Konstantinos Angelopoulos, V. Souza, J. Mylopoulos
Adaptive software systems monitor the environment to ensure that their requirements are being fullled. When this is not the case, their adaptation mechanism proposes an adaptation (a change to the behaviour/configuration) that can lead to restored satisfaction of system requirements. Unfortunately, such adaptation mechanisms don't work very well in cases where there are multiple failures (divergence of system behaviour relative to several requirements). This paper proposes an adaptation mechanism that can handle multiple failures. The proposal consists of extending the Qualia adaptation mechanism of Zanshin enriched with features adopted from Control Theory. The proposed framework supports the definition of requirements for the adaptation process prescribing how to deal at runtime with problems such as conflicting requirements and synchronization, enhancing the precision and effectiveness of the adaptation mechanism. The proposed mechanism, named Qualia+ is illustrated and evaluated with an example using the meeting scheduling exemplar.
{"title":"Dealing with multiple failures in zanshin: a control-theoretic approach","authors":"Konstantinos Angelopoulos, V. Souza, J. Mylopoulos","doi":"10.1145/2593929.2593936","DOIUrl":"https://doi.org/10.1145/2593929.2593936","url":null,"abstract":"Adaptive software systems monitor the environment to ensure that their requirements are being fullled. When this is not the case, their adaptation mechanism proposes an adaptation (a change to the behaviour/configuration) that can lead to restored satisfaction of system requirements. Unfortunately, such adaptation mechanisms don't work very well in cases where there are multiple failures (divergence of system behaviour relative to several requirements). This paper proposes an adaptation mechanism that can handle multiple failures. The proposal consists of extending the Qualia adaptation mechanism of Zanshin enriched with features adopted from Control Theory. The proposed framework supports the definition of requirements for the adaptation process prescribing how to deal at runtime with problems such as conflicting requirements and synchronization, enhancing the precision and effectiveness of the adaptation mechanism. The proposed mechanism, named Qualia+ is illustrated and evaluated with an example using the meeting scheduling exemplar.","PeriodicalId":168314,"journal":{"name":"International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122182814","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 self-adaptive software system should be able to monitor and analyze its runtime behavior and make adaptation decisions accordingly to meet certain desirable objectives. Traditional software adaptation techniques and recent "models@runtime" approaches usually require an a priori model for a system's dynamic behavior. Oftentimes the model is difficult to define and labor-intensive to maintain, and tends to get out of date due to adaptation and architecture decay. We propose an alternative approach that does not require defining the system's behavior model beforehand, but instead involves mining software component interactions from system execution traces to build a probabilistic usage model, which is in turn used to analyze, plan, and execute adaptations. Our preliminary evaluation of the approach against an Emergency Deployment System shows that the associations mining model can be used to effectively address a variety of adaptation needs, including (1) safely applying dynamic changes to a running software system without creating inconsistencies, (2) identifying potentially malicious (abnormal) behavior for self-protection, and (3) our ongoing research on improving deployment of software components in a distributed setting for performance self-optimization.
