ACM Cloud and Autonomic Computing Conference最新文献

Autonomously improving query evaluations over multidimensional data in distributed hash tables 自主改进对分布式哈希表中多维数据的查询计算

ACM Cloud and Autonomic Computing Conference Pub Date : 2013-08-09 DOI: 10.1145/2494621.2494638

Matthew Malensek, S. Pallickara, S. Pallickara

{"title":"Autonomously improving query evaluations over multidimensional data in distributed hash tables","authors":"Matthew Malensek, S. Pallickara, S. Pallickara","doi":"10.1145/2494621.2494638","DOIUrl":"https://doi.org/10.1145/2494621.2494638","url":null,"abstract":"The proliferation of observational devices and sensors with networking capabilities has led to growth in both the rates and sources of data that ultimately contribute to extreme-scale data volumes. Datasets generated in such settings are often multidimensional, with each dimension accounting for a feature of interest. We posit that efficient evaluation of queries over such datasets must account for both the distribution of data values and the patterns in the queries themselves. Configuring query evaluation by hand is infeasible given the data volumes, dimensionality, and the rates at which new data and queries arrive. In this paper, we describe our algorithm to autonomously improve query evaluations over voluminous, distributed datasets. Our approach autonomously tunes for the most dominant query patterns and distribution of values across a dimension. We evaluate our algorithm in the context of our system, Galileo, which is a hierarchical distributed hash table used for managing geospatial, time-series data. Our system strikes a balance between memory utilization, fast evaluations, and search space reductions. Empirical evaluations reported here are performed on a dataset that is multidimensional and comprises a billion files. The schemes described in this work are broadly applicable to any system that leverages distributed hash tables as a storage mechanism.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122105549","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: 11

SecureDropbox: a file encryption system suitable for cloud storage services SecureDropbox:适用于云存储服务的文件加密系统

ACM Cloud and Autonomic Computing Conference Pub Date : 2013-08-09 DOI: 10.1145/2494621.2494642

Min-Yu Chen, Chi-Wei Liu, M. Hwang

引用次数: 7

Simulation process support for climate data analysis 模拟过程支持气候数据分析

ACM Cloud and Autonomic Computing Conference Pub Date : 2013-08-09 DOI: 10.1145/2494621.2494651

Yunhee Kang, S. Kung, Haengjin Jang

引用次数: 2

Autonomic performance-per-watt management (APM) of cloud resources and services 云资源和服务的自主性能管理(APM)

ACM Cloud and Autonomic Computing Conference Pub Date : 2013-08-09 DOI: 10.1145/2494621.2494624

Farah Fargo, Cihan Tunc, Y. Al-Nashif, S. Hariri

{"title":"Autonomic performance-per-watt management (APM) of cloud resources and services","authors":"Farah Fargo, Cihan Tunc, Y. Al-Nashif, S. Hariri","doi":"10.1145/2494621.2494624","DOIUrl":"https://doi.org/10.1145/2494621.2494624","url":null,"abstract":"With the rapid growth of data centers and clouds, the power cost and power consumption of their computing and storage resources become critically important to be managed efficiently. Several research studies have shown that data servers typically operate at a low utilization of 10% to 15%, while their power consumption is close to those at peak loads. With this significant fluctuation in the workloads, an elastic delivery of computing services with an efficient power provisioning mechanism becomes an important design goal. Live workload migrations and virtualization are important techniques to optimize power and performance in large-scale data centers [5], [25] This paper presents an application specific autonomic adaptive power and performance management system that utilizes AppFlow-based reasoning to configure dynamically datacenter resources and workload allocations. This system will continuously monitor the workload to determine the current operating point of both workloads and the virtual machines (VMs) running these workloads and then predict the next operating points for these VMs. This enables the system to allocate the appropriate amount of hardware resources that can run efficiently the VM workloads with minimum power consumption. We have experimented with and evaluated our approach to manage the VMs running RUBiS bidding application. Our experimental results showed that our approach can reduce the VMs' power consumption up to 84% compared to static resource allocation and up to 30% compared to other methods with minimum performance degradation.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129622986","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

A virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling 一种虚拟机重新打包方法，用于云自动伸缩的水平与垂直弹性权衡

ACM Cloud and Autonomic Computing Conference Pub Date : 2013-08-09 DOI: 10.1145/2494621.2494628

M. Sedaghat, F. Hernández-Rodriguez, E. Elmroth

引用次数: 91

B-MAPS: a self-adaptive resource scheduling framework for heterogeneous cloud systems B-MAPS:异构云系统的自适应资源调度框架

ACM Cloud and Autonomic Computing Conference Pub Date : 2013-08-09 DOI: 10.1145/2494621.2494640

Joal Wood, B. Romoser, I. Zecena, Ziliang Zong

引用次数: 3

A model-based approach to self-protection in computing system 基于模型的计算系统自我保护方法

ACM Cloud and Autonomic Computing Conference Pub Date : 2013-08-09 DOI: 10.1145/2494621.2494639

