2006 IEEE International Conference on Autonomic Computing最新文献_第2页

Weatherman: Automated, Online and Predictive Thermal Mapping and Management for Data Centers 天气预报员:数据中心的自动化、在线和预测热测绘和管理

2006 IEEE International Conference on Autonomic Computing Pub Date : 2006-06-12 DOI: 10.1109/ICAC.2006.1662394

Justin D. Moore, J. Chase, Parthasarathy Ranganathan

{"title":"Weatherman: Automated, Online and Predictive Thermal Mapping and Management for Data Centers","authors":"Justin D. Moore, J. Chase, Parthasarathy Ranganathan","doi":"10.1109/ICAC.2006.1662394","DOIUrl":"https://doi.org/10.1109/ICAC.2006.1662394","url":null,"abstract":"Recent advances have demonstrated the potential benefits of coordinated management of thermal load in data centers, including reduced cooling costs and improved resistance to cooling system failures. A key unresolved obstacle to the practical implementation of thermal load management is the ability to predict the effects of workload distribution and cooling configurations on temperatures within a data center enclosure. The interactions between workload, cooling and temperature are dependent on complex factors that are unique to each data center, including physical room layout, hardware power consumption and cooling capacity; this dictates an approach that formulates management policies for each data center based on these properties. We propose and evaluate a simple, flexible method to infer a detailed model of thermal behavior within a data center from a stream of instrumentation data. This data - taken during normal data center operation - includes continuous readings taken from external temperature sensors, server instrumentation and computer room air conditioning units. Experimental results from a representative data center show that automatic thermal mapping can predict accurately the heat distribution resulting from a given workload distribution and cooling configuration, thereby removing the need for static or manual configuration of thermal load management systems. We also demonstrate how our approach adapts to preserve accuracy across changes to cluster attributes that affect thermal behavior - such as cooling settings, workload distribution and power consumption.","PeriodicalId":146874,"journal":{"name":"2006 IEEE International Conference on Autonomic Computing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126898276","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: 218

Enabling Self-Managing Applications using Model-based Online Control Strategies 使用基于模型的在线控制策略启用自管理应用程序

2006 IEEE International Conference on Autonomic Computing Pub Date : 2006-06-12 DOI: 10.1109/ICAC.2006.1662377

V. Bhat, M. Parashar, Hua Liu, M. Khandekar, Nagarajan Kandasamy, S. Abdelwahed

{"title":"Enabling Self-Managing Applications using Model-based Online Control Strategies","authors":"V. Bhat, M. Parashar, Hua Liu, M. Khandekar, Nagarajan Kandasamy, S. Abdelwahed","doi":"10.1109/ICAC.2006.1662377","DOIUrl":"https://doi.org/10.1109/ICAC.2006.1662377","url":null,"abstract":"The increasing heterogeneity, dynamism and uncertainty of emerging DCE (Distributed Computing Environment) systems imply that an application must be able to detect and adapt to changes in its state, its requirements and the state of the system to meet its desired QoS constraints. As system and application scales increase, ad hoc heuristic-based approaches to application adaptation and self-management quickly become insufficient. This paper builds on the Accord programming system for rule-based self-management and extends it with model-based control and optimization strategies. This paper also presents the development of a self-managing data streaming service based on online control using Accord. This service is part of a Grid-based fusion simulation workflow consisting of long-running simulations, executing on remote supercomputing sites and generating several terabytes of data, which must then be streamed over a wide-area network for live analysis and visualization. The self-managing data streaming service minimize data streaming overheads on the simulations, adapt to dynamic network bandwidth and prevent data loss. An evaluation of the service demonstrating its feasibility is presented.","PeriodicalId":146874,"journal":{"name":"2006 IEEE International Conference on Autonomic Computing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124154051","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: 48

Automatic Online Validation of Network Configuration in the Emulab Network Testbed 网络配置在Emulab网络试验台中的自动在线验证

2006 IEEE International Conference on Autonomic Computing Pub Date : 2006-06-12 DOI: 10.1109/ICAC.2006.1662391

David S. Anderson, Mike Hibler, L. Stoller, Tim Stack, Jay Lepreau

{"title":"Automatic Online Validation of Network Configuration in the Emulab Network Testbed","authors":"David S. Anderson, Mike Hibler, L. Stoller, Tim Stack, Jay Lepreau","doi":"10.1109/ICAC.2006.1662391","DOIUrl":"https://doi.org/10.1109/ICAC.2006.1662391","url":null,"abstract":"Emulab is a large-scale, remotely-accessible network and distributed systems testbed used by over a thousand researchers around the world. In Emulab, users create \"experiments\" composed of arbitrarily interconnected groups of dedicated machines that are automatically configured according to user specifications. In the last year alone, users have run over 18,000 such experiments, expecting consistent and correct behavior in the face of the ever-evolving 500,000 line code base and 3,000 discrete hardware components that comprise Emulab. We have found normal testing to be insufficient to meet these expectations and have therefore provided continuous, automatic validation. This paper describes Linktest, an integral part of our validation framework that is responsible for end-to-end validation during the configuration of every experiment. Developing and deploying such a validation approach faces numerous challenges, including the need for a code path entirely independent of the rest of the Emulab software. We describe our system's motivation, its design and implementation and our experience.","PeriodicalId":146874,"journal":{"name":"2006 IEEE International Conference on Autonomic Computing","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121735444","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: 29

