| 1. |
- Ali, Shaukat, et al.
(author)
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Big data from the cloud to the edge : The aggregate computing solution
- 2019
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In: PervasiveHealth. - New York, NY, USA : ACM Publications. - 9781450371421 ; , s. 177-182
-
Conference paper (peer-reviewed)abstract
- We advocate a novel concept of dependable intelligent edge systems (DIES) i.e., the edge systems ensuring a high degree of dependability (e.g., security, safety, and robustness) and autonomy because of their applications in critical domains. Building DIES entail a paradigm shift in architectures for acquiring, storing, and processing potentially large amounts of complex data: data management is placed at the edge between the data sources and local processing entities, with loose coupling to storage and processing services located in the cloud. As such, the literal definition of edge and intelligence is adopted, i.e., the ability to acquire and apply knowledge and skills is shifted towards the edge of the network, outside the cloud infrastructure. This paradigm shift offers flexibility, auto configuration, and auto diagnosis, but also introduces novel challenges. © 2019 ACM.
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| 2. |
- Alkhabbas, Fahed, et al.
(author)
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A Goal driven Approach for Deploying Self-adaptive IoT Systems
- 2020
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In: Proceedings. - 9781728146591 - 9781728146607 ; , s. 146-156
-
Conference paper (peer-reviewed)abstract
- Engineering Internet of Things (IoT) systems is a challenging task partly due to the dynamicity and uncertainty of the environment including the involvement of the human in the loop. Users should be able to achieve their goals seamlessly in different environments, and IoT systems should be able to cope with dynamic changes. Several approaches have been proposed to enable the automated formation, enactment, and self-adaptation of goal-driven IoT systems. However, they do not address deployment issues. In this paper, we propose a goal-driven approach for deploying self-adaptive IoT systems in the Edge-Cloud continuum. Our approach supports the systems to cope with the dynamicity and uncertainty of the environment including changes in their deployment topologies, i.e., the deployment nodes and their interconnections. We describe the architecture and processes of the approach and the simulations that we conducted to validate its feasibility. The results of the simulations show that the approach scales well when generating and adapting the deployment topologies of goal-driven IoT systems in smart homes and smart buildings.
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| 3. |
- Baresi, Luciano, et al.
(author)
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Building Software for the Internet of Things
- 2015. - 9
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In: IEEE Internet Computing. - 1089-7801 .- 1941-0131. ; 19:2, s. 6-8
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Journal article (peer-reviewed)abstract
- The guest editors present a special issue on building software for the Internet of Things (IoT).
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| 4. |
- Bucchiarone, Antonio, et al.
(author)
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From Monolithic to Microservices An Experience Report from the Banking Domain
- 2018
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In: IEEE Software. - : IEEE Computer Society. - 0740-7459 .- 1937-4194. ; 35:3, s. 50-55
-
Journal article (peer-reviewed)abstract
- Microservices have seen their popularity blossoming with an explosion of concrete applications in real-life software. Several companies are currently involved in a major refactoring of their back-end systems in order to improve scalability. This article presents an experience report of a real-world case study, from the banking domain, in order to demonstrate how scalability is positively affected by reimplementing a monolithic architecture into microservices. The case study is based on the FX Core system for converting from one currency to another. FX Core is a mission-critical system of Danske Bank, the largest bank in Denmark and one of the leading financial institutions in Northern Europe.
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| 5. |
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| 6. |
- de Lemos, Rogerio, et al.
(author)
-
Software Engineering for Self-Adaptive Systems : A Second Research Roadmap
- 2013
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In: Software Engineering for Self-Adaptive Systems II. - Berlin, Heidelberg : Springer. - 9783642358128 ; , s. 1-32
-
Conference paper (other academic/artistic)abstract
- The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.
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| 7. |
- Dehury, Chinmaya Kumar, et al.