Qian Chen, S. Abdelwahed, A. Erradi

引用次数: 40

Autonomous, failure-resilient orchestration of distributed discrete event simulations 分布式离散事件模拟的自治、故障弹性编排

ACM Cloud and Autonomic Computing Conference Pub Date : 2013-08-09 DOI: 10.1145/2494621.2494625

Matthew Malensek, Z. Sui, Neil Harvey, S. Pallickara

{"title":"Autonomous, failure-resilient orchestration of distributed discrete event simulations","authors":"Matthew Malensek, Z. Sui, Neil Harvey, S. Pallickara","doi":"10.1145/2494621.2494625","DOIUrl":"https://doi.org/10.1145/2494621.2494625","url":null,"abstract":"Discrete event simulations model the behavior of complex, real-world systems. Simulating a wide range of relevant events and conditions naturally provides a more accurate model, but also increases the computational workload associated with the simulation. To manage these processing requirements in a scalable manner, a discrete event simulation can be distributed across a number of computing resources. However, individual tasks in the simulation are stateful, and therefore require inter-task communication and synchronization to produce an accurate model. This property not only complicates the orchestration of the discrete event simulation in a distributed setting, but also makes providing reliable, fault-tolerant execution a challenge, especially when compared to conventional distributed fault tolerance schemes.\u0000 In this paper, we propose an autonomous agent that provides fault tolerance functionality for discrete event simulations by predicting state changes in the simulation and adjusting its fault tolerance policy accordingly. This allows the system to avoid negatively impacting overall execution times while preserving reliability guarantees. To underscore the viability of our solution, we provide benchmarks of a production discrete event simulation that can sustain failures while running under the supervision of our fault tolerance framework.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114347388","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

A case for MapReduce over the internet MapReduce在互联网上的一个案例

ACM Cloud and Autonomic Computing Conference Pub Date : 2013-08-09 DOI: 10.1145/2494621.2494632

Hrishikesh Gadre, I. Rodero, J. Montes, M. Parashar

{"title":"A case for MapReduce over the internet","authors":"Hrishikesh Gadre, I. Rodero, J. Montes, M. Parashar","doi":"10.1145/2494621.2494632","DOIUrl":"https://doi.org/10.1145/2494621.2494632","url":null,"abstract":"In recent years, MapReduce programming model and specifically its open source implementation Hadoop has been widely used by organizations to perform large-scale data processing tasks such as web-indexing, data mining as well as scientific simulations. The key benefits of this programming model include its simple programming interface and ability to process massive datasets in a scalable fashion without requiring high-end computing infrastructure. We observe that the current design of Hadoop framework assumes a centralized execution environment involving a single datacenter. This assumption leads to simplified design decisions in the Hadoop architecture regarding efficient network usage, specifically in the replica-selection policy in Hadoop Distributed File System (HDFS) and in the reduce phase scheduling algorithm. In this paper, we investigate real-world scenarios in which MapReduce programming model and specifically Hadoop framework could be used for processing large-scale, geographically scattered datasets. We show that using the Hadoop framework with default policies can cause severe performance degradation in such geographically distributed environment. We propose and evaluate extensions to Hadoop MapReduce framework to improve its performance in such environments. The evaluation demonstrates that the proposed extensions substantially outperform default policies in the Hadoop framework.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116146990","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: 2

A quantitative study of virtual machine live migration 虚拟机动态迁移的定量研究

ACM Cloud and Autonomic Computing Conference Pub Date : 2013-08-09 DOI: 10.1145/2494621.2494622

Wenjin Hu, Andrew Hicks, Long Zhang, Eli M. Dow, Vinay Soni, Hao Jiang, Ronny L. Bull, Jeanna Neefe Matthews

{"title":"A quantitative study of virtual machine live migration","authors":"Wenjin Hu, Andrew Hicks, Long Zhang, Eli M. Dow, Vinay Soni, Hao Jiang, Ronny L. Bull, Jeanna Neefe Matthews","doi":"10.1145/2494621.2494622","DOIUrl":"https://doi.org/10.1145/2494621.2494622","url":null,"abstract":"Virtual machine (VM) live migration is a critical feature for managing virtualized environments, enabling dynamic load balancing, consolidation for power management, preparation for planned maintenance, and other management features. However, not all virtual machine live migration is created equal. Variants include memory migration, which relies on shared backend storage between the source and destination of the migration, and storage migration, which migrates storage state as well as memory state. We have developed an automated testing framework that measures important performance characteristics of live migration, including total migration time, the time a VM is unresponsive during migration, and the amount of data transferred over the network during migration. We apply this testing framework and present the results of studying live migration, both memory migration and storage migration, in various virtualization systems including KVM, XenServer, VMware, and Hyper-V. The results provide important data to guide the migration decisions of both system administrators and autonomic cloud management systems.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127779843","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: 93