2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)最新文献

{"title":"Digital Shadows in Self-Improving System Integration: A Concept U sing Generative Modelling","authors":"Ghassan Al-Falouji, Christian Gruhl, Sven Tomforde","doi":"10.1109/ACSOS-C52956.2021.00047","DOIUrl":"https://doi.org/10.1109/ACSOS-C52956.2021.00047","url":null,"abstract":"Self-improving system integration (SISSY) aims at a continuous self-assessment and goal-oriented modification of the integration status of component systems within an overall system constellation. In this article, we argue that the process of self-assessing the conditions and the integration status can be supported by a continuously generated and updated model of the self - the digital shadow. We briefly summarise the concept by showing how this can serve as a basis for establishing the self-improvement part of SISSY and introduce three recent application scenarios, where we apply the technology.","PeriodicalId":268224,"journal":{"name":"2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)","volume":"29 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120976360","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}

{"title":"Research directions for Aggregate Computing with Machine Learning","authors":"Gianluca Aguzzi","doi":"10.1109/ACSOS-C52956.2021.00078","DOIUrl":"https://doi.org/10.1109/ACSOS-C52956.2021.00078","url":null,"abstract":"Collective adaptive systems are challenging from the engineering perspective. Different approaches aim at taming these systems either by specifying the behaviour programmatically or by using Machine Learning techniques. Aggregate programming is part of the first group and is a novel technique by which developers can express collective system behaviours from a global perspective, using a compositional and functional programming approach. Over the years, Aggregate Computing has been applied in different scenarios, ranging from smart cities to crowd of augmented people. Despite its promising capabilities, it is sometimes challenging to describe aggregate behaviours, so we aim at merging Aggregate Computing with Machine Learning techniques to simplify the aggregate program synthesis.","PeriodicalId":268224,"journal":{"name":"2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132483859","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}

{"title":"Modeling and Integration for Complex Systems","authors":"C. Landauer","doi":"10.1109/ACSOS-C52956.2021.00056","DOIUrl":"https://doi.org/10.1109/ACSOS-C52956.2021.00056","url":null,"abstract":"In this paper, we propose a way forward in modeling complex software-managed systems that relies on two well-known mathematical constructs, the subdirect product and the quotient by a congruence relation. These mathematical techniques allow us to build models that correspond to different aspects of / concerns about the system (which we call viewpoints), and to integrate them afterwards. It does require models of the interactions among these viewpoints, but that kind of consideration should occur no matter what kind of modeling is being carried out. This provides an explicit way for designers to enumerate and analyze all the design considerations, which, of course, they should be considering anyway, but often do not because there are difficulties in studying model interactions, and no particular theoretical base for doing so. This paper is our first attempt to address that problem. We describe some of the relevant viewpoint models, show how some of their interactions might be modeled, and explain the underlying mathematical methods.","PeriodicalId":268224,"journal":{"name":"2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125698195","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}

{"title":"Employing Stochastic Multiplayer Games to Support Self-Organization over Ad Hoc Networks","authors":"Ian Riley, R. Gamble","doi":"10.1109/ACSOS-C52956.2021.00032","DOIUrl":"https://doi.org/10.1109/ACSOS-C52956.2021.00032","url":null,"abstract":"Self-organization over ad hoc networks is of growing interest due to their application to collective adaptive systems of IoT devices, such as wearables and drones. These devices can possess situational goals that depend on the availability of external services provided by service providers (SPs) within the ad hoc network. SPs have quality-of-service (QoS) attributes whose values are based on the service(s) they have available. These QoS attributes can be negatively affected by environmental sources of uncertainty as well as behavioral constraints imposed on the SP to support self-organization via integration. Novel mechanisms are needed to evaluate the impact of an integration configuration on the SP's QoS attributes to produce Pareto optimal configurations. We construct a stochastic multiplayer game (SMG) that evaluates a SP's expected satisficing level given its privileged data access, sources of uncertainty, and QoS values. Polynomial regression is applied to the output of the SMG to produce a model to evaluate an integration configuration at runtime. We demonstrate the model on a rescue scenario involving wearables and drones and examine the efficacy of the resulting configurations.","PeriodicalId":268224,"journal":{"name":"2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)","volume":"12 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124913196","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}

{"title":"Combining Central Control with Collective Adaptive Systems","authors":"Christian Kröher, Klaus Schmid, Simon Paasche, C. Sauer","doi":"10.1109/ACSOS-C52956.2021.00035","DOIUrl":"https://doi.org/10.1109/ACSOS-C52956.2021.00035","url":null,"abstract":"Collective Adaptive Systems (CAS) achieve high resilience by providing distributed self-* properties through autonomous elements. While they are very beneficial for achieving high reliability, they are more difficult for dealing with targeted external inputs or ensuring globally optimal results. It is easier to achieve these properties with centralized approaches. Ideally, one could combine the benefits of collective adaptive systems with aspects of central control to achieve the best of both paradigms. While such combinations have already been shown in previous work, here, we aim at a systematic discussion of the range of approaches to integrate both control paradigms. As a result, we present a taxonomy of control action types, which describes how a central control unit can interact with a CAS to achieve the desired overall behavior, providing a first step towards CAS control patterns and identifying the involved trade-offs. Ideally, one could combine the benefits of collective adaptive systems with aspects of central control to achieve the best of both paradigms. While such combinations have already been shown in previous work, here, we aim at a systematic discussion of the range of approaches to integrate both control paradigms. As a result, we present a taxonomy of control action types, which describes how a central control unit can interact with a CAS to achieve the desired overall behavior, providing a first step towards CAS control patterns and identifying the involved trade-offs.","PeriodicalId":268224,"journal":{"name":"2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124987422","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}

