2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)最新文献_第10页

OptCon: An Adaptable SLA-Aware Consistency Tuning Framework for Quorum-Based Stores OptCon:基于群体存储的适应性sla感知一致性调优框架

2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) Pub Date : 2016-03-25 DOI: 10.1109/CCGrid.2016.9

Subhajit Sidhanta, W. Golab, S. Mukhopadhyay, Saikat Basu

{"title":"OptCon: An Adaptable SLA-Aware Consistency Tuning Framework for Quorum-Based Stores","authors":"Subhajit Sidhanta, W. Golab, S. Mukhopadhyay, Saikat Basu","doi":"10.1109/CCGrid.2016.9","DOIUrl":"https://doi.org/10.1109/CCGrid.2016.9","url":null,"abstract":"Users of distributed datastores that employquorum-based replication are burdened with the choice of asuitable client-centric consistency setting for each storage operation. The above matching choice is difficult to reason about asit requires deliberating about the tradeoff between the latencyand staleness, i.e., how stale (old) the result is. The latencyand staleness for a given operation depend on the client-centricconsistency setting applied, as well as dynamic parameters such asthe current workload and network condition. We present OptCon, a novel machine learning-based predictive framework, that canautomate the choice of client-centric consistency setting underuser-specified latency and staleness thresholds given in the servicelevel agreement (SLA). Under a given SLA, OptCon predictsa client-centric consistency setting that is matching, i.e., it isweak enough to satisfy the latency threshold, while being strongenough to satisfy the staleness threshold. While manually tunedconsistency settings remain fixed unless explicitly reconfigured, OptCon tunes consistency settings on a per-operation basis withrespect to changing workload and network state. Using decisiontree learning, OptCon yields 0.14 cross validation error in predictingmatching consistency settings under latency and stalenessthresholds given in the SLA. We demonstrate experimentally thatOptCon is at least as effective as any manually chosen consistencysettings in adapting to the SLA thresholds for different usecases. We also demonstrate that OptCon adapts to variationsin workload, whereas a given manually chosen fixed consistencysetting satisfies the SLA only for a characteristic workload.","PeriodicalId":103641,"journal":{"name":"2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134015846","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: 9

OptEx: A Deadline-Aware Cost Optimization Model for Spark OptEx:一个面向Spark的截止日期感知成本优化模型

2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) Pub Date : 2016-03-25 DOI: 10.1109/CCGrid.2016.10

Subhajit Sidhanta, W. Golab, S. Mukhopadhyay

引用次数: 49

DocLite: A Docker-Based Lightweight Cloud Benchmarking Tool docclite:基于docker的轻量级云基准测试工具

2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) Pub Date : 2016-03-23 DOI: 10.1109/CCGrid.2016.14

B. Varghese, Lawan Thamsuhang Subba, Long Thai, A. Barker

{"title":"DocLite: A Docker-Based Lightweight Cloud Benchmarking Tool","authors":"B. Varghese, Lawan Thamsuhang Subba, Long Thai, A. Barker","doi":"10.1109/CCGrid.2016.14","DOIUrl":"https://doi.org/10.1109/CCGrid.2016.14","url":null,"abstract":"Existing benchmarking methods are time consuming processes as they typically benchmark the entire Virtual Machine (VM) in order to generate accurate performance data, making them less suitable for real-time analytics. The research in this paper is aimed to surmount the above challenge by presenting DocLite - Docker Container-based Lightweight benchmarking tool. DocLite explores lightweight cloud benchmarking methods for rapidly executing benchmarks in near real-time. DocLite is built on the Docker container technology, which allows a user-defined memory size and number of CPU cores of the VM to be benchmarked. The tool incorporates two benchmarking methods - the first referred to as the native method employs containers to benchmark a small portion of the VM and generate performance ranks, and the second uses historic benchmark data along with the native method as a hybrid to generate VM ranks. The proposed methods are evaluated on three use-cases and are observed to be up to 91 times faster than benchmarking the entire VM. In both methods, small containers provide the same quality of rankings as a large container. The native method generates ranks with over 90% and 86% accuracy for sequential and parallel execution of an application compared against benchmarking the whole VM. The hybrid method did not improve the quality of the rankings significantly.","PeriodicalId":103641,"journal":{"name":"2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128437073","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

