2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)最新文献

{"title":"MAPE-SAC: A Framework to Dynamically Manage Security Assurance Cases","authors":"Sharmin Jahan, M. Pasco, R. Gamble, P. McKinley, B. Cheng","doi":"10.1109/FAS-W.2019.00045","DOIUrl":"https://doi.org/10.1109/FAS-W.2019.00045","url":null,"abstract":"Assuring security compliance in self-adaptive systems is challenging, notably as both functional and security conditions may change at run time, where adaptation of functional behavior may violate security requirements or vice versa. In traditional systems, certification is performed at design time on the mechanisms that will be deployed to guarantee the effectiveness of organizationally chosen and instantiated security controls defined by standards bodies (e.g., NIST SP800-53). In contrast, adaptive systems benefit by run-time adaptations for which dynamic certification could be difficult. Confidence in an information system's compliance with security constraints can be expressed using security assurance cases (SACs). Specifically, NIST security controls follow a repeated structure that make them amenable to their specification in terms of SACs. The collection of SACs for the related security controls form a network that can be used to assess the level of the system's compliance through certification-based evidence. Once the system is deployed, environmental and functional uncertainties may require more complex adaptations that include the coordination of functional and security adaptations. This paper introduces the MAPE-SAC control loop and its interaction with the MAPE-K control loop to dynamically manage run-time adaptations in response to changes in functional and security conditions. We illustrate the use of both control loops and their interaction using an example of an autonomous rover responding to a potential security incident.","PeriodicalId":368308,"journal":{"name":"2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131741837","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":"Integration of Pervasive Platforms with iCasa","authors":"P. Lalanda, A. Diaconescu","doi":"10.1109/FAS-W.2019.00025","DOIUrl":"https://doi.org/10.1109/FAS-W.2019.00025","url":null,"abstract":"The purpose of this paper is to present a recent project called INTEROP conducted between Mannheim and Grenoble universities. Its goal is to define and develop architectures and models enabling interoperability between platforms in pervasive environments.","PeriodicalId":368308,"journal":{"name":"2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129082693","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":"Towards an Autonomic and Distributed Device Management for the Internet of Things","authors":"Neil Ayeb, É. Rutten, Sébastien Bolle, T. Coupaye, Marc Douet","doi":"10.1109/FAS-W.2019.00065","DOIUrl":"https://doi.org/10.1109/FAS-W.2019.00065","url":null,"abstract":"Device Management (DM) is currently industrially deployed for LAN devices, phones and workstation management. Internet of Things (IoT) devices are massive, dynamic, heterogeneous, and inter-operable. Existing solutions are not suitable for IoT management. This doctoral research in an industrial environment addresses these limitations with a novel autonomic and distributed approach for the DM.","PeriodicalId":368308,"journal":{"name":"2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124768654","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":"Towards Predicting GPGPU Performance for Concurrent Workloads","authors":"Sunggon Kim, Dongwhan Kim, Hyeonsang Eom, Yongseok Son","doi":"10.1109/FAS-W.2019.00048","DOIUrl":"https://doi.org/10.1109/FAS-W.2019.00048","url":null,"abstract":"General-Purpose Graphics Processing Units (GPGPUs) have been widely adapted to the industry due to the high parallelism of Graphics Processing Units (GPUs) compared with Central Processing Units (CPUs). To handle the ever-increasing demand, multiple applications often run concurrently in the GPGPU device. However, the GPGPU device can be under-utilized when various types of GPGPU applications are running concurrently. In this paper, we analyze various types of scientific applications and identify factors that impact the performance during the concurrent execution of the applications in the GPGPU device. Our analysis results show that each application has a distinct characteristic and a certain combination of applications has better performance compared with the others when executed concurrently. Based on the finding of our analysis, we propose a simulator which predicts the performance of GPGPU. Our simulator collects performance metrics during the execution of applications and predicts the performance benefits. The experimental result shows that the best combination of applications can increase the performance by 39.44% and 65.98% compared with