Browsing by Author "Truong, Hong Linh"
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- Architecturing elastic edge storage services for data-driven decision making
A4 Artikkeli konferenssijulkaisussa(2019-01-01) Lujic, Ivan; Truong, Hong LinhIn the IoT era, a massive number of smart sensors produce a variety of data at unprecedented scale. Edge storage has limited capacities posing a crucial challenge for maintaining only the most relevant IoT data for edge analytics. Currently, this problem is addressed mostly considering traditional cloud-based database perspectives, including storage optimization and resource elasticity, while separately investigating data analytics approaches and system operations. For better support of future edge analytics, in this work, we propose a novel, holistic approach for architecturing elastic edge storage services, featuring three aspects, namely, (i) data/system characterization (e.g., metrics, key properties), (ii) system operations (e.g., filtering, sampling), and (iii) data processing utilities (e.g., recovery, prediction). In this regard, we present seven engineering principles for the architecture design of edge data services. - Benchmarking Blockchain Interactions in Mobile Edge Cloud Software Systems
A4 Artikkeli konferenssijulkaisussa(2021) Truong, Hong Linh; Rydzi, FilipAs blockchain becomes an essential part of many software systems in the edge and cloud, the developer starts to treat blockchain features like commodity software components that can be integrated into edge and cloud software systems. For the developer it is quite challenging to determine, customize, and evaluate suitable blockchain features for software systems in the edge and cloud environments. In this paper, we conceptualize important blockchain interactions in mobile edge computing software systems (MECSS) and present generic techniques for evaluating these interactions. We determine different interaction patterns for different deployments of compute resources and networks. We abstract and represent application-level mobile edge computing (MEC) features and blockchain features to create MECSS deployment models to be coupled with testbed deployments for benchmarking application-level interactions within application contexts. Based on that, we develop a generic framework for building and executing benchmarks of application-level blockchain interactions within MECSS. We will demonstrate our framework for vehicle-to-everything communication scenarios with two main blockchain technologies, Hyperledger Fabric and Ethereum, using various types of compute resources in edge and cloud infrastructures. - Novel contract-based runtime explainability framework for end-to-end ensemble machine learning serving
A4 Artikkeli konferenssijulkaisussa(2024-04-14) Nguyen, Minh Tri; Truong, Hong Linh; Truong-Huu, TramThe growing complexity of end-to-end Machine Learning (ML) serving across the edge-cloud continuum has raised the necessity for runtime explainability to support service optimizations, transparency, and trustworthiness. That involves many challenges in managing ML service quality and engineering runtime explainability based on ML service contracts. Currently, consumers use ML services almost as a black box with insufficient explainability for not only inference decisions but also other contractual aspects, such as data/service quality and costs. The generic explainability for ML models is inadequate to explain the runtime ML usage for individual consumers. Moreover, ML-specific metrics have not been addressed in existing service contracts. In this work, we introduce a novel contract-based runtime explainability framework for end-to-end ensemble ML serving. The framework provides a comprehensive engineering toolset, including explainability constraints in ML contracts, report schemas, and interactions between ML consumers and the components of the ML serving for evaluating service quality with contract-based explanations. We develop new monitoring probes to measure ML-specific metrics on data quality, inference confidence, inference accuracy, and capture runtime ML usage. Finally, we present essential quality analyses via an observation agent. That interprets ML inferences and evaluates contributions of ML inference microservices, assisting ML serving optimization. The agent also integrates ML algorithms for detecting relations among metrics, supporting constraint developments. We demonstrate our work with two real-world applications for malware and object detection. - On Coordinating LLMs and Platform Knowledge for Software Modernization and New Developments