{"title":"Automated mining of software component interactions for self-adaptation","authors":"E. Yuan, N. Esfahani, S. Malek","doi":"10.1145/2593929.2593934","DOIUrl":"https://doi.org/10.1145/2593929.2593934","url":null,"abstract":"A self-adaptive software system should be able to monitor and analyze its runtime behavior and make adaptation decisions accordingly to meet certain desirable objectives. Traditional software adaptation techniques and recent \"models@runtime\" approaches usually require an a priori model for a system's dynamic behavior. Oftentimes the model is difficult to define and labor-intensive to maintain, and tends to get out of date due to adaptation and architecture decay. We propose an alternative approach that does not require defining the system's behavior model beforehand, but instead involves mining software component interactions from system execution traces to build a probabilistic usage model, which is in turn used to analyze, plan, and execute adaptations. Our preliminary evaluation of the approach against an Emergency Deployment System shows that the associations mining model can be used to effectively address a variety of adaptation needs, including (1) safely applying dynamic changes to a running software system without creating inconsistencies, (2) identifying potentially malicious (abnormal) behavior for self-protection, and (3) our ongoing research on improving deployment of software components in a distributed setting for performance self-optimization.","PeriodicalId":168314,"journal":{"name":"International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124295152","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 uncertain and dynamic demand for Quality of Service (QoS) in cloud-based systems, engineering self-adaptivity in cloud architectures require novel approaches to support on-demand elasticity. The architecture should dynamically select an elastic strategy, which optimizes the global benefit for QoS and cost objectives for all cloud-based services. The architecture shall also provide mechanisms for reaching the strategy with minimal overhead. However, the challenge in the cloud is that the nature of objectives (e.g., throughput and the required cost) and QoS interference could cause overlapping sensitivity amongst intra- and inter-services objectives, which leads to objective-dependency (i.e., conflicted or harmonic) during optimization. In this paper, we propose a symbiotic and sensitivity-aware architecture for optimizing global-benefit with reduced overhead in the cloud. The architecture dynamically partitions QoS and cost objectives into sensitivity independent regions, where the local optimums are achieved. In addition, the architecture realizes the concept of symbiotic feedback loop, which is a bio-directional self-adaptive action that not only allows to dynamically monitor and adapt the managed services by scaling to their demand, but also to adaptively consolidate the managing system by re-partitioning the regions based on symptoms. We implement the architecture as a prototype extending on decentralized MAPE loop by introducing an Adaptor component. We then experimentally analyze and evaluate our architecture using hypothetical scenarios. The results reveal that our symbiotic and sensitivity-aware architecture is able to produce even better global benefit and smaller overhead in contrast to other non sensitivity-aware architectures.
{"title":"Symbiotic and sensitivity-aware architecture for globally-optimal benefit in self-adaptive cloud","authors":"Tao Chen, R. Bahsoon","doi":"10.1145/2593929.2593931","DOIUrl":"https://doi.org/10.1145/2593929.2593931","url":null,"abstract":"Due to the uncertain and dynamic demand for Quality of Service (QoS) in cloud-based systems, engineering self-adaptivity in cloud architectures require novel approaches to support on-demand elasticity. The architecture should dynamically select an elastic strategy, which optimizes the global benefit for QoS and cost objectives for all cloud-based services. The architecture shall also provide mechanisms for reaching the strategy with minimal overhead. However, the challenge in the cloud is that the nature of objectives (e.g., throughput and the required cost) and QoS interference could cause overlapping sensitivity amongst intra- and inter-services objectives, which leads to objective-dependency (i.e., conflicted or harmonic) during optimization. In this paper, we propose a symbiotic and sensitivity-aware architecture for optimizing global-benefit with reduced overhead in the cloud. The architecture dynamically partitions QoS and cost objectives into sensitivity independent regions, where the local optimums are achieved. In addition, the architecture realizes the concept of symbiotic feedback loop, which is a bio-directional self-adaptive action that not only allows to dynamically monitor and adapt the managed services by scaling to their demand, but also to adaptively consolidate the managing system by re-partitioning the regions based on symptoms. We implement the architecture as a prototype extending on decentralized MAPE loop by introducing an Adaptor component. We then experimentally analyze and evaluate our architecture using hypothetical scenarios. The results reveal that our symbiotic and sensitivity-aware architecture is able to produce even better global benefit and smaller overhead in contrast to other non sensitivity-aware architectures.","PeriodicalId":168314,"journal":{"name":"International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131735207","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}
Stefano Sebastio, M. Amoretti, Alberto Lluch-Lafuente
The increasing diffusion of cloud technologies offers new opportunities for distributed and collaborative computing. Volunteer clouds are a prominent example, where participants join and leave the platform and collaborate by sharing computational resources. The high complexity, dynamism and unpredictability of such scenarios call for decentralized self-* approaches. We present in this paper a framework for the design and evaluation of self-adaptive collaborative task execution strategies in volunteer clouds. As a byproduct, we propose a novel strategy based on the Ant Colony Optimization paradigm, that we validate through simulation-based statistical analysis over Google cluster data.
