2015 IEEE International Conference on Autonomic Computing最新文献_第4页

Performance-Based Vertical Memory Elasticity 基于性能的垂直内存弹性

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.51

Soodeh Farokhi, Pooyan Jamshidi, Dražen Lučanin, I. Brandić

引用次数: 22

QoS-Driven Cloud Resource Management through Fuzzy Model Predictive Control 基于模糊模型预测控制的qos驱动云资源管理

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.41

Lixi Wang, Jing Xu, H. Duran-Limon, Ming Zhao

{"title":"QoS-Driven Cloud Resource Management through Fuzzy Model Predictive Control","authors":"Lixi Wang, Jing Xu, H. Duran-Limon, Ming Zhao","doi":"10.1109/ICAC.2015.41","DOIUrl":"https://doi.org/10.1109/ICAC.2015.41","url":null,"abstract":"Virtualized systems such as public and private clouds are emerging as important new computing platforms with great potential to conveniently deliver computing across the Internet and efficiently utilize resources consolidated via virtualization. Resource management in virtualized systems remains a key challenge because of their intrinsically dynamic and complex nature, where the applications have dynamically changing workloads and virtual machines (VMs) compete for the shared resources in a convolved manner. To address this challenge, this paper proposes a new resource management approach that can effectively capture the nonlinear behaviors in VM resource usages through fuzzy modeling and quickly adapt to the changes in the system through predictive control. The resulting fuzzy-model-predictive-control (FMPC) approach is capable of optimizing the VM resource allocations to applications according to their QoS targets. This approach is incorporated in a two-level cloud resource management framework where at the VM host level the node controllers employ FMPC to optimize dynamic VM resource allocations within individual hosts, and at the cloud zone level the global scheduler coordinates the node controllers to optimize resource utilization across hosts through dynamic VM migrations. The proposed approaches were implemented for Xen-based virtualized systems and evaluated using typical benchmarks (RUBiS, Free Bench) on a test bed with over 100 concurrent VMs. The results demonstrate that FMPC can accurately model the resource demands for dynamic applications and optimize the resource allocations to VMs with complex contentions. It substantially outperforms the traditional linear modeling based predictive control approach. The two-level resource management can make effective use of VM migrations to further improve performance across hosts as the host-level loads vary over time.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"13 1","pages":"81-90"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88334951","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: 22

A Virtual Machine Resource Management Method with Millisecond Precision 一种毫秒精度的虚拟机资源管理方法

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.24

Y. Kaneko, Toshio Ito, Tomonori Maegawa

引用次数: 0

Structure and Governance of Communities for the Digital Society 数字社会的社区结构与治理

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.62

J. Pitt, A. Diaconescu

引用次数: 22

Reflecting on Planning Models: A Challenge for Self-Modeling Systems 反思规划模型:对自我建模系统的挑战

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.72

J. Frank

{"title":"Reflecting on Planning Models: A Challenge for Self-Modeling Systems","authors":"J. Frank","doi":"10.1109/ICAC.2015.72","DOIUrl":"https://doi.org/10.1109/ICAC.2015.72","url":null,"abstract":"We discuss the opportunities for autonomous systems to perform reflection on their planners by adapting the models used to build plans. We first describe model-based planning systems, a form of automated planning system driven by declarative models of the planning domain. These models include descriptions of the conditions and effects of actions on the state of the world. When planning the activities of cyber-physical systems, the command and data representation of the system must be formally abstracted to the actions and states described in the planning system model. When the execution of a plan either fails or produces unexpected outcomes, the execution trace can be abstracted and compared to the predicted state according to the planning model, producing a list of discrepancies, these discrepancies can then be used to fix the model. This provides part of a reflection capability, namely, a set of well-formed problems with the domain model, the abstractions, or both. The challenge lies in the rest of the reflection capability, namely, a set of techniques for changing the models or the abstractions. We discuss these challenges and describe some of the options for addressing them.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"13 1","pages":"255-260"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86764256","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

System of Systems Integration Also Includes Hardware Integration: A Small Demonstration of Providing Some Reflection Processes for HW 系统集成的系统也包括硬件集成:为硬件提供一些反射过程的一个小例子

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.74

Phyllis R. Nelson, C. Landauer, K. Bellman, Shotaro Goto, Jesse Taylor

引用次数: 0

An Accusation-Based Strategy to Handle Undesirable Behaviour in Multi-agent Systems 多智能体系统中基于指责的不良行为处理策略

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.69

Sarah Edenhofer, Christopher Stifter, Uwe Jänen, Jan Kantert, Sven Tomforde, J. Hähner, C. Müller-Schloer

引用次数: 10

Self-Managed Component-Based Software Architecture for Business Process Management 基于自管理组件的业务流程管理软件体系结构

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.25

Bassem Debbabi, T. Calmant, Olivier Gattaz, Sandra Massonnat, Patrick Emin

引用次数: 0

CADRE: Carbon-Aware Data Replication for Geo-Diverse Services 地理多样化服务的碳感知数据复制

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.15

Zichen Xu, Nan Deng, Christopher Stewart, Xiaorui Wang

{"title":"CADRE: Carbon-Aware Data Replication for Geo-Diverse Services","authors":"Zichen Xu, Nan Deng, Christopher Stewart, Xiaorui Wang","doi":"10.1109/ICAC.2015.15","DOIUrl":"https://doi.org/10.1109/ICAC.2015.15","url":null,"abstract":"Internet services replicate data to geo-diverse sites around the world, often via consistent hashing. Collectively, these sites span multiple power authorities that independently control carbon emissions at each site. Serving data from a carbon-heavy site increases the service's carbon footprint, but it is hard to place data at sites that will have low emission rates without replicating to too many sites. We present CADRE, a carbon-aware data replication approach. CADRE forecasts emission rates at each site and replicates data to sites that combine together to yield low carbon footprints. It makes replication decisions online, i.e., When data is created, and thus avoids emissions caused by moving data frequently in response to changing emission rates. CADRE uses the multiple-choice secretary algorithm to replicate objects with large footprints to low emission sites. It models carbon footprints for each object using the footprint-replication curve, a graph that maps replication factors to expected carbon footprints. CADRE also achieves availability goals, respects storage capacity limits and balances data across sites. Compared to consistent hashing, our approach reduces carbon footprints by 70%. It also supports and enhances the state-of-the-art green load balancing, reducing the carbon footprint by an additional 21%.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"13 1","pages":"177-186"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84964842","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: 31

Specifying Distributed Adaptation through Software Component Relocation 通过软件组件重新定位指定分布式适配

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.65

Jingtao Sun, I. Satoh

引用次数: 3