2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)最新文献

Hybrid Planning Using Learning and Model Checking for Autonomous Systems 基于学习和模型检验的自治系统混合规划

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) Pub Date : 2020-08-01 DOI: 10.1109/ACSOS49614.2020.00026

Ashutosh Pandey, I. Ruchkin, B. Schmerl, D. Garlan

引用次数: 7

Understanding Uncertainty in Self-adaptive Systems 理解自适应系统中的不确定性

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) Pub Date : 2020-08-01 DOI: 10.1109/ACSOS49614.2020.00047

R. Calinescu, R. Mirandola, Diego Perez-Palacin, Danny Weyns

{"title":"Understanding Uncertainty in Self-adaptive Systems","authors":"R. Calinescu, R. Mirandola, Diego Perez-Palacin, Danny Weyns","doi":"10.1109/ACSOS49614.2020.00047","DOIUrl":"https://doi.org/10.1109/ACSOS49614.2020.00047","url":null,"abstract":"Ensuring that systems achieve their goals under uncertainty is a key driver for self-adaptation. Nevertheless, the concept of uncertainty in self-adaptive systems (SAS) is still insufficiently understood. Although several taxonomies of uncertainty have been proposed, taxonomies alone cannot convey the SAS research community’s perception of uncertainty. To explore and to learn from this perception, we conducted a survey focused on the SAS ability to deal with unanticipated change and to model uncertainty, and on the major challenges that limit this ability. In this paper, we analyse the responses provided by the 51 participants in our survey. The insights gained from this analysis include the view—held by 71% of our participants—that SAS can be engineered to cope with unanticipated change, e.g., through evolving their actions, synthesising new actions, or using default actions to deal with such changes. To handle uncertainties that affect SAS models, the participants recommended the use of confidence intervals and probabilities for parametric uncertainty, and the use of multiple models with model averaging or selection for structural uncertainty. Notwithstanding this positive outlook, the provision of assurances for safety-critical SAS continues to pose major challenges according to our respondents. We detail these findings in the paper, in the hope that they will inspire valuable future research on self-adaptive systems.","PeriodicalId":310362,"journal":{"name":"2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125463057","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

PRESTO: a latency-aware power-capping orchestrator for cloud-native microservices PRESTO:用于云原生微服务的延迟感知功率上限编排器

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) Pub Date : 2020-08-01 DOI: 10.1109/ACSOS49614.2020.00021

Rolando Brondolin, M. Santambrogio

引用次数: 3

Self-Patch: Beyond Patch Tuesday for Containerized Applications 自我补丁:容器化应用程序的补丁星期二之后

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) Pub Date : 2020-08-01 DOI: 10.1109/ACSOS49614.2020.00022

Olufogorehan Tunde-Onadele, Yuhang Lin, Jingzhu He, Xiaohui Gu

引用次数: 9

Assessing Adaptations based on Change Impacts 评估基于变化影响的适应

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) Pub Date : 2020-08-01 DOI: 10.1109/ACSOS49614.2020.00025

Sharmin Jahan, Ian Riley, R. Gamble

{"title":"Assessing Adaptations based on Change Impacts","authors":"Sharmin Jahan, Ian Riley, R. Gamble","doi":"10.1109/ACSOS49614.2020.00025","DOIUrl":"https://doi.org/10.1109/ACSOS49614.2020.00025","url":null,"abstract":"When a self-adaptive system alters its functionality to operate in a dynamic environment, it may impact whether the system can remain in compliance with its security requirements. Security assurance cases (SACs) provide confidence in system compliance by expressing security requirements as claims, arguments grounded in deployed mechanisms, and techniques that assure their satisfiability. A security control network (SCN) is comprised of SACs connected through sharing of state variables and conditions that support neighboring claims, as well as shared mechanisms and techniques. When a security mechanism is affected by an adaptation, the effect can propagate across the SCN. A dynamic change impact assessment (CIA) is necessary to select the least impactful adaptation plan from the set of possible plans. Performing a procedural CIA at runtime can be used to maintain system confidence after an adaptation has been applied, yet it remains a significant research challenge. In this paper, we estimate the change impact of an adaptation based on the level influence of the affected nodes in the SCN. The influence of each node is determined by a dependency weight, which is a function of the node’s three centrality measures from network flow analysis: degree, betweenness, and closeness. We demonstrate the applicability of the approach towards providing a dynamic CIA for security requirements without the need for human intervention.","PeriodicalId":310362,"journal":{"name":"2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114950461","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: 1

Design-Time Validation of Runtime Reconfiguration Strategies: An Environmental-Driven Approach 运行时重构策略的设计时验证:环境驱动的方法

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) Pub Date : 2020-08-01 DOI: 10.1109/ACSOS49614.2020.00028