(author)
-
Securing Clustered Edge Intelligence With Blockchain
- 2024
-
In: IEEE Consumer Electronics Magazine. - : Institute of Electrical and Electronics Engineers (IEEE). - 2162-2248 .- 2162-2256. ; 13:1, s. 22-29
-
Journal article (peer-reviewed)abstract
- The devices at the edge of a network are not only responsible for sensing the surrounding environment but are also made intelligent enough to learn and react to the environment. Clustered Edge Intelligence (CEI) emphasizes intelligence-centric clustering instead of device-centric clustering. It allows the devices to share their knowledge and events with other devices and the remote fog or cloud servers. However, recent advancements facilitate the traceability of the events’ history by analyzing edge devices’ event logs, which are compute intensive and easy to alter. This article focuses on a blockchain-based solution for CEI that makes the edge devices’ events history immutable and easily traceable. This article further explains how the edge devices’ activities and the environmental data can be secured from the source device to the cloud servers. Such a secured CEI mechanism can be applied in establishing a transparent and efficient smart city, supply chain, logistics, and transportation systems.
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| 8. |
- Deng, Shuiguang, et al.
(author)
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Optimal Application Deployment in Resource Constrained Distributed Edges
- 2021
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In: IEEE Transactions on Mobile Computing. - : IEEE Computer Society. - 1536-1233 .- 1558-0660. ; 20:5, s. 1907-1923
-
Journal article (peer-reviewed)abstract
- The dramatically increasing of mobile applications make it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on a mobile edge computing (MEC) paradigm. In the MEC paradigm, plenty of machines are placed at the edge of the network so that the performance of applications can be optimized by using the involved microservice instances deployed on them. In this paper, we explore the deployment problem of microserivce-based applications in the MEC environment and propose an approach to help to optimize the cost of application deployment with the constraints of resources and the requirement of performance. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of mobile services.
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| 9. |
- Donta, Praveen Kumar, Dr. 1990-, et al.
(author)
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Towards Intelligent Data Protocols for the Edge
- 2023
-
Conference paper (peer-reviewed)abstract
- The computing continuum is growing because multiple devices are added daily. Edge devices play a key role in this because computation is decentralized or distributed. Edge computing is advanced by using AI/ML algorithms to become more intelligent. Besides, Edge data protocols are useful for transmitting or receiving data between devices. Since, computation efficiency is possible when the data is received at the Edge timely, and it is possible only when the data protocols are efficient, reliable and fast. Most edge data protocols are defined with static set of rules and their primary purpose is to provide standardized and reliable data communications. Edge devices need autonomous or dynamic protocols that enable interoperability, autonomous decision making, scalability, and adaptability. This paper examines the limitations of popular data protocols used in edge networks, the need for intelligent data protocols, and their implications. We also explore possible ways to simplify learning for edge devices and discuss how intelligent data protocols can mitigate challenges such as congestion, message filtering, message expiration, prioritization, and resource handling.
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| 10. |
- Dustdar, Schahram, et al.
(author)
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On Distributed Computing Continuum Systems
- 2023
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In: IEEE Transactions on Knowledge and Data Engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1041-4347 .- 1558-2191 .- 2326-3865. ; 35:4, s. 4092-4105
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Journal article (peer-reviewed)abstract
- This article presents our vision on the need of developing new managing technologies to harness distributed “computing continuum” systems. These systems are concurrently executed in multiple computing tiers: Cloud, Fog, Edge and IoT. This simple idea develops manifold challenges due to the inherent complexity inherited from the underlying infrastructures of these systems. This makes inappropriate the use of current methodologies for managing Internet distributed systems, which are based on the early systems that were based on client/server architectures and were completely specified by the application software. We present a new methodology to manage distributed “computing continuum” systems. This is based on a mathematical artifact called Markov Blanket, which sets these systems in a Markovian space, more suitable to cope with their complex characteristics. Furthermore, we develop the concept of equilibrium for these systems, providing a more flexible management framework compared with the one based on thresholds, currently in use for Internet-based distributed systems. Finally, we also link the equilibrium with the development of adaptive mechanisms. However, we are aware that developing the entire methodology requires a big effort and the use of learning techniques, therefore, we finish this article with an overview of the techniques required to develop this methodology.
