J. Fernandez-Marquez, J. Arcos, G. Serugendo, Mirko Viroli, Sara Montagna
Bio-inspired mechanisms have been extensively used in the last decade for solving optimisation problems and for decentralised control of sensors, robots or nodes in P2P systems. Different attempts at describing some of these mechanisms have been proposed, some of them under the form of design patterns. However, there is not so far a clear catalogue of these mechanisms, described as patterns, showing the relations between the different patterns and identifying the precise boundaries of each mechanism. To ease engineering of artificial bio-inspired systems, this paper describes a group of bio-inspired mechanisms in terms of design patterns organised into different layers. This approach is exemplified through the description of 7 bio-inspired mechanisms: three basic ones (Spreading, Aggregation, and Evaporation), a mid-level one (Gradient) obtained by composing the basic ones, and three top-level ones (Chemotaxis, Morphogenesis, and Quorum sensing) exploiting the mid-level one.
{"title":"Description and composition of bio-inspired design patterns: the gradient case","authors":"J. Fernandez-Marquez, J. Arcos, G. Serugendo, Mirko Viroli, Sara Montagna","doi":"10.1145/1998570.1998575","DOIUrl":"https://doi.org/10.1145/1998570.1998575","url":null,"abstract":"Bio-inspired mechanisms have been extensively used in the last decade for solving optimisation problems and for decentralised control of sensors, robots or nodes in P2P systems. Different attempts at describing some of these mechanisms have been proposed, some of them under the form of design patterns. However, there is not so far a clear catalogue of these mechanisms, described as patterns, showing the relations between the different patterns and identifying the precise boundaries of each mechanism. To ease engineering of artificial bio-inspired systems, this paper describes a group of bio-inspired mechanisms in terms of design patterns organised into different layers. This approach is exemplified through the description of 7 bio-inspired mechanisms: three basic ones (Spreading, Aggregation, and Evaporation), a mid-level one (Gradient) obtained by composing the basic ones, and three top-level ones (Chemotaxis, Morphogenesis, and Quorum sensing) exploiting the mid-level one.","PeriodicalId":340028,"journal":{"name":"BADS '11","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125132425","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-organized Invasive Parallel Optimization (SIPO) is a new framework for solving optimization problems on parallel platforms. In contrast to existing approaches, the resources in SIPO are self-organized and represented as a unified resource to the user who specifies the optimization problem and its preferences to the system. SIPO starts working with one resource and automatically divides the optimization task stepwise into smaller tasks which are assigned to more resources. This job assignment is decided on demand by the resources. The novelty here is that there is no need to specify the number of parallel computing resources in the beginning of the optimization. This number is estimated during the optimization process by the resources. The proposed new framework of SIPO is described in this paper with respect to multi-objective optimization problems but it has a much larger scope. A comparative evaluation of using SIPO in multi-objective optimization problems shows that this adaptive approach can obtain equally good or sometimes even better solutions than other parallel and non-parallel methods which are not self-organized.
{"title":"Self-organized invasive parallel optimization","authors":"Sanaz Mostaghim, Friederike Pfeiffer, H. Schmeck","doi":"10.1145/1998570.1998581","DOIUrl":"https://doi.org/10.1145/1998570.1998581","url":null,"abstract":"Self-organized Invasive Parallel Optimization (SIPO) is a new framework for solving optimization problems on parallel platforms. In contrast to existing approaches, the resources in SIPO are self-organized and represented as a unified resource to the user who specifies the optimization problem and its preferences to the system. SIPO starts working with one resource and automatically divides the optimization task stepwise into smaller tasks which are assigned to more resources. This job assignment is decided on demand by the resources. The novelty here is that there is no need to specify the number of parallel computing resources in the beginning of the optimization. This number is estimated during the optimization process by the resources. The proposed new framework of SIPO is described in this paper with respect to multi-objective optimization problems but it has a much larger scope. A comparative evaluation of using SIPO in multi-objective optimization problems shows that this adaptive approach can obtain equally good or sometimes even better solutions than other parallel and non-parallel methods which are not self-organized.","PeriodicalId":340028,"journal":{"name":"BADS '11","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129641694","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}
Binary Particle Swarm Optimization (BPSO) is a population based stochastic algorithm for discrete optimization inspired by social behavior of bird flocking or fish schooling that has been successfully applied in different areas. However, its potential has not been sufficiently explored. Recent works have proposed hybridization of BPSO with promising results. This paper aims to present three variants of hybrid BPSO algorithm, which is differently to the previous approaches. This work, maintains the main BPSO concept for the update of the velocity of the particle and position, one additional step is added to the method that is crossover technique of Genetic Algorithm. The paper describes the three proposed algorithms and a set of experiments with the standard benchmark functions. The hybrid algorithm shows competitive results compared to Classical BPSO.
