Self-organising systems are a popular engineering concept for designing decentralised autonomic computing systems. They are able to find solutions in complex and versatile problem domains, but as they capture more complexity in their own design, they are becoming less and less comprehensible to their users (be they humans or intelligent agents). We describe a design challenge that relates to usability theory in general and in particular resembles an observation made by Phoebe Senger, who noted that software agents tend to become incomprehensible in their behaviour as they grow more complex. In the manifestation of self-organising systems, the problem is more urgent (since we find ourselves using them more and more) and harder to solve at the same time (since these systems are not centrally controlled). We describe the problem domain and propose three system properties that could be used as quality indicators in this regard: Stability, Learn ability and Engage ability. We demonstrate their usage in a simple model of dynamic pricing markets (e.g. the electricity domain) and evaluate them in different ways.
{"title":"Designing Comprehensible Self-Organising Systems","authors":"N. Höning, H. L. Poutré","doi":"10.1109/SASO.2010.18","DOIUrl":"https://doi.org/10.1109/SASO.2010.18","url":null,"abstract":"Self-organising systems are a popular engineering concept for designing decentralised autonomic computing systems. They are able to find solutions in complex and versatile problem domains, but as they capture more complexity in their own design, they are becoming less and less comprehensible to their users (be they humans or intelligent agents). We describe a design challenge that relates to usability theory in general and in particular resembles an observation made by Phoebe Senger, who noted that software agents tend to become incomprehensible in their behaviour as they grow more complex. In the manifestation of self-organising systems, the problem is more urgent (since we find ourselves using them more and more) and harder to solve at the same time (since these systems are not centrally controlled). We describe the problem domain and propose three system properties that could be used as quality indicators in this regard: Stability, Learn ability and Engage ability. We demonstrate their usage in a simple model of dynamic pricing markets (e.g. the electricity domain) and evaluate them in different ways.","PeriodicalId":370044,"journal":{"name":"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116923400","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}
In this paper a flexible self-reconfiguration mechanism for industrial mobile robots in car manufacturing is presented. Implemented in a holonic multi-agent system, user-inserted process changes are executed self-organised. Starting with a natural-language based description of the desired process change, an uncoupled recombination mechanism is the core part of the presented approach. The novel recombination of already existing system functionalities enables the addition of new functions to an already existing system while stability and productivity are assured. A hierarchical-organised standard holon and heterarchical-organised reconfiguration holons are provided to separate the execution of routine tasks and self-reconfiguration tasks. By using the principles of holonic manufacturing and skill evolvability, the self-reconfiguration mechanism supplies functional flexibility for industrial mobile robots without uncontrolled loss of productivity.
{"title":"Self-Reconfiguration of Industrial Mobile Robots","authors":"S. Angerer, R. Pooley, R. Aylett","doi":"10.1109/SASO.2010.43","DOIUrl":"https://doi.org/10.1109/SASO.2010.43","url":null,"abstract":"In this paper a flexible self-reconfiguration mechanism for industrial mobile robots in car manufacturing is presented. Implemented in a holonic multi-agent system, user-inserted process changes are executed self-organised. Starting with a natural-language based description of the desired process change, an uncoupled recombination mechanism is the core part of the presented approach. The novel recombination of already existing system functionalities enables the addition of new functions to an already existing system while stability and productivity are assured. A hierarchical-organised standard holon and heterarchical-organised reconfiguration holons are provided to separate the execution of routine tasks and self-reconfiguration tasks. By using the principles of holonic manufacturing and skill evolvability, the self-reconfiguration mechanism supplies functional flexibility for industrial mobile robots without uncontrolled loss of productivity.","PeriodicalId":370044,"journal":{"name":"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126780157","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}
Planning is an important method in self-adaptive systems. Existing approaches emphasize the functional properties of the systems but do not consider possible alternative adaptations resulting in system functionality with different grades of quality. In compositional adaptation, the adaptation process should identify not only a feasible system configuration, but a good one. In safety-critical systems such as cars, the adaptation process has to fulfill special requirements. The sequence of reconfiguration activities has to maintain constraints over the entire state trajectory defined by the adaptation process, e.g., that certain processes are always running or even a minimal number of redundant instances. At the same time, in modern cars, many optional processes, such as learning of the engine model or optimization of control processes, improve the performance of the car. Possible optimization objectives are fuel consumption, driving comfort, and wear. Thus, this paper introduces a model of a self-adaptation process by reconfiguration, which considers the quality of alternative configurations. Furthermore, a planning process is introduced that generates a sequence of reconfiguration activities, which result in good configuration. The introduced process can be used to maintain the basic system functionality and also to select the currently most appropriate task implementations and optional tasks to run in a recovered system, e.g. after hardware failures.
