Autonomous robots have a great potential to help in disaster scenarios. Nevertheless, each robot is faced with the variety of these scenarios and the difficulties of cooperation with heterogeneous robots and human actors of other rescue forces. Furthermore, approaches using predefined adaptation models are not flexible enough for these scenarios. Therefore, we propose an adaptive configuration of the information processing to enable the collaboration of heterogeneous groups. We present two contributions in this paper: a) An ontology to describe the semantics of information processing components and the structure of information, b) The composition of these components to an adaptation model at run-time. To coordinate the information sharing and integration of new discovered information sources at run-time, we further provide a general framework. Finally, we describe how to apply our approach in a fictitious scenario.
{"title":"Adaptive Run-Time Models for Groups of Autonomous Robots","authors":"S. Niemczyk, K. Geihs","doi":"10.1109/SEAMS.2015.21","DOIUrl":"https://doi.org/10.1109/SEAMS.2015.21","url":null,"abstract":"Autonomous robots have a great potential to help in disaster scenarios. Nevertheless, each robot is faced with the variety of these scenarios and the difficulties of cooperation with heterogeneous robots and human actors of other rescue forces. Furthermore, approaches using predefined adaptation models are not flexible enough for these scenarios. Therefore, we propose an adaptive configuration of the information processing to enable the collaboration of heterogeneous groups. We present two contributions in this paper: a) An ontology to describe the semantics of information processing components and the structure of information, b) The composition of these components to an adaptation model at run-time. To coordinate the information sharing and integration of new discovered information sources at run-time, we further provide a general framework. Finally, we describe how to apply our approach in a fictitious scenario.","PeriodicalId":144594,"journal":{"name":"2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127895947","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}
Research on adaptive and self-managing systems is hindered by a lack of prototypical applications that researchers could use to evaluate and compare new methods, techniques and tools. To address this limitation, we introduce a reference implementation of a Tele Assistance System (TAS) for research on self-adaptation in the domain of service-based systems. Our TAS exemplar of service-based systems comes with pre-defined scenarios for comparing the effectiveness of different self-adaptation solutions. Other researchers can easily exploit the underlying service platform, reusable components and development method we devised for TAS to speed up the engineering of additional research exemplars for service-based systems.
{"title":"Tele Assistance: A Self-Adaptive Service-Based System Exemplar","authors":"Danny Weyns, R. Calinescu","doi":"10.1109/SEAMS.2015.27","DOIUrl":"https://doi.org/10.1109/SEAMS.2015.27","url":null,"abstract":"Research on adaptive and self-managing systems is hindered by a lack of prototypical applications that researchers could use to evaluate and compare new methods, techniques and tools. To address this limitation, we introduce a reference implementation of a Tele Assistance System (TAS) for research on self-adaptation in the domain of service-based systems. Our TAS exemplar of service-based systems comes with pre-defined scenarios for comparing the effectiveness of different self-adaptation solutions. Other researchers can easily exploit the underlying service platform, reusable components and development method we devised for TAS to speed up the engineering of additional research exemplars for service-based systems.","PeriodicalId":144594,"journal":{"name":"2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126979050","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 concept of command and control (C2) is generally associated with the exercise of authority, direction and coordination of assets and capabilities. Traditionally, the concept has encompassed important operational functions such as the establishment of intent, allocation of roles and responsibilities, definition of rules and constraints, and the monitoring and estimation of system state, situation, and progress. More recently, the notion of C2 has been extended beyond military applications to include cyber operation environments and assets. Unfortunately this evolution has enjoyed faster progress and adoption on the offensive, rather than defensive side of cyber operations. One example is the adoption of advanced peer-to-peer C2 infrastructures for the control of malicious botnets and coordinated attacks, which have successfully yielded very effective and resilient control infrastructures in many instances. Defensive C2 is normally associated with a system's ability to monitor, interpret, reason, and respond to cyber events, often through advanced human-machine interfaces, or automated actions. For defensive operations, the concept is gradually evolving and gaining momentum. Recent research activities in this area are now showing great potential to enable truly resilient cyber defense infrastructures. In this talk I will introduce some of the motivations, requirements, and challenges associated with the design of distributed command and control infrastructures for cyber operations. The talk will primarily focus on the resilience aspects of distributed C2, and will cover a brief overview of the prior research in the field, as well as discussions on some of the current and future challenges in this important research domain.
