2015 International Conference on Cloud and Autonomic Computing最新文献

Parqua: Online Reconfigurations in Virtual Ring-Based NoSQL Systems 基于虚拟环的NoSQL系统中的在线重新配置

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.23

Yosub Shin, Mainak Ghosh, Indranil Gupta

引用次数: 4

Optimal Pricing and Capacity Planning of a New Economy Cloud Computing Service Class 新经济云计算服务类别的最优定价与容量规划

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.8

Jie Xu, Chenbo Zhu

{"title":"Optimal Pricing and Capacity Planning of a New Economy Cloud Computing Service Class","authors":"Jie Xu, Chenbo Zhu","doi":"10.1109/ICCAC.2015.8","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.8","url":null,"abstract":"Resource under-utilization in cloud computing systems is widespread due to workload fluctuations and drives up the cost of cloud computing service. Offering service using slack resources in an opportunistic way improves the utilization of resources and the economics of cloud service providers. But Opportunistic service class comes with virtually no service level objectives (SLO) and thus is of limited use. In a recent study, a new Economy class was introduced to provide long-term SLOs using reclaimed cloud computing resources. Analysis based on the workload collected on six production cloud computing clusters at Google demonstrated the potential of the Economy class. This paper presents an analytic study on the optimal pricing and capacity planning of this new Economy class. We show that depending on the terms of the service level agreements and the characteristics of the cloud computing workloads, a cloud service provider may either choose a penalty averse or penalty preference strategy when allocating reclaimed computing resources to the Economy class cloud computing service. We also derive conditions under which the new Economy class will be profitable.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128486750","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: 7

Fine-Grained, Adaptive Resource Sharing for Real Pay-Per-Use Pricing in Clouds 细粒度、自适应的资源共享，实现云计算中真正的按使用付费定价

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.36

Young Choon Lee, Youngjin Kim, Hyuck Han, Sooyong Kang

{"title":"Fine-Grained, Adaptive Resource Sharing for Real Pay-Per-Use Pricing in Clouds","authors":"Young Choon Lee, Youngjin Kim, Hyuck Han, Sooyong Kang","doi":"10.1109/ICCAC.2015.36","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.36","url":null,"abstract":"Cloud computing is characterized by its essentially pay-per-use pricing with elasticity. Typically, the granularity of usage for such pricing is at virtual machine (VM) level in IaaS clouds, e.g., a multiple of machine hours. The elasticity and cost effectiveness in these clouds are primarily achieved through the exploitation of resource virtualization and sharing. However, a majority of applications running on VMs in clouds struggle to fully utilize resources allocated to them. Since co-location granularity is strictly restricted to VM level and resources allocated to VMs are space-shared, the unused resources are apt to be wasted while users are still charged for such wastage. In this paper, we address the problem of fine-grained and adaptive resource sharing for real pay-per-use pricing. To this end, we devise a resource management mechanism as a cost efficiency solution for both users and providers of clouds. The mechanism consists of a container-based resource allocator and a real-usage based pricing scheme. We demonstrate the efficacy of this mechanism via experiments, in Amazon EC2, using two typical workloads in clouds, web services and database services, and a compute-intensive high energy physics application. Our results show that the mechanism can achieve near-optimal cost efficiency.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127428452","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

Performance Analysis of Loosely Coupled Applications in Heterogeneous Distributed Computing Systems 异构分布式计算系统中松耦合应用的性能分析

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.38

Eunji Hwang, Seontae Kim, Tae-kyung Yoo, Jik-Soo Kim, Soonwook Hwang, Young-ri Choi

