Internet-of-Things (IoT) is an emergent paradigm that is increasingly applied in smart cities. A popular technology used in IoT is LoRa that supports long-range wireless communication. In this research, we study LoRa-based IoT systems with battery-powered end nodes that collect and communicate data to a gateway for further processing. Existing approaches in such IoT systems usually only consider stationary end nodes. We focus on systems with mobile end nodes, paving the way to new applications such as target tracking. Key Quality of Service (QoS) requirements for these settings are the reliability of the communication and energy consumption. With mobile end nodes, ensuring these QoS is challenging as the system is subject to continuous changes. In this paper, we investigate how the settings of a mobile end node impact key performance indicators for reliability and energy consumption. Based on insights obtained from extensive field experiments, we devise an algorithm that automatically adapts the settings of a mobile end node to ensure its QoS requirements for a setup with a single gateway. We then extend the algorithm to a setup with multiple gateways. We demonstrate how the algorithms achieve the QoS requirements of a mobile end node in a concrete IoT deployment.
{"title":"A QoS-Aware Adaptive Mobility Handling Approach for LoRa-Based IoT Systems","authors":"Berrevoets Robbe, Weyns Danny","doi":"10.1109/SASO.2018.00024","DOIUrl":"https://doi.org/10.1109/SASO.2018.00024","url":null,"abstract":"Internet-of-Things (IoT) is an emergent paradigm that is increasingly applied in smart cities. A popular technology used in IoT is LoRa that supports long-range wireless communication. In this research, we study LoRa-based IoT systems with battery-powered end nodes that collect and communicate data to a gateway for further processing. Existing approaches in such IoT systems usually only consider stationary end nodes. We focus on systems with mobile end nodes, paving the way to new applications such as target tracking. Key Quality of Service (QoS) requirements for these settings are the reliability of the communication and energy consumption. With mobile end nodes, ensuring these QoS is challenging as the system is subject to continuous changes. In this paper, we investigate how the settings of a mobile end node impact key performance indicators for reliability and energy consumption. Based on insights obtained from extensive field experiments, we devise an algorithm that automatically adapts the settings of a mobile end node to ensure its QoS requirements for a setup with a single gateway. We then extend the algorithm to a setup with multiple gateways. We demonstrate how the algorithms achieve the QoS requirements of a mobile end node in a concrete IoT deployment.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115713385","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}
{"title":"[Copyright notice]","authors":"","doi":"10.1109/saso.2018.00003","DOIUrl":"https://doi.org/10.1109/saso.2018.00003","url":null,"abstract":"","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115256050","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}
Monitoring is a key prerequisite for self-adaptive software and many other forms of operating software. Monitoring relevant lower level phenomena like the occurrences of exceptions and diagnosis data requires to carefully examine which detailed information is really necessary and feasible to monitor. Adaptive monitoring permits observing a greater variety of details with less overhead, if most of the time the MAPE-K loop can operate using only a small subset of all those details. However, engineering such an adaptive monitoring is a major engineering effort on its own that further complicates the development of self-adaptive software. The proposed approach overcomes the outlined problems by providing generic adaptive monitoring via runtime models. It reduces the effort to introduce and apply adaptive monitoring by avoiding additional development effort for controlling the monitoring adaptation. Although the generic approach is independent from the monitoring purpose, it still allows for substantial savings regarding the monitoring resource consumption as demonstrated by an example.