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| 11. |
- Fazio, Maria, et al.
(author)
-
A Note on the Convergence of IoT, Edge, and Cloud Computing in Smart Cities
- 2018
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In: IEEE Cloud Computing. - : IEEE. - 2325-6095. ; 5:5, s. 22-24
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Journal article (peer-reviewed)abstract
- The purpose of the special issue is to cover all aspects of design and implementation, as well as deployment and evaluation of solutions aimed at the osmotic convergence of IoT, edge, and cloud computing, with specific reference to the smart cities application scenario.
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| 12. |
- Gholami, Ali, 1978-
(author)
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Security and Privacy of Sensitive Data in Cloud Computing
- 2016
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Doctoral thesis (other academic/artistic)abstract
- Cloud computing offers the prospect of on-demand, elastic computing, provided as a utility service, and it is revolutionizing many domains of computing. Compared with earlier methods of processing data, cloud computing environments provide significant benefits, such as the availability of automated tools to assemble, connect, configure and reconfigure virtualized resources on demand. These make it much easier to meet organizational goals as organizations can easily deploy cloud services. However, the shift in paradigm that accompanies the adoption of cloud computing is increasingly giving rise to security and privacy considerations relating to facets of cloud computing such as multi-tenancy, trust, loss of control and accountability. Consequently, cloud platforms that handle sensitive information are required to deploy technical measures and organizational safeguards to avoid data protection breakdowns that might result in enormous and costly damages. Sensitive information in the context of cloud computing encompasses data from a wide range of different areas and domains. Data concerning health is a typical example of the type of sensitive information handled in cloud computing environments, and it is obvious that most individuals will want information related to their health to be secure. Hence, with the growth of cloud computing in recent times, privacy and data protection requirements have been evolving to protect individuals against surveillance and data disclosure. Some examples of such protective legislation are the EU Data Protection Directive (DPD) and the US Health Insurance Portability and Accountability Act (HIPAA), both of which demand privacy preservation for handling personally identifiable information. There have been great efforts to employ a wide range of mechanisms to enhance the privacy of data and to make cloud platforms more secure. Techniques that have been used include: encryption, trusted platform module, secure multi-party computing, homomorphic encryption, anonymization, container and sandboxing technologies. However, it is still an open problem about how to correctly build usable privacy-preserving cloud systems to handle sensitive data securely due to two research challenges. First, existing privacy and data protection legislation demand strong security, transparency and audibility of data usage. Second, lack of familiarity with a broad range of emerging or existing security solutions to build efficient cloud systems. This dissertation focuses on the design and development of several systems and methodologies for handling sensitive data appropriately in cloud computing environments. The key idea behind the proposed solutions is enforcing the privacy requirements mandated by existing legislation that aims to protect the privacy of individuals in cloud-computing platforms. We begin with an overview of the main concepts from cloud computing, followed by identifying the problems that need to be solved for secure data management in cloud environments. It then continues with a description of background material in addition to reviewing existing security and privacy solutions that are being used in the area of cloud computing. Our first main contribution is a new method for modeling threats to privacy in cloud environments which can be used to identify privacy requirements in accordance with data protection legislation. This method is then used to propose a framework that meets the privacy requirements for handling data in the area of genomics. That is, health data concerning the genome (DNA) of individuals. Our second contribution is a system for preserving privacy when publishing sample availability data. This system is noteworthy because it is capable of cross-linking over multiple datasets. The thesis continues by proposing a system called ScaBIA for privacy-preserving brain image analysis in the cloud. The final section of the dissertation describes a new approach for quantifying and minimizing the risk of operating system kernel exploitation, in addition to the development of a system call interposition reference monitor for Lind - a dual sandbox.
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| 13. |
- Hazra, Abhishek, et al.
