2017 International Conference on Networking, Architecture, and Storage (NAS)最新文献_第3页

Performance Optimization of In-Memory File System in Distributed Storage System 分布式存储系统中内存文件系统的性能优化

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026870

Zhaowei Li, Yunlong Yan, Jintao Mo, Zhaocong Wen, Junmin Wu

引用次数: 5

Service Migrations in the Cloud for Mobile Accesses: A Reinforcement Learning Approach 移动访问云中的服务迁移:一种强化学习方法

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026876

Shan Cao, Yang Wang, Chengzhong Xu

{"title":"Service Migrations in the Cloud for Mobile Accesses: A Reinforcement Learning Approach","authors":"Shan Cao, Yang Wang, Chengzhong Xu","doi":"10.1109/NAS.2017.8026876","DOIUrl":"https://doi.org/10.1109/NAS.2017.8026876","url":null,"abstract":"Migrating service to certain vantage locations that are close to its clients can not only reduce the service access latency,but also minimize the network costs for its service provider. As such, this problem is particularly important for time-bounded services to achieve both enhanced QoS and cost effectiveness as well. However, the service migration is not free, coming at costs of bulk-data transfer and likely service disruption, as a result, increasing the overall service costs. To gain the benefits of service migration while minimizing service costs, in this paper, we leverage reinforecement learning (RL) methods to propose an efficient algorithm, called Mig- RL, for the service migration in a cloud environment. The Mig-RL utilizes an agent to learn the optimal policy that determines service migration status by using a typical RL algorithm, called Q-learning. Specifically, the agent learns from the historical access information to decide when and to where the service should be migrated, without requiring any prior information regarding the service accesses. Therefore, the agent can dynamically adapt to the environment and achieve online migration in real time. Experimental results on the real and synthesized access sequences from cloud networks show that Mig-RL can minimize the service costs, and in the meantime, improve the quality of service (QoS) by adapting to the changes of mobile access patterns.","PeriodicalId":222161,"journal":{"name":"2017 International Conference on Networking, Architecture, and Storage (NAS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125434319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Kaleido: Enabling Efficient Scientific Data Processing on Big-Data Systems Kaleido:在大数据系统中实现高效的科学数据处理

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026864

Saman Biookaghazadeh, Shujia Zhou, Ming Zhao

{"title":"Kaleido: Enabling Efficient Scientific Data Processing on Big-Data Systems","authors":"Saman Biookaghazadeh, Shujia Zhou, Ming Zhao","doi":"10.1109/NAS.2017.8026864","DOIUrl":"https://doi.org/10.1109/NAS.2017.8026864","url":null,"abstract":"Big-Data systems are increasingly important for solving the data-driven problems in many science domains including geosciences. However, existing big- data systems cannot support the efficient processing of self-describing data formats such as NetCDF which are commonly used by scientific communities for data distribution and sharing. This limitation presents a serious hurdle to the further adoption of big-data systems by science domains. This paper presents Kaleido, a solution to this problem by enabling big- data systems to efficiently store and process scientific data. Specifically, it enables Hadoop to directly store NetCDF data on HDFS, and process them in MapReduce using convenient APIs. It also enables Hive to support queries on NetCDF data, transparent to the users. Moreover, it employs optimizations tailored to scientific data, particularly dimension-aware layout which allows efficient execution of subset queries targeting any dimension of the multi- dimensional data. The paper presents a comprehensive evaluation of Kaleido using representative queries on a typical geoscientific dataset. The results show that Kaleido achieves substantial speedup and space saving compared to existing solutions for storing and processing NetCDF data on Hadoop, and it also substantially outperforms the state-of-the-art solutions for supporting subset queries on scientific data.","PeriodicalId":222161,"journal":{"name":"2017 International Conference on Networking, Architecture, and Storage (NAS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134633099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Binary Index and Journal Embedding in the Linear Tape File System 线性磁带文件系统中的二进制索引和日志嵌入

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026847

K. Jensen, B. Vinter

引用次数: 0

Enhancing Next Generation Passive Optical Network Stage 2 (NG-PON2) with Channel Bonding 利用通道键合技术增强下一代无源光网络第二阶段(NG-PON2)

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026856

Liang Zhang, Yuanqiu Luo, N. Ansari, Bo Gao, Xiang Liu, F. Effenberger

引用次数: 4

DualStack: A High Efficient Dynamic Page Scheduling Scheme in Hybrid Main Memory 双栈:一种高效的混合主存动态页面调度方案

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026855

Zhen Zhang, Yinjin Fu, Guyu Hu

{"title":"DualStack: A High Efficient Dynamic Page Scheduling Scheme in Hybrid Main Memory","authors":"Zhen Zhang, Yinjin Fu, Guyu Hu","doi":"10.1109/NAS.2017.8026855","DOIUrl":"https://doi.org/10.1109/NAS.2017.8026855","url":null,"abstract":"With the development of big data and multi-core processors technology, DRAM-only based main memory cannot satisfy the requirements of in-memory computing in high memory capacity and low energy consumption. The emerging memory technology-phase change memory (PCM) is proposed to break the bottleneck of the current memory system. However, its weaknesses in write endurance and long access latency make it cannot fully replace DRAM. Consequently, researchers presented the architectural design aimed at DRAM/ PCM hybrids and the corresponding page migration scheme to give full play to their merits. The urgent challenges facing by existed page migration schemes are poor performed under weak locality in data streams and further improvement need in prediction of future access tendency. In this paper, we propose an efficient page migration policy called DualStack which features dynamic page management according to global read and write information and temporal locality. It is designed to keep write-intensive pages to DRAM and read-intensive pages to PCM, and specially avoid frequent and unnecessary migration between hybrid memory media. Compared to the state-of-the-art of hybrid main memory, our experimental results indicate that DualStack can effectively improve the system I/O latency by 38%~58% on the premise of reducing the system power consumption by 20%~30%.","PeriodicalId":222161,"journal":{"name":"2017 International Conference on Networking, Architecture, and Storage (NAS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127815134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Performance Tuning and Modeling for Big Data Applications in Docker Containers Docker容器中大数据应用的性能调优和建模

