DITAS: Unleashing the Potential of Fog Computing to Improve Data-Intensive Applications

Plebani, Pierluigi; García-Pérez, David; Anderson, Maya; Bermbach, David; Cappiello, Cinzia; Kat, Ronen I.; Marinakis, Achilleas; Moulos, Vrettos; Pallas, Frank; Pernici, Barbara; Tai, Stefan; Vitali, Monica

doi:10.1007/978-3-319-79090-9_11

Cited by 5 publications

(4 citation statements)

References 3 publications

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“…It is possible to assume that performances can increase if data are processed closer to where they are stored [30]. While the effects of the latency can be controlled when all the resources are managed by the same authority, the scenario becomes more complex when considering a DaaS 2.…”

Section: Motivating Scenariomentioning

confidence: 99%

Efficient Data as a Service in Fog Computing: An Adaptive Multi-Agent Based Approach

Mangiaracina

Plebani

Salnitri

et al. 2023

IEEE Trans. Cloud Comput.

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Data as a Service (DaaS) offers an effective provisioning model able to exploit the advantages of cloud computing in terms of accessibility and scalability when data providers need to make their data available to different data consumers. Nevertheless, in settings where data are generated at the edge and they need to be propagated (e.g., Industry 4.0, Smart Cities), DaaS model suffers of some limitations: data transfer from the edge to the cloud -and viceversa -could require a significant time and privacy issues could hamper the possibility to move the data. Goal of this paper is to propose a DaaS model based on the Fog Computing paradigm, which combines the advantages of both cloud and edge computing. The proposed solution implements an adaptive multi-agent system where each agent autonomously manages the placement of data in the most convenient location considering the quality of service requirements of the user that it is serving. To guarantee the collaboration of the agents without imposing a centralized control, a reinforcement learning algorithm will be enacted to balance between the local optimum for the single data consumers and the satisfaction of the global requirements of all consumers.

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Section: Motivating Scenariomentioning

confidence: 99%

Efficient Data as a Service in Fog Computing: An Adaptive Multi-Agent Based Approach

Mangiaracina

Plebani

Salnitri

et al. 2023

IEEE Trans. Cloud Comput.

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“…Acting as middleware, it lets the data consumers simply define their requirements of the needed data, expressed as data utility, and takes the responsibility for providing those data timely, securely, and accurately, while also hiding the complexity of the underlying infrastructure [29]. Following the general definition of the VDC, its corresponding Blueprint is a JSON file that describes, except for the implementation details and the API that the VDC exposes, all the attributes and properties of the VDC, such as availability, throughput, and price [30]. This part is critical for the VDC to be considered as a product with specific characteristics that could be used as information metadata by the data buyers.…”

Section: Semantic Resolution Enginementioning

confidence: 99%

A Scalable and Semantic Data as a Service Marketplace for Enhancing Cloud-Based Applications

et al. 2020

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Data handling and provisioning play a dominant role in the structure of modern cloud–fog-based architectures. Without a strict, fast, and deterministic method of exchanging data we cannot be sure about the performance and efficiency of transactions and applications. In the present work we propose an architecture for a Data as a Service (DaaS) Marketplace, hosted exclusively in a cloud environment. The architecture includes a storage management engine that ensures the Quality of Service (QoS) requirements, a monitoring component that enables real time decisions about the resources used, and a resolution engine that provides semantic data discovery and ranking based on user queries. We show that the proposed system outperforms the classic ElasticSearch queries in data discovery use cases, providing more accurate results. Furthermore, the semantic enhancement of the process adds extra results which extend the user query with a more abstract definition to each notion. Finally, we show that the real-time scaling, provided by the data storage manager component, limits QoS requirements by decreasing the latency of the read and write data requests.

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“…Among the published results, several related works utilizing model-driven engineering and automated code generation have been identified. Moreover, the relevance of this approach has been approved by the fact that there have been several recent Horizon 2020 projects (DICE 3 [8] and DITAS 4 [4,9], for example) based on it.…”

Section: Related Workmentioning

confidence: 99%

“…On the other side, [4,9] deals with mechanisms for automated deployment of data-intensive applications that have to comply with given data movement policies and constraints with support for both Cloud and Edge computing. While it supports both the computation task and data movement, this solution does not provide full materialization of deployment diagrams and does not benefit from SDN for performance tuning, but is rather focused on data utility assessment, data movement issues and how data movement affects the QoS and application performance.…”

Section: Related Workmentioning

confidence: 99%

Model-based approach for semantic-driven deployment of containerized applications to support future internet services and architectures

Petrović

2019

Serb J Electr Eng

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Due to increasing number of connected Internet of Things (IoT) devices, enormous amount of data needs to be transmitted to the Cloud for processing, while the network is becoming Cloud computing's bottleneck. On the other side, the privacy and security issues in more sensitive application domains could dramatically restrict the freedom of data movement, so it is not possible to offload all the data to the Cloud for processing. Furthermore, the manual operations related to tuning and deployment of these applications are time-consuming and require additional effort. In this paper, a model-based framework for automated, semantic-driven (re-)deployment of containerized applications is presented, leveraging the synergy of Virtual Network Functions (VNFs) and SDN, tackling the mentioned issues.

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DITAS: Unleashing the Potential of Fog Computing to Improve Data-Intensive Applications

Cited by 5 publications

References 3 publications

Efficient Data as a Service in Fog Computing: An Adaptive Multi-Agent Based Approach

Efficient Data as a Service in Fog Computing: An Adaptive Multi-Agent Based Approach

A Scalable and Semantic Data as a Service Marketplace for Enhancing Cloud-Based Applications

Model-based approach for semantic-driven deployment of containerized applications to support future internet services and architectures

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