{"title":"Towards Generic Adaptive Monitoring","authors":"Thomas Brand, H. Giese","doi":"10.1109/SASO.2018.00027","DOIUrl":"https://doi.org/10.1109/SASO.2018.00027","url":null,"abstract":"Monitoring is a key prerequisite for self-adaptive software and many other forms of operating software. Monitoring relevant lower level phenomena like the occurrences of exceptions and diagnosis data requires to carefully examine which detailed information is really necessary and feasible to monitor. Adaptive monitoring permits observing a greater variety of details with less overhead, if most of the time the MAPE-K loop can operate using only a small subset of all those details. However, engineering such an adaptive monitoring is a major engineering effort on its own that further complicates the development of self-adaptive software. The proposed approach overcomes the outlined problems by providing generic adaptive monitoring via runtime models. It reduces the effort to introduce and apply adaptive monitoring by avoiding additional development effort for controlling the monitoring adaptation. Although the generic approach is independent from the monitoring purpose, it still allows for substantial savings regarding the monitoring resource consumption as demonstrated by an example.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131679596","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}
Clouds abstract the provisioning of computing resources and offer them in a pay-as-you-go manner. Containerized platform clouds, in particular, offer large parts of the software/hardware stack to their clients. Through multitenancy, cloud resources can be transparently shared among the client-tenants of the cloud provider. However, multitenancy causes performance interference that leads to unpredictable behavior and Service Level Objective (SLO) violations. The applications commonly deployed on clouds use high-level languages, which provide automatic memory management, also known as Garbage Collection (GC). Depending on the selected GC policy and the cloud's resource management strategy, CPU utilization spikes by the GC threads can severely affect any co-located tenants. This paper proposes a technique of GC elasticity that scales down the GC threads and heap size at times of low load, while executing preemptive GCs. Experimental results on top of an isolated Cloud Foundry installation using the IBM JVM/Liberty stack, suggest that our prototype reduced SLO violations on co-located tenants, only when the GC-scaled tenant maintained a large object graph and used a GC policy with long stop-the-world phases.
{"title":"Mitigating Garbage Collection Interference on Containerized Clouds","authors":"Panagiotis Patros, K. Kent, Michael H. Dawson","doi":"10.1109/SASO.2018.00029","DOIUrl":"https://doi.org/10.1109/SASO.2018.00029","url":null,"abstract":"Clouds abstract the provisioning of computing resources and offer them in a pay-as-you-go manner. Containerized platform clouds, in particular, offer large parts of the software/hardware stack to their clients. Through multitenancy, cloud resources can be transparently shared among the client-tenants of the cloud provider. However, multitenancy causes performance interference that leads to unpredictable behavior and Service Level Objective (SLO) violations. The applications commonly deployed on clouds use high-level languages, which provide automatic memory management, also known as Garbage Collection (GC). Depending on the selected GC policy and the cloud's resource management strategy, CPU utilization spikes by the GC threads can severely affect any co-located tenants. This paper proposes a technique of GC elasticity that scales down the GC threads and heap size at times of low load, while executing preemptive GCs. Experimental results on top of an isolated Cloud Foundry installation using the IBM JVM/Liberty stack, suggest that our prototype reduced SLO violations on co-located tenants, only when the GC-scaled tenant maintained a large object graph and used a GC policy with long stop-the-world phases.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128677305","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}
Recently, the study of social norms has attracted much attention in multi-agent system research. We here focus on the case that agents are aware of the social value of each action under prisoner's dilemma games. During decision-making, agents weigh different actions based on the social values and their individual payoffs. We model such agents' behaviours with the notion of social value orientation. An experimental study is conducted on different kinds of agent societies. It is shown that a stable state can be generally reached in these societies, however, global social norms do not always emerge. In particular, we identify dynamic equilibrium phenomena as the ultimate state in some agent societies. Moreover, we show that in dynamic societies, agents eventually develop individualistic or altruistic orientations.
