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检索条件"任意字段=21st International Symposium on Computer Architecture and High Performance Computing"

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Hermes: Improving server utilization by colocation-aware runtime systems 21

Hermes: Improving server utilization by colocation-aware run...

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21st IEEE international Conference on high performance computing and Communications, 17th IEEE international Conference on Smart City and 5th IEEE international Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Liu, Shenming Li, Kaiming Huang, Hao State Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9781728120584

Improving server utilization is increasingly important to service providers. Latency-critical services have strict tail latency service-level objects and safe colocation of the latencycritical service with other workloads on the same machine is difficult. This would underutilize server resources. We present Hermes, a user-level resource management layer to address this dilemma. We implement two kinds of runtime systems in Hermes, one for latency-critical workloads (LC runtime) and one for besteffort workloads (BE runtime). LC runtime implements userlevel thread management and controls the dedicated computing resources occupied by latency-critical workload through a feedback-based controller. BE runtimes schedule threads of besteffort workloads to take advantage of simultaneous multithreading technology. Runtime systems are aware of their colocation and work in a cooperative approach to improve server utlization without violating the tail latency service-level objects of the latency-critical workload. Hermes is implemented entirely at user-level on Linux. We evaluate Hermes using memcached and several sythetic micro-benchmarks, the result shows that Hermes could achieve both safe colocation and improvement of core utilization. © 2019 IEEE.

关键词： Quality of service

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POEM+: Pricing longer for mobile blockchain computation offloading with edge computing 21

POEM+: Pricing longer for mobile blockchain computation offl...

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作者： Li, Yinan Wu, Jigang Chen, Long School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9781728120584

Blockchain as an effective security solution has been applied in many mobile devices. But due to the limits of storage and computational capacities, it is hard for mobile devices to run blockchain applications locally. To solve this challenge, blockchain applications are offloaded to edge servers with mobile edge computing (MEC). However, most existing auction mechanisms on mobile blockchain fail to utilize parallel execution and the long term performance has not been well addressed. This paper investigates the mobile blockchain computation task offloading problem to improve total utility of auction participants. We propose an auction mechanism named POEM+ by solving an NP-hard multiple-choice multi-dimensional knapsack problem. POEM+ can offload tasks of one mobile user to edge servers on heterogeneous edge serves. We theoretically prove that POEM+ satisfies economic properties, such as individual rationality, budget balance, truthfulness and computation efficiency. Both simulation and real world experiments demonstrate that POEM+ achieves 130.6% higher utility than the existing heterogeneous tasks double auction WBD and 138.77% higher allocation efficiency averagely. Besides, the long-term performance of POEM+ can be guaranteed. © 2019 IEEE.

关键词： Efficiency

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A sustainable strategy for multi-cloud service composition based on formal concept analysis 21

A sustainable strategy for multi-cloud service composition b...

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作者： Pang, Beibei Yang, Yixuan Hao, Fei School of Computer Science Shaanxi Normal University Xi'an China

ISBN: (纸本)9781728120584

Multi-cloud composed by multiple clouds environment can take advantages of cloud computing which is becoming increasingly popular among new scientific and technological undertakings. The requested services of users could often be distributed across some providers hosted by several clouds. The multi-cloud services composition problem requires new scalable approaches to efficiently perform searching to reduce the energy consumption and monetary cost for fulfilling user's request. In this paper, we present a sustainable strategy for multi-cloud service composition, using Formal Concept Analysis and social network by considering some critical factors such as energy consumption between clouds, monetary cost and trust between users and clouds. In addition, a novel algorithm is proposed regarding to the sustainable multi-cloud service composition. We also evaluated the proposed approach through experiments and the results reveal the feasibility and effectiveness of our approach. Finally, we point out future directions for the selection of cloud providers. © 2019 IEEE.

关键词： Formal concept analysis

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high-elasticity virtual cluster placement in multi-tenant cloud data centers 21

High-elasticity virtual cluster placement in multi-tenant cl...