{"title":"Resilient Command and Control Infrastructures for Cyber Operations","authors":"Marco M. Carvalho","doi":"10.1109/SEAMS.2015.17","DOIUrl":"https://doi.org/10.1109/SEAMS.2015.17","url":null,"abstract":"The concept of command and control (C2) is generally associated with the exercise of authority, direction and coordination of assets and capabilities. Traditionally, the concept has encompassed important operational functions such as the establishment of intent, allocation of roles and responsibilities, definition of rules and constraints, and the monitoring and estimation of system state, situation, and progress. More recently, the notion of C2 has been extended beyond military applications to include cyber operation environments and assets. Unfortunately this evolution has enjoyed faster progress and adoption on the offensive, rather than defensive side of cyber operations. One example is the adoption of advanced peer-to-peer C2 infrastructures for the control of malicious botnets and coordinated attacks, which have successfully yielded very effective and resilient control infrastructures in many instances. Defensive C2 is normally associated with a system's ability to monitor, interpret, reason, and respond to cyber events, often through advanced human-machine interfaces, or automated actions. For defensive operations, the concept is gradually evolving and gaining momentum. Recent research activities in this area are now showing great potential to enable truly resilient cyber defense infrastructures. In this talk I will introduce some of the motivations, requirements, and challenges associated with the design of distributed command and control infrastructures for cyber operations. The talk will primarily focus on the resilience aspects of distributed C2, and will cover a brief overview of the prior research in the field, as well as discussions on some of the current and future challenges in this important research domain.","PeriodicalId":144594,"journal":{"name":"2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133176636","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}
M. Mongiello, Patrizio Pelliccione, Massimo Sciancalepore
Modern software systems are increasingly complex and are controlling critical activities in many different domains. The traditional assumption that these systems will work in a controlled context is slightly vanishing. Therefore, it emerges the need of methodologies able to determine under what conditions desired goals will be achieved and behavioural strategies will be preserved despite (often unavoidable) adaptations. In this paper we use the cognitive psychology concept of schema to identify the set of properties that an adaptable system has to maintain when adapting to changed context. The methodology we propose, called AC-contract (Adaptable Code-contract), starts from high-level requirements and identifies properties that should hold locally on single parts of the system. Local properties are represented as contracts directly on the programming language. Specifically, AC-contract is able to embed logical propositions in the source code as annotations, moreover, it enables verification of adaptable code by exploiting a preprocessor that executes the annotations. The methodology is applied to a mobile application supporting travellers during their journey.
{"title":"AC-Contract: Run-Time Verification of Context-Aware Applications","authors":"M. Mongiello, Patrizio Pelliccione, Massimo Sciancalepore","doi":"10.1109/SEAMS.2015.11","DOIUrl":"https://doi.org/10.1109/SEAMS.2015.11","url":null,"abstract":"Modern software systems are increasingly complex and are controlling critical activities in many different domains. The traditional assumption that these systems will work in a controlled context is slightly vanishing. Therefore, it emerges the need of methodologies able to determine under what conditions desired goals will be achieved and behavioural strategies will be preserved despite (often unavoidable) adaptations. In this paper we use the cognitive psychology concept of schema to identify the set of properties that an adaptable system has to maintain when adapting to changed context. The methodology we propose, called AC-contract (Adaptable Code-contract), starts from high-level requirements and identifies properties that should hold locally on single parts of the system. Local properties are represented as contracts directly on the programming language. Specifically, AC-contract is able to embed logical propositions in the source code as annotations, moreover, it enables verification of adaptable code by exploiting a preprocessor that executes the annotations. The methodology is applied to a mobile application supporting travellers during their journey.","PeriodicalId":144594,"journal":{"name":"2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116240614","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-adaptation is a prominent property for developing complex distributed software systems. Notable approaches to deal with self-adaptation are the runtime goal model artifacts. Goals are generally invariant along the system lifecycle but contain points of variability for allowing the system to decide among many alternative behaviors. This work investigates how it is possible to provide goal models at run-time that do not contain tasks, i.e. The description of how to address goals, thus breaking the design-time tie up between Tasks and Goals, generally outcome of a means-end analysis. In this vision the system is up to decide how to combine its available Capabilities: the Proactive Means-End Reasoning. The impact of this research line is to implement a goal-oriented form of self-adaptation where goal models can be injected at runtime. The paper also introduces MUSA, a Middleware for User-driven Service self-Adaptation.