A4 Artikkeli konferenssijulkaisussa(2024) Truong, Hong Linh; Vukovic, Maja; Pavuluri, RajuEmerging generative and fine-tuning LLMs services have been widely benchmarked and used for various software development tasks. These LLMs services are powerful but have different output qualities for software development tasks and may not be able to deal with complex development tasks in edge-cloud software modernization and new developments due to their generative capabilities and lack of up-ro-date (domain) knowledge. Many queries and solutions related to target platforms, deploy-ment configurations, policies, data regulation, observability, to name just a few, are not well integrated with these LLMs, but are accessed by the developer through other sources. In this work, we discuss situations where the gaps between the needs and the offerings from LLMs can be compensated by Platform Knowledge, which captures knowledge about, e.g., software, service and infrastructure catalogs, architectural decision records and code patterns. We propose COLLMS - a framework for coordinating LLMs services and Platform Knowledge. At the starting point of the framework, we will discuss challenges for achieving the coordination centered around Platform Knowledge, LLMs management and integration, quality-aware coordination of LLMs, and observability and knowledge updating. - On-the-fly collaboration for legacy business process systems in an open service environment
A4 Artikkeli konferenssijulkaisussa(2019-07-01) Ye, Lin; Zhu, Biqi; Hu, Chenglong; Zhang, Liang; Truong, Hong LinhDynamic, distributed and open business forces enterprises to support various critical requirements, such as, timely reacting to changes, properly reusing business assets and smoothly collaborating with external partners. Existing approaches focus on mechanisms dealing with heterogeneity, but there is a lack of frameworks enabling legacy business processes performing collaboration in an open service environment. This paper proposes the L2L service framework featuring reactive IoT event messaging and coordinator-based collaborating between autonomous enterprises. Along with the emerging of coordinators, L2L empowers on-the-fly business process collaboration with dynamic changes. We present our experiments with a real-world scenario from the shipping industry of China. - Quality of Service Aware Orchestration for Cloud–Edge Continuum Applications
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2022-03-01) Orive, Adrián; Agirre, Aitor; Truong, Hong Linh; Sarachaga, Isabel; Marcos, MargaThe fast growth in the amount of connected devices with computing capabilities in the past years has enabled the emergence of a new computing layer at the Edge. Despite being resource-constrained if compared with cloud servers, they offer lower latencies than those achievable by Cloud computing. The combination of both Cloud and Edge computing paradigms can provide a suitable infrastructure for complex applications’ quality of service requirements that cannot easily be achieved with either of these paradigms alone. These requirements can be very different for each application, from achieving time sensitivity or assuring data privacy to storing and processing large amounts of data. Therefore, orchestrating these applications in the Cloud–Edge computing raises new challenges that need to be solved in order to fully take advantage of this layered infrastructure. This paper proposes an architecture that enables the dynamic orchestration of applications in the Cloud– Edge continuum. It focuses on the application’s quality of service by providing the scheduler with input that is commonly used by modern scheduling algorithms. The architecture uses a distributed scheduling approach that can be customized in a per-application basis, which ensures that it can scale properly even in setups with high number of nodes and complex scheduling algorithms. This architecture has been implemented on top of Kubernetes and evaluated in order to asses its viability to enable more complex scheduling algorithms that take into account the quality of service of applications. - TENSAI - Practical and Responsible Observability for Data Quality-aware Large-scale Analytics
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2024-12-19) Truong, Hong Linh; Nguyen, Ngoc Nhu TrangGiven a large-scale mobile network with a variety of equipment and radio access network technologies for an approximate 20 million subscribers, there are many types of data that can be used for big data analytics and machine learning (ML) tasks for network operations, monitoring, and optimization. However, a variety of data is measured, collected, and propagated through numerous complex data and software systems. Thus, people, software components, and data-driven operations for big data and ML pipelines face great challenges in dealing with data quality impacts. Data quality related problems occur and are propagated through complex operations involving different types of data, people, software components, and analytics that cannot be solved purely through data quality engineering. This article discusses our TENSAI framework, as a practical and responsible observability for ensuring data quality in such a mobile network. TENSAI focuses on methods of communication, strategy specifications, and data quality engineering for diverse types of data and analytics among different types of operations. TENSAI presents techniques for capturing and communicating causes/effects of data quality problems clearly to all relevant stakeholders, developing data quality-aware adaptation strategies for actions on data that can be integrated into analytics processes, and engineering data quality awareness in software and data pipelines. Thus, TENSAI supports full visibility of data quality problems and impacts among related systems to empower the utilization and adaptation of data analytics for different types of operations. We will illustrate our TENSAI with several real-world data types, pipelines, and cases based on a real-world mobile network.