{"title":"9th International Workshop on Autonomic Management of High Performance Grid and Cloud Computing (AMGCC 2021) [Organizing Committee and Program Committee]","authors":"","doi":"10.1109/acsos-c52956.2021.00013","DOIUrl":"https://doi.org/10.1109/acsos-c52956.2021.00013","url":null,"abstract":"","PeriodicalId":268224,"journal":{"name":"2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124934398","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}

{"title":"MeLoN: Distributed Deep Learning meets the Big Data Platform","authors":"Dae-Cheol Kang, Seoungbeom Heo, Hyeounji Jang, Hyeock-Jin Lee, Minkyoung Cho, Jik-Soo Kim","doi":"10.1109/ACSOS-C52956.2021.00028","DOIUrl":"https://doi.org/10.1109/ACSOS-C52956.2021.00028","url":null,"abstract":"Recent advancements in Artificial Intelligence have brought “Deep Learning” frameworks to be a cornerstone for the 4th Industrial Revolution along with “Big Data” platform technologies such as Apache Hadoop. However, efficient processing of deep learning applications has become challenging as the overall sizes of data and model increase rapidly. To address this problem, we can leverage big data platforms that have successfully provided stable storage and data processing capability during the past decade. In this paper, we present design and implementation of MeLoN (Multi-tenant dEep Learning framework On yarN) that can effectively run distributed deep learning applications on top of the big data platform Hadoop. MeLoN takes expected GPU memory usages of a deep learning application as an input parameter, and employs a GPU over-provisioning policy that can improve the overall resource utilization. Evaluation results show that MeLoN can improve the overall system throughput for concurrently running multiple deep learning applications in a Hadoop cluster. MeLoN can bring many interesting research issues related to profiling of expected GPU memory usages of deep learning applications, storage optimizations for deep learning processing, supporting complex deep learning related jobs based on queuing systems which can ultimately contribute to a new data processing framework in the YARN-based Hadoop ecosystem. In this paper, we present design and implementation of MeLoN (Multi-tenant dEep Learning framework On yarN) that can effectively run distributed deep learning applications on top of the big data platform Hadoop. MeLoN takes expected GPU memory usages of a deep learning application as an input parameter, and employs a GPU over-provisioning policy that can improve the overall resource utilization. Evaluation results show that MeLoN can improve the overall system throughput for concurrently running multiple deep learning applications in a Hadoop cluster. MeLoN can bring many interesting research issues related to profiling of expected GPU memory usages of deep learning applications, storage optimizations for deep learning processing, supporting complex deep learning related jobs based on queuing systems which can ultimately contribute to a new data processing framework in the YARN-based Hadoop ecosystem.","PeriodicalId":268224,"journal":{"name":"2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121114998","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}

{"title":"Challenges of Big Data and Vehicle Data","authors":"C. Prehofer","doi":"10.1109/ACSOS-C52956.2021.00070","DOIUrl":"https://doi.org/10.1109/ACSOS-C52956.2021.00070","url":null,"abstract":"In this short paper, we outline the challenges of Big Data processing for vehicle data, considering different use cases as well as different locations. On an architectural level, we show the benefits and possible limitations of the different approaches for in-vehicle processing, edge computing as well as cloud processing. We also discuss different approaches for Big Data Processing, including batch processing as well as distributed stream processing.","PeriodicalId":268224,"journal":{"name":"2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121308953","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}

{"title":"Engineering Adaptive Authentication","authors":"Alzubair Hassan, B. Nuseibeh, L. Pasquale","doi":"10.1109/ACSOS-C52956.2021.00068","DOIUrl":"https://doi.org/10.1109/ACSOS-C52956.2021.00068","url":null,"abstract":"Adaptive authentication systems identify and enforce suitable methods to verify that someone (user) or something (device) is eligible to access a service or a resource. An authentication method is usually adapted in response to changes in the security risk or the user's behaviour. Previous work on adaptive authentication systems provides limited guidance about i) what and how contextual factors can affect the selection of an authentication method; ii) which requirements are relevant to an adaptive authentication system and iii) how authentication methods can affect the satisfaction of the relevant requirements. In this paper, we provide a holistic framework informed by previous research to characterize the adaptive authentication problem and support the development of an adaptive authentication system. Our framework explicitly considers the contextual factors that can trigger an adaptation, the requirements that are relevant during decision making and their trade-offs, as well as the authentication methods that can change as a result of an adaptation. From the gaps identified in the literature, we elicit a set of challenges that can be addressed in future research on adaptive authentication.","PeriodicalId":268224,"journal":{"name":"2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127050417","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}

{"title":"BDI-Dojo: developing robust BDI agents in evolving adversarial environments","authors":"S. Pulawski, K. Dam, A. Ghose","doi":"10.1109/ACSOS-C52956.2021.00066","DOIUrl":"https://doi.org/10.1109/ACSOS-C52956.2021.00066","url":null,"abstract":"The Belief-Desire-Intention (BDI) architecture is a widely-used model for developing multi-agent systems. BDI agents pursue their goals over time using a collection of plan recipes that are programmed by the developers. Thus, traditional BDI agents are limited in dealing with dynamic environments where uncertainties are not known beforehand, such as those introduced by adversarial forces. In this paper, we present the BDI-Dojo framework for developing robust BDI agents by training them using reinforcement learning against similarly learning-equipped adversarial agents. This adversarial training approach empowers BDI agents to become more resilient in uncertain, dynamic environments.","PeriodicalId":268224,"journal":{"name":"2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114643904","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}