2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)最新文献

Beyond Sync: Distributed Temporal Coordination and Its Implementation in a Multi-Robot System 超越同步:分布式时间协调及其在多机器人系统中的实现

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) Pub Date : 2019-06-16 DOI: 10.1109/SASO.2019.00020

Agata Barciś, C. Bettstetter

引用次数: 2

Ops-Scale: Scalable and Elastic Cloud Operations by a Functional Abstraction and Feedback Loops Ops-Scale:基于功能抽象和反馈循环的可伸缩和弹性云操作

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) Pub Date : 2019-06-16 DOI: 10.1109/saso.2019.00017

Kamal Hakimzadeh, J. Dowling

{"title":"Ops-Scale: Scalable and Elastic Cloud Operations by a Functional Abstraction and Feedback Loops","authors":"Kamal Hakimzadeh, J. Dowling","doi":"10.1109/saso.2019.00017","DOIUrl":"https://doi.org/10.1109/saso.2019.00017","url":null,"abstract":"Recent research has proposed new techniques to streamline the autoscaling of cloud applications, but little effort has been made to advance configuration management (CM) systems for such elastic operations. Existing practices use CM systems, from the DevOps paradigm, to automate operations. However, these practices still require human intervention to program ad hoc procedures to fully automate reconfiguration. Moreover, even after careful programming of cloud operations, the backing models are insufficient for re-running such programs unchanged in other platforms—which implies an overhead in rewriting the programs. We argue that CM programs can be designed to be deployment-agnostic and highly elastic with well-defined abstractions. In this paper, we introduce our abstraction based on declarative functional programming, and we demonstrate it using a feedback loop control mechanism. Our proposal, called Ops-Scale, is a family of cloud operations that are derived by making a functional abstraction over existing configuration programs. The hypothesis in this paper is twofold: 1) it should be possible to make a highly declarative CM system rich enough to capture fine-grained reconfigurations of autoscaling automatically, and; 2) that a program written for a specific deployment can be re-used in other deployments. To test this hypothesis, we have implemented an open source configuration engine called Karamel that is already used in industry for large-scale cluster deployments. Results show that at scale Ops-Scale can capture a polynomial order of reconfiguration growth in a fully automated manner. In practice, recent deployments have demonstrated that Karamel can provision clusters of 100 virtual machines consisting of many-layers distributed services on Google's IaaS Cloud in 'less than 10 minutes'.","PeriodicalId":259990,"journal":{"name":"2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127696859","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: 2

Distributed Self-Monitoring Sensor Networks Via Markov Switching Dynamic Linear Models 基于马尔可夫切换动态线性模型的分布式自监测传感器网络

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) Pub Date : 2019-06-16 DOI: 10.1109/SASO.2019.00014

L. Fang, Juan Ye, S. Dobson

{"title":"Distributed Self-Monitoring Sensor Networks Via Markov Switching Dynamic Linear Models","authors":"L. Fang, Juan Ye, S. Dobson","doi":"10.1109/SASO.2019.00014","DOIUrl":"https://doi.org/10.1109/SASO.2019.00014","url":null,"abstract":"Wireless sensor networks empowered with low-cost sensing devices and wireless communications present an opportunity to enable continuous, fine-grained data collection over a wide environment. However, the quality of data collected is susceptible to the hardware conditions and also adversarial external factors such as high variance in temperature and humidity. Over time, the sensors report erroneous readings, which deviate from true readings. To tackle the problem, we propose an efficient self-monitoring, self-managing and self-adaptive sensing framework based on a dynamic hybrid Bayesian network that combines Hidden Markov Model and Dynamic Linear Model. The framework does not only enable automatic on-line inference of true readings robustly but also monitor the working status of sensor nodes at the same time, which can uncover important insights on hardware management. The whole process also benefits from the derived approximation algorithm and thus supports on-line one-pass computation with minimum human intervention, which make the accurate formal inference affordable for distributed edge processing.","PeriodicalId":259990,"journal":{"name":"2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125036928","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: 0

Generic Adaptive Monitoring Based on Executed Architecture Runtime Model Queries and Events 基于已执行体系结构运行时模型查询和事件的通用自适应监控

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) Pub Date : 2019-06-16 DOI: 10.1109/SASO.2019.00012