Checkpointing to Minimize Completion Time for Inter-Dependent Parallel Processes on Volunteer Grids 志愿网格上相互依赖的并行进程的检查点最小化完成时间

2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) Pub Date : 2016-03-11 DOI: 10.1109/CCGrid.2016.78

M. T. Rahman, Hien Nguyen, J. Subhlok, Gopal Pandurangan

引用次数: 9

A Distributed System for Storing and Processing Data from Earth-Observing Satellites: System Design and Performance Evaluation of the Visualisation Tool 地球观测卫星数据存储与处理的分布式系统:可视化工具的系统设计与性能评估

2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) Pub Date : 2015-11-24 DOI: 10.1109/CCGrid.2016.19

M. Szuba, P. Ameri, U. Grabowski, Jörg Meyer, A. Streit

引用次数: 5

Medusa: An Efficient Cloud Fault-Tolerant MapReduce Medusa:一个高效的云容错MapReduce

2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) Pub Date : 2015-11-23 DOI: 10.1109/CCGrid.2016.20

Pedro Costa, Xiao Bai, Fernando M. V. Ramos, M. Correia

{"title":"Medusa: An Efficient Cloud Fault-Tolerant MapReduce","authors":"Pedro Costa, Xiao Bai, Fernando M. V. Ramos, M. Correia","doi":"10.1109/CCGrid.2016.20","DOIUrl":"https://doi.org/10.1109/CCGrid.2016.20","url":null,"abstract":"Applications such as web search and social networking have been moving from centralized to decentralized cloud architectures to improve their scalability. MapReduce, a programming framework for processing large amounts of data using thousands of machines in a single cloud, also needs to be scaled out to multiple clouds to adapt to this evolution. The challenge of building a multi-cloud distributed architecture is substantial. Notwithstanding, the ability to deal with the new types of faults introduced by such setting, such as the outage of a whole datacenter or an arbitrary fault caused by a malicious cloud insider, increases the endeavor considerably. In this paper we propose Medusa, a platform that allows MapReduce computations to scale out to multiple clouds and tolerate several types of faults. Our solution fulfills four objectives. First, it is transparent to the user, who writes her typical MapReduce application without modification. Second, it does not require any modification to the widely used Hadoop framework. Third, the proposed system goes well beyond the fault-tolerance offered by MapReduce to tolerate arbitrary faults, cloud outages, and even malicious faults caused by corrupt cloud insiders. Fourth, it achieves this increased level of fault tolerance at reasonable cost. We performed an extensive experimental evaluation in the ExoGENI testbed, demonstrating that our solution significantly reduces execution time when compared to traditional methods that achieve the same level of resilience.","PeriodicalId":103641,"journal":{"name":"2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129421055","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: 18

De-Fragmenting the Cloud 对云进行碎片化

2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) Pub Date : 2015-06-23 DOI: 10.1109/CCGrid.2016.21

Mayank Mishra, U. Bellur

{"title":"De-Fragmenting the Cloud","authors":"Mayank Mishra, U. Bellur","doi":"10.1109/CCGrid.2016.21","DOIUrl":"https://doi.org/10.1109/CCGrid.2016.21","url":null,"abstract":"Existing Virtual Machine (VM) placement schemes have looked to conserve either CPU and Memory on the physical machine (PM) OR network resources (bandwidth) but not both. However, real applications use all resource types to varying degrees. The result of applying existing placement schemes to VMs running real applications is a fragmented data center where resources along one dimension become unusable even though they are available because of the unavailability of resources along other dimensions. An example of this fragmentation is unusable CPU because of a bottlenecked network link from the PM which has available CPU. To date, evaluations of the efficacy of VM placement schemes has not recognized this fragmentation and it's ill effects, let alone try to measure it and avoid it. In this paper, we first define the notion of what we term \"relative resource fragmentation\" and illustrate how it can be measured in a data center. The metric we put forth for capturing the degree of fragmentation is comprehensive and includes all key data center resource types. We then propose a VM placement scheme that minimizes this fragmentation and therefore maximizes the utility of data center resources. Results of empirical evaluations of our placement scheme compared to existing placement schemes show a reduction of fragmentation by as much as 15% and an increase in the number of successfully placed applications by as much as 20%.","PeriodicalId":103641,"journal":{"name":"2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124068507","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: 5