Xinwei Fang, R. Calinescu, Colin Paterson, Julie A. Wilson
Self-adaptive systems are expected to mitigate disruptions by continually adjusting their configuration and behaviour. This mitigation is often reactive. Typically, environmental or internal changes trigger a system response only after a violation of the system requirements. Despite a broad agreement that prevention is better than cure in self-adaptation, proactive adaptation methods are underrepresented within the repertoire of solutions available to the developers of self-adaptive systems. To address this gap, we present a work-in-progress approach for the prediction of-system-level disruptions (PRESTO) through parametric model checking. Intended for use in the analysis step of the MAPE-K (MonitorAnalyse-Plan-Execute over a shared Knowledge) feedback control loop of self-adaptive systems, PRESTO comprises two stages. First, time-series analysis is applied to monitoring data in order to identify trends in the values of individual system and/or environment parameters. Next, future non-functional requirement violations are predicted by using parametric model checking, in order to establish the potential impact of these trends on the reliability and performance of the system. We illustrate the application of PRESTO in a case study from the autonomous farming domain.
{"title":"PRESTO: Predicting System-level Disruptions through Parametric Model Checking","authors":"Xinwei Fang, R. Calinescu, Colin Paterson, Julie A. Wilson","doi":"10.1145/3524844.3528059","DOIUrl":"https://doi.org/10.1145/3524844.3528059","url":null,"abstract":"Self-adaptive systems are expected to mitigate disruptions by continually adjusting their configuration and behaviour. This mitigation is often reactive. Typically, environmental or internal changes trigger a system response only after a violation of the system requirements. Despite a broad agreement that prevention is better than cure in self-adaptation, proactive adaptation methods are underrepresented within the repertoire of solutions available to the developers of self-adaptive systems. To address this gap, we present a work-in-progress approach for the prediction of-system-level disruptions (PRESTO) through parametric model checking. Intended for use in the analysis step of the MAPE-K (MonitorAnalyse-Plan-Execute over a shared Knowledge) feedback control loop of self-adaptive systems, PRESTO comprises two stages. First, time-series analysis is applied to monitoring data in order to identify trends in the values of individual system and/or environment parameters. Next, future non-functional requirement violations are predicted by using parametric model checking, in order to establish the potential impact of these trends on the reliability and performance of the system. We illustrate the application of PRESTO in a case study from the autonomous farming domain.","PeriodicalId":227173,"journal":{"name":"2022 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131531107","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}
Software is playing an increasingly crucial role for any modern enterprise. Due to the high volatility of software and its environment, management of software change has become a crucial ability of a digital enterprise. Indeed, the ever-changing technical aspects of cloud-based software might influence its economic aspects at the same rate. This PhD work proposes an autonomic management framework for economic-driven adaptive resource allocation, during which performance and economics are considered simultaneously. The framework consists of a hybrid performance-economic model for cloud software systems and a self-adaptive system for resource management at run time. The proposed framework will be developed and evaluated on use cases from the domain of financial services. The objective in these use cases is to improve productivity of data scientists when they train and deploy analytical and ML models, while maintaining a low resource consumption. Considering productivity and cost as the economic factors, and latency and resource consumption as the technical ones, we will use the proposed hybrid model to guide initial deployment, and the self-adaptive system to adjust the deployment at run time.