{"title":"From Means-End Analysis to Proactive Means-End Reasoning","authors":"L. Sabatucci, M. Cossentino","doi":"10.1109/SEAMS.2015.9","DOIUrl":"https://doi.org/10.1109/SEAMS.2015.9","url":null,"abstract":"Self-adaptation is a prominent property for developing complex distributed software systems. Notable approaches to deal with self-adaptation are the runtime goal model artifacts. Goals are generally invariant along the system lifecycle but contain points of variability for allowing the system to decide among many alternative behaviors. This work investigates how it is possible to provide goal models at run-time that do not contain tasks, i.e. The description of how to address goals, thus breaking the design-time tie up between Tasks and Goals, generally outcome of a means-end analysis. In this vision the system is up to decide how to combine its available Capabilities: the Proactive Means-End Reasoning. The impact of this research line is to implement a goal-oriented form of self-adaptation where goal models can be injected at runtime. The paper also introduces MUSA, a Middleware for User-driven Service self-Adaptation.","PeriodicalId":144594,"journal":{"name":"2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129827466","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}
Designers of self-adaptive systems often formulate adaptive design decisions, making unrealistic or myopic assumptions about the system's requirements and environment. The decisions taken during this formulation are crucial for satisfying requirements. In environments which are characterized by uncertainty and dynamism, deviation from these assumptions is the norm and may trigger "surprises". Our method allows designers to make explicit links between the possible emergence of surprises, risks and design trade-offs. The method can be used to explore the design decisions for self-adaptive systems and choose among decisions that better fulfil (or rather partially fulfil) non-functional requirements and address their trade-offs. The analysis can also provide designers with valuable input for refining the adaptation decisions to balance, for example, resilience (i.e. Satisfiability of non-functional requirements and their trade-offs) and stability (i.e. Minimizing the frequency of adaptation). The objective is to provide designers of self adaptive systems with a basis for multi-dimensional what-if analysis to revise and improve the understanding of the environment and its effect on non-functional requirements and thereafter decision-making. We have applied the method to a wireless sensor network for flood prediction. The application shows that the method gives rise to questions that were not explicitly asked before at design-time and assists designers in the process of risk-aware, what-if and trade-off analysis.
{"title":"Minimizing Nasty Surprises with Better Informed Decision-Making in Self-Adaptive Systems","authors":"S. Hassan, N. Bencomo, R. Bahsoon","doi":"10.1109/SEAMS.2015.13","DOIUrl":"https://doi.org/10.1109/SEAMS.2015.13","url":null,"abstract":"Designers of self-adaptive systems often formulate adaptive design decisions, making unrealistic or myopic assumptions about the system's requirements and environment. The decisions taken during this formulation are crucial for satisfying requirements. In environments which are characterized by uncertainty and dynamism, deviation from these assumptions is the norm and may trigger \"surprises\". Our method allows designers to make explicit links between the possible emergence of surprises, risks and design trade-offs. The method can be used to explore the design decisions for self-adaptive systems and choose among decisions that better fulfil (or rather partially fulfil) non-functional requirements and address their trade-offs. The analysis can also provide designers with valuable input for refining the adaptation decisions to balance, for example, resilience (i.e. Satisfiability of non-functional requirements and their trade-offs) and stability (i.e. Minimizing the frequency of adaptation). The objective is to provide designers of self adaptive systems with a basis for multi-dimensional what-if analysis to revise and improve the understanding of the environment and its effect on non-functional requirements and thereafter decision-making. We have applied the method to a wireless sensor network for flood prediction. The application shows that the method gives rise to questions that were not explicitly asked before at design-time and assists designers in the process of risk-aware, what-if and trade-off analysis.","PeriodicalId":144594,"journal":{"name":"2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115817602","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 Dynamic Software Product Line (DSPL) is a widely used approach to handle variability at runtime, e.g., By activating or deactivating features to adapt the running configuration. With the emergence of highly configurable and evolvable systems, DSPLs have to cope with the evolution of their structural variability, i.e., The Feature Model (FM) used to derive the configuration. So far, little is known about the evolution of the FM while a configuration derived from this FM is running. In particular, such a dynamic evolution changes the DSPL configuration space, which is thus unsynchronized with the running configuration and its adaptation capabilities. In this position paper, we propose and describe an initial architecture to manage the dynamic evolution of DSPLs and their synchronization. In particular, we explain how this architecture supports the evolution of DSPLs based on FMs extended with cardinality and attributes, which, to the best of our knowledge, has never been addressed yet.