{"title":"Discrete optimization problem solving with three variants of hybrid binary particle swarm optimization","authors":"Vikas Singh, Deepak Singh, R. Tiwari, A. Shukla","doi":"10.1145/1998570.1998580","DOIUrl":"https://doi.org/10.1145/1998570.1998580","url":null,"abstract":"Binary Particle Swarm Optimization (BPSO) is a population based stochastic algorithm for discrete optimization inspired by social behavior of bird flocking or fish schooling that has been successfully applied in different areas. However, its potential has not been sufficiently explored. Recent works have proposed hybridization of BPSO with promising results. This paper aims to present three variants of hybrid BPSO algorithm, which is differently to the previous approaches. This work, maintains the main BPSO concept for the update of the velocity of the particle and position, one additional step is added to the method that is crossover technique of Genetic Algorithm. The paper describes the three proposed algorithms and a set of experiments with the standard benchmark functions. The hybrid algorithm shows competitive results compared to Classical BPSO.","PeriodicalId":340028,"journal":{"name":"BADS '11","volume":"274 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134087901","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 current research trend in Software Engineering concerns the development of new techniques to deal intelligently and efficiently with the design of complex and distributed systems that are able to evolve overtime and adapt to rapid changes of their requirements. Runtime evolution is mainly achieved by instrumenting the system with a conceptually centralized controller that is able to monitor the system execution, analyze the opportunities for evolution, plan the evolution, and, finally, executing it by transforming in some way the controlled system. While this approach makes sense in most cases, when system decentralization is significant, it could not be feasible for scalability issues. An interesting class of approaches that replaces the conceptually centralized intelligence with a completely decentralized solution is the one based on self-organization inspired by the natural world. In nature, it often happens that a global behavior emerges from simple and local decisions made autonomously by each element of the system (think, for example, of the ability of an ants colony to quickly find the shortest path to the food). Applying the same idea to software systems, we have that each system component becomes an autonomous entity able to perform local actions affecting its state, behavior, and the relationships it holds with its neighbors. While the proposals for decentralized self-organization available in the literature appear to be very interesting, in most cases, they are still only defined in terms of analytical or simulative models. We argue that applying self-organization approaches to real running systems is a non-trivial task as it has to account for problems such as synchronization issues, race conditions, loss of messages and the like. In this talk we introduce the concept of self-organization and present some examples of applications in various domains, ranging from energy saving to cloud computing optimization. Moreover, we try to offer a roadmap to the definition of some design guidelines that support the adoption of self-organization.