{"title":"Devops for digital business: Optimizing the performance and economic efficiency of software products for digital business","authors":"Soude Ghari","doi":"10.1145/3524844.3528069","DOIUrl":"https://doi.org/10.1145/3524844.3528069","url":null,"abstract":"Software is playing an increasingly crucial role for any modern enterprise. Due to the high volatility of software and its environment, management of software change has become a crucial ability of a digital enterprise. Indeed, the ever-changing technical aspects of cloud-based software might influence its economic aspects at the same rate. This PhD work proposes an autonomic management framework for economic-driven adaptive resource allocation, during which performance and economics are considered simultaneously. The framework consists of a hybrid performance-economic model for cloud software systems and a self-adaptive system for resource management at run time. The proposed framework will be developed and evaluated on use cases from the domain of financial services. The objective in these use cases is to improve productivity of data scientists when they train and deploy analytical and ML models, while maintaining a low resource consumption. Considering productivity and cost as the economic factors, and latency and resource consumption as the technical ones, we will use the proposed hybrid model to guide initial deployment, and the self-adaptive system to adjust the deployment at run time.","PeriodicalId":227173,"journal":{"name":"2022 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129689881","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}
Thomas E. F. Witte, Raffaela Groner, Alexander Raschke, Matthias Tichy, Irdin Pekaric, M. Felderer
Self-adaptive systems offer several attack surfaces due to the communication via different channels and the different sensors required to observe the environment. Often, attacks cause safety to be compromised as well, making it necessary to consider these two aspects together. Furthermore, the approaches currently used for safety and security analysis do not sufficient take into account the intermediate steps of an adaptation. Current work in this area ignores the fact that a self-adaptive system also reveals possible vulnerabilities (even if only temporarily) during the adaptation. To address this issue, we propose a modeling approach that takes into account the different relevant aspects of a system, its adaptation process, as well as safety hazards and security attacks. We present several models that describe different aspects of a self-adaptive system and we outline our idea of how these models can then be combined into an Attack-Fault Tree. This allows modeling aspects of the system on different levels of abstraction and co-evolve the models using transformations according to the adaptation of the system. Finally, analyses can then be performed as usual on the resulting Attack-Fault Tree.CCS CONCEPTS• Software and its engineering → System description languages; Fault tree analysis; • Computer systems organization → Embedded and cyber-physical systems; Dependable and fault-tolerant systems and networks.
{"title":"Towards Model Co-evolution Across Self-Adaptation Steps for Combined Safety and Security Analysis","authors":"Thomas E. F. Witte, Raffaela Groner, Alexander Raschke, Matthias Tichy, Irdin Pekaric, M. Felderer","doi":"10.1145/3524844.3528062","DOIUrl":"https://doi.org/10.1145/3524844.3528062","url":null,"abstract":"Self-adaptive systems offer several attack surfaces due to the communication via different channels and the different sensors required to observe the environment. Often, attacks cause safety to be compromised as well, making it necessary to consider these two aspects together. Furthermore, the approaches currently used for safety and security analysis do not sufficient take into account the intermediate steps of an adaptation. Current work in this area ignores the fact that a self-adaptive system also reveals possible vulnerabilities (even if only temporarily) during the adaptation. To address this issue, we propose a modeling approach that takes into account the different relevant aspects of a system, its adaptation process, as well as safety hazards and security attacks. We present several models that describe different aspects of a self-adaptive system and we outline our idea of how these models can then be combined into an Attack-Fault Tree. This allows modeling aspects of the system on different levels of abstraction and co-evolve the models using transformations according to the adaptation of the system. Finally, analyses can then be performed as usual on the resulting Attack-Fault Tree.CCS CONCEPTS• Software and its engineering → System description languages; Fault tree analysis; • Computer systems organization → Embedded and cyber-physical systems; Dependable and fault-tolerant systems and networks.","PeriodicalId":227173,"journal":{"name":"2022 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133102001","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}
Danny Weyns, I. Gerostathopoulos, Nadeem Abbas, J. Andersson, S. Biffl, Přemek Brada, T. Bures, A. D. Salle, P. Lago, Angelika Musil, Juergen Musil, Patrizio Pelliccione
Self-adaptation equips a software system with a feedback loop that automates tasks that otherwise need to be performed by operators. Such feedback loops have found their way to a variety of practical applications, one typical example is an elastic cloud. Yet, the state of the practice in self-adaptation is currently not clear. To get insights into the use of self-adaptation in practice, we are running a largescale survey with industry. This paper reports preliminary results based on survey data that we obtained from 113 practitioners spread over 16 countries, 62 of them work with concrete self-adaptive systems. We highlight the main insights obtained so far: motivations for self-adaptation, concrete use cases, and difficulties encountered when applying self-adaptation in practice. We conclude the paper with outlining our plans for the remainder of the study.CCS CONCEPTS • Software and its engineering $rightarrow$Software system structures; Designing software; Maintaining software.