{"title":"Dynamically Evolving the Structural Variability of Dynamic Software Product Lines","authors":"L. Baresi, Clément Quinton","doi":"10.1109/SEAMS.2015.24","DOIUrl":"https://doi.org/10.1109/SEAMS.2015.24","url":null,"abstract":"A Dynamic Software Product Line (DSPL) is a widely used approach to handle variability at runtime, e.g., By activating or deactivating features to adapt the running configuration. With the emergence of highly configurable and evolvable systems, DSPLs have to cope with the evolution of their structural variability, i.e., The Feature Model (FM) used to derive the configuration. So far, little is known about the evolution of the FM while a configuration derived from this FM is running. In particular, such a dynamic evolution changes the DSPL configuration space, which is thus unsynchronized with the running configuration and its adaptation capabilities. In this position paper, we propose and describe an initial architecture to manage the dynamic evolution of DSPLs and their synchronization. In particular, we explain how this architecture supports the evolution of DSPLs based on FMs extended with cardinality and attributes, which, to the best of our knowledge, has never been addressed yet.","PeriodicalId":144594,"journal":{"name":"2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127219454","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}
M. Kit, I. Gerostathopoulos, T. Bures, P. Hnetynka, F. Plášil
Recent advances in embedded devices capabilities and wireless networks paved the way for creating ubiquitous Cyber-Physical Systems (CPS) grafted with self-configuring and self-adaptive capabilities. As these systems need to strike a balance between dependability, open-endedness and adaptability, and operate in dynamic and opportunistic environments, their design and development is particularly challenging. We take an architecture-based approach to this problem and advocate the use of component-based abstractions and related machinery to engineer self-adaptive CPS. Our approach is structured around DEECo -- a component framework that introduces the concept of component ensembles to deal with the dynamicity of CPS at the middleware level. DEECo provides the architecture abstractions of autonomous components and component ensembles on top of which different adaptation techniques can be deployed. This makes DEECo a vehicle for seamless experiments with self-adaptive systems where the physical distribution and mobility of nodes, and the limited data availability play an important role.
{"title":"An Architecture Framework for Experimentations with Self-Adaptive Cyber-physical Systems","authors":"M. Kit, I. Gerostathopoulos, T. Bures, P. Hnetynka, F. Plášil","doi":"10.1109/SEAMS.2015.28","DOIUrl":"https://doi.org/10.1109/SEAMS.2015.28","url":null,"abstract":"Recent advances in embedded devices capabilities and wireless networks paved the way for creating ubiquitous Cyber-Physical Systems (CPS) grafted with self-configuring and self-adaptive capabilities. As these systems need to strike a balance between dependability, open-endedness and adaptability, and operate in dynamic and opportunistic environments, their design and development is particularly challenging. We take an architecture-based approach to this problem and advocate the use of component-based abstractions and related machinery to engineer self-adaptive CPS. Our approach is structured around DEECo -- a component framework that introduces the concept of component ensembles to deal with the dynamicity of CPS at the middleware level. DEECo provides the architecture abstractions of autonomous components and component ensembles on top of which different adaptation techniques can be deployed. This makes DEECo a vehicle for seamless experiments with self-adaptive systems where the physical distribution and mobility of nodes, and the limited data availability play an important role.","PeriodicalId":144594,"journal":{"name":"2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128427996","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}
This paper investigates the accuracy of predictive auto-scaling systems in the Infrastructure as a Service (IaaS) layer of cloud computing. The hypothesis in this research is that prediction accuracy of auto-scaling systems can be increased by choosing an appropriate time-series prediction algorithm based on the performance pattern over time. To prove this hypothesis, an experiment has been conducted to compare the accuracy of time-series prediction algorithms for different performance patterns. In the experiment, workload was considered as the performance metric, and Support Vector Machine (SVM) and Neural Networks (NN) were utilized as time-series prediction techniques. In addition, we used Amazon EC2 as the experimental infrastructure and TPC-W as the benchmark to generate different workload patterns. The results of the experiment show that prediction accuracy of SVM and NN depends on the incoming workload pattern of the system under study. Specifically, the results show that SVM has better prediction accuracy in the environments with periodic and growing workload patterns, while NN outperforms SVM in forecasting unpredicted workload pattern. Based on these experimental results, this paper proposes an architecture for a self-adaptive prediction suite using an autonomic system approach. This suite can choose the most suitable prediction technique based on the performance pattern, which leads to more accurate prediction results.