{"title":"Performance Analysis of Loosely Coupled Applications in Heterogeneous Distributed Computing Systems","authors":"Eunji Hwang, Seontae Kim, Tae-kyung Yoo, Jik-Soo Kim, Soonwook Hwang, Young-ri Choi","doi":"10.1109/ICCAC.2015.38","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.38","url":null,"abstract":"Loosely coupled applications composed of a potentially very large number (from tens of thousands to even billions) of tasks are commonly used in High-Throughput Computing (HTC) and Many-Task Computing (MTC) paradigms. To efficiently execute large-scale computations which can exceed the capability in a single type of computing resources within expected time, we should be able to effectively integrate resources from Heterogeneous Distributed Computing (HDC) systems such as Clusters, Grids, and Clouds. In this paper, we quantitatively analyze the performance of three different real scientific applications consisting of many tasks on top of HDC systems based on a Partnership of Distributed Computing Clusters, Grids, and Clouds to show practical issues that normal scientific users can face during the course of leveraging these systems. Our experimental results show that the performance of a loosely coupled application can be significantly affected by the characteristics of a HDC system, along with hardware specification of a node, and their impacts on the performance can vary widely depending on the resource usage pattern of each application. Throughout our extensive performance study with representative HDC systems and real scientific applications, we aim to give an insight to the research community on design and implementation of a next generation middleware system that can intelligently support large-scale loosely coupled applications by considering both of resource and application perspectives.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123665750","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: 3

Towards Proactive Fault Management of Enterprise Systems 面向企业系统主动故障管理

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.18

R. Jia, S. Abdelwahed, A. Erradi

引用次数: 2

Feasibility of the Computation Task Offloading to GPGPU-enabled Devices in Mobile Cloud 移动云中计算任务向支持gpgpu的设备卸载的可行性

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.37

Kihan Choi, Jaehun Lee, Youngjin Kim, Sooyong Kang, Hyuck Han

{"title":"Feasibility of the Computation Task Offloading to GPGPU-enabled Devices in Mobile Cloud","authors":"Kihan Choi, Jaehun Lee, Youngjin Kim, Sooyong Kang, Hyuck Han","doi":"10.1109/ICCAC.2015.37","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.37","url":null,"abstract":"Smart mobile devices including smart phones and tablets have become one of the most popular devices in the personal computing environment. Users spend much time using smart mobile devices to the extent that it exceeds their time spent using PC. One of the major characteristics of applications used by users through smart mobile devices is that the applications in the field of entertainment like games and augmented reality require a great deal of computations. In order to deal with this, smart mobile devices began to be loaded with an application processor equipped with high performance GPU. In this study, the feasibility of having computation-intensive mobile applications to use the GPU resource of another GPGPU-enabled device in the same space for their computation tasks was verified. If benefits can be obtained in terms of the performance by having the high performance GPU of a remote device perform the complex computations that are currently performed on local device CPU, such an approach can be used as an essential technology for mobile clouds that can be established based on the mobile devices. In order to verify this, we not only implemented the game `Reversi' using the Monte Carlo Tree Search (MCTS) algorithm but also implemented a remote GPU support framework to Android platform so that it supports task offloading to GPGPU-enabled remote mobile devices. The Reversi game offloads computationally heavy parts of the MCTS to a remote GPU through our remote GPU support framework. We compare its performance with the case where the MCTS was completely performed on a local CPU. The results of experiments showed that the winning rate dramatically increases when the remote GPU was used. This result indicates workload offloading between the mobile devices can be a meaningful approach for the mobile cloud implementation.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128325751","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

Experimental Analysis on Autonomic Strategies for Cloud Elasticity 云弹性自治策略的实验分析

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.22

Simon Dupont, Jonathan Lejeune, F. D. Oliveira, T. Ledoux

{"title":"Experimental Analysis on Autonomic Strategies for Cloud Elasticity","authors":"Simon Dupont, Jonathan Lejeune, F. D. Oliveira, T. Ledoux","doi":"10.1109/ICCAC.2015.22","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.22","url":null,"abstract":"In spite of the indubitable advantages of elasticity in Cloud infrastructures, some technical and conceptual limitations are still to be considered. For instance, resource start up time is generally too long to react to unexpected workload spikes. Also, the billing cycles' granularity of existing pricing models may incur consumers to suffer from partial usage waste. We advocate that the software layer can take part in the elasticity process as the overhead of software reconfigurations can be usually considered negligible if compared to infrastructure one. Thanks to this extra level of elasticity, we are able to define cloud reconfigurations that enact elasticity in both software and infrastructure layers so as to meet demand changes while tackling those limitations. This paper presents an autonomic approach to manage cloud elasticity in a crosslayered manner. First, we enhance cloud elasticity with the software elasticity model. Then, we describe how our autonomic cloud elasticity model relies on dynamic selection of elasticity tactics. We present an experimental analysis of a sub-set of those elasticity tactics under different scenarios in order to provide insights on strategies that could drive the autonomic selection of the proper tactics to be applied.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128637494","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: 20