{"title":"Preliminary Results of a Survey on the Use of Self-Adaptation in Industry","authors":"Danny Weyns, I. Gerostathopoulos, Nadeem Abbas, J. Andersson, S. Biffl, Přemek Brada, T. Bures, A. D. Salle, P. Lago, Angelika Musil, Juergen Musil, Patrizio Pelliccione","doi":"10.1145/3524844.3528077","DOIUrl":"https://doi.org/10.1145/3524844.3528077","url":null,"abstract":"Self-adaptation equips a software system with a feedback loop that automates tasks that otherwise need to be performed by operators. Such feedback loops have found their way to a variety of practical applications, one typical example is an elastic cloud. Yet, the state of the practice in self-adaptation is currently not clear. To get insights into the use of self-adaptation in practice, we are running a largescale survey with industry. This paper reports preliminary results based on survey data that we obtained from 113 practitioners spread over 16 countries, 62 of them work with concrete self-adaptive systems. We highlight the main insights obtained so far: motivations for self-adaptation, concrete use cases, and difficulties encountered when applying self-adaptation in practice. We conclude the paper with outlining our plans for the remainder of the study.CCS CONCEPTS • Software and its engineering $rightarrow$Software system structures; Designing software; Maintaining software.","PeriodicalId":227173,"journal":{"name":"2022 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128597113","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}
Jürgen Dobaj, A. Riel, T. Krug, Matthias Seidl, Georg Macher, M. Egretzberger
Background: Industrial Product-Service Systems (IPSS) denote a service-oriented way of providing access to cyber-physical systems’ (CPS) capabilities. The design of such systems bears high risk due to uncertainty in requirements related to service function and behavior, operation environments, and evolving customer needs. Such risks and uncertainties are well known in the IT sector, where DevOps principles ensure continuous system improvement through reliable and frequent delivery processes. A modular and service-oriented system architecture complements these processes to facilitate IT system adaptation and evolution.Objective: This work proposes a method to use and extend the Digital Twins (DTs) of IPSS assets for enabling the continuous optimization of CPS service delivery and the latter’s adaptation to changing needs and environments. This reduces uncertainty during design and operations by assuring IPSS integrity and availability, especially for design and service adaptations at CPS runtime.Methodology: The method builds on transferring IT DevOps principles to DT-enabled CPS IPSS. The chosen design approach integrates, reuses, and aligns the DT processing and communication resources with DevOps requirements derived from literature.Results: We use these requirements to propose a DT-enabled self-adaptive CPS model, which guides the realization of DT-enabled DevOps in CPS IPSS. We further propose detailed design models for operation-critical DTs that integrate CPS closed-loop control and architecture-based CPS adaptation. This integrated approach enables the implementation of A/B testing as a use case and central concept to enable CPS IPSS service adaptation and reconfiguration.Conclusion: The self-adaptive CPS model and DT design concept have been validated in an evaluation environment for operation-critical CPS IPSS. The demonstrator achieved sub-millisecond cycle times during service A/B testing at runtime without causing CPS operation interferences and downtime.CCS CONCEPTS• Computer systems organization~Embedded and cyber-physical systems •Computer systems organization~Architectures
{"title":"Towards Digital Twin-enabled DevOps for CPS providing Architecture-Based Service Adaptation & Verification at Runtime","authors":"Jürgen Dobaj, A. Riel, T. Krug, Matthias Seidl, Georg Macher, M. Egretzberger","doi":"10.1145/3524844.3528057","DOIUrl":"https://doi.org/10.1145/3524844.3528057","url":null,"abstract":"Background: Industrial Product-Service Systems (IPSS) denote a service-oriented way of providing access to cyber-physical systems’ (CPS) capabilities. The design of such systems bears high risk due to uncertainty in requirements related to service function and behavior, operation environments, and evolving customer needs. Such risks and uncertainties are well known in the IT sector, where DevOps principles ensure continuous system improvement through reliable and frequent delivery processes. A modular and service-oriented system architecture complements these processes to facilitate IT system adaptation and evolution.Objective: This work proposes a method to use and extend the Digital Twins (DTs) of IPSS assets for enabling the continuous optimization of CPS service delivery and the latter’s adaptation to changing needs and environments. This reduces uncertainty during design and operations by assuring IPSS integrity and availability, especially for design and service adaptations at CPS runtime.Methodology: The method builds on transferring IT DevOps principles to DT-enabled CPS IPSS. The chosen design approach integrates, reuses, and aligns the DT processing and communication resources with DevOps requirements derived from literature.Results: We use these requirements to propose a DT-enabled self-adaptive CPS model, which guides