{"title":"Do Social Norms Emerge? The Evolution of Agents' Decisions with the Awareness of Social Values under Iterated Prisoner's Dilemma","authors":"Shuyue Hu, Ho-fung Leung","doi":"10.1109/SASO.2018.00012","DOIUrl":"https://doi.org/10.1109/SASO.2018.00012","url":null,"abstract":"Recently, the study of social norms has attracted much attention in multi-agent system research. We here focus on the case that agents are aware of the social value of each action under prisoner's dilemma games. During decision-making, agents weigh different actions based on the social values and their individual payoffs. We model such agents' behaviours with the notion of social value orientation. An experimental study is conducted on different kinds of agent societies. It is shown that a stable state can be generally reached in these societies, however, global social norms do not always emerge. In particular, we identify dynamic equilibrium phenomena as the ultimate state in some agent societies. Moreover, we show that in dynamic societies, agents eventually develop individualistic or altruistic orientations.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129934156","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}
Elias Alevizos, NCSR “Demokritos”, GR André Bauer, University of Würzburg, DE Kyle Canavera, University of California Irvine, US Roberto Casadei, University of Bologna, IT Simon Eismann, University of Würzburg, DE Aymen Fakhreddine, University of Klagenfurt, AT Nugroho Fredivianus, University of Kassel, DE Mahmoud Hammad, University of California Irvine, US Jonathan Hudson, University of Calgary, CA Stefan Jakob, University of Kassel, DE Oliver Kosak, University of Augsburg, DE Christian Medeiros Adriano, Hasso Plattner Institute, DE Marie Ossenkopf, University of Kassel, DE Danilo Pianini, University of Bologna, IT Jorge F. Schmidt, University of Klagenfurt, AT Norbert Schmitt, University of Würzburg, DE Simon Stieber, University of Augsburg, DE Arke Vogell, University of Klagenfurt, AT Constantin Wanninger, University of Augsburg, DE Christian Zöllner, Hasso Plattner Institute, DE
Elias Alevizos, NCSR“Demokritos”，GR andr Bauer，维尔茨堡大学，DE Kyle Canavera，加州大学欧文分校，美国Roberto Casadei，博洛尼亚大学，IT Simon Eismann，维尔茨堡大学，DE Aymen Fakhreddine，克拉根福大学，AT Nugroho Fredivianus，卡塞尔大学，DE Mahmoud Hammad，加州大学欧文分校，美国Jonathan Hudson，卡尔加里大学，CA Stefan Jakob，卡塞尔大学，DE Oliver Kosak，奥格斯堡大学，DE Christian Medeiros Adriano, Hasso Plattner研究所，DE Marie Ossenkopf，卡塞尔大学，DE Danilo Pianini，博洛尼亚大学，Jorge F. Schmidt，克拉根福大学，AT Norbert Schmitt, w茨堡大学，DE Simon Stieber，奥格斯堡大学，DE Arke Vogell，克拉根福大学，AT Constantin Wanninger，奥格斯堡大学，DE Christian Zöllner, Hasso Plattner研究所，DE DE
{"title":"SASO 2018 Subreviewers","authors":"E. Alevizos, Roberto Casadei, Aymen Fakhreddine","doi":"10.1109/saso.2018.00010","DOIUrl":"https://doi.org/10.1109/saso.2018.00010","url":null,"abstract":"Elias Alevizos, NCSR “Demokritos”, GR André Bauer, University of Würzburg, DE Kyle Canavera, University of California Irvine, US Roberto Casadei, University of Bologna, IT Simon Eismann, University of Würzburg, DE Aymen Fakhreddine, University of Klagenfurt, AT Nugroho Fredivianus, University of Kassel, DE Mahmoud Hammad, University of California Irvine, US Jonathan Hudson, University of Calgary, CA Stefan Jakob, University of Kassel, DE Oliver Kosak, University of Augsburg, DE Christian Medeiros Adriano, Hasso Plattner Institute, DE Marie Ossenkopf, University of Kassel, DE Danilo Pianini, University of Bologna, IT Jorge F. Schmidt, University of Klagenfurt, AT Norbert Schmitt, University of Würzburg, DE Simon Stieber, University of Augsburg, DE Arke Vogell, University of Klagenfurt, AT Constantin Wanninger, University of Augsburg, DE Christian Zöllner, Hasso Plattner Institute, DE","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116090018","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}
The coordination of multiple agents which are collectively responsible for accomplishing a system's task is among the fundamental issues of organizational design. This paper studies which mode of coordination emerges in systems when the task complexity is imperfectly known, for example, as the system has gone through an external shock. For this, an agent-based simulation based on NK fitness landscapes is employed. The simulation model controls for different levels of task complexity and different degrees of knowledge about the task. The results provide broad support for the conjecture that intense coordination emerges when the complexity of the task to be accomplished collaboratively is high. Moreover, the results suggest that the precision of knowledge about the task subtly may affect which coordination mode emerges.