{"title":"A computational field framework for collaborative task execution in volunteer clouds","authors":"Stefano Sebastio, M. Amoretti, Alberto Lluch-Lafuente","doi":"10.1145/2593929.2593943","DOIUrl":"https://doi.org/10.1145/2593929.2593943","url":null,"abstract":"The increasing diffusion of cloud technologies offers new opportunities for distributed and collaborative computing. Volunteer clouds are a prominent example, where participants join and leave the platform and collaborate by sharing computational resources. The high complexity, dynamism and unpredictability of such scenarios call for decentralized self-* approaches. We present in this paper a framework for the design and evaluation of self-adaptive collaborative task execution strategies in volunteer clouds. As a byproduct, we propose a novel strategy based on the Ant Colony Optimization paradigm, that we validate through simulation-based statistical analysis over Google cluster data.","PeriodicalId":168314,"journal":{"name":"International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122083448","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}
Personalized Web-Tasking (PWT) proposes the automation of user-centric and repetitive web interactions to assist users in the fulfilment of personal goals using internet systems. In PWT, both personal goals and internet systems are affected by unpredictable changes in user preferences, situations, system infrastructures and environments. Therefore, self-adaptation enhanced with dynamic context monitoring is required to guarantee the effectiveness of PWT systems that, despite context uncertainty, must guarantee the accomplishment of personal goals and deliver pleasant user experiences. This position paper describes our approach to the development of PWT systems, which relies on self-adaptation and its enabling technologies. In particular, it presents our runtime modelling approach that is comprised of our PWT Ontology and Goal-oriented Context-sensitive web-tasking (GCT) models, and the way we exploit previous SEAMS contributions developed in our research group, the DYNAMICO reference model and the SmarterContext Monitoring Infrastructure and Reasoning Engine. The main goal of this paper is to demonstrate how the most crucial challenges in the engineering of PWT systems can be addressed by implementing them as self-adaptive software.
{"title":"Self-adaptive applications: on the development of personalized web-tasking systems","authors":"Lorena Castaneda, Norha M. Villegas, H. Müller","doi":"10.1145/2593929.2593942","DOIUrl":"https://doi.org/10.1145/2593929.2593942","url":null,"abstract":"Personalized Web-Tasking (PWT) proposes the automation of user-centric and repetitive web interactions to assist users in the fulfilment of personal goals using internet systems. In PWT, both personal goals and internet systems are affected by unpredictable changes in user preferences, situations, system infrastructures and environments. Therefore, self-adaptation enhanced with dynamic context monitoring is required to guarantee the effectiveness of PWT systems that, despite context uncertainty, must guarantee the accomplishment of personal goals and deliver pleasant user experiences. This position paper describes our approach to the development of PWT systems, which relies on self-adaptation and its enabling technologies. In particular, it presents our runtime modelling approach that is comprised of our PWT Ontology and Goal-oriented Context-sensitive web-tasking (GCT) models, and the way we exploit previous SEAMS contributions developed in our research group, the DYNAMICO reference model and the SmarterContext Monitoring Infrastructure and Reasoning Engine. The main goal of this paper is to demonstrate how the most crucial challenges in the engineering of PWT systems can be addressed by implementing them as self-adaptive software.","PeriodicalId":168314,"journal":{"name":"International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133533930","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}
Cloud elasticity provides a software system with the ability to maintain optimal user experience by automatically acquiring and releasing resources, while paying only for what has been consumed. The mechanism for automatically adding or removing resources on the fly is referred to as auto-scaling. The state-of-the-practice with respect to auto-scaling involves specifying threshold-based rules to implement elasticity policies for cloud-based applications. However, there are several shortcomings regarding this approach. Firstly, the elasticity rules must be specified precisely by quantitative values, which requires deep knowledge and expertise. Furthermore, existing approaches do not explicitly deal with uncertainty in cloud-based software, where noise and unexpected events are common. This paper exploits fuzzy logic to enable qualitative specification of elasticity rules for cloud-based software. In addition, this paper discusses a control theoretical approach using type-2 fuzzy logic systems to reason about elasticity under uncertainties. We conduct several experiments to demonstrate that cloud-based software enhanced with such elasticity controller can robustly handle unexpected spikes in the workload and provide acceptable user experience. This translates into increased profit for the cloud application owner.