{"title":"Methods for self-organizing distributed software","authors":"E. D. Nitto, Daniel J. Dubois","doi":"10.1145/1998570.1998577","DOIUrl":"https://doi.org/10.1145/1998570.1998577","url":null,"abstract":"A current research trend in Software Engineering concerns the development of new techniques to deal intelligently and efficiently with the design of complex and distributed systems that are able to evolve overtime and adapt to rapid changes of their requirements. Runtime evolution is mainly achieved by instrumenting the system with a conceptually centralized controller that is able to monitor the system execution, analyze the opportunities for evolution, plan the evolution, and, finally, executing it by transforming in some way the controlled system. While this approach makes sense in most cases, when system decentralization is significant, it could not be feasible for scalability issues.\u0000 An interesting class of approaches that replaces the conceptually centralized intelligence with a completely decentralized solution is the one based on self-organization inspired by the natural world. In nature, it often happens that a global behavior emerges from simple and local decisions made autonomously by each element of the system (think, for example, of the ability of an ants colony to quickly find the shortest path to the food). Applying the same idea to software systems, we have that each system component becomes an autonomous entity able to perform local actions affecting its state, behavior, and the relationships it holds with its neighbors. While the proposals for decentralized self-organization available in the literature appear to be very interesting, in most cases, they are still only defined in terms of analytical or simulative models.\u0000 We argue that applying self-organization approaches to real running systems is a non-trivial task as it has to account for problems such as synchronization issues, race conditions, loss of messages and the like.\u0000 In this talk we introduce the concept of self-organization and present some examples of applications in various domains, ranging from energy saving to cloud computing optimization. Moreover, we try to offer a roadmap to the definition of some design guidelines that support the adoption of self-organization.","PeriodicalId":340028,"journal":{"name":"BADS '11","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123645544","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}
Load balancing in distributed computing systems is an important requirement to make efficient use of all available resources. Envisioning a increase in the scale and dynamicity of future grid systems, fully distributed autonomic solutions are required to address this problem. In this regard, we introduce a load balancing mechanism, called ozmos, that follows the principle of osmosis to relocate tasks between nodes in a P2P based grid. Our solution is based on a Chord overlay upon which bio-inspired agents are deployed to share information about the status of the grid as well as to reschedule tasks between nodes. The key based routing capabilities of Chord are exploited to discover other nodes in the overlay, and to efficiently support relocation of incompatible tasks in heterogeneous grids. By means of a simulation study conducted in various scenarios, we highlight the efficacy of the proposed algorithm in achieving system-wide load balance in grids of different scales, and with both homogenous and heterogeneous resources.
{"title":"ozmos: bio-inspired load balancing in a chord-based P2P grid","authors":"Amos Brocco","doi":"10.1145/1998570.1998573","DOIUrl":"https://doi.org/10.1145/1998570.1998573","url":null,"abstract":"Load balancing in distributed computing systems is an important requirement to make efficient use of all available resources. Envisioning a increase in the scale and dynamicity of future grid systems, fully distributed autonomic solutions are required to address this problem. In this regard, we introduce a load balancing mechanism, called ozmos, that follows the principle of osmosis to relocate tasks between nodes in a P2P based grid. Our solution is based on a Chord overlay upon which bio-inspired agents are deployed to share information about the status of the grid as well as to reschedule tasks between nodes. The key based routing capabilities of Chord are exploited to discover other nodes in the overlay, and to efficiently support relocation of incompatible tasks in heterogeneous grids. By means of a simulation study conducted in various scenarios, we highlight the efficacy of the proposed algorithm in achieving system-wide load balance in grids of different scales, and with both homogenous and heterogeneous resources.","PeriodicalId":340028,"journal":{"name":"BADS '11","volume":"256 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133773053","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}
Due to the changing nature of service-oriented environments, the ability to locate services of interest in such open, dynamic, and distributed environments has become an essential requirement. Current service-oriented architecture standards mainly rely on functional properties, however, service registries lack mechanisms for managing services' non-functional properties. Such non-functional properties are expressed in terms of quality of service (QoS) attributes. QoS for web services allows consumers to have confidence in the use of services by aiming to experience good service performance in terms of waiting time, reliability, and availability. This paper investigates the service selection process, and proposes two approaches; one that is based on a genetic algorithm, and the other is based on a memetic algorithm to match consumers with services based on QoS attributes as closely as possible. Both approaches are compared with an optimal assignment algorithm called the Munkres algorithm, as well as a Random approach. Measurements are performed to quantify the overall match score, the execution time, and the scalability of all approaches.
