| 1. |
- Ali, Shaukat, et al.
(författare)
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Big data from the cloud to the edge : The aggregate computing solution
- 2019
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Ingår i: PervasiveHealth. - New York, NY, USA : ACM Publications. - 9781450371421 ; , s. 177-182
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Konferensbidrag (refereegranskat)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.
(författare)
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A Goal driven Approach for Deploying Self-adaptive IoT Systems
- 2020
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Ingår i: Proceedings. - 9781728146591 - 9781728146607 ; , s. 146-156
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Konferensbidrag (refereegranskat)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.
(författare)
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Building Software for the Internet of Things
- 2015. - 9
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Ingår i: IEEE Internet Computing. - 1089-7801 .- 1941-0131. ; 19:2, s. 6-8
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Tidskriftsartikel (refereegranskat)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.
(författare)
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From Monolithic to Microservices An Experience Report from the Banking Domain
- 2018
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Ingår i: IEEE Software. - : IEEE Computer Society. - 0740-7459 .- 1937-4194. ; 35:3, s. 50-55
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Software Engineering for Self-Adaptive Systems : A Second Research Roadmap
- 2013
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Ingår i: Software Engineering for Self-Adaptive Systems II. - Berlin, Heidelberg : Springer. - 9783642358128 ; , s. 1-32
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)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. |
- Deng, Shuiguang, et al.
(författare)
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Optimal Application Deployment in Resource Constrained Distributed Edges
- 2021
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Ingår i: IEEE Transactions on Mobile Computing. - : IEEE Computer Society. - 1536-1233 .- 1558-0660. ; 20:5, s. 1907-1923
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Tidskriftsartikel (refereegranskat)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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| 8. |
- Donta, Praveen Kumar, et al.
(författare)
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Governance and sustainability of distributed continuum systems: a big data approach
- 2023
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Ingår i: Journal of Big Data. - 2196-1115. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Distributed computing continuum systems (DCCS) make use of a vast number of computing devices to process data generated by edge devices such as the Internet of Things and sensor nodes. Besides performing computations, these devices also produce data including, for example, event logs, configuration files, network management information. When these data are analyzed, we can learn more about the devices, such as their capabilities, processing efficiency, resource usage, and failure prediction. However, these data are available in different forms and have different attributes due to the highly heterogeneous nature of DCCS. The diversity of data poses various challenges which we discuss by relating them to big data, so that we can utilize the advantages of big data analytical tools. We enumerate several existing tools that can perform the monitoring task and also summarize their characteristics. Further, we provide a general governance and sustainable architecture for DCCS, which reflects the human body’s self-healing model. The proposed model has three stages: first, it analyzes system data to acquire knowledge; second, it can leverage the knowledge to monitor and predict future conditions; and third, it takes further actions to autonomously solve any issue or to alert administrators. Thus, the DCCS model is designed to minimize the system’s downtime while optimizing resource usage. A small set of data is used to illustrate the monitoring and prediction of the performance of a system through Bayesian network structure learning. Finally, we discuss the limitations of the governance and sustainability model, and we provide possible solutions to overcome them and make the system more efficient.
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| 9. |
- Fazio, Maria, et al.
(författare)
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A Note on the Convergence of IoT, Edge, and Cloud Computing in Smart Cities
- 2018
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Ingår i: IEEE Cloud Computing. - : IEEE. - 2325-6095. ; 5:5, s. 22-24
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Tidskriftsartikel (refereegranskat)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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| 10. |
- Gholami, Ali, 1978-
(författare)
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Security and Privacy of Sensitive Data in Cloud Computing
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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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