{"title":"Planning with Utility and State Trajectory Constraints in Self-Healing Automotive Systems","authors":"B. Klöpper, S. Honiden, Jan Meyer, M. Tichy","doi":"10.1109/SASO.2010.16","DOIUrl":"https://doi.org/10.1109/SASO.2010.16","url":null,"abstract":"Planning is an important method in self-adaptive systems. Existing approaches emphasize the functional properties of the systems but do not consider possible alternative adaptations resulting in system functionality with different grades of quality. In compositional adaptation, the adaptation process should identify not only a feasible system configuration, but a good one. In safety-critical systems such as cars, the adaptation process has to fulfill special requirements. The sequence of reconfiguration activities has to maintain constraints over the entire state trajectory defined by the adaptation process, e.g., that certain processes are always running or even a minimal number of redundant instances. At the same time, in modern cars, many optional processes, such as learning of the engine model or optimization of control processes, improve the performance of the car. Possible optimization objectives are fuel consumption, driving comfort, and wear. Thus, this paper introduces a model of a self-adaptation process by reconfiguration, which considers the quality of alternative configurations. Furthermore, a planning process is introduced that generates a sequence of reconfiguration activities, which result in good configuration. The introduced process can be used to maintain the basic system functionality and also to select the currently most appropriate task implementations and optional tasks to run in a recovered system, e.g. after hardware failures.","PeriodicalId":370044,"journal":{"name":"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114661693","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}
Sensor networks (SN) have arisen as one of the most promising monitoring technologies. The recent emergence of small and inexpensive sensors ease the development and proliferation of this kind of networks in a wide range of actualworldapplications.1 So far the majority of SN deployments have assumed that sensors can be configured prior to their deployment because the area and events to monitor are well known at design time. Nevertheless, when the purpose of anSN is to monitor the events of an environment such that the distribution and nature of its events is uncertain, we cannot longer assume that sensors can be configured at design time. Instead, sensors must be endowed with the capacity of autonomously reconfiguring and coordinating in order to maximize the amount of information they perceive over time. In this paper, we propose a low cost (in terms of energy and computation) collective distributed algorithm, the so-called collective search diffusion (CDS) algorithm, which allows the sensors in an SN to collaboratively search for the configurations that maximize the information that they perceive based only on their local knowledge. We empirically show that the CDSalgorithm helps an SN efficiently monitor environments where various dynamic events occur while showing high degrees of resilience to sensor failures. Both features make the CDSalgorithm a suitable tool for monitoring remote and/or hostile uncharted environments.
{"title":"Self-Configuring Sensors for Uncharted Environments","authors":"N. Salazar, J. Rodríguez-Aguilar, J. Arcos","doi":"10.1109/SASO.2010.38","DOIUrl":"https://doi.org/10.1109/SASO.2010.38","url":null,"abstract":"Sensor networks (SN) have arisen as one of the most promising monitoring technologies. The recent emergence of small and inexpensive sensors ease the development and proliferation of this kind of networks in a wide range of actualworldapplications.1 So far the majority of SN deployments have assumed that sensors can be configured prior to their deployment because the area and events to monitor are well known at design time. Nevertheless, when the purpose of anSN is to monitor the events of an environment such that the distribution and nature of its events is uncertain, we cannot longer assume that sensors can be configured at design time. Instead, sensors must be endowed with the capacity of autonomously reconfiguring and coordinating in order to maximize the amount of information they perceive over time. In this paper, we propose a low cost (in terms of energy and computation) collective distributed algorithm, the so-called collective search diffusion (CDS) algorithm, which allows the sensors in an SN to collaboratively search for the configurations that maximize the information that they perceive based only on their local knowledge. We empirically show that the CDSalgorithm helps an SN efficiently monitor environments where various dynamic events occur while showing high degrees of resilience to sensor failures. Both features make the CDSalgorithm a suitable tool for monitoring remote and/or hostile uncharted environments.","PeriodicalId":370044,"journal":{"name":"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126060983","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}
S. M. Allen, M. J. Chorley, Gualtiero Colombo, R. Whitaker
This paper considers an adaptive data dissemination scenario that applies an autonomic trust protocol to a network of agents. The protocol uses social network structures to incentivize cooperation. Validation is conducted through simulation of content sharing between peers which uses similarity of interest between peers to define payoff. Positive correlation is observed between the number of social links placed and payoff received by single agents. Content sharing allows calculation of similarity between agents within a system. Prior interaction history drives the formation of social links between nodes and allows estimation of an individuals cooperation by another. Agents may adaptively change their cooperation levels when forming social relation-ships by copying those of the most ‘popular’ members of their own social groups. Adaptation mechanisms can be prioritized within communities sharing similar interests. Similarity of interest communities and their initial cooperation levels both have an effect on the self-adaptation of cooperation. The most divergent and least cooperative nodes have fewer opportunities to form new social links, increase their cooperation levels, and consequently increase their payoff. Self-adaptation results in higher payoff for the population compared to the static scenario in which adaptation of agents cooperation does not occur.
