Fourth International Conference on Autonomic Computing (ICAC'07)最新文献_第3页

Appliance-Based Autonomic Provisioning Framework for Virtualized Outsourcing Data Center 虚拟化外包数据中心的基于设备的自主供应框架

Fourth International Conference on Autonomic Computing (ICAC'07) Pub Date : 2007-06-11 DOI: 10.1109/ICAC.2007.6

Xiaoyin Wang, Dong-Jun Lan, G. Wang, Xing Fang, Meng Ye, Ying Chen, Qingbo Wang

引用次数: 86

Autonomous Return on Investment Analysis of Additional Processing Resources 附加加工资源自主投资收益分析

Fourth International Conference on Autonomic Computing (ICAC'07) Pub Date : 2007-06-11 DOI: 10.1504/IJAC.2010.033010

Jonathan Wildstrom, P. Stone, E. Witchel

引用次数: 7

Evaluation of Optimization Methods for Network Bottleneck Diagnosis 网络瓶颈诊断优化方法的评价

Fourth International Conference on Autonomic Computing (ICAC'07) Pub Date : 2007-06-11 DOI: 10.1109/ICAC.2007.15

A. Beygelzimer, J. Kephart, I. Rish

引用次数: 7

Time-Sharing Parallel Applications with Performance Isolation and Control 具有性能隔离和控制的分时并行应用程序

Fourth International Conference on Autonomic Computing (ICAC'07) Pub Date : 2007-06-11 DOI: 10.1109/ICAC.2007.39

Bin Lin, Ananth I. Sundararaj, P. Dinda

{"title":"Time-Sharing Parallel Applications with Performance Isolation and Control","authors":"Bin Lin, Ananth I. Sundararaj, P. Dinda","doi":"10.1109/ICAC.2007.39","DOIUrl":"https://doi.org/10.1109/ICAC.2007.39","url":null,"abstract":"Most parallel machines, such as clusters, are space-shared in order to isolate batch parallel applications from each other and optimize their performance. However, this leads to low utilization or potentially long waiting times. We propose a self-adaptive approach to time-sharing such machines that provides isolation and allows the execution rate of an application to be tightly controlled by the administrator. Our approach combines a periodic real-time scheduler on each node with a global feedback-based control system that governs the local schedulers. We have developed an online system that implements our approach. The system takes as input a target execution rate for each application, and automatically and continuously adjusts the applications' realtime schedules to achieve those rates with proportional CPU utilization. Target rates can be dynamically adjusted. Applications are performance-isolated from each other and from other work that is not using our system. We present an extensive evaluation that shows that the system remains stable with low response times, and that our focus on CPU isolation and control does not come at the significant expense of network I/O, disk I/O, or memory isolation.","PeriodicalId":179923,"journal":{"name":"Fourth International Conference on Autonomic Computing (ICAC'07)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129055661","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: 14

Online Monitoring of Database Structural Deterioration 数据库结构退化的在线监测

Fourth International Conference on Autonomic Computing (ICAC'07) Pub Date : 2007-06-11 DOI: 10.1109/ICAC.2007.29

T. Hoshino, K. Goda, M. Kitsuregawa

引用次数: 1

Adaptive Multi-levels Dictionaries and Singular Value Decomposition Techniques for Autonomic Problem Determination 自主问题确定的自适应多层字典和奇异值分解技术

Fourth International Conference on Autonomic Computing (ICAC'07) Pub Date : 2007-06-11 DOI: 10.1109/ICAC.2007.4

H. Chan, T. Kwok

引用次数: 1

Adaptive Learning of Metric Correlations for Temperature-Aware Database Provisioning 温度感知数据库配置中度量相关性的自适应学习

Fourth International Conference on Autonomic Computing (ICAC'07) Pub Date : 2007-06-11 DOI: 10.1109/ICAC.2007.3