the average of combinations and the worst case, respectively.","PeriodicalId":368308,"journal":{"name":"2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131358937","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":"\"When you Believe in Things that you don't Understand\": the Effect of Cross-Generational Habits on Self-Improving System Integration","authors":"Chloe M. Barnes, Lukas Esterle, John N. A. Brown","doi":"10.1109/FAS-W.2019.00020","DOIUrl":"https://doi.org/10.1109/FAS-W.2019.00020","url":null,"abstract":"Humans experiencing unexpected feedback to certain actions which they are not able to explain, might develop superstitious behaviour. In this paper, we discuss that similar behaviour might also occur in engineered systems. We provide a thought-experiment regarding such behaviour in computational systems. In this paper, we propose a first step towards improved runtime systems integration based on a the ability to become aware of previously-unknown others and their actions, as described in networked self-awareness.","PeriodicalId":368308,"journal":{"name":"2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121408353","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":"Optimizing Cloud Caches For Free: A Case for Autonomic Systems with a Serverless Computing Approach","authors":"Xavier Andrade, J. Cedeño, Edwin F. Boza, Harold Aragon, Cristina L. Abad, Jorge R. Murillo","doi":"10.1109/FAS-W.2019.00044","DOIUrl":"https://doi.org/10.1109/FAS-W.2019.00044","url":null,"abstract":"While significant advances have been made towards realizing self-tuning cloud caches, existing products still require manual tuning. These systems are built to serve requests extremely fast and anything that consumes resources not directly related to the request-serving control path is avoided. We show that severless computing platforms can be leveraged to solve complex optimization problems that arise during self-tuning loops, and thus can be used to optimize resources in cloud caches, for free. To show that our approach is feasible and useful, we implement SPREDS (Self-Partitioning REDis), a modified version of Redis that optimizes memory management in the multi-instance Redis scenario. Through this case study and cost analysis, we make a case for implementing the controller of autonomic systems using a serverless computing approach.","PeriodicalId":368308,"journal":{"name":"2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127788554","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":"Profiling Dynamic Data Access Patterns with Bounded Overhead and Accuracy","authors":"Seongjae Park, Yunjae Lee, Yoonhee Kim, H. Yeom","doi":"10.1109/FAS-W.2019.00054","DOIUrl":"https://doi.org/10.1109/FAS-W.2019.00054","url":null,"abstract":"One common characteristic of modern workloads such as cloud, big data, and machine learning is memory intensiveness. In detail, such workloads tend to have a huge working set and low locality. Especially, the size of working sets is rapidly growing so that cannot be fully accommodated by a DRAM based main memory. Worse yet, the cloud computing systems, which has been pervasive since few decades ago, are continuously reducing the size of DRAM per CPU and encouraging memory overcommitment. Consequently, efficient and effective out-of-core memory management is becoming more important. Though a number of memory management mechanisms for such situations have proposed, manual analysis and optimization are still required for optimal performance of each workload due to the wide variety of data access patterns. However, existing tools for memory access analysis are not appropriate to be used here because those are not designed for extraction of the dynamic data access pattern of modern workloads. When those tools are used for the purpose, those incur unacceptably high overheads for unnecessarily accurate analysis results. To mitigate this situation, we introduce a tool that is designed for the purpose. Basically, the tool employs a memory access tracking technique based on page table entry access bit, which incurs only minimal overhead. It also provides a technique for an effective tradeoff between profiling overheads and accuracy of the output by dynamically adjusting number of tracking regions. By adopting the technique, this tool can control the level of overheads and output accuracy in bounded range that user specified regardless of the size of target workloads. The overhead can be lowered even enough to be used for online target workloads while still providing useful quality of the extracted data access pattern. The main contributions of this paper are: 1) introduce of the data access patterns profiler tool designed for modern memory-intensive workloads, and 2) empirical memory access pattern analysis of various realistic