{"title":"Self Adaptation of Cooperation in Multi-agent Content Sharing Systems","authors":"S. M. Allen, M. J. Chorley, Gualtiero Colombo, R. Whitaker","doi":"10.1109/SASO.2010.15","DOIUrl":"https://doi.org/10.1109/SASO.2010.15","url":null,"abstract":"This paper considers an adaptive data dissemination scenario that applies an autonomic trust protocol to a network of agents. The protocol uses social network structures to incentivize cooperation. Validation is conducted through simulation of content sharing between peers which uses similarity of interest between peers to define payoff. Positive correlation is observed between the number of social links placed and payoff received by single agents. Content sharing allows calculation of similarity between agents within a system. Prior interaction history drives the formation of social links between nodes and allows estimation of an individuals cooperation by another. Agents may adaptively change their cooperation levels when forming social relation-ships by copying those of the most ‘popular’ members of their own social groups. Adaptation mechanisms can be prioritized within communities sharing similar interests. Similarity of interest communities and their initial cooperation levels both have an effect on the self-adaptation of cooperation. The most divergent and least cooperative nodes have fewer opportunities to form new social links, increase their cooperation levels, and consequently increase their payoff. Self-adaptation results in higher payoff for the population compared to the static scenario in which adaptation of agents cooperation does not occur.","PeriodicalId":370044,"journal":{"name":"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems","volume":"91 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128774254","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}
Haifeng Chen, Hui Kang, Guofei Jiang, K. Yoshihira, Akhilesh Saxena
Server consolidation through virtualization is becoming an effective way to save power and space in enterprise data centers. However, it also brings additional operational risks for the consolidated system because the impacts of hardware failures, human errors, and security breaches can be vastly magnified in that densely packed environment. In order to mitigate the above issues, this paper proposes a new virtualization and consolidation analysis engine(VCAE), which exploits and utilizes various constraints in the consolidation process. VCAE provides a comprehensive framework to discover, represent, check, and combine various constraints in server consolidation. It can assist system operators to effectively deal with the large number of constraints in the consolidation planning. In addition, VCAE proposes an evolution based method to discover the optimal consolidation scheme under multiple constraints. As a consequence, the consolidation solution generated by VCAE can not only maximize the utilization of system resources but also keep the hidden risks as low as possible in the consolidated system. The experimental results from an real enterprise system have demonstrated the advantages of our analysis engine.
