A scalable Edge Computing architecture enabling smart offloading for Location Based Services

Spatharakis, Dimitrios; Dimolitsas, Ioannis; Dechouniotis, Dimitrios; Papathanail, George; Fotoglou, Ioakeim; Papadimitriou, Panagiotis; Papavassiliou, Symeon

doi:10.1016/j.pmcj.2020.101217

Cited by 33 publications

(19 citation statements)

References 26 publications

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“…In this way, when users arrive at the coverage area of the next Edge site, they receive the product of their completed offloaded task, with the minimum additional delay [144]. As an example, a two-step offloading mechanism for smart touristic services [145], [146] is based on estimating the location and density of users. Every mobile device takes the initial offloading decision based on a dead reckoning technique and measurements of its WiFi signal strength.…”

Section: Mobile (High-mobility)mentioning

confidence: 99%

“…Intermittent connectivity, the innate management features of the virtualization technologies and the limited resources at the edge of the network, lead to a highly volatile dynamic environment that necessitates advanced modeling and control methodologies. Regarding the modeling of the offloading-based applications, control theory provides many modeling alternatives involving switching systems [145], [284], Linear Parameter Varying (LPV) systems [285], [286] and Fuzzy Takagi-Sugeno systems [287], [288], that allow the natural incorporation of uncertainties and disturbances in the performance model.…”

Section: Control-related Challengesmentioning

confidence: 99%

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Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

et al. 2021

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Next generation communication networks are expected to accommodate a high number of new and resource-voracious applications that can be offered to a large range of end users. Even though end devices are becoming more powerful, the available local resources cannot cope with the requirements of these applications. This has created a new challenge called task offloading, where computation intensive tasks need to be offloaded to more resource powerful remote devices. Naturally, the Cloud Computing is a well-tested infrastructure that can facilitate the task offloading. However, Cloud Computing as a centralized and distant infrastructure creates significant communication delays that cannot satisfy the requirements of the emerging delay-sensitive applications. To this end, the concept of Edge Computing has been proposed, where the Cloud Computing capabilities are repositioned closer to the end devices at the edge of the network. This paper provides a detailed survey of how the Edge and/or Cloud can be combined together to facilitate the task offloading problem. Particular emphasis is given on the mathematical, artificial intelligence and control theory optimization approaches that can be used to satisfy the various objectives, constraints and dynamic conditions of this end-to-end application execution approach. The survey concludes with identifying open challenges and future directions of the problem at hand.

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Section: Mobile (High-mobility)mentioning

confidence: 99%

Section: Control-related Challengesmentioning

confidence: 99%

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

et al. 2021

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“…Such a massive heterogeneous environment leads to the scalability aspect [ 16 ]. The growth of a client marketplace or business is made possible due to the tremendous ability to create specific Cloud resources, enabling improvement or reducing costs.…”

Section: Background On Computing Paradigmsmentioning

confidence: 99%

A Survey of Security in Cloud, Edge, and Fog Computing

Ometov

Molua

Komarov

et al. 2022

Sensors

123

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The field of information security and privacy is currently attracting a lot of research interest. Simultaneously, different computing paradigms from Cloud computing to Edge computing are already forming a unique ecosystem with different architectures, storage, and processing capabilities. The heterogeneity of this ecosystem comes with certain limitations, particularly security and privacy challenges. This systematic literature review aims to identify similarities, differences, main attacks, and countermeasures in the various paradigms mentioned. The main determining outcome points out the essential security and privacy threats. The presented results also outline important similarities and differences in Cloud, Edge, and Fog computing paradigms. Finally, the work identified that the heterogeneity of such an ecosystem does have issues and poses a great setback in the deployment of security and privacy mechanisms to counter security attacks and privacy leakages. Different deployment techniques were found in the review studies as ways to mitigate and enhance security and privacy shortcomings.

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“…Performance efficiency is the most commonly studied quality attribute, some of the indicators to quantify performance can be response and reaction time, worst case and average execution time, throughput, CPU, memory, and network utilization, performance under different loads, and energy consumption [37,38,39,40]. Similarly, scalability can be achieved by deploying more nodes, however, node management, SW parallelism, load balancing, devices orchestration, etc., can be a complex process [41,42,43,44]. The standardization of interfaces, protocols, and APIs used in the EC will improve interoperability [45,46].…”

Section: Functional and Non-functional Requirementsmentioning

confidence: 99%

Non-functional requirements elicitation for edge computing

Gupta

2022

Internet of Things

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A scalable Edge Computing architecture enabling smart offloading for Location Based Services

Cited by 33 publications

References 26 publications

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

A Survey of Security in Cloud, Edge, and Fog Computing

Non-functional requirements elicitation for edge computing

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