A self-organization of efficient and robust networks is important for a future design of communication or transportation systems, however both characteristics are incompatible in many real networks. Recently, it has been found that the robustness of onion-like structure with positive degree-degree correlations is optimal against intentional attacks. We show that, by biologically inspired copying, an onion-like network emerges in the incremental growth with functions of proxy access and reinforced connectivity on a space. The proposed network consists of the backbone of tree-like structure by copyings and the peripheral by adding shortcut links between low degree nodes to enhance the connectivity. It has the fine properties of the statistically self-averaging unlike the conventional duplication-divergence model, exponential-like degree distribution without overloaded hubs, strong robustness against both malicious attacks and random failures, and the efficiency with short paths counted by the number of hops as mediators and by the Euclidean distances. The adaptivity to heal over and to recover the performance of networking is also discussed for a change of environment in such disasters or battlefields on a geographical map. These properties will be useful for a resilient and scalable infrastructure of network systems even in emergent situations or poor environments.
{"title":"Growing Self-Organized Design of Efficient and Robust Complex Networks","authors":"Y. Hayashi","doi":"10.1109/SASO.2014.17","DOIUrl":"https://doi.org/10.1109/SASO.2014.17","url":null,"abstract":"A self-organization of efficient and robust networks is important for a future design of communication or transportation systems, however both characteristics are incompatible in many real networks. Recently, it has been found that the robustness of onion-like structure with positive degree-degree correlations is optimal against intentional attacks. We show that, by biologically inspired copying, an onion-like network emerges in the incremental growth with functions of proxy access and reinforced connectivity on a space. The proposed network consists of the backbone of tree-like structure by copyings and the peripheral by adding shortcut links between low degree nodes to enhance the connectivity. It has the fine properties of the statistically self-averaging unlike the conventional duplication-divergence model, exponential-like degree distribution without overloaded hubs, strong robustness against both malicious attacks and random failures, and the efficiency with short paths counted by the number of hops as mediators and by the Euclidean distances. The adaptivity to heal over and to recover the performance of networking is also discussed for a change of environment in such disasters or battlefields on a geographical map. These properties will be useful for a resilient and scalable infrastructure of network systems even in emergent situations or poor environments.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"1 1","pages":"50-59"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85715470","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}
A common requirement of distributed multi-agent systems is for the agents themselves to negotiate pairwise agreements on performing a joint action. In systems with endogenous resources, the cost of computing the decision-making has to be taken into account. If the computational resources expended in negotiating an optimal solution exceed the marginal benefits gained from that negotiation, then it would be more expedient and efficient to use the memory of past interactions to short-cut the complexity of decision-making in joint or collective actions of this kind. In social systems, it has been observed that social capital is an attribute of individuals that enhances their ability to solve collective action problems. In this paper, we define a new computational framework for representing and reasoning about electronic social capital, in which actions enhance or diminish three different forms of social capital (individual trustworthiness, social network, and institutions), and a decision-making model which uses social capital to decide whether to cooperate or defect in strategic games. A set of scenarios are presented where we believe that social capital can support effective collective action sustained over time, avoid suboptimal dominant strategies, and short-cut the computational costs involved in repetitious solving of strategic games.
{"title":"Social Capital as a Complexity Reduction Mechanism for Decision Making in Large Scale Open Systems","authors":"Patricio E. Petruzzi, D. Busquets, J. Pitt","doi":"10.1109/SASO.2014.27","DOIUrl":"https://doi.org/10.1109/SASO.2014.27","url":null,"abstract":"A common requirement of distributed multi-agent systems is for the agents themselves to negotiate pairwise agreements on performing a joint action. In systems with endogenous resources, the cost of computing the decision-making has to be taken into account. If the computational resources expended in negotiating an optimal solution exceed the marginal benefits gained from that negotiation, then it would be more expedient and efficient to use the memory of past interactions to short-cut the complexity of decision-making in joint or collective actions of this kind. In social systems, it has been observed that social capital is an attribute of individuals that enhances their ability to solve collective action problems. In this paper, we define a new computational framework for representing and reasoning about electronic social capital, in which actions enhance or diminish three different forms of social capital (individual trustworthiness, social network, and institutions), and a decision-making model which uses social capital to decide whether to cooperate or defect in strategic games. A set of scenarios are presented where we believe that social capital can support effective collective action sustained over time, avoid suboptimal dominant strategies, and short-cut the computational costs involved in repetitious solving of strategic games.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"15 1","pages":"145-150"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86892426","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}
Arnaud Cordier, R. Domingues, Anthony Labaere, N. Noel, Adrien Thiery, Thomas Cerqueus, S. Clarke, P. Idziak, Hui Song, P. Perry, Anthony Ventresque
This paper demonstrates how we applied a constraint-based dynamic adaptation approach on CarDemo, a traffic management system. The approach allows domain experts to describe the adaptation goals as declarative constraints, and automatically plan the adaptation decisions to satisfy these constraints. We demonstrate how to utilise this approach to realise the dynamic switch of routing services of the traffic management system, according to the change of global system states and user requests.