{"title":"Autonomic resource provisioning for cloud-based software","authors":"Pooyan Jamshidi, Aakash Ahmad, C. Pahl","doi":"10.1145/2593929.2593940","DOIUrl":"https://doi.org/10.1145/2593929.2593940","url":null,"abstract":"Cloud elasticity provides a software system with the ability to maintain optimal user experience by automatically acquiring and releasing resources, while paying only for what has been consumed. The mechanism for automatically adding or removing resources on the fly is referred to as auto-scaling. The state-of-the-practice with respect to auto-scaling involves specifying threshold-based rules to implement elasticity policies for cloud-based applications. However, there are several shortcomings regarding this approach. Firstly, the elasticity rules must be specified precisely by quantitative values, which requires deep knowledge and expertise. Furthermore, existing approaches do not explicitly deal with uncertainty in cloud-based software, where noise and unexpected events are common. This paper exploits fuzzy logic to enable qualitative specification of elasticity rules for cloud-based software. In addition, this paper discusses a control theoretical approach using type-2 fuzzy logic systems to reason about elasticity under uncertainties. We conduct several experiments to demonstrate that cloud-based software enhanced with such elasticity controller can robustly handle unexpected spikes in the workload and provide acceptable user experience. This translates into increased profit for the cloud application owner.","PeriodicalId":168314,"journal":{"name":"International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117027319","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}
Although different approaches to decision-making in self-adaptive systems have shown their effectiveness in the past by factoring in predictions about the system and its environment (e.g., resource availability), no proposal considers the latency associated with the execution of tactics upon the target system. However, dierent adaptation tactics can take different amounts of time until their effects can be observed. In reactive adaptation, ignoring adaptation tactic latency can lead to suboptimal adaptation decisions (e.g., activating a server that takes more time to boot than the transient spike in traffic that triggered its activation). In proactive adaptation, taking adaptation latency into account is necessary to get the system into the desired state to deal with an upcoming situation. In this paper, we introduce a formal analysis technique based on model checking of stochastic multiplayer games (SMGs) that enables us to quantify the potential benefits of employing dierent types of algorithms for self-adaptation. In particular, we apply this technique to show the potential benefit of considering adaptation tactic latency in proactive adaptation algorithms. Our results show that factoring in tactic latency in decision making improves the outcome of adaptation. We also present an algorithm to do proactive adaptation that considers tactic latency, and show that it achieves higher utility than an algorithm that under the assumption of no latency is optimal.
{"title":"Stochastic game analysis and latency awareness for proactive self-adaptation","authors":"J. Cámara, Gabriel A. Moreno, D. Garlan","doi":"10.1145/2593929.2593933","DOIUrl":"https://doi.org/10.1145/2593929.2593933","url":null,"abstract":"Although different approaches to decision-making in self-adaptive systems have shown their effectiveness in the past by factoring in predictions about the system and its environment (e.g., resource availability), no proposal considers the latency associated with the execution of tactics upon the target system. However, dierent adaptation tactics can take different amounts of time until their effects can be observed. In reactive adaptation, ignoring adaptation tactic latency can lead to suboptimal adaptation decisions (e.g., activating a server that takes more time to boot than the transient spike in traffic that triggered its activation). In proactive adaptation, taking adaptation latency into account is necessary to get the system into the desired state to deal with an upcoming situation. In this paper, we introduce a formal analysis technique based on model checking of stochastic multiplayer games (SMGs) that enables us to quantify the potential benefits of employing dierent types of algorithms for self-adaptation. In particular, we apply this technique to show the potential benefit of considering adaptation tactic latency in proactive adaptation algorithms. Our results show that factoring in tactic latency in decision making improves the outcome of adaptation. We also present an algorithm to do proactive adaptation that considers tactic latency, and show that it achieves higher utility than an algorithm that under the assumption of no latency is optimal.","PeriodicalId":168314,"journal":{"name":"International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131200408","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. Harman, Yue Jia, W. Langdon, J. Petke, Iman Hemati Moghadam, S. Yoo, Fan Wu
This paper presents a brief outline of an approach to online genetic improvement. We argue that existing progress in genetic improvement can be exploited to support adaptivity. We illustrate our proposed approach with a 'dreaming smart device' example that combines online and offline machine learning and optimisation.