A CPU Overhead-Aware VM Placement Algorithm for Network Bandwidth Guarantee in Virtualized Data Centers 面向虚拟化数据中心网络带宽保障的CPU开销感知虚拟机布局算法

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.40

Kwonyong Lee, Sungyong Park

{"title":"A CPU Overhead-Aware VM Placement Algorithm for Network Bandwidth Guarantee in Virtualized Data Centers","authors":"Kwonyong Lee, Sungyong Park","doi":"10.1109/ICCAC.2015.40","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.40","url":null,"abstract":"As server consolidations based on the virtualization techniques become popular and cloud services continue to grow rapidly, more and more data centers are being built to accommodate a number of virtual clusters running various workloads. Since these virtual clusters often share the resources provided by physical machines (PMs), it is more likely that the interferences between virtual machines (VMs) affect the performance of applications running on top of the virtual clusters. While a lot of studies have proposed different virtual machine placement algorithms to investigate this issue, the problem caused by network performance variability still remains as a challenging issue. Since they usually ignore the CPU overhead to process the communications between VMs, the network bandwidth allocated to a VM cannot be fully utilized when a PM has not enough CPU resources to cover the CPU overhead for VM networking functions. This results in unpredictable application performance running on the virtual clusters. This paper proposes a virtual machine placement algorithm that considers the CPU overhead incurred to reserve network bandwidth in a virtualized data center environment. In order to decide the CPU overhead necessary to guarantee the network bandwidth allocated to a VM, a performance model based on standard linear regression using the data collected from a real environment is used. By comparing the amount of CPU resource available in the driver domain with the CPU overhead obtained from the performance model, the proposed algorithm decides whether the network bandwidth for the VM can be provided or not and selects an appropriate location for VM placement. The benchmarking results show that the proposed algorithm guarantees the network bandwidth allocated to each VM without violations when the CPU resources are shared by multiple VMs.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127466650","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: 1

Reducing Noisy-Neighbor Impact with a Fuzzy Affinity-Aware Scheduler 用模糊亲和感知调度器减少噪声邻居影响

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.14

Luis Tomás, C. Vázquez, Johan Tordsson, G. Moreno

引用次数: 13

Autonomic Provisioning and Application Mapping on Spot Cloud Resources 点云资源的自主供应与应用映射

2015 International Conference on Cloud and Autonomic Computing Pub Date : 2015-09-21 DOI: 10.1109/ICCAC.2015.21

Daniel J. Dubois, G. Casale

{"title":"Autonomic Provisioning and Application Mapping on Spot Cloud Resources","authors":"Daniel J. Dubois, G. Casale","doi":"10.1109/ICCAC.2015.21","DOIUrl":"https://doi.org/10.1109/ICCAC.2015.21","url":null,"abstract":"The spot instance model is a virtual machine pricing scheme in which unused resources of cloud providers are offered to the highest bidder. This leads to the formation of a spot price, whose fluctuations can determine customers to be overbid by other users and lose the virtual machine they rented. In this paper we propose a heuristic to automate the decision on: (i) which and how many resources to rent in order to run a cloud application, (ii) how to map the application components to the rented resources, and (iii) what spot price bids to use in order to minimize the total bid price while maintaining an acceptable level of performance. To drive the decision making, our algorithm combines a multi-class queueing network model of the application with a Markov model that describes the stochastic evolution of the spot price and its influence on virtual machine reliability. We show, using a model developed for a real enterprise application and historical traces of the Amazon EC2 spot instance prices, that our heuristic finds low cost solutions that indeed guarantee the required levels of performance. The performance of our heuristic method is compared to that of nonlinear programming and shown to markedly accelerate the finding of low-cost optimal solutions.","PeriodicalId":133491,"journal":{"name":"2015 International Conference on Cloud and Autonomic Computing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116317318","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