{"title":"Self-Adaptation of Coordination in Imperfectly Known Task Environments","authors":"F. Wall","doi":"10.1109/SASO.2018.00030","DOIUrl":"https://doi.org/10.1109/SASO.2018.00030","url":null,"abstract":"The coordination of multiple agents which are collectively responsible for accomplishing a system's task is among the fundamental issues of organizational design. This paper studies which mode of coordination emerges in systems when the task complexity is imperfectly known, for example, as the system has gone through an external shock. For this, an agent-based simulation based on NK fitness landscapes is employed. The simulation model controls for different levels of task complexity and different degrees of knowledge about the task. The results provide broad support for the conjecture that intense coordination emerges when the complexity of the task to be accomplished collaboratively is high. Moreover, the results suggest that the precision of knowledge about the task subtly may affect which coordination mode emerges.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116608528","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}
Analyzing how agent interactions affect macro-level self-organized behaviors can yield a deeper understanding of how complex adaptive systems work. The dynamic nature of complex systems makes it difficult to determine if, or when, a system has reached a state of equilibrium or is about to undergo a major transition reflecting the appearance of self-organized states. Using the notion of local neighborhood entropy, this paper presents a metric for evaluating the macro-level order of a system. The metric is tested in two dissimilar complex adaptive systems with self-organizing properties: an autonomous swarm searching for multiple dynamic targets and Conway's Game of Life. In both domains, the proposed metric is able to graphically capture periods of increasing and decreasing self-organization (i.e. changes in macro-level order), equilibrium and points of criticality; displaying its general applicability in identifying these behaviors in complex adaptive systems.
{"title":"A Macro-Level Order Metric for Self-Organizing Adaptive Systems","authors":"David W. King, Gilbert L. Peterson","doi":"10.1109/SASO.2018.00017","DOIUrl":"https://doi.org/10.1109/SASO.2018.00017","url":null,"abstract":"Analyzing how agent interactions affect macro-level self-organized behaviors can yield a deeper understanding of how complex adaptive systems work. The dynamic nature of complex systems makes it difficult to determine if, or when, a system has reached a state of equilibrium or is about to undergo a major transition reflecting the appearance of self-organized states. Using the notion of local neighborhood entropy, this paper presents a metric for evaluating the macro-level order of a system. The metric is tested in two dissimilar complex adaptive systems with self-organizing properties: an autonomous swarm searching for multiple dynamic targets and Conway's Game of Life. In both domains, the proposed metric is able to graphically capture periods of increasing and decreasing self-organization (i.e. changes in macro-level order), equilibrium and points of criticality; displaying its general applicability in identifying these behaviors in complex adaptive systems.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121350844","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}
Agent-based models (ABMs) involve large numbers of individual agents, each governed by a common behavior program (Agent-Level Parameters, or ALPs), whose collective behavior (System-Level Parameters, or SLPs) is emergent due to interactions among the agents and the environment. Applications of ABMs include modeling the spread of epidemics, supply chain optimization, and representing the dynamics of financial markets. A typical application involves specifying one ALP to get a desired SLP. In this work, we explore emergent behavior sequences, such as a swarm of drones transitioning from broad area search to focused search to airlifting disaster victims. The central question is how one achieves graceful and ef?cient changes between SLPs by manipulating ALPs. We explore three different ways of transitioning between ALPs and observe their behavior on SLPs, with the goal of fast and stable convergence on the desired SLPs. All of the empirical work is done in an existing framework that allows users to specify ALPs by demonstrating desired SLPs, thereby removing the need for deep ABM knowledge on the part of users.
{"title":"Optimizing Transitions between Abstract ABM Demonstrations","authors":"B. Seipp, K. K. Budhraja, T. Oates","doi":"10.1109/SASO.2018.00021","DOIUrl":"https://doi.org/10.1109/SASO.2018.00021","url":null,"abstract":"Agent-based models (ABMs) involve large numbers of individual agents, each governed by a common behavior program (Agent-Level Parameters, or ALPs), whose collective behavior (System-Level Parameters, or SLPs) is emergent due to interactions among the agents and the environment. Applications of ABMs include modeling the spread of epidemics, supply chain optimization, and representing the dynamics of financial markets. A typical application involves specifying one ALP to get a desired SLP. In this work, we explore emergent behavior sequences, such as a swarm of drones transitioning from broad area search to focused search to airlifting disaster victims. The central question is how one achieves graceful and ef?cient changes between SLPs by manipulating ALPs. We explore three different ways of transitioning between ALPs and observe their behavior on SLPs, with the goal of fast and stable convergence on the desired SLPs. All of the empirical work is done in an existing framework that allows users to specify ALPs by demonstrating desired SLPs, thereby removing the need for deep ABM knowledge on the part of users.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127071094","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}
{"title":"SASO 2018 Reviewers","authors":"","doi":"10.1109/saso.2018.00009","DOIUrl":"https://doi.org/10.1109/saso.2018.00009","url":null,"abstract":"","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133653467","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}
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