{"title":"Towards an Autonomic Auto-scaling Prediction System for Cloud Resource Provisioning","authors":"A. Nikravesh, S. Ajila, Chung-Horng Lung","doi":"10.1109/SEAMS.2015.22","DOIUrl":"https://doi.org/10.1109/SEAMS.2015.22","url":null,"abstract":"This paper investigates the accuracy of predictive auto-scaling systems in the Infrastructure as a Service (IaaS) layer of cloud computing. The hypothesis in this research is that prediction accuracy of auto-scaling systems can be increased by choosing an appropriate time-series prediction algorithm based on the performance pattern over time. To prove this hypothesis, an experiment has been conducted to compare the accuracy of time-series prediction algorithms for different performance patterns. In the experiment, workload was considered as the performance metric, and Support Vector Machine (SVM) and Neural Networks (NN) were utilized as time-series prediction techniques. In addition, we used Amazon EC2 as the experimental infrastructure and TPC-W as the benchmark to generate different workload patterns. The results of the experiment show that prediction accuracy of SVM and NN depends on the incoming workload pattern of the system under study. Specifically, the results show that SVM has better prediction accuracy in the environments with periodic and growing workload patterns, while NN outperforms SVM in forecasting unpredicted workload pattern. Based on these experimental results, this paper proposes an architecture for a self-adaptive prediction suite using an autonomic system approach. This suite can choose the most suitable prediction technique based on the performance pattern, which leads to more accurate prediction results.","PeriodicalId":144594,"journal":{"name":"2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128034537","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}
Sebastian Götz, I. Gerostathopoulos, Filip Krikava, Adnan Shahzada, Romina Spalazzese
Future software systems will be highly dynamic. We are already experiencing, for example, a world where Cyber-Physical Systems (CPSs) play a more and more crucial role. CPSs integrate computational, physical, and networking elements, they comprise a number of subsystems, or entities, that are connected and work together. The open and highly distributed nature of the resulting system gives rise to unanticipated runtime management issues such as the organization of subsystems and resource optimization. In this paper, we focus on the problem of knowledge sharing among cooperating entities of a highly distributed and self-adaptive CPS. Specifically, the research question we address is how to minimize the knowledge that needs to be shared among the entities of a CPS. If all entities share all their knowledge with each other, the performance, energy and memory consumption as well as privacy are unnecessarily negatively impacted. To reduce the amount of knowledge to share between CPS entities, we envision a role-based adaptive knowledge exchange technique working on partial runtime models, i.e., Models reflecting only part of the state of the CPS. Our approach supports two adaptation dimensions: the runtime type of knowledge and conditions over the knowledge. We illustrate the feasibility of our technique by discussing its realization based on two state-of-the-art approaches.
{"title":"Adaptive Exchange of Distributed Partial Models@run.time for Highly Dynamic Systems","authors":"Sebastian Götz, I. Gerostathopoulos, Filip Krikava, Adnan Shahzada, Romina Spalazzese","doi":"10.1109/SEAMS.2015.25","DOIUrl":"https://doi.org/10.1109/SEAMS.2015.25","url":null,"abstract":"Future software systems will be highly dynamic. We are already experiencing, for example, a world where Cyber-Physical Systems (CPSs) play a more and more crucial role. CPSs integrate computational, physical, and networking elements, they comprise a number of subsystems, or entities, that are connected and work together. The open and highly distributed nature of the resulting system gives rise to unanticipated runtime management issues such as the organization of subsystems and resource optimization. In this paper, we focus on the problem of knowledge sharing among cooperating entities of a highly distributed and self-adaptive CPS. Specifically, the research question we address is how to minimize the knowledge that needs to be shared among the entities of a CPS. If all entities share all their knowledge with each other, the performance, energy and memory consumption as well as privacy are unnecessarily negatively impacted. To reduce the amount of knowledge to share between CPS entities, we envision a role-based adaptive knowledge exchange technique working on partial runtime models, i.e., Models reflecting only part of the state of the CPS. Our approach supports two adaptation dimensions: the runtime type of knowledge and conditions over the knowledge. We illustrate the feasibility of our technique by discussing its realization based on two state-of-the-art approaches.","PeriodicalId":144594,"journal":{"name":"2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131032173","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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