A Policy-Definition Language and Prototype Implementation Library for Policy-based Autonomic Systems 基于策略的自治系统策略定义语言及原型实现库

2006 IEEE International Conference on Autonomic Computing Pub Date : 2006-06-12 DOI: 10.1109/ICAC.2006.1662407

R. Anthony

{"title":"A Policy-Definition Language and Prototype Implementation Library for Policy-based Autonomic Systems","authors":"R. Anthony","doi":"10.1109/ICAC.2006.1662407","DOIUrl":"https://doi.org/10.1109/ICAC.2006.1662407","url":null,"abstract":"This paper presents work towards generic policy toolkit support for autonomic computing systems in which the policies themselves can be adapted dynamically and automatically. The work is motivated by three needs: the need for longer-term policy-based adaptation where the policy itself is dynamically adapted to continually maintain or improve its effectiveness despite changing environmental conditions; the need to enable non autonomics-expert practitioners to embed self-managing behaviours with low cost and risk; and the need for adaptive policy mechanisms that are easy to deploy into legacy code. A policy definition language is presented; designed to permit powerful expression of self-managing behaviours. The language is very flexible through the use of simple yet expressive syntax and semantics and facilitates a very diverse policy behaviour space through both hierarchical and recursive uses of language elements. A prototype library implementation of the policy support mechanisms is described. The library reads and writes policies in well-formed XML script. The implementation extends the state of the art in policy-based autonomics through innovations which include support for multiple policy versions of a given policy type, multiple configuration templates and meta-policies to dynamically select between policy instances and templates. Most significantly, the scheme supports hot-swapping between policy instances. To illustrate the feasibility and generalised applicability of these tools, two dissimilar example deployment scenarios are examined. The first is taken from an exploratory implementation of self-managing parallel processing and is used to demonstrate the simple and efficient use of the tools.","PeriodicalId":146874,"journal":{"name":"2006 IEEE International Conference on Autonomic Computing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125032426","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: 42

A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation 自主资源分配的混合强化学习方法

2006 IEEE International Conference on Autonomic Computing Pub Date : 2006-06-12 DOI: 10.1109/ICAC.2006.1662383

G. Tesauro, Nicholas K. Jong, R. Das, M. Bennani

{"title":"A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation","authors":"G. Tesauro, Nicholas K. Jong, R. Das, M. Bennani","doi":"10.1109/ICAC.2006.1662383","DOIUrl":"https://doi.org/10.1109/ICAC.2006.1662383","url":null,"abstract":"Reinforcement Learning (RL) provides a promising new approach to systems performance management that differs radically from standard queuing-theoretic approaches making use of explicit system performance models. In principle, RL can automatically learn high-quality management policies without an explicit performance model or traffic model and with little or no built-in system specific knowledge. In our original work [1], [2], [3] we showed the feasibility of using online RL to learn resource valuation estimates (in lookup table form) which can be used to make high-quality server allocation decisions in a multi-application prototype Data Center scenario. The present work shows how to combine the strengths of both RL and queuing models in a hybrid approach in which RL trains offline on data collected while a queuing model policy controls the system. By training offline we avoid suffering potentially poor performance in live online training. We also now use RL to train nonlinear function approximators (e.g. multi-layer perceptrons) instead of lookup tables; this enables scaling to substantially larger state spaces. Our results now show that in both open-loop and closed-loop traffic, hybrid RL training can achieve significant performance improvements over a variety of initial model-based policies. We also find that, as expected, RL can deal effectively with both transients and switching delays, which lie outside the scope of traditional steady-state queuing theory.","PeriodicalId":146874,"journal":{"name":"2006 IEEE International Conference on Autonomic Computing","volume":"305 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131837251","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: 367

Informed data distribution selection in a self-predicting storage system 自预测存储系统中的知情数据分布选择

2006 IEEE International Conference on Autonomic Computing Pub Date : 2006-06-12 DOI: 10.1109/ICAC.2006.1662398

Eno Thereska, M. Abd-El-Malek, J. Wylie, D. Narayanan, G. Ganger

引用次数: 69

Collaborative Self-Configuration and Learning in Autonomic Computing Systems: Applications to Supply Chain 自主计算系统中的协同自配置和学习:在供应链中的应用

2006 IEEE International Conference on Autonomic Computing Pub Date : 2006-06-12 DOI: 10.1109/ICAC.2006.1662418

Hina Arora, T. S. Raghu, A. Vinze, Peter Brittenham

引用次数: 14

Service System Resource Management Based on a Tracked Layered Performance Model 基于跟踪分层性能模型的服务系统资源管理

2006 IEEE International Conference on Autonomic Computing Pub Date : 2006-06-12 DOI: 10.1109/ICAC.2006.1662396

C. Woodside, T. Zheng, Marin Litoiu

引用次数: 56

Autonomic Feature Selection for Application Classification 应用分类的自主特征选择

2006 IEEE International Conference on Autonomic Computing Pub Date : 2006-06-12 DOI: 10.1109/ICAC.2006.1662380

Jian Zhang, R. Figueiredo

引用次数: 26

The Making of SimEAC SimEAC的制作

2006 IEEE International Conference on Autonomic Computing Pub Date : 2006-06-12 DOI: 10.1109/ICAC.2006.1662417

D. Chisnall, Min Chen

引用次数: 3