{"title":"Dynamic Adaptation of the Traffic Management System CarDemo","authors":"Arnaud Cordier, R. Domingues, Anthony Labaere, N. Noel, Adrien Thiery, Thomas Cerqueus, S. Clarke, P. Idziak, Hui Song, P. Perry, Anthony Ventresque","doi":"10.1109/SASO.2014.40","DOIUrl":"https://doi.org/10.1109/SASO.2014.40","url":null,"abstract":"This paper demonstrates how we applied a constraint-based dynamic adaptation approach on CarDemo, a traffic management system. The approach allows domain experts to describe the adaptation goals as declarative constraints, and automatically plan the adaptation decisions to satisfy these constraints. We demonstrate how to utilise this approach to realise the dynamic switch of routing services of the traffic management system, according to the change of global system states and user requests.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"43 1","pages":"193-194"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91528171","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}
Current trends in information and communication technology show that systems are increasingly influencing each other -- which is seldom completely anticipated at design-time. As a result, mastering system integration with traditional methods becomes infeasible due to the resulting complexity. In this paper we argue that self-improving system integration is the most promising solution to counter the resulting challenges. Thereby, we highlight the different aspects of such a process with special attention to the optimisation question and discuss how approaches from the domain of self-organising systems -- in particular Organic and Autonomic Computing -- will be beneficial when researching possible solutions.
{"title":"Interwoven Systems: Self-Improving Systems Integration","authors":"K. Bellman, Sven Tomforde, R. Würtz","doi":"10.1109/SASOW.2014.21","DOIUrl":"https://doi.org/10.1109/SASOW.2014.21","url":null,"abstract":"Current trends in information and communication technology show that systems are increasingly influencing each other -- which is seldom completely anticipated at design-time. As a result, mastering system integration with traditional methods becomes infeasible due to the resulting complexity. In this paper we argue that self-improving system integration is the most promising solution to counter the resulting challenges. Thereby, we highlight the different aspects of such a process with special attention to the optimisation question and discuss how approaches from the domain of self-organising systems -- in particular Organic and Autonomic Computing -- will be beneficial when researching possible solutions.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"81 1","pages":"123-127"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83246513","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}
Self-Improving System Integration -- Preface for the SISSY14 Workshop.
自我完善的系统集成——SISSY14研讨会前言。
{"title":"\"Self-Improving System Integration\" - Preface for the SISSY14 Workshop","authors":"K. Bellman, Sven Tomforde, R. Würtz","doi":"10.1109/SASOW.2014.20","DOIUrl":"https://doi.org/10.1109/SASOW.2014.20","url":null,"abstract":"Self-Improving System Integration -- Preface for the SISSY14 Workshop.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"4 1","pages":"122-122"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79083916","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}
Hyper-heuristics is a very active field that is developing all the time. This area of bio-inspired intelligent systems covers a wide range of algorithms selection techniques. This type of self-organising mechanism uses heuristics to optimise heuristics. Many discussions focus on the quality of solutions of the problems obtained from the hyper-heuristics, very little discussion concentrates on the generated algorithms themselves. Some hyper-heuristic frameworks tend to be highly constrained, their limited instruction sets prevent the state-of-the-art algorithms from being expressed. In addition, often the generated algorithms are not human-readable. In this paper, we propose a possible extension of some existing hyper-heuristic formulations, so that some of the current open issues can be addressed and it becomes possible to produce self-organizing heuristics that adapt themselves automatically to the environment when the class of problems changes. This together with the analysis of the evolved algorithms, may lead to human-competitive results.