Max Scheerer, Martina Rapp, Ralf H. Reussner

{"title":"Design-Time Validation of Runtime Reconfiguration Strategies: An Environmental-Driven Approach","authors":"Max Scheerer, Martina Rapp, Ralf H. Reussner","doi":"10.1109/ACSOS49614.2020.00028","DOIUrl":"https://doi.org/10.1109/ACSOS49614.2020.00028","url":null,"abstract":"Validating the effectiveness of reconfiguration strategies of Self-Adaptive Systems (SAS) regarding their impact on runtime quality properties is a challenging problem at design time. Since quality properties, such as performance or reliability, are effectively observable at runtime, it is inherently difficult to validate reconfiguration strategies at design-time during their design (e.g., during the definition of the software architecture). Furthermore, engineering and validating SAS at design-time involves uncertainties that are difficult to manage due to a dynamic operating environment. Therefore, we propose a novel model-based analysis approach that is driven by a temporal probabilistic model which captures the stochastic nature of the operating environment. The sampled trajectories through the state space serve as a basis for validation. Software engineers benefit from the framework by validating their reconfiguration strategy regarding quality objectives before implementation. The validated strategy serves as starting point for further model-based analyses such as correctness verification of adaptation logic or scenario-based analysis for local optimization.","PeriodicalId":310362,"journal":{"name":"2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130001953","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: 8

Reconfigurable Embedded Devices Using Reinforcement Learning to Develop Action-Policies 使用强化学习开发行动策略的可重构嵌入式设备

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) Pub Date : 2020-08-01 DOI: 10.1109/ACSOS49614.2020.00046

Alwyn Burger, David W. King, Gregor Schiele

{"title":"Reconfigurable Embedded Devices Using Reinforcement Learning to Develop Action-Policies","authors":"Alwyn Burger, David W. King, Gregor Schiele","doi":"10.1109/ACSOS49614.2020.00046","DOIUrl":"https://doi.org/10.1109/ACSOS49614.2020.00046","url":null,"abstract":"The size of sensor networks supporting smart cities is ever increasing. Sensor network resiliency becomes vital for critical networks such as emergency response and waste water treatment. One approach is to engineer ‘self-aware’ sensors that can proactively change their component composition in response to changes in work load when critical devices fail. By extension, these devices could anticipate their own termination, such as battery depletion, and offload current tasks onto connected devices. These neighboring devices can then reconFigure themselves to process these tasks, thus avoiding catastrophic network failure. In this article, we present an array of self-aware sensors who use Q-learning to develop a policy that guides device reaction to various environmental stimuli. The novelty lies in the use of field programmable gate arrays embedded on the sensors that take into account internal system state, configuration, and learned state-action pairs, that guide device decisions in order to meet system demands. Experiments show that even relatively simple reward functions develop Q-learning policies that yield positive device behaviors in dynamic environments.","PeriodicalId":310362,"journal":{"name":"2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116199505","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

ACSOS 2020 Breaker Page ACSOS 2020断路器页面

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) Pub Date : 2020-08-01 DOI: 10.1109/acsos49614.2020.00003

引用次数: 0

Evaluating the Effect of User-Given Guiding Attention on the Learning Process 评价用户给予的引导注意力对学习过程的影响

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) Pub Date : 2020-08-01 DOI: 10.1109/ACSOS49614.2020.00044

R. Nordsieck, Michael Heider, A. Angerer, J. Hähner

引用次数: 1

Reasoning about When to Provide Explanation for Human-involved Self-Adaptive Systems 关于何时为涉及人类的自适应系统提供解释的推理

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) Pub Date : 2020-08-01 DOI: 10.1109/ACSOS49614.2020.00042

Nianyu Li, J. Cámara, D. Garlan, B. Schmerl

{"title":"Reasoning about When to Provide Explanation for Human-involved Self-Adaptive Systems","authors":"Nianyu Li, J. Cámara, D. Garlan, B. Schmerl","doi":"10.1109/ACSOS49614.2020.00042","DOIUrl":"https://doi.org/10.1109/ACSOS49614.2020.00042","url":null,"abstract":"Many self-adaptive systems benefit from human involvement, where a human operator can provide expertise not available to the system and perform adaptations involving physical changes that cannot be automated. However, a lack of transparency and intelligibility of system goals and the autonomous behaviors enacted to achieve them may hinder a human operator’s effort to make such involvement effective. Explanation is sometimes helpful to allow the human to understand why the system is making certain decisions. However, explanations come with costs in terms of, e.g., delayed actions. Hence, it is not always obvious whether explanations will improve the satisfaction of system goals and, if so, when to provide them to the operator. In this work, we define a formal framework for reasoning about explanations of adaptive system behaviors and the conditions under which they are warranted. Specifically, we characterize explanations in terms of their impact on a human operator’s ability to effectively engage in adaptive actions. We then present a decision-making approach for planning in self-adaptation that leverages a probabilistic reasoning tool to determine when the explanation should be used in an adaptation strategy in order to improve overall system utility. We illustrate our approach in a representative scenario for the application of an adaptive news website in the context of potential denial-of-service attacks.","PeriodicalId":310362,"journal":{"name":"2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122971085","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