(author)
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Cooperative Transmission Scheduling and Computation Offloading With Collaboration of Fog and Cloud for Industrial IoT Applications
- 2023
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In: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662 .- 2372-2541. ; 10:5, s. 3944-3953
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Journal article (peer-reviewed)abstract
- Energy consumption for large amounts of delay-sensitive applications brings serious challenges with the continuous development and diversity of Industrial Internet of Things (IIoT) applications in fog networks. In addition, conventional cloud technology cannot adhere to the delay requirement of sensitive IIoT applications due to long-distance data travel. To address this bottleneck, we design a novel energy–delay optimization framework called transmission scheduling and computation offloading ( TSCO ), while maintaining energy and delay constraints in the fog environment. To achieve this objective, we first present a heuristic-based transmission scheduling strategy to transfer IIoT-generated tasks based on their importance. Moreover, we also introduce a graph-based task-offloading strategy using constrained-restricted mixed linear programming to handle high traffic in rush-hour scenarios. Extensive simulation results illustrate that the proposed TSCO approach significantly optimizes energy consumption and delay up to 12%–17% during computation and communication over the traditional baseline algorithms.
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| 14. |
- Hazra, Abhishek, et al.
(author)
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Distributed AI in Zero-Touch Provisioning for Edge Networks: Challenges and Research Directions
- 2024
-
In: Computer. - 0018-9162 .- 1558-0814. ; 57:3, s. 69-78
-
Journal article (peer-reviewed)abstract
- This article combines Distributed Artificial Intelligence (DAI) with zero-touch provisioning (ZTP) for edge networks. Several advantages are also highlighted that come with incorporating DAI into ZTP in the context of edge networks. Further, we draw potential research directions to foster novel studies in this field.
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| 15. |
- Li, Fei, et al.
(author)
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Proactive Service Discovery in Pervasive Environments
- 2010
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In: Proceedings of the 7th ACM International Conference on Pervasive Services (ICPS). ; , s. 126-133
-
Conference paper (peer-reviewed)abstract
- Pervasive environments are characterized by rich and dy-namic context, where users need to be continuously informed about services relevant to their current context. Implicit discovery requests, triggered by changes of user context, avail-able services, or user preferences are prevalent in such environments.This paper proposes a proactive service discovery approach for pervasive environments to address these implicit requests. Services and user preferences are described by a formal context model, which effectively captures the dynamics of context and the relationship between services and users. Based on the model, we propose a proactive discovery algorithm to continuously present the most relevant services to the user in response to changes of context, services or user preferences. Numeric coding methods are applied in different phases of the algorithm to improve its performance. A proactive service discovery system is proposed and the context model is grounded in a smart home environment. Experimental results show that our approach can efficiently provide the user with up-to-date information about useful services.
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| 16. |
- Mazzara, Manuel, et al.
(author)
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Microservices : Migration of a Mission Critical System
- 2021
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In: IEEE Transactions on Services Computing. - : IEEE Press. - 1939-1374. ; 14:5, s. 1464-1477
-
Journal article (peer-reviewed)abstract
- An increasing interest is growing around the idea of microservices and the promise of improving scalability when compared to monolithic systems. Several companies are evaluating pros and cons of a complex migration. In particular, financial institutions are positioned in a difficult situation due to the economic climate and the appearance of agile competitors that can navigate in a more flexible legal framework and started their business since day one with more agile architectures and without being bounded to outdated technological standard. In this paper, we present a real world case study in order to demonstrate how scalability is positively affected by re-implementing a monolithic architecture (MA) into a microservices architecture (MSA). The case study is based on the FX Core system, a mission critical system of Danske Bank, the largest bank in Denmark and one of the leading financial institutions in Northern Europe. The technical problem that has been addressed and solved in this paper is the identification of a repeatable migration process that can be used to convert a real world Monolithic architecture into a Microservices architecture in the specific setting of financial domain, typically characterized by legacy systems and batch-based processing on heterogeneous data sources.
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| 17. |
- Meuser, Tobias, et al.