Thomas Brand, H. Giese

{"title":"Generic Adaptive Monitoring Based on Executed Architecture Runtime Model Queries and Events","authors":"Thomas Brand, H. Giese","doi":"10.1109/SASO.2019.00012","DOIUrl":"https://doi.org/10.1109/SASO.2019.00012","url":null,"abstract":"Monitoring is a key functionality for automated decision making as it is performed by self-adaptive systems, too. Effective monitoring provides the relevant information on time. This can be achieved with exhaustive monitoring causing a high overhead consumption of economical and ecological resources. In contrast, our generic adaptive monitoring approach supports effectiveness with increased efficiency. Also, it adapts to changes regarding the information demand and the monitored system without additional configuration and software implementation effort. The approach observes the executions of runtime model queries and processes change events to determine the currently required monitoring configuration. In this paper we explicate different possibilities to use the approach and evaluate their characteristics regarding the phenomenon detection time and the monitoring effort. Our approach allows balancing between those two characteristics. This makes it an interesting option for the monitoring function of self-adaptive systems because for them usually very short-lived phenomena are not relevant.","PeriodicalId":259990,"journal":{"name":"2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"18 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123267986","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: 6

Constructivist Approach to State Space Adaptation in Reinforcement Learning 强化学习中状态空间适应的建构主义方法

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) Pub Date : 2019-06-16 DOI: 10.1109/SASO.2019.00016

Maxime Guériau, Nicolás Cardozo, Ivana Dusparic

{"title":"Constructivist Approach to State Space Adaptation in Reinforcement Learning","authors":"Maxime Guériau, Nicolás Cardozo, Ivana Dusparic","doi":"10.1109/SASO.2019.00016","DOIUrl":"https://doi.org/10.1109/SASO.2019.00016","url":null,"abstract":"Reinforcement learning (RL) is increasingly used to achieve adaptive behaviours in Internet of Things systems relying on large amounts of sensor data. To address the need for self-adaptation in such environments, techniques for detecting environment changes and re-learning behaviours appropriate to those changes have been proposed. However, with the heterogeneity of sensor inputs, the problem of self-adaptation permeates one level deeper; in order for the learnt behaviour to adapt, the underlying environment representation needs to adapt first. The granularity of the RL state space might need to be adapted to learn more efficiently, or to match the new granularity of input data. This paper proposes an implementation of Constructivist RL (Con-RL), enabling RL to learn and continuously adapt its state space representations. We propose a Multi-Layer Growing Neural Gas (ML-GNG) technique, as an extension of the GNG clustering algorithm, to autonomously learn suitable state spaces based on sensor data and learnt actions at runtime. We also create and continuously update a repository of state spaces, selecting the most appropriate one to use at each time step. We evaluate Con-RL in two scenarios: the canonical RL mountain car single-agent scenario, and a large-scale multi-agent car and ride-sharing scenario. We demonstrate its ability to adapt to new sensor inputs, to increase the speed of learning through state space optimization, and to maintain stable long-term performance.","PeriodicalId":259990,"journal":{"name":"2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121673165","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

Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes 利用马尔科夫过程支持自适应权衡的知识库K模型

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) Pub Date : 2019-06-01 DOI: 10.1109/SASO.2019.00011

L. H. Paucar, N. Bencomo

引用次数: 9

The Emergence of Division of Labor in Multi-Agent Systems 多智能体系统中劳动分工的出现

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) Pub Date : 2019-06-01 DOI: 10.1109/SASO.2019.00022

David W. King, Gilbert L. Peterson

引用次数: 3

Title Page i 第1页

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) Pub Date : 2019-06-01 DOI: 10.1109/saso.2019.00001

引用次数: 0

Maintaining SLOs of Cloud-Native Applications Via Self-Adaptive Resource Sharing 通过自适应资源共享维护云原生应用的slo

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) Pub Date : 2019-06-01 DOI: 10.1109/SASO.2019.00018

Vladimir Podolskiy, Michael Mayo, Abigail M. Y. Koay, M. Gerndt, Panos Patros

引用次数: 16

Multi-Scale Feedbacks for Large-Scale Coordination in Self-Systems 自系统大尺度协调的多尺度反馈

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) Pub Date : 2019-06-01 DOI: 10.1109/SASO.2019.00025

A. Diaconescu, L. Felice, P. Mellodge

引用次数: 15