{"title":"Memetic algorithm for web service selection","authors":"Simone A. Ludwig","doi":"10.1145/1998570.1998572","DOIUrl":"https://doi.org/10.1145/1998570.1998572","url":null,"abstract":"Due to the changing nature of service-oriented environments, the ability to locate services of interest in such open, dynamic, and distributed environments has become an essential requirement. Current service-oriented architecture standards mainly rely on functional properties, however, service registries lack mechanisms for managing services' non-functional properties. Such non-functional properties are expressed in terms of quality of service (QoS) attributes. QoS for web services allows consumers to have confidence in the use of services by aiming to experience good service performance in terms of waiting time, reliability, and availability. This paper investigates the service selection process, and proposes two approaches; one that is based on a genetic algorithm, and the other is based on a memetic algorithm to match consumers with services based on QoS attributes as closely as possible. Both approaches are compared with an optimal assignment algorithm called the Munkres algorithm, as well as a Random approach. Measurements are performed to quantify the overall match score, the execution time, and the scalability of all approaches.","PeriodicalId":340028,"journal":{"name":"BADS '11","volume":"307 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132555020","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 proposes and evaluates a model-driven performance engineering framework for wireless sensor networks (WSNs). The proposed framework, called Moppet, is designed for application developers to rapidly implement WSN applications and estimate their performance. It leverages the notion of feature modeling so that it allows developers to graphically and intuitively specify features (e.g., functionalities and configuration policies) in their applications. It also validates a set of constraints among features and generates application code. Moppet also uses event calculus in order to estimate a WSN application's performance without generating its code nor running it on simulators and real networks. Currently, it can estimate power consumption and lifetime of each sensor node. Experimental results show that, in a small-scale WSN of 16 iMote nodes, Moppet's average performance estimation error is 8%. In a large-scale simulated WSN of 400 nodes, its average estimation error is 2%. Moppet scales well to the network size with respect to estimation accuracy. Moppet generates lightweight nesC code that can be deployed with TinyOS on resource-limited nodes. The current experimental results show that Moppet is well-applicable to implement biologically-inspired routing protocols such as pheromone-based gradient routing protocols and estimate their performance.
{"title":"Model-driven performance engineering for wireless sensor networks with feature modeling and event calculus","authors":"P. Boonma, J. Suzuki","doi":"10.1145/1998570.1998574","DOIUrl":"https://doi.org/10.1145/1998570.1998574","url":null,"abstract":"This paper proposes and evaluates a model-driven performance engineering framework for wireless sensor networks (WSNs). The proposed framework, called Moppet, is designed for application developers to rapidly implement WSN applications and estimate their performance. It leverages the notion of feature modeling so that it allows developers to graphically and intuitively specify features (e.g., functionalities and configuration policies) in their applications. It also validates a set of constraints among features and generates application code. Moppet also uses event calculus in order to estimate a WSN application's performance without generating its code nor running it on simulators and real networks. Currently, it can estimate power consumption and lifetime of each sensor node. Experimental results show that, in a small-scale WSN of 16 iMote nodes, Moppet's average performance estimation error is 8%. In a large-scale simulated WSN of 400 nodes, its average estimation error is 2%. Moppet scales well to the network size with respect to estimation accuracy. Moppet generates lightweight nesC code that can be deployed with TinyOS on resource-limited nodes. The current experimental results show that Moppet is well-applicable to implement biologically-inspired routing protocols such as pheromone-based gradient routing protocols and estimate their performance.","PeriodicalId":340028,"journal":{"name":"BADS '11","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123831294","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}
Particle swarm optimization (PSO) is a powerful technique for computer aided prediction of proteins' three-dimensional structure. In this work, employing an all-atom force field we demonstrate the efficiency of the standard PSO algorithm, as implemented in the ArFlock library, for finding the folded state of two proteins of different sizes starting from completely extended conformations. In particular, the predicted structure of the larger protein is in good agreement with the structure from the Protein Data Bank within the experimental resolution. We also show that parallelization of the PSO speeds up the simulation linearly with the number of workers and reduces the time for predictions dramatically without loss of accuracy.
{"title":"Protein structure prediction using particle swarm optimization and a distributed parallel approach","authors":"Ivan Kondov, R. Berlich","doi":"10.1145/1998570.1998579","DOIUrl":"https://doi.org/10.1145/1998570.1998579","url":null,"abstract":"Particle swarm optimization (PSO) is a powerful technique for computer aided prediction of proteins' three-dimensional structure. In this work, employing an all-atom force field we demonstrate the efficiency of the standard PSO algorithm, as implemented in the ArFlock library, for finding the folded state of two proteins of different sizes starting from completely extended conformations. In particular, the predicted structure of the larger protein is in good agreement with the structure from the Protein Data Bank within the experimental resolution. We also show that parallelization of the PSO speeds up the simulation linearly with the number of workers and reduces the time for predictions dramatically without loss of accuracy.","PeriodicalId":340028,"journal":{"name":"BADS '11","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128695965","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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