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026871

Kejiang Ye, Yunjie Ji

{"title":"Performance Tuning and Modeling for Big Data Applications in Docker Containers","authors":"Kejiang Ye, Yunjie Ji","doi":"10.1109/NAS.2017.8026871","DOIUrl":"https://doi.org/10.1109/NAS.2017.8026871","url":null,"abstract":"Docker container is experiencing a rapid development with the support from industry and being widely used in large scale production cloud environment, due to the benefits of speedy launching time and tiny memory footprint. However the performance of big data applications (e.g., Spark) running in Docker containers is still not clear due to the complex parameter configuration and interference between neighbor containers. This paper investigates the impacts of docker configuration and resource interference on the performance of big data applications in a typical container environment. In particular, we first conduct a series of experiments to measure the performance impact by adjusting the docker configuration parameters, such as resource limits, and observe the Spark performance is not linear with increasing resource allocation for containers. Then, we evaluate the interference between multiple containers by controlling the resource competition and detect the performance interference phenomenon between multiple containers. Finally, we propose a performance prediction model based on the Support Vector Regression (SVR) to predict the application performance with different configurations and resource competition settings. Experimental results show the prediction error is less than 10% for all the four typical Spark applications.","PeriodicalId":222161,"journal":{"name":"2017 International Conference on Networking, Architecture, and Storage (NAS)","volume":"292 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127410884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Towards Robust and Accurate Similar Trajectory Discovery: Weak-Parametric Approaches 实现鲁棒和精确的相似轨迹发现:弱参数方法

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026879

Yupeng Tuo, Xiao-chun Yun, Yongzheng Zhang

{"title":"Towards Robust and Accurate Similar Trajectory Discovery: Weak-Parametric Approaches","authors":"Yupeng Tuo, Xiao-chun Yun, Yongzheng Zhang","doi":"10.1109/NAS.2017.8026879","DOIUrl":"https://doi.org/10.1109/NAS.2017.8026879","url":null,"abstract":"Trajectory analysis is crucial and has been more and more widely used in various fields, such as location-based services (LBS), urban traffic control, user classification and route planner, etc. In this paper, we propose GSIM and ASIM, two novel approaches that are weak-parametric and can effectively measure and discover similar trajectories. The proposed methods are based on the key insight that the similarity can be reflected by observing the growth rate of specific indicators. (1) GSIM defines a 3-layer grid structure and statistics the total overlapping points for all grids between trajectories in each layer, it finally calculates the growth rate of the total counts as the grid radius grows from layer 1 to layer 3. (2) ASIM assumes that any two trajectories are similar and calculates the area of the minimum boundary rectangle that contains all the points. Then it cuts the rectangle from four directions one point by one to get the maximum boundary rectangle that contains the other two percentage of total points. Finally it utilizes the average change rate of the areas as the similarity. Further, we design parameter-learning modules to learn the setting of corresponding parameters automatically. Extensive experiments on real-world dataset show that, compared with typical approaches like LCSS, EDIT, DTW, etc., the proposed methods can significantly improve the effectiveness and achieve better efficiency in most test cases. Meanwhile, they are not sensitive to parameter settings.","PeriodicalId":222161,"journal":{"name":"2017 International Conference on Networking, Architecture, and Storage (NAS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130204024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Facilitating Workload Aware Storage Platform by Using Machine Learning Technics 利用机器学习技术实现负载感知存储平台

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026859

Wubin Li, F. F. Moghaddam, P. Heidari, Y. Lemieux, Abdelouahed Gherbi

引用次数: 1

Optimizing Energy Consumption on HPC Systems with a Multi-Level Checkpointing Mechanism 基于多级检查点机制的高性能计算系统能耗优化

2017 International Conference on Networking, Architecture, and Storage (NAS) Pub Date : 2017-08-01 DOI: 10.1109/NAS.2017.8026868

Muhammad Alfian Amrizal, H. Takizawa

{"title":"Optimizing Energy Consumption on HPC Systems with a Multi-Level Checkpointing Mechanism","authors":"Muhammad Alfian Amrizal, H. Takizawa","doi":"10.1109/NAS.2017.8026868","DOIUrl":"https://doi.org/10.1109/NAS.2017.8026868","url":null,"abstract":"Coordinated checkpointing is a widely-used checkpoint/restart (CPR) technique for fault-tolerance in large-scale HPC systems. However, this CPR technique will involve massive amounts of I/O concentration, resulting in considerably high checkpoint overhead and high energy consumption. This paper focuses on multi-level checkpointing that allows the use of different kinds of fast but less reliable storages to reduce the checkpointing frequency to parallel file system (PFS). This paper presents an energy model of multi-level checkpointing and proposes an iterative algorithm that minimizes energy consumption by optimizing the checkpoint interval of each level and selecting the best combination of checkpoint levels. It is confirmed that the algorithm is very fast and effective since it can reach convergence in a relatively small number of iteration steps. This paper also clarifies the fact that it is actually unnecessary to use all the available checkpoint levels in a multi-level CPR mechanism. By selectively using only appropriate checkpoint levels, a significant increase in energy efficiency (9 to 21%) is observed.","PeriodicalId":222161,"journal":{"name":"2017 International Conference on Networking, Architecture, and Storage (NAS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123555748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6