{"title":"Plug-and-Play Hyper-heuristics: An Extended Formulation","authors":"Patricia Ryser-Welch, J. Miller","doi":"10.1109/SASO.2014.33","DOIUrl":"https://doi.org/10.1109/SASO.2014.33","url":null,"abstract":"Hyper-heuristics is a very active field that is developing all the time. This area of bio-inspired intelligent systems covers a wide range of algorithms selection techniques. This type of self-organising mechanism uses heuristics to optimise heuristics. Many discussions focus on the quality of solutions of the problems obtained from the hyper-heuristics, very little discussion concentrates on the generated algorithms themselves. Some hyper-heuristic frameworks tend to be highly constrained, their limited instruction sets prevent the state-of-the-art algorithms from being expressed. In addition, often the generated algorithms are not human-readable. In this paper, we propose a possible extension of some existing hyper-heuristic formulations, so that some of the current open issues can be addressed and it becomes possible to produce self-organizing heuristics that adapt themselves automatically to the environment when the class of problems changes. This together with the analysis of the evolved algorithms, may lead to human-competitive results.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"1 1","pages":"179-180"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75811472","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 focus of this tutorial is to provide a synopsis of self-managing computing also known as Autonomic Computing. In doing so, we will introduce the techniques that enable computer systems to manage themselves so as to minimise the need for human input. This will also discuss how self-managing systems can address some of the issues resulting from the ever-increasing complexity of software administration and the growing difficulty encountered by software administrators in performing their job effectively.
{"title":"Self-Managing Pervasive Computing","authors":"P. Lalanda, J. Mccann, A. Diaconescu","doi":"10.1109/SASOW.2014.42","DOIUrl":"https://doi.org/10.1109/SASOW.2014.42","url":null,"abstract":"The focus of this tutorial is to provide a synopsis of self-managing computing also known as Autonomic Computing. In doing so, we will introduce the techniques that enable computer systems to manage themselves so as to minimise the need for human input. This will also discuss how self-managing systems can address some of the issues resulting from the ever-increasing complexity of software administration and the growing difficulty encountered by software administrators in performing their job effectively.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"14 1","pages":"5-5"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82503902","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}
Hiroyuki Nakagawa, Takumitsu Kudo, Y. Sei, Yasuyuki Tahara, Akihiko Ohsuga
Software evolution is an essential activity that adapts existing software to changes in requirements. Because of recent rapid requirements changes, systems are strongly required to evolve even if the target systems are embedded systems, whose implementation code is generally hard to be changed. This paper discusses the feasibility of applying self-adaptation mechanism for software evolution. We use the MAPE loop mechanism to evolve embedded systems without changing code inside the existing systems. This paper also reports preliminary results that we experimentally evolved a cleaning robot by following our approach. Our demonstrations show a part of additional behaviors as the results of software evolution that makes the cleaning robot possible to move obstacles. We also discuss the future directions of software evolution for embedded systems with the self-adaptive mechanism.
{"title":"Towards Software Evolution for Embedded Systems Based on MAPE Loop Encapsulation","authors":"Hiroyuki Nakagawa, Takumitsu Kudo, Y. Sei, Yasuyuki Tahara, Akihiko Ohsuga","doi":"10.1109/SASO.2014.45","DOIUrl":"https://doi.org/10.1109/SASO.2014.45","url":null,"abstract":"Software evolution is an essential activity that adapts existing software to changes in requirements. Because of recent rapid requirements changes, systems are strongly required to evolve even if the target systems are embedded systems, whose implementation code is generally hard to be changed. This paper discusses the feasibility of applying self-adaptation mechanism for software evolution. We use the MAPE loop mechanism to evolve embedded systems without changing code inside the existing systems. This paper also reports preliminary results that we experimentally evolved a cleaning robot by following our approach. Our demonstrations show a part of additional behaviors as the results of software evolution that makes the cleaning robot possible to move obstacles. We also discuss the future directions of software evolution for embedded systems with the self-adaptive mechanism.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"16 1","pages":"203-204"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88292838","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}
Simon Spinner, Samuel Kounev, Xiaoyun Zhu, Lei Lu, Mustafa Uysal, Anne M. Holler, Rean Griffith
Applications in virtualized data centers are often subject to Service Level Objectives (SLOs) regarding their performance (e.g., latency or throughput). In order to fulfill these SLOs, it is necessary to allocate sufficient resources of different types (CPU, memory, I/O, etc.) to an application. However, the relationship between the application performance and the resource allocation is complex and depends on multiple factors including application architecture, system configuration, and workload demands. In this paper, we present a model-based approach to ensure that the application performance meets the user-defined SLO efficiently by runtime "vertical scaling" (i.e., adding or removing resources) of individual virtual machines (VMs) running the application. A layered performance model describing the relationship between the resource allocation and the observed application performance is automatically extracted and updated online using resource demand estimation techniques. Such a model is then used in a feedback controller to dynamically adapt the number of virtual CPUs of individual VMs. We have implemented the controller on top of the VMware vSphere platform and evaluated it in a case study using a real-world email and groupware server. The experimental results show that our approach allows the managed application to achieve SLO satisfaction in spite of workload demand variation while avoiding oscillations commonly observed with state-of-the-art threshold-based controllers.