{"title":"VCAE: A Virtualization and Consolidation Analysis Engine for Large Scale Data Centers","authors":"Haifeng Chen, Hui Kang, Guofei Jiang, K. Yoshihira, Akhilesh Saxena","doi":"10.1109/SASO.2010.25","DOIUrl":"https://doi.org/10.1109/SASO.2010.25","url":null,"abstract":"Server consolidation through virtualization is becoming an effective way to save power and space in enterprise data centers. However, it also brings additional operational risks for the consolidated system because the impacts of hardware failures, human errors, and security breaches can be vastly magnified in that densely packed environment. In order to mitigate the above issues, this paper proposes a new virtualization and consolidation analysis engine(VCAE), which exploits and utilizes various constraints in the consolidation process. VCAE provides a comprehensive framework to discover, represent, check, and combine various constraints in server consolidation. It can assist system operators to effectively deal with the large number of constraints in the consolidation planning. In addition, VCAE proposes an evolution based method to discover the optimal consolidation scheme under multiple constraints. As a consequence, the consolidation solution generated by VCAE can not only maximize the utilization of system resources but also keep the hidden risks as low as possible in the consolidated system. The experimental results from an real enterprise system have demonstrated the advantages of our analysis engine.","PeriodicalId":370044,"journal":{"name":"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115567943","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}
To keep pace with constantly changing markets, many companies are seeking strategic partnerships. In this paper, we assume that a company can electronically provide a profile of the product or service it has to offer. This profile is described in such a way that potential partners can assess the fitness of the company for eventually teaming up. We concentrate on the fully decentralized optimal formation of teams consisting of k members. This problem boils down to developing a decentralized, efficient algorithm for solving a variant of the maximal weighted k-sub graph problem. We provide a first solution, along with an assessment of its performance, thereby concentrating on the feasibility of an actual embedding in real-world scenarios consisting of thousands of companies. In particular, any solution should be highly adaptive when new or fresh information concerning potential partners comes available.
{"title":"Optimal Decentralized Formation of k-Member Partnerships","authors":"A. Chmielowiec, M. Steen","doi":"10.1109/SASO.2010.14","DOIUrl":"https://doi.org/10.1109/SASO.2010.14","url":null,"abstract":"To keep pace with constantly changing markets, many companies are seeking strategic partnerships. In this paper, we assume that a company can electronically provide a profile of the product or service it has to offer. This profile is described in such a way that potential partners can assess the fitness of the company for eventually teaming up. We concentrate on the fully decentralized optimal formation of teams consisting of k members. This problem boils down to developing a decentralized, efficient algorithm for solving a variant of the maximal weighted k-sub graph problem. We provide a first solution, along with an assessment of its performance, thereby concentrating on the feasibility of an actual embedding in real-world scenarios consisting of thousands of companies. In particular, any solution should be highly adaptive when new or fresh information concerning potential partners comes available.","PeriodicalId":370044,"journal":{"name":"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114189145","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}
Positive feedback and a consensus-building procedure are the key elements of a self-organized decision-making mechanism that allows a population of agents to collectively determine which of two actions is the fastest to execute. Such a mechanism can be seen as a collective learning algorithm because even though individual agents do not directly compare the available alternatives, the population is able to select the action that takes less time to perform, thus potentially improving the efficiency of the system. However, when a large population is involved, the time required to reach consensus on one of the available choices may render impractical such a decision-making mechanism. In this paper, we tackle this problem by applying the incremental social learning approach, which consists of a growing population size coupled with a social learning mechanism. The obtained experimental results show that by using the incremental social learning approach, the collective learning process can be accelerated substantially. The conditions under which this is true are described.
{"title":"Incremental Social Learning Applied to a Decentralized Decision-Making Mechanism: Collective Learning Made Faster","authors":"M. M. D. Oca, T. Stützle, M. Birattari, M. Dorigo","doi":"10.1109/SASO.2010.28","DOIUrl":"https://doi.org/10.1109/SASO.2010.28","url":null,"abstract":"Positive feedback and a consensus-building procedure are the key elements of a self-organized decision-making mechanism that allows a population of agents to collectively determine which of two actions is the fastest to execute. Such a mechanism can be seen as a collective learning algorithm because even though individual agents do not directly compare the available alternatives, the population is able to select the action that takes less time to perform, thus potentially improving the efficiency of the system. However, when a large population is involved, the time required to reach consensus on one of the available choices may render impractical such a decision-making mechanism. In this paper, we tackle this problem by applying the incremental social learning approach, which consists of a growing population size coupled with a social learning mechanism. The obtained experimental results show that by using the incremental social learning approach, the collective learning process can be accelerated substantially. The conditions under which this is true are described.","PeriodicalId":370044,"journal":{"name":"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124136377","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. Al-Shishtawy, Muhammad Fayyaz, K. Popov, Vladimir Vlassov
Achieving self-management can be challenging, particularly in dynamic environments with resource churn (joins/leaves/failures). Dealing with the effect of churn on management increases the complexity of the management logic and thus makes its development time consuming and error prone. We propose the abstraction of robust management elements (RMEs), which are able to heal themselves under continuous churn. Using RMEs allows the developer to separate the issue of dealing with the effect of churn on management from the management logic. This facilitates the development of robust management by making the developer focus on managing the application while relying on the platform to provide the robustness of management. RMEs can be implemented as fault-tolerant long-living services. We present a generic approach and an associated algorithm to achieve fault-tolerant long-living services. Our approach is based on replicating a service using finite state machine replication with a reconfigurable replica set. Our algorithm automates the reconfiguration (migration) of the replica set in order to tolerate continuous churn. The algorithm uses P2P replica placement schemes to place replicas and uses the P2P overlay to monitor them. The replicated state machine is extended to analyze monitoring data in order to decide on when and where to migrate. We describe how to use our approach to achieve robust management elements. We present a simulation-based evaluation of our approach which shows its feasibility.