(author)
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Revisiting Edge AI : Opportunities and Challenges
- 2024
-
In: IEEE Internet Computing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1089-7801 .- 1941-0131. ; 28:4, s. 49-59
-
Journal article (peer-reviewed)abstract
- Edge artificial intelligence (AI) is an innovative computing paradigm that aims to shift the training and inference of machine learning models to the edge of the network. This paradigm offers the opportunity to significantly impact our everyday lives with new services such as autonomous driving and ubiquitous personalized health care. Nevertheless, bringing intelligence to the edge involves several major challenges, which include the need to constrain model architecture designs, the secure distribution and execution of the trained models, and the substantial network load required to distribute the models and data collected for training. In this article, we highlight key aspects in the development of edge AI in the past and connect them to current challenges. This article aims to identify research opportunities for edge AI, relevant to bring together the research in the fields of artificial intelligence and edge computing.
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| 18. |
- Noor, Ayman, et al.
(author)
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A Framework for Monitoring Microservice-Oriented Cloud Applications in Heterogeneous Virtualization Environments
- 2019
-
In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD). - : IEEE. - 9781728127057
-
Conference paper (peer-reviewed)abstract
- Microservices have emerged as a new approach for developing and deploying cloud applications that require higher levels of agility, scale, and reliability. To this end, a microservice-based cloud application architecture advocates decomposition of monolithic application components into independent software components called "microservices". As the independent microservices can be developed, deployed, and updated independently of each other, it leads to complex run-time performance monitoring and management challenges. To solve this problem, we propose a generic monitoring framework, Multi-microservices Multi-virtualization Multi-cloud (M3) that monitors the performance of microservices deployed across heterogeneous virtualization platforms in a multi-cloud environment. We validated the efficacy and efficiency of M3 using a Book-Shop application executing across AWS and Azure.
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| 19. |
- Patros, Panos, et al.
(author)
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Towards Sustainable Serverless Computing
- 2021
-
In: IEEE Internet Computing. - 1089-7801 .- 1941-0131. ; 25:6, s. 42-50
-
Journal article (peer-reviewed)abstract
- Although serverless computing generally involves executing short-lived “functions,” the increasing migration to this computing paradigm requires careful consideration of energy and power requirements. serverless computing is also viewed as an economically-driven computational approach, often influenced by the cost of computation, as users are charged for per-subsecond use of computational resources rather than the coarse-grained charging that is common with virtual machines and containers. To ensure that the startup times of serverless functions do not discourage their use, resource providers need to keep these functions hot, often by passing in synthetic data. We describe the real power consumption characteristics of serverless, based on execution traces reported in the literature, and describe potential strategies (some adopted from existing VM and container-based approaches) that can be used to reduce the energy overheads of serverless execution. Our analysis is, purposefully, biased toward the use of machine learning workloads because: (1) workloads are increasingly being used widely across different applications; (2) functions that implement machine learning algorithms can range in complexity from long-running (deep learning) versus short-running (inference only), enabling us to consider serverless across a variety of possible execution behaviors. The general findings are easily translatable to other domains.
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| 20. |
- Pinto, George P., et al.
(author)
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A Systematic Review on Privacy-Aware IoT Personal Data Stores
- 2024
-
In: Sensors. - 1424-8220. ; 24:7, s. 2197-2197
-
Journal article (peer-reviewed)abstract
- Data from the Internet of Things (IoT) enables the design of new business models and services that improve user experience and satisfaction. These data serve as important information sources for many domains, including disaster management, biosurveillance, smart cities, and smart health, among others. However, this scenario involves the collection of personal data, raising new challenges related to data privacy protection. Therefore, we aim to provide state-of-the-art information regarding privacy issues in the context of IoT, with a particular focus on findings that utilize the Personal Data Store (PDS) as a viable solution for these concerns. To achieve this, we conduct a systematic mapping review to identify, evaluate, and interpret the relevant literature on privacy issues and PDS-based solutions in the IoT context. Our analysis is guided by three well-defined research questions, and we systematically selected 49 studies published until 2023 from an initial pool of 176 papers. We analyze and discuss the most common privacy issues highlighted by the authors and position the role of PDS technologies as a solution to privacy issues in the IoT context. As a result, our findings reveal that only a small number of works (approximately 20%) were dedicated to presenting solutions for privacy issues. Most works (almost 82%) were published between 2018 and 2023, demonstrating an increased interest in the theme in recent years. Additionally, only two works used PDS-based solutions to deal with privacy issues in the IoT context.