{"title":"Genetic improvement for adaptive software engineering (keynote)","authors":"M. Harman, Yue Jia, W. Langdon, J. Petke, Iman Hemati Moghadam, S. Yoo, Fan Wu","doi":"10.1145/2593929.2600116","DOIUrl":"https://doi.org/10.1145/2593929.2600116","url":null,"abstract":"This paper presents a brief outline of an approach to online genetic improvement. We argue that existing progress in genetic improvement can be exploited to support adaptivity. We illustrate our proposed approach with a 'dreaming smart device' example that combines online and offline machine learning and optimisation.","PeriodicalId":168314,"journal":{"name":"International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126599651","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 notion of Contextual Requirements refers to the inter-relation between the requirements of a system, both functional and non-functional (NFRs), and the dynamic environment in which the system operates. Dependability requirements are NFRs which could also be context-dependent. The meaning and the consequence of faults affecting dependability vary in relation to the context in which a fault occurs. In this paper, we elaborate on the need to consider the contextual nature of failures and dependability. Then, we extend a contextual requirements model, the contextual goal model, to capture contextual failures and utilize that to enrich the semantic of dependability requirements. We provide techniques to analyse and reason about the effects of contexts on failures and their consequences. This analysis helps evaluate the possible alternative configurations to reach goals from dependability perspective and, hence, take adaptation decisions. Finally, we demonstrate the feasibility and applicability of our approach on a Mobile Personal Emergency Response system.
{"title":"Modelling and analysing contextual failures for dependability requirements","authors":"D. Mendonça, Raian Ali, G. Rodrigues","doi":"10.1145/2593929.2593947","DOIUrl":"https://doi.org/10.1145/2593929.2593947","url":null,"abstract":"The notion of Contextual Requirements refers to the inter-relation between the requirements of a system, both functional and non-functional (NFRs), and the dynamic environment in which the system operates. Dependability requirements are NFRs which could also be context-dependent. The meaning and the consequence of faults affecting dependability vary in relation to the context in which a fault occurs. In this paper, we elaborate on the need to consider the contextual nature of failures and dependability. Then, we extend a contextual requirements model, the contextual goal model, to capture contextual failures and utilize that to enrich the semantic of dependability requirements. We provide techniques to analyse and reason about the effects of contexts on failures and their consequences. This analysis helps evaluate the possible alternative configurations to reach goals from dependability perspective and, hence, take adaptation decisions. Finally, we demonstrate the feasibility and applicability of our approach on a Mobile Personal Emergency Response system.","PeriodicalId":168314,"journal":{"name":"International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126021537","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}
Designing an adaptive system to meet its quality constraints in the face of environmental uncertainties can be a challenging task. In cloud environment, a designer has to also consider and evaluate different control points, i.e., those variables that affect the quality of the software system. This paper presents a method for eliciting, evaluating and ranking control points for web applications deployed in cloud environments. The proposed method consists of several phases that take high-level stakeholders' adaptation goals and transform them into lower level MAPE-K loop control points. The MAPE-K loops are then activated at runtime using search-based algorithms. We conducted several experiments to evaluate the different phases of our methodology.
{"title":"Designing search based adaptive systems: a quantitative approach","authors":"P. Zoghi, Mark Shtern, Marin Litoiu","doi":"10.1145/2593929.2593935","DOIUrl":"https://doi.org/10.1145/2593929.2593935","url":null,"abstract":"Designing an adaptive system to meet its quality constraints in the face of environmental uncertainties can be a challenging task. In cloud environment, a designer has to also consider and evaluate different control points, i.e., those variables that affect the quality of the software system. This paper presents a method for eliciting, evaluating and ranking control points for web applications deployed in cloud environments. The proposed method consists of several phases that take high-level stakeholders' adaptation goals and transform them into lower level MAPE-K loop control points. The MAPE-K loops are then activated at runtime using search-based algorithms. We conducted several experiments to evaluate the different phases of our methodology.","PeriodicalId":168314,"journal":{"name":"International Symposium on Software Engineering for Adaptive and Self-Managing Systems","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115724082","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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