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作者： Lu, Shuaibing Wu, Jie Fang, Zhiyi Faculty of Information Technology Beijing University of Technology Beijing China Center for Networked Computing Temple University United States College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781728120584

In recent years, Data Center Network (DCN) has become a promising and efficient data processing infrastructure for cloud computing. One important mission of DCN is to serve the ever-growing demand for computation, storage, and networking for multiple tenants in cloud computing. This paper uses the notion of elasticity to measure the potential growth of multiple tenants in terms of both computation and communication resources. Our objective is to maximize the elasticity for DCNs. We consider the multiple virtual cluster placement problem with the hose model under the computation and communication constraints. We first formulate this problem as an Integer Linear Programming (ILP) problem. Unfortunately, the formulated ILP problem cannot be solved by the simplex or eclipse methods because of a large number of variables and constraints. Therefore, we propose an efficient scheme based on the Dynamic Programming (DP) and analyze its optimality and complexity. Furthermore, we propose a heuristic algorithm for placement that maximizes the elasticity and guarantees the bandwidth demand as well as lower complexity. Extensive evaluations demonstrate that our schemes outperform existing state-of-the-art methods in terms of both elasticity and efficiency. © 2019 IEEE.

关键词： Elasticity

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A new approach for scheduling job with the heterogeneity-aware resource in HPC systems 21

A new approach for scheduling job with the heterogeneity-awa...

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作者： Chung, Minh Thanh Pham, Kien Thoai, Nam Kranzlmueller, Dieter Faculty of Computer Science AND Engineering Ho Chi Minh City University of Technology Viet Nam Ludwig Maximilian University of Munich Germany

ISBN: (纸本)9781728120584

Scheduling in high performance computing (HPC) platforms is often a challenge with a huge range of researches and a noticeable phenomenon is a divergence between theory and practice. Especially, the heterogeneity of jobs today is becoming more popular, while a static scheduler is often prohibited by the runtime behavior as well as the multi-demand of programs running on a specific system. The hitherto conventional solution of scheduling jobs is only partially satisfying, because the program's behavior may differ significantly when comparing to the original scale. In this paper, we develop a dynamic scheduler based on characteristics and runtime behaviors of submitted jobs on a CPU/coprocessor-based cluster. Fundamentally, the behaviors of large-scale programs on an HPC system can be extracted from the history or log files, then draw a relational model between its characteristic and an objective function. This problem can be fitful in applying machine learning to find a scheduling function for job submission. We use a two-stage approach: first using simulation to simulate all situations of run jobs, in order to generate a dataset which then is used for training models with machine learning. This model enables to improve the scheduling performance and fit well the relationship between the job's characteristics and criteria in practice. More importantly, our experiments highlight the influence of the heterogeneity-aware resource over schedulers. © 2019 IEEE.

关键词： Machine learning

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Pream: Enhancing HPC storage system performance with pre-allocated metadata management mechanism 21

Pream: Enhancing HPC storage system performance with pre-all...

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作者： Li, Hongbo Cheng, Peng Chen, Zhiguang Xiao, Nong School of Data and Computer Science Sun Yat-Sen University Guangzhou China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

ISBN: (纸本)9781728120584

With the convergence of high performance computing (HPC) and big data, processing a large volume of scientific data on HPC systems is getting increased attentions. However, supporting these data-intensive workloads on HPC systems that are geared for compute-intensive workloads presents a new challenge in data management. Typical HPC systems consist of a large collection of compute nodes and use parallel file systems (PFSs) for persistent data storage. Although PFSs provide concurrent I/O bandwidth and perform well for large sequential write/read requests, its performance is bottlenecked by expensive metadata operations. Moreover, data-intensive applications with bursty I/O patterns or generate a large number of temporary files exacerbate the shortcomings. In this paper, we propose Pream, a light-weight metadata management framework that aim to address these challenges. Pream targets scenarios of supporting data-intensive workloads that generates a huge number of temporary files on diskless compute nodes. Pream pre-allocates file metadata from the metadata server, and manages these metadata locally to accelerate metadata operations. While newly created temporary files keep residing in PFSs, open/create requests of these temporary files can be handled by Pream locally without connecting with PFSs. Our evaluation demonstrates that Pream can outperform Lustre in many workloads and reduce latency of metadata operation efficiently. © 2019 IEEE.

关键词： Metadata

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Minimum cost task assignment for vehicle-based crowdsensing with deterministic and nondeterministic trajectory 21

Minimum cost task assignment for vehicle-based crowdsensing ...

