Drop computing: Ad-hoc dynamic collaborative computing

Ciobanu, Radu-Ioan; Negru, Cătălin; Pop, Florin; Dobre, Ciprian; Mavromoustakis, Constandinos X.; Mastorakis, George

doi:10.1016/j.future.2017.11.044

Cited by 44 publications

(21 citation statements)

References 10 publications

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“…Edge and Fog computing have emerged recently as a means to address such requirements. Proponents of Edge and Fog computing diverge from existing centralised high-performance Cloud data centres and advocate the use of highly decentralised computing platforms which provide computing nodes in close proximity of the data sources [17,18]. Edge computing is a highly distributed approach that performs computational operations on multiprocessor devices (e.g.…”

Section: Computing In the Edge-to-cloud Continuummentioning

confidence: 99%

Trust management in a blockchain based fog computing platform with trustless smart oracles

Kochovski

Gec

Stankovski

et al. 2019

Future Generation Computer Systems

155

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Trust is a crucial aspect when cyber-physical systems have to rely on resources and services under ownership of various entities, such as in the case of Edge, Fog and Cloud computing. The DECENTER's Fog Computing Platform is developed to support Big Data pipelines, which start from the Internet of Things (IoT), such as cameras that provide video-streams for subsequent analysis. It is used to implement Artificial Intelligence (AI) algorithms across the Edge-Fog-Cloud computing continuum which provide benefits to applications, including high Quality of Service (QoS), improved privacy and security, lower operational costs and similar. In this article, we present a trust management architecture for DECENTER that relies on the use of blockchain-based Smart Contracts (SCs) and specifically designed trustless Smart Oracles. The architecture is implemented on Ethereum ledger (testnet) and three trust management scenarios are used for illustration. The scenarios (trust management for cameras, trusted data flow and QoS based computing node selection) are used to present the benefits of establishing trust relationships among entities, services and stakeholders of the platform.

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Section: Computing In the Edge-to-cloud Continuummentioning

confidence: 99%

Trust management in a blockchain based fog computing platform with trustless smart oracles

Kochovski

Gec

Stankovski

et al. 2019

Future Generation Computer Systems

155

View full text Add to dashboard Cite

show abstract

“…This research has been focusing on advancing the current note-taking method. The main problem is the inefficiency of current practice which the nurses need to wrote down notes then turns it to digital notes [14,15]. They also noticed the high cost of the technologically-advanced simulation system which hinder the hospital into taking such route.…”

Section: Peaynotes: a Generic Web-based Patient Clinical Notes Sharinmentioning

confidence: 99%

A review on various methods of collaborative computing

Zamanhuri

Aziz

Rauf

et al. 2019

IJEECS

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Currently, mosques in Malaysia distribute their lecture schedules either on paper-based form or by uploading the schedule on their social media platform. This has some disadvantages such as paper schedules are susceptible to damages and information on social media platform is often not updated to current changes. Collaborative Computing is a system that enable individuals to work together remotely by making use of the reach ability of the internet. In order to utilise the Internet’s obvious advantages over paper-based and rapid information distribution and asynchronous communication, a review is conducted to study the available methods of collaborative computing, further analyse current research papers. Result shows that Centralized Computing method is the most suitable method for developing collaborative mobile application for Islamic Lectures schedule.

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“…They proposed a heuristic offloading decision algorithm in order to optimize the offloading decision, and computation resources to maximize system utility. Ciobanu et al [13] introduced a Drop Computing paradigm that employs the mobile crowd formed of devices in close proximity for quicker and more efficient access. It was different from traditional method, where every data or computation request going directly to the cloud.…”

Section: Related Workmentioning

confidence: 99%

A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

Wang

Ren

2018

Future Internet

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Abstract:Mobile devices could augment their ability via cloud resources in mobile cloud computing environments. This paper developed a novel two-layered reinforcement learning (TLRL) algorithm to consider task offloading for resource-constrained mobile devices. As opposed to existing literature, the utilization rate of the physical machine and the delay for offloaded tasks are taken into account simultaneously by introducing a weighted reward. The high dimensionality of the state space and action space might affect the speed of convergence. Therefore, a novel reinforcement learning algorithm with a two-layered structure is presented to address this problem. First, k clusters of the physical machines are generated based on the k-nearest neighbors algorithm (k-NN). The first layer of TLRL is implemented by a deep reinforcement learning to determine the cluster to be assigned for the offloaded tasks. On this basis, the second layer intends to further specify a physical machine for task execution. Finally, simulation examples are carried out to verify that the proposed TLRL algorithm is able to speed up the optimal policy learning and can deal with the tradeoff between physical machine utilization rate and delay.

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Drop computing: Ad-hoc dynamic collaborative computing

Cited by 44 publications

References 10 publications

Trust management in a blockchain based fog computing platform with trustless smart oracles

Trust management in a blockchain based fog computing platform with trustless smart oracles

A review on various methods of collaborative computing

A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

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