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| 21. |
- Pujol, Víctor Casamayor, et al.
(author)
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On Causality in Distributed Continuum Systems
- 2024
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In: IEEE Internet Computing. - 1089-7801 .- 1941-0131. ; 28:2, s. 57-64
-
Journal article (peer-reviewed)abstract
- As distributed continuum systems (DCSs) are envisioned, they will have a massive impact on our future society. Hence, it is of utmost importance to ensure that their impact is socially responsible. Equipping these systems with causal models brings features such as explainability, accountability, and auditability, which are needed to provide the right level of trust. Furthermore, by combining causality with graph-based service-level objectives, we can cope with dynamic and complex system requirements while achieving sustainable development of DCSs’ capacities and applications.
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| 22. |
- Schwiegelshohn, Uwe, et al.
(author)
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Perspectives on grid computing
- 2010
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In: Future Generation Computer Systems. - : Elsevier BV. - 0167-739X .- 1872-7115. ; 26:8, s. 1104-1115
-
Journal article (peer-reviewed)abstract
- Grid computing has been the subject of many large national and international IT projects. However, not all goals of these projects have been achieved. In particular. the number of users lags behind the initial forecasts laid out by proponents of grid technologies. This underachievement may have led to claims that the grid concept as a whole is on its way to being replaced by Cloud computing and various X-as-a-Service approaches. In this paper, we try to analyze the current situation and to identify promising directions for future grid development. Although there are shortcomings in current grid systems, we are convinced that the concept as a whole remains valid and can benefit from new developments, including Cloud computing. Furthermore, we strongly believe that some future applications will require the grid approach and that, as a result, further research is required in order to turn this concept into reliable, efficient and user-friendly computing platforms. (c) 2010 Elsevier B.V. All rights reserved.
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| 23. |
-
SE4COG '18: Proceedings of the 1st International Workshop on Software Engineering for Cognitive Services
- 2018
-
Editorial proceedings (other academic/artistic)abstract
- Welcome to the 1st International Workshop on Software Engineering for Cognitive Services (SE4COG 2018). We are excited and happy to have you here. The motivation for the workshop comes from the recognition that we are entering a new era of computing and we are moving in a somewhat uncharted territory. We are transitioning from a deterministic model where services are invoked with known, fixed, understood parameters and the service performs exactly what is requested, barring systems error, to a scenario where services try to interpret the user request in the best possible way and access the resources they consider appropriate for fulfilling the request, amongst a large and rapidly evolving set of available base APIs to be invoked. In this workshop, we will discuss the issues and challenges that such services bring from a software engineering perspective. Through a set of talks, panels, and open discussions we will try to understand and identify which are the fundamental differences of cognitive services and how we need to approach both the challenges they present but also the opportunities. Are current approaches to service design valid and applicable for cognitive services? What about requirements, testing, and even entirely new problems from a service engineering perspective, such as training? Even basic notions of correctness change and become somewhat blurred in cognitive service design, and it seems that disciplines such as HCI, information retrieval, knowledge management, and various sides of artificial intelligence become deeply intertwined in the service engineering process. How about service deployment in distributed elastic cloud infrastructures that are ubiquitous? How do we address service quality assurance at run-time such as response time, security, regulatory compliance of data mobility in the face of non-deterministic fluctuations in workloads and available computing resources? Are current software engineering practices adequate to harness the multi-core servers, GPUs, Optical software defined WANs to provide the scale, resiliency and efficiency demanded by global communication, collaboration and commerce services? These are only some of the issues and questions we will consider during the workshop. We hope in your active participation as we will need all your brainpower to identify the promising research directions and lay out a map that is hopefully useful for researchers exploring this new ground.