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作者： Tu, Meimei Li, Peng Zhang, Tao Zhou, Qiang Liu, Qin College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System China Department of Computer Science New York Institute of Technology United States School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9781728120584

Vehicle-based crowdsensing (VCS) is a special case in crowdsourcing, and task assignment is a basic and important problem. In this paper, we investigate the minimum cost task assignment (MCTA) problem for vehicle-based crowdsensing. The VCS platform hopes to recruit some vehicles to complete given spatial-temporal tasks with the minimum cost. As vehicle trajectories are dynamic, we divide the MCTA problem into two sub-problems, the deterministic trajectory (D-MCTA) problem, and nondeterministic trajectory (N-MCTA) problem. For D-MCTA problem, we prove that the D-MCTA problem is NP-hard, and we propose a greedy algorithm to solve this problem. For N-MCTA problem, firstly, we determine the probability of each vehicle's trajectory through the logistic regression method. Then, we exploit the semi-Markov method to calculate the probability of each vehicle executing the task. Moreover, a greedy algorithm is proposed and the theoretical analysis is given. Finally, extensive simulations have been conducted to show the performance of our proposed algorithms is superior to the other algorithms. © 2019 IEEE.

关键词： Vehicles

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Multi-scale convolutional neural networks for scene recognition 21

Multi-scale convolutional neural networks for scene recognit...

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作者： Yi, Zheng Zhang, HanLing College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781728120584

In recent years, convolutional neural network (CNN) has performed well in a number of image classification tasks, but it hit a bottleneck on scene recognition task, due to the multilevel semantic information in a scene. This paper is dedicated to studying the deep learning methods in scene recognition task, and making contributes to improving the classification performance in the field of scene recognition, and an effective method that captures and fuses multi-level semantic information is proposed. First of all, we compare the differences between object classification task and scene recognition task in order to apply the successful replication of CNN in object classification task to scene recognition task after resolving the differences. Then we use a multi-scale learning method to capture different scale visual features at multiple levels. In addition, on the basis of multi-scale learning, we propose a method of feature fusion at the level of category, aiming to effectively combine different scale features. The experimental results show that the success of the object classification task can be applied to the scene recognition task by our method. © 2019 IEEE.

关键词： Intelligent systems

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Enhanced style transfer in real-time with histogram-matched instance normalization 21

Enhanced style transfer in real-time with histogram-matched ...

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作者： Peng, Manman Wu, Qiang College of Computer Science and Electric Engineering Hunan University Changsha China

ISBN: (纸本)9781728120584

Since the neural networks are utilized to extract information from an image, Gatys et al. found that they could separate the content and style of images and reconstruct them to another image which called style Transfer. Moreover, there are many feed-forward neural networks have been suggested to speeding up the original method to make style Transfer become practical application. However, this takes a price: these feed-forward networks are unchangeable because of their fixed parameters which mean we cannot transfer arbitrary styles but only single one in real-time. Some coordinated approaches have been offered to relieve this dilemma. Such as a style-swap layer and an adaptive normalization layer (AdaIN) and soon. Its worth mentioning that we observed that the AdaIN layer only aligns the means and variance of the content feature maps with those of the style feature maps. Our method is aimed at presenting an operational approach that enables arbitrary style transfer in real-time, reserving more statistical information by histogram matching, providing more reliable texture clarity and more humane user control. We achieve performance more cheerful than existing approaches without adding calculation, complexity. And the speed comparable to the fastest style Transfer method. Our method provides more flexible user control and trustworthy quality and stability. © 2019 IEEE.

关键词： Textures

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作者： Xu, Chaoting Han, Kai Gui, Fei Xu, Jingxin School of Computer Science and Technology University of Science and Technology of China China

ISBN: (纸本)9781728120584

The main task of recommender systems is to recommend items or any other information which the particular user is interested in accurately and efficiently. However, traditional recommender systems usually suffer from the data sparsity problem and cold start problem. To alleviate these problems, social recommender systems usually combine the social network data and rating data together to improve the accuracy of prediction results. In this paper, we propose SimilarMF, a novel and accurate social recommender system trying to improve the performance by means of utilizing the implicit similarity information among users and items. Firstly, SimilarMF integrates social network with rating data to construct a user similarity graph and an item similarity graph. Secondly, it adopts an embedding method to acquire enough similar user-user pairs and item-item pairs. It also performs negative sampling for dissimilar pairs. Lastly, it incorporates the extracted information into matrix factorization framework to predict ratings more accurately. The experimental results show that our method offers better recommendations than its competitors. © 2019 IEEE.

关键词： Recommender systems

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