the realization of DT-enabled DevOps in CPS IPSS. We further propose detailed design models for operation-critical DTs that integrate CPS closed-loop control and architecture-based CPS adaptation. This integrated approach enables the implementation of A/B testing as a use case and central concept to enable CPS IPSS service adaptation and reconfiguration.Conclusion: The self-adaptive CPS model and DT design concept have been validated in an evaluation environment for operation-critical CPS IPSS. The demonstrator achieved sub-millisecond cycle times during service A/B testing at runtime without causing CPS operation interferences and downtime.CCS CONCEPTS• Computer systems organization~Embedded and cyber-physical systems •Computer systems organization~Architectures","PeriodicalId":227173,"journal":{"name":"2022 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133593307","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 the past years, machine learning (ML) has become a popular approach to support self-adaptation. While ML techniques enable dealing with several problems in self-adaptation, such as scalable decision-making, they are also subject to inherent challenges. In this paper, we focus on one such challenge that is particularly important for self-adaptation: ML techniques are designed to deal with a set of predefined tasks associated with an operational domain; they have problems to deal with new emerging tasks, such as concept shift in input data that is used for learning. To tackle this challenge, we present lifelong self-adaptation: a novel approach to self-adaptation that enhances self-adaptive systems that use ML techniques with a lifelong ML layer. The lifelong ML layer tracks the running system and its environment, associates this knowledge with the current tasks, identifies new tasks based on differentiations, and updates the learning models of the self-adaptive system accordingly. We present a reusable architecture for lifelong self-adaptation and apply it to the case of concept drift caused by unforeseen changes of the input data of a learning model that is used for decision-making in self-adaptation. We validate lifelong self-adaptation for two types of concept drift using two cases.
{"title":"Lifelong Self-Adaptation: Self-Adaptation Meets Lifelong Machine Learning","authors":"Omid Gheibi, Danny Weyns","doi":"10.1145/3524844.3528052","DOIUrl":"https://doi.org/10.1145/3524844.3528052","url":null,"abstract":"In the past years, machine learning (ML) has become a popular approach to support self-adaptation. While ML techniques enable dealing with several problems in self-adaptation, such as scalable decision-making, they are also subject to inherent challenges. In this paper, we focus on one such challenge that is particularly important for self-adaptation: ML techniques are designed to deal with a set of predefined tasks associated with an operational domain; they have problems to deal with new emerging tasks, such as concept shift in input data that is used for learning. To tackle this challenge, we present lifelong self-adaptation: a novel approach to self-adaptation that enhances self-adaptive systems that use ML techniques with a lifelong ML layer. The lifelong ML layer tracks the running system and its environment, associates this knowledge with the current tasks, identifies new tasks based on differentiations, and updates the learning models of the self-adaptive system accordingly. We present a reusable architecture for lifelong self-adaptation and apply it to the case of concept drift caused by unforeseen changes of the input data of a learning model that is used for decision-making in self-adaptation. We validate lifelong self-adaptation for two types of concept drift using two cases.","PeriodicalId":227173,"journal":{"name":"2022 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130380212","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}
J. Cleland-Huang, Ankit Agrawal, Michael Vierhauser, Michael Murphy, Mike Prieto
The MAPE-K feedback loop has been established as the primary reference model for self-adaptive and autonomous systems in domains such as autonomous driving, robotics, and Cyber-Physical Systems. At the same time, the Human Machine Teaming (HMT) paradigm is designed to promote partnerships between humans and autonomous machines. It goes far beyond the degree of collaboration expected in human-on-the-loop and human-in-the-loop systems and emphasizes interactions, partnership, and teamwork between humans and machines. However, while MAPE-K enables fully autonomous behavior, it does not explicitly address the interactions between humans and machines as intended by HMT. In this paper, we present the MAPE-KHMT framework which augments the traditional MAPE-K loop with support for HMT. We identify critical human-machine teaming factors and describe the infrastructure needed across the various phases of the MAPE-K loop in order to effectively support HMT. This includes runtime models that are constructed and populated dynamically across monitoring, analysis, planning, and execution phases to support human-machine partnerships. We illustrate MAPE-KHMT using examples from an autonomous multi-UAV emergency response system, and present guidelines for integrating HMT into MAPE-K.CCS CONCEPTS• Human-centered computing → Collaborative interaction; HCI theory, concepts and models.