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| 24. |
- Sudharsan, Bharath, et al.
(author)
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Toward Distributed, Global, Deep Learning Using IoT Devices
- 2021
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In: IEEE Internet Computing. - : IEEE. - 1089-7801 .- 1941-0131. ; 25:3
-
Journal article (peer-reviewed)abstract
- Deep learning (DL) using large scale, high-quality IoT datasets can be computationally expensive. Utilizing such datasets to produce a problem-solving model within a reasonable time frame requires a scalable distributed training platform/system. We present a novel approach where to train one DL model on the hardware of thousands of mid-sized IoT devices across the world, rather than the use of GPU cluster available within a data center. We analyze the scalability and model convergence of the subsequently generated model, identify three bottlenecks that are: high computational operations, time consuming dataset loading I/O, and the slow exchange of model gradients. To highlight research challenges for globally distributed DL training and classification, we consider a case study from the video data processing domain. A need for a two-step deep compression method, which increases the training speed and scalability of DL training processing, is also outlined. Our initial experimental validation shows that the proposed method is able to improve the tolerance of the distributed training process to varying internet bandwidth, latency, and Quality of Service metrics.
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| 25. |
- Taheri, Javid, et al.
(author)
-
Edge Intelligence : From Theory to Practice
- 2023. - 1
-
Book (other academic/artistic)abstract
- This graduate-level textbook is ideally suited for lecturing the most relevant topics of Edge Computing and its ties to Artificial Intelligence (AI) and Machine Learning (ML) approaches. It starts from basics and gradually advances, step-by-step, to ways AI/ML concepts can help or benefit from Edge Computing platforms. The book is structured into seven chapters; each comes with its own dedicated set of teaching materials (practical skills, demonstration videos, questions, lab assignments, etc.). Chapter 1 opens the book and comprehensively introduces the concept of distributed computing continuum systems that led to the creation of Edge Computing. Chapter 2 motivates the use of container technologies and how they are used to implement programmable edge computing platforms. Chapter 3 introduces ways to employ AI/ML approaches to optimize service lifecycles at the edge. Chapter 4 goes deeper in the use of AI/ML and introduces ways to optimize spreading computational tasks along edge computing platforms. Chapter 5 introduces AI/ML pipelines to efficiently process generated data on the edge. Chapter 6 introduces ways to implement AI/ML systems on the edge and ways to deal with their training and inferencing procedures considering the limited resources available at the edge-nodes. Chapter 7 motivates the creation of a new orchestrator independent object model to descriptive objects (nodes, applications, etc.) and requirements (SLAs) for underlying edge platforms. To provide hands-on experience to students and step-by-step improve their technical capabilities, seven sets of Tutorials-and-Labs (TaLs) are also designed. Codes and Instructions for each TaL is provided on the book website, and accompanied by videos to facilitate their learning process.
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| 26. |
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| 27. |
- Truong, Hong-Linh, 1975, et al.
(author)
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Governing Bot-as-a-Service in Sustainability Platforms–Issues and Approaches
- 2012
-
In: Procedia Computer Science. - : Elsevier BV. - 1877-0509. ; 10, s. 561-568
-
Conference paper (peer-reviewed)abstract
- The emerging cloud computing models for Internet-of-Things have fostered the development of lightweight applications using cloud services for monitoring and optimizing devices and equipment hosted in distributed facilities. Such applications – called bots in our work – can be composed and deployed with multiple types of governance policies from cloud platforms to distributed hosting environments and they can access not only local data and devices but also cloud data and features. Therefore, it is a great challenge to govern them. In this paper, we discuss governance issues and state-of-the-art on supporting the emerging Bot-as-a-Service in sustainability governance platforms. Based on that we outline our approaches to policy development and enforcement for the Bot-as-a-Service model
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