{"title":"Achieving Robust Self-Management for Large-Scale Distributed Applications","authors":"A. Al-Shishtawy, Muhammad Fayyaz, K. Popov, Vladimir Vlassov","doi":"10.1109/SASO.2010.42","DOIUrl":"https://doi.org/10.1109/SASO.2010.42","url":null,"abstract":"Achieving self-management can be challenging, particularly in dynamic environments with resource churn (joins/leaves/failures). Dealing with the effect of churn on management increases the complexity of the management logic and thus makes its development time consuming and error prone. We propose the abstraction of robust management elements (RMEs), which are able to heal themselves under continuous churn. Using RMEs allows the developer to separate the issue of dealing with the effect of churn on management from the management logic. This facilitates the development of robust management by making the developer focus on managing the application while relying on the platform to provide the robustness of management. RMEs can be implemented as fault-tolerant long-living services. We present a generic approach and an associated algorithm to achieve fault-tolerant long-living services. Our approach is based on replicating a service using finite state machine replication with a reconfigurable replica set. Our algorithm automates the reconfiguration (migration) of the replica set in order to tolerate continuous churn. The algorithm uses P2P replica placement schemes to place replicas and uses the P2P overlay to monitor them. The replicated state machine is extended to analyze monitoring data in order to decide on when and where to migrate. We describe how to use our approach to achieve robust management elements. We present a simulation-based evaluation of our approach which shows its feasibility.","PeriodicalId":370044,"journal":{"name":"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125768562","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. Marco, Francesco Gallo, P. Inverardi, R. Ippoliti
In the software domain, self-adaptive systems are able to modify their behavior at run-time to respond to internal and external changes. In life science, biological cells are power entities able to adapt to the (unpredictable) situations they incur in, in a complete decentralized fashion. We are working on a new architectural paradigm for self-adaptive software systems inspired by the adaptation mechanism coming from the cell life-cycle. In order to address the complexity and the variety of self adaptive systems we found that the cell lifecycle is interesting for three main features: i) their ability to specialize behaviors starting from the most general one, i.e. the stem cell, ii) the cell ability to make regular use of the programmed death mechanism (apoptosis) to get rid of obsolete behaviors. iii) a sort of architectural principle that allows the living organisms to be very efficient systems by maintaining the right trade off between general/universal cells (stem cells)and specialized/labouring ones. In this poster paper we present our STEM paradigm by proposing new roles and architectural structure that will be part of the STEM architecture description language.
{"title":"Towards a Stem Architecture Description Language for Self-Adaptive Systems","authors":"A. Marco, Francesco Gallo, P. Inverardi, R. Ippoliti","doi":"10.1109/SASO.2010.37","DOIUrl":"https://doi.org/10.1109/SASO.2010.37","url":null,"abstract":"In the software domain, self-adaptive systems are able to modify their behavior at run-time to respond to internal and external changes. In life science, biological cells are power entities able to adapt to the (unpredictable) situations they incur in, in a complete decentralized fashion. We are working on a new architectural paradigm for self-adaptive software systems inspired by the adaptation mechanism coming from the cell life-cycle. In order to address the complexity and the variety of self adaptive systems we found that the cell lifecycle is interesting for three main features: i) their ability to specialize behaviors starting from the most general one, i.e. the stem cell, ii) the cell ability to make regular use of the programmed death mechanism (apoptosis) to get rid of obsolete behaviors. iii) a sort of architectural principle that allows the living organisms to be very efficient systems by maintaining the right trade off between general/universal cells (stem cells)and specialized/labouring ones. In this poster paper we present our STEM paradigm by proposing new roles and architectural structure that will be part of the STEM architecture description language.","PeriodicalId":370044,"journal":{"name":"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116726044","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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