Saeed Ghanbari, G. Soundararajan, Jin Chen, C. Amza

{"title":"Adaptive Learning of Metric Correlations for Temperature-Aware Database Provisioning","authors":"Saeed Ghanbari, G. Soundararajan, Jin Chen, C. Amza","doi":"10.1109/ICAC.2007.3","DOIUrl":"https://doi.org/10.1109/ICAC.2007.3","url":null,"abstract":"This paper introduces a transparent self-configuring architecture for automatic scaling with temperature awareness in the database tier of a dynamic content Web server. We use a unified approach to achieving the joint objectives of performance, efficient resource usage and avoiding temperature hot-spots in a replicated database cluster. The key novelty in our approach is a lightweight on-line learning method for fast adaptations to bottleneck situations. Our approach is based on deriving a lightweight performance model of the replicated database cluster on the fly. The system trains its own model based on perceived correlations between various system and application metrics and the query latency for the application. The model adjusts itself dynamically to changes in the application workload mix. We use our performance model for query latency pre diction and determining the number of database replicas necessary to meet the incoming load. We adapt by adding the necessary replicas, pro-actively in anticipation of a bottleneck situation and we remove them automatically in underload. Finally, the system adjusts its query scheduling algorithm dynamically in order to avoid temperature hot- spots within the replicated database cluster. We investigate our transparent database provisioning mechanism in the database tier using the TPC-W industry- standard e-commerce benchmark. We demonstrate that our technique provides quality of service in terms of both performance and avoiding hot-spot machines under different load scenarios. We further show that our method is robust to dynamic changes in the workload mix of the application.","PeriodicalId":179923,"journal":{"name":"Fourth International Conference on Autonomic Computing (ICAC'07)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131737540","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: 44

Towards Autonomic Fault Recovery in System-S 系统故障自动恢复研究

Fourth International Conference on Autonomic Computing (ICAC'07) Pub Date : 2007-06-11 DOI: 10.1109/ICAC.2007.40

Gabriela Jacques-Silva, J. Challenger, Lou Degenaro, J. Giles, R. Wagle

{"title":"Towards Autonomic Fault Recovery in System-S","authors":"Gabriela Jacques-Silva, J. Challenger, Lou Degenaro, J. Giles, R. Wagle","doi":"10.1109/ICAC.2007.40","DOIUrl":"https://doi.org/10.1109/ICAC.2007.40","url":null,"abstract":"System-S is a stream processing infrastructure which enables program fragments to be distributed and connected to form complex applications. There may be potentially tens of thousands of interdependent and heterogeneous program fragments running across thousands of nodes. While the scale and interconnection imply the need for automation to manage the program fragments, the need is intensified because the applications operate on live streaming data and thus need to be highly available. System-S has been designed with components that autonomically manage the program fragments, but the system components themselves are also susceptible to failures which can jeopardize the system and its applications. The work we present addresses the self healing nature of these management components in System-S. In particular, we show how one key component of System-S, the job management orchestrator, can be abruptly terminated and then recover without interrupting any of the running program fragments by reconciling with other autonomous system components. We also describe techniques that we have developed to validate that the system is able to autonomically respond to a wide variety of error conditions including the abrupt termination and recovery of key system components. Finally, we show the performance of the job management orchestrator recovery for a variety of workloads.","PeriodicalId":179923,"journal":{"name":"Fourth International Conference on Autonomic Computing (ICAC'07)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129946151","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: 36

Server-Level Power Control 服务器级电源控制

Fourth International Conference on Autonomic Computing (ICAC'07) Pub Date : 2007-06-11 DOI: 10.1109/ICAC.2007.35

C. Lefurgy, Xiaorui Wang, Malcolm S. Allen-Ware

{"title":"Server-Level Power Control","authors":"C. Lefurgy, Xiaorui Wang, Malcolm S. Allen-Ware","doi":"10.1109/ICAC.2007.35","DOIUrl":"https://doi.org/10.1109/ICAC.2007.35","url":null,"abstract":"We present a technique that controls the peak power consumption of a high-density server by implementing a feedback controller that uses precise, system-level power measurement to periodically select the highest performance state while keeping the system within a fixed power constraint. A control theoretic methodology is applied to systematically design this control loop with analytic assurances of system stability and controller performance, despite unpredictable workloads and running environments. In a real server we are able to control power over a 1 second period to within 1 W. Additionally, we have observed that power over an 8 second period can be controlled to within 0.1 W. We believe that we are the first to demonstrate such precise control of power in a real server. Conventional servers respond to power supply constraint situations by using simple open-loop policies to set a safe performance level in order to limit peak power consumption. We show that closed-loop control can provide higher performance under these conditions and test this technique on an IBM BladeCenter HS20 server. Experimental results demonstrate that closed-loop control provides up to 82% higher application performance compared to open-loop control and up to 17% higher performance compared to a widely used ad-hoc technique.","PeriodicalId":179923,"journal":{"name":"Fourth International Conference on Autonomic Computing (ICAC'07)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131225122","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: 304

Fault-Adaptive Control for Robust Performance Management of Computing Systems 计算系统鲁棒性能管理的故障自适应控制

Fourth International Conference on Autonomic Computing (ICAC'07) Pub Date : 2007-06-11 DOI: 10.1109/ICAC.2007.17

S. Abdelwahed, Nagarajan Kandasamy

引用次数: 1