Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are su...
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Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL’s model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model.
The MODELS series of conferences is the premier venue for the exchange of - novative technical ideas and experiences focusing on a very important new te- nical discipline: model-driven software and systemsengineering...
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ISBN:
(数字)9783642161292
ISBN:
(纸本)9783642161285
The MODELS series of conferences is the premier venue for the exchange of - novative technical ideas and experiences focusing on a very important new te- nical discipline: model-driven software and systemsengineering. The expansion ofthisdisciplineisadirectconsequenceoftheincreasingsigni?canceandsuccess of model-based methods in practice. Numerous e?orts resulted in the invention of concepts, languagesand tools for the de?nition, analysis,transformation, and veri?cationofdomain-speci?cmodelinglanguagesandgeneral-purposemodeling language standards, as well as their use for software and systemsengineering. MODELS 2010, the 13th edition of the conference series, took place in Oslo, Norway, October 3-8, 2010, along with numerous satellite workshops, symposia and tutorials. The conference was fortunate to have three prominent keynote speakers: Ole Lehrmann Madsen (Aarhus University, Denmark), Edward A. Lee (UC Berkeley, USA) and Pamela Zave (AT&T Laboratories, USA). To provide a broader forum for reporting on scienti?c progress as well as on experience stemming from practical applications of model-based methods, the 2010 conference accepted submissions in two distinct tracks: Foundations and Applications. The primary objective of the ?rst track is to present new research results dedicated to advancing the state-of-the-art of the discipline, whereas the second aims to provide a realistic and veri?able picture of the current state-- the-practice of model-based engineering, so that the broader community could be better informed of the capabilities and successes of this relatively young discipline. This volume contains the ?nal version of the papers accepted for presentation at the conference from both tracks.
On behalf of the Organizing Committee I am pleased to present the proceedings of the 2006 Symposium on Component-Based softwareengineering (CBSE). CBSE is concerned with the development of software-intensive systems ...
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ISBN:
(数字)9783540356295
ISBN:
(纸本)9783540356288
On behalf of the Organizing Committee I am pleased to present the proceedings of the 2006 Symposium on Component-Based softwareengineering (CBSE). CBSE is concerned with the development of software-intensive systems from reusable parts (components), the development of reusable parts, and system maintenance and improvement by means of component replacement and customization. CBSE 2006 was the ninth in a series of events that promote a science and technology foundation for achieving predictable quality in softwaresystems through the use of software component technology and its associated softwareengineering practices. We were fortunate to have a dedicated Program Committee comprising 27 internationally recognized researchers and industrial practitioners. We received 77 submissions and each paper was reviewed by at least three Program Committee members (four for papers with an author on the Program Committee). The entire reviewing process was supported by Microsoft’s CMT technology. In total, 22 submissions were accepted as full papers and 9 submissions were accepted as short papers. This was the first time CBSE was not held as a co-located event at ICSE. Hence special thanks are due to Ivica Crnkovic for hosting the event. We also wish to thank the ACM Special Interest Group on softwareengineering (SIGSOFT) for their sponsorship of CBSE 2005. The proceedings you now hold were published by Springer and we are grateful for their support. Finally, we must thank the many authors who contributed the high-quality papers contained within these proceedings.
作者：
NACHTSHEIM, JOHN J.BALLOU, L. DENNISJohn J. Nachtsheim:is currently the Deputy Assistant Administrator for Research & Development for the Maritime Administration. His duties are the planning
coordinating organizing evaluating and directing of the R&D activities of MarAd. His past experiences include: Naval Architect for the Naval Ship Engineering Center 1959 Deputy Chief Design Engineer for the Puget Sound Naval Shipyard
1958 to 1959 and Naval Architect
the former Bureau of Ships 1948 to 1958. His education is comprised of a B.S. degree from the Webb Institute of Naval Architecture an L.L.B. degree from the George Washington University Law School completion of the Advanced Management Program at Harvard University and current study of Transportation at the American University. He is a Registered Professional Engineer in the District of Columbia and a Member of the Bar in the District of Columbia and the State of Maryland. In addition to ASNE his other professional memberships include the Society of Naval Architects and Marine Engineers the Society of Aeronautical Weight Engineers and the Association of Senior Engineers of the Naval Ships Systems Command (Honorary). USNCommander L. Dennis Ballou:
USN is the Head of the Engineering Service Office Naval Ship Engineering Center. He is involved in computer hardware and software services to support engineering design automatic data processing systems design work study and quality assurance. Prior to NavSec duty Commander Ballou served in various billets afloat and ashore: tours on the USS Skagit and Tang supervision of the USS Skipjack's first overhulconstruction of the USS Nathanael Greene and helping to establish the Polaris overhaul program. He is a graduate of the U.S. Naval Academy
Officers' Submarine School and the Webb Institute of Naval Architecture. He holds BS and MS degrees in marine engineering and naval architecture respectively. He has also completed many graduate
Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data manag...
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Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.
The rapid advancements in big data and the Internet of Things (IoT) have significantly accelerated the digital transformation of medical institutions, leading to the widespread adoption of Digital Twin Healthcare (DTH...
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The rapid advancements in big data and the Internet of Things (IoT) have significantly accelerated the digital transformation of medical institutions, leading to the widespread adoption of Digital Twin Healthcare (DTH). The Cloud DTH Platform (CDTH) serves as a cloud-based framework that integrates DTH models, healthcare resources, patient data, and medical services. By leveraging real-time data from medical devices, the CDTH platform enables intelligent healthcare services such as disease prediction and medical resource optimization. However, the platform functions as a system of systems (SoS), comprising interconnected yet independent healthcare services. This complexity is further compounded by the integration of both black-box AI models and domain-specific mechanistic models, which pose challenges in ensuring the interpretability and trustworthiness of DTH models. To address these challenges, we propose a Model-Based systemsengineering (MBSE)-driven DTH modeling methodology derived from systematic requirement and functional analyses. To implement this methodology effectively, we introduce a DTH model development approach using the X language, along with a comprehensive toolchain designed to streamline the development process. Together, this methodology and toolchain form a robust framework that enables engineers to efficiently develop interpretable and trustworthy DTH models for the CDTH platform. By integrating domain-specific mechanistic models with AI algorithms, the framework enhances model transparency and reliability. Finally, we validate our approach through a case study involving elderly patient care, demonstrating its effectiveness in supporting the development of DTH models that meet healthcare and interpretability requirements.
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