workloads.","PeriodicalId":368308,"journal":{"name":"2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126349457","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":"Learning Approach for Smart Self-Adaptive Cyber-Physical Systems","authors":"A. Petrovska, A. Pretschner","doi":"10.1109/FAS-W.2019.00061","DOIUrl":"https://doi.org/10.1109/FAS-W.2019.00061","url":null,"abstract":"Modern Cyber-Physical Systems (CPSs) need to be able to operate efficiently and reliably within continually changing, uncertain, and unanticipated environments. Namely, these systems should be capable of learning, automatically reconfiguring themselves, and be able to cooperate and collaborate with other CPSs. In a nutshell, exhibit human-like, smart capabilities in an autonomic manner. However, engineering such systems is all but trivial, primarily because we need to develop systems at design-time that are capable of autonomously coping with the uncertainty and change at runtime. Therefore, not only the importance of self-adaptivity as a system's feature increases, but it becomes a fundamental approach for the systems to continue meeting their functional specifications, fulfilling their business objectives while preserving the performance-despite all the runtime changes that the system may encounter. To tackle these challenges, this paper proposes the initial research vision and agenda with the envisioned contributions towards an approach for self-adaptation of cooperative, smart CPSs through shared knowledge and learning.","PeriodicalId":368308,"journal":{"name":"2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121325169","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":"Crossing the Adaptation Boundaries of Distinct Testbeds","authors":"Charles Walter, R. Gamble","doi":"10.1109/FAS-W.2019.00022","DOIUrl":"https://doi.org/10.1109/FAS-W.2019.00022","url":null,"abstract":"Testbeds to experiment with self-adaptive systems allow for the examination of a range of domain-specific problems without the need for specialized or proprietary equipment. They can focus on a need for adaptive control, such as mission completion and security threats, or study mechanisms to allow self-adaptation, such as embedding component awareness and performing the actual deployment of executable code changes during runtime for dynamic adaptation. Individually, needs often narrow the underlying models and functionality of the testbeds so that the experiments can be controlled and understood. There are multiple options to extend the experiments, such as significantly increasing the testbed components, functional requirements, and potential adaptations. However, these options, while necessary to have a fuller understanding of the scalability of the testbed, could maintain an inherent bias based on how the testbed was intended to perform its original operation, limiting its potential for self-improvement. Another option is to introduce some form of integration with a different testbed to determine how each can influence the other's adaptation mechanisms to improve self-awareness techniques. In this paper, we overview our two existing testbeds created for experimenting with self-adaptation concepts. Each testbed employs different runtime model checking and adaptation risk assessment mechanisms, with distinct functional goals. We discuss the difficulties in crossing the adaptation boundaries to perform self-improvement and to increase the potential for valuable communication and awareness.","PeriodicalId":368308,"journal":{"name":"2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126843380","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":"Efficient Adaptive Resource Provisioning for Cloud Applications using Reinforcement Learning","authors":"I. John, Aiswarya Sreekantan, S. Bhatnagar","doi":"10.1109/FAS-W.2019.00077","DOIUrl":"https://doi.org/10.1109/FAS-W.2019.00077","url":null,"abstract":"An appealing feature of cloud computing is elasticity, that allows shrinking or expanding the resources allocated to an application in order to adjust to workload variations. The resource provisioning algorithm must also adhere to the performance requirements specified in the Service Level Agreement between the cloud provider and the client who runs the application. While the use of Reinforcement learning algorithms such as Q-learning has been proposed already to address this problem, those suffer from slow convergence and scalability issues. In this paper, we explore methods for overcoming such challenges and ensuring effective resource utilization. Preliminary experiments on CloudSim platform demonstrate the superiority of some of these methods over static, threshold-based and other reinforcement learning based allocation schemes.","PeriodicalId":368308,"journal":{"name":"2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124207424","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}