{"title":"Runtime Vertical Scaling of Virtualized Applications via Online Model Estimation","authors":"Simon Spinner, Samuel Kounev, Xiaoyun Zhu, Lei Lu, Mustafa Uysal, Anne M. Holler, Rean Griffith","doi":"10.1109/SASO.2014.29","DOIUrl":"https://doi.org/10.1109/SASO.2014.29","url":null,"abstract":"Applications in virtualized data centers are often subject to Service Level Objectives (SLOs) regarding their performance (e.g., latency or throughput). In order to fulfill these SLOs, it is necessary to allocate sufficient resources of different types (CPU, memory, I/O, etc.) to an application. However, the relationship between the application performance and the resource allocation is complex and depends on multiple factors including application architecture, system configuration, and workload demands. In this paper, we present a model-based approach to ensure that the application performance meets the user-defined SLO efficiently by runtime \"vertical scaling\" (i.e., adding or removing resources) of individual virtual machines (VMs) running the application. A layered performance model describing the relationship between the resource allocation and the observed application performance is automatically extracted and updated online using resource demand estimation techniques. Such a model is then used in a feedback controller to dynamically adapt the number of virtual CPUs of individual VMs. We have implemented the controller on top of the VMware vSphere platform and evaluated it in a case study using a real-world email and groupware server. The experimental results show that our approach allows the managed application to achieve SLO satisfaction in spite of workload demand variation while avoiding oscillations commonly observed with state-of-the-art threshold-based controllers.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"23 1","pages":"157-166"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78943607","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}
Pub Date : 2014-09-08DOI: 10.12694/scpe.v16i3.1098
L. Varga
It is widely believed that road traffic as a whole self-adapts to the current situation to make travel times shorter by avoiding congestions, if the autonomously operating navigation devices exploit real-time traffic information. The classical theoretical models do not have definite answer if car navigation based on real-time data is able to self-adapt and produce better traffic or not. The novel theoretical approach to study this belief is the online routing game model. Current commercial car navigation systems are modelled with the class of simple naive online routing games. It is already proved that simple naive online routing games may show undesirable phenomena. One of the approaches to improve car navigation is intention-propagation-based prediction where agents share their intention and can forecast future travel times. In this paper we prove that in spite of exploiting prediction in online routing games, the phenomena studied in simple naive online routing games are still possible, although in a different way. With these results we point out where improvements are needed in collective adaptive systems composed of navigation devices.
{"title":"On Intention-Propagation-Based Prediction in Autonomously Self-Adapting Navigation","authors":"L. Varga","doi":"10.12694/scpe.v16i3.1098","DOIUrl":"https://doi.org/10.12694/scpe.v16i3.1098","url":null,"abstract":"It is widely believed that road traffic as a whole self-adapts to the current situation to make travel times shorter by avoiding congestions, if the autonomously operating navigation devices exploit real-time traffic information. The classical theoretical models do not have definite answer if car navigation based on real-time data is able to self-adapt and produce better traffic or not. The novel theoretical approach to study this belief is the online routing game model. Current commercial car navigation systems are modelled with the class of simple naive online routing games. It is already proved that simple naive online routing games may show undesirable phenomena. One of the approaches to improve car navigation is intention-propagation-based prediction where agents share their intention and can forecast future travel times. In this paper we prove that in spite of exploiting prediction in online routing games, the phenomena studied in simple naive online routing games are still possible, although in a different way. With these results we point out where improvements are needed in collective adaptive systems composed of navigation devices.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"84 1","pages":"38-43"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79151247","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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