2015 IEEE International Conference on Autonomic Computing最新文献_第5页

Constructing Execution and Life-Cycle Models for Smart City Services with Self-Aware IoT 构建具有自我感知物联网的智慧城市服务执行和生命周期模型

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.57

Masahide Nakamura, L. D. Bousquet

引用次数: 7

Toward Hierarchical Mixed Integer Programming for Pack-to-Swad Placement in Datacenters 数据中心中分组到交换位置的分层混合整数规划

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.23

Ye Xia, Maurício O. Tsugawa, J. Fortes, Shigang Chen

引用次数: 6

An Extensible Autonomous Reconfiguration Framework for Complex Component-Based Embedded Systems 基于复杂组件的嵌入式系统的可扩展自治重构框架

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.18

Johannes Schlatow, Mischa Moestl, R. Ernst

引用次数: 17

Demonstrating Voice over an Autonomic Network 在自主网络上演示语音

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.14

Lan Wang, E. Gelenbe

引用次数: 11

Distributed Real-Time Event Analysis 分布式实时事件分析

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.12

J. Stephen, D. Gmach, Rob Block, A. Madan, Alvin AuYoung

引用次数: 10

Towards Integrating Trusted Execution Environment into Embedded Autonomic Systems 嵌入式自治系统可信执行环境集成研究

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.27

M. Sabt, Mohammed Achemlal, A. Bouabdallah

引用次数: 2

Adding a Deliberative Layer to an Autonomic System 在自治系统中加入协商层

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.32

Marius Pol

引用次数: 0

Replication for Predictability in a Java RPC Framework Java RPC框架中的可预测性复制

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.49

Jianwei Tu, Christopher Stewart

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引用次数: 1

Fossa: Learning ECA Rules for Adaptive Distributed Systems Fossa:学习适应性分布式系统的ECA规则

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.37

Alexander Frömmgen, R. Rehner, Max Lehn, A. Buchmann

{"title":"Fossa: Learning ECA Rules for Adaptive Distributed Systems","authors":"Alexander Frömmgen, R. Rehner, Max Lehn, A. Buchmann","doi":"10.1109/ICAC.2015.37","DOIUrl":"https://doi.org/10.1109/ICAC.2015.37","url":null,"abstract":"The development of adaptive distributed systems is complex. Due to a large amount of interdependencies and feedback loops between network nodes and software components, distributed systems respond nonlinearly to changes in the environment and system adaptations. Although Event Condition Action (ECA) rules allow a crisp definition of the adaptive behavior and a loose coupling with the actual system implementation, defining concrete rules is nontrivial. It requires specifying the events and conditions which trigger adaptations, as well as the selection of appropriate actions leading to suitable new configurations. In this paper, we present the idea of Fossa, an ECA framework for adaptive distributed systems. Following a methodology that separates the adaptation logic from the actual application implementation, we propose learning ECA rules by automatically executing a multitude of tests. Rule sets are generated by algorithms such as genetic programming, and the results are evaluated using a utility function provided by the developer. Fossa therefore provides an automated offline learner that derives suitable ECA rules for a given utility function.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"10 1","pages":"207-210"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72795232","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: 27

Optimizing Energy, Locality and Priority in a MapReduce Cluster MapReduce集群的能量、位置和优先级优化

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-07-07 DOI: 10.1109/ICAC.2015.30

Yijun Ying, R. Birke, Cheng Wang, L. Chen, N. Gautam

{"title":"Optimizing Energy, Locality and Priority in a MapReduce Cluster","authors":"Yijun Ying, R. Birke, Cheng Wang, L. Chen, N. Gautam","doi":"10.1109/ICAC.2015.30","DOIUrl":"https://doi.org/10.1109/ICAC.2015.30","url":null,"abstract":"To strike a balance between optimizing for energy versus performance in data centers is extremely tricky because the workloads are significantly different with varying constraints on performance. This issue is exacerbated with the introduction of MapReduce over and above conventional web applications. In particular, with batch versus interactive MapReduce, e.g., Spark system, data availability and locality drive performance while exhibiting different degrees of delay sensitivities. In this paper we consider an energy minimization framework (which is formulated as a concave minimization problem) with explicit modeling of (i) time variability, (ii) data locality, and (iii) delay sensitivity of web applications, batch MapReduce, and interactive MapReduce. Our objective is to maximize the usage of MapReduce servers by delaying the batch MapReduce and offering the execution to web workloads whenever capacity permits. We propose a two-step approach which first employs a controller dynamically allocating servers to the three types of workloads and secondly designs a MapReduce scheduler achieving the optimal data locality. To cater to the stochastic nature of workloads, we use a Makov Decision Process model to design the allocation algorithm at the controller and derive the structure of the optimal. The proposed locality-aware scheduler is specifically engineered to sustain the throughput during the transient overload caused by insufficient server allocation for the batch-MapReduce. We conclude by presenting simulation results from an extensive set of experiments, and these results indicate the efficacy of the methodology proposed by keeping the data center costs to a minimum while ensuring the delay constraints of workloads are met.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"1 1","pages":"21-30"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87494630","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: 21