che Zhigang Luo College of Computer National University of Defense Technology Changsha China Institute for Quantum Information & State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (数字)9781728146447

ISBN: (纸本)9781728146454

In crowd counting, regression-based method shows better performance in extreme density scenes by introduces a density map. However, the regression-based method fails to locate the positions of each head, which significantly restricts its applications. Detection-based method counts each head with their accurate locations but works only in middle or low-level density scenes. In this paper, a joint learning method, named Density-attentive Head Detector (DAHD) is developed to overcome their respective shortcomings via a multi-task training procedure. Specifically, to guarantee the sensitivity of the detector to small or partially occluded heads, we carefully equip the detector with a density map which learned from regression module. Moreover, a novel Dilated Feature Pyramid network (DFPN) is introduced to our method to enlarge the receptive field of convolutional kernel, bringing confirmative additional benefits to identify small heads. Experiments on the popular ShanghaiTech and Mall datasets confirm the improved performance of DAHD compared with the current detection-based approaches, and a comparable performance to regression-based approaches in term of counting.

关键词： Detectors Feature extraction Object detection Faces Kernel Training Task analysis

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Global-view based Task Migration for Deep Learning Processor

Global-view based Task Migration for Deep Learning Processor

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Jinyu Cheng Kai Zhao Yuanchao Xu College of Information Engineering Capital Normal University Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology CAS Beijing China

In order to cope with the deep neural network application characterized by big data and intensive computation and memory access, deep learning processors often adopt NUMA architecture and multi-core architecture to alleviate the bandwidth bottleneck and contention problems existing in a single storage node and improve the parallelism of the system. This design also increases the complexity of task scheduling, considering not only the data affinity but also the utilization of DLP. When the load of DLP is uneven, the existing local view scheduling will make forward view scheduling according to the status of tasks waiting in line, instead of following the principle of data affinity, and forcibly migrate tasks to DLP in other quadrants with lower load. It is observed that this strategy produces unnecessary task migration in heterogeneous programming model. The reason is that the load is uneven from local view but is even from global view. This unnecessary migration leads to bandwidth fluctuation and overall performance degradation. In order to overcome this problem, this paper proposes global view scheduling, which schedules according to the status of tasks waiting in line for execution and tasks waiting in line for scheduling. Lazy migration is adopted when DLP load is balanced from the global view, and eager migration is adopted immediately when DLP load is unbalanced from the global view. Experimental results show that this scheduling method can reduce unnecessary migration, alleviate bandwidth fluctuation and improve overall performance without reducing NPU utilization.

关键词： Deep learning Degradation Program processors Fluctuations Processor scheduling Bandwidth Computer architecture

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