{"title":"Extending MAPE-K to support Human-Machine Teaming","authors":"J. Cleland-Huang, Ankit Agrawal, Michael Vierhauser, Michael Murphy, Mike Prieto","doi":"10.1145/3524844.3528054","DOIUrl":"https://doi.org/10.1145/3524844.3528054","url":null,"abstract":"The MAPE-K feedback loop has been established as the primary reference model for self-adaptive and autonomous systems in domains such as autonomous driving, robotics, and Cyber-Physical Systems. At the same time, the Human Machine Teaming (HMT) paradigm is designed to promote partnerships between humans and autonomous machines. It goes far beyond the degree of collaboration expected in human-on-the-loop and human-in-the-loop systems and emphasizes interactions, partnership, and teamwork between humans and machines. However, while MAPE-K enables fully autonomous behavior, it does not explicitly address the interactions between humans and machines as intended by HMT. In this paper, we present the MAPE-KHMT framework which augments the traditional MAPE-K loop with support for HMT. We identify critical human-machine teaming factors and describe the infrastructure needed across the various phases of the MAPE-K loop in order to effectively support HMT. This includes runtime models that are constructed and populated dynamically across monitoring, analysis, planning, and execution phases to support human-machine partnerships. We illustrate MAPE-KHMT using examples from an autonomous multi-UAV emergency response system, and present guidelines for integrating HMT into MAPE-K.CCS CONCEPTS• Human-centered computing → Collaborative interaction; HCI theory, concepts and models.","PeriodicalId":227173,"journal":{"name":"2022 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115314084","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 assure performance on the fly, planning is arguably one of the most important steps for self-adaptive systems (SASs), especially when they are highly configurable with a daunting number of adaptation options. However, there has been little understanding of the planning landscape or ways by which it can be analyzed. This inevitably creates barriers to the design of better and tailored planners for SASs. In this paper, we showcase how the planning landscapes of SASs can be quantified and reasoned, particularly with respect to the different environments. By studying four diverse real-world SASs and 14 environments, we found that (1) the SAS planning landscapes often provide strong guidance to the planner, but their ruggedness and multi-modality can be the major obstacle; (2) the extents of guidance and number of global/local optima are sensitive to the changing environment, but not the ruggedness of the surface; (3) the local optima are often closer to the global optimum than other random points; and (4) there are considerable (and useful) overlaps on the global/local optima between landscapes under different environments. We then discuss the potential implications to the future work of planner designs for SASs. CCS CONCEPTS • Software and its engineering $rightarrow$ Software performance; Software configuration management and version control systems.
{"title":"Planning Landscape Analysis for Self-Adaptive Systems","authors":"Tao Chen","doi":"10.1145/3524844.3528060","DOIUrl":"https://doi.org/10.1145/3524844.3528060","url":null,"abstract":"To assure performance on the fly, planning is arguably one of the most important steps for self-adaptive systems (SASs), especially when they are highly configurable with a daunting number of adaptation options. However, there has been little understanding of the planning landscape or ways by which it can be analyzed. This inevitably creates barriers to the design of better and tailored planners for SASs. In this paper, we showcase how the planning landscapes of SASs can be quantified and reasoned, particularly with respect to the different environments. By studying four diverse real-world SASs and 14 environments, we found that (1) the SAS planning landscapes often provide strong guidance to the planner, but their ruggedness and multi-modality can be the major obstacle; (2) the extents of guidance and number of global/local optima are sensitive to the changing environment, but not the ruggedness of the surface; (3) the local optima are often closer to the global optimum than other random points; and (4) there are considerable (and useful) overlaps on the global/local optima between landscapes under different environments. We then discuss the potential implications to the future work of planner designs for SASs. CCS CONCEPTS • Software and its engineering $rightarrow$ Software performance; Software configuration management and version control systems.","PeriodicalId":227173,"journal":{"name":"2022 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123874941","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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