Decision Support for Computational Offloading by Probing Unknown Services

Meurisch, Christian; Gedeon, Julien; Kaup, Fabian; Mühlhäuser, Max

doi:10.1109/icccn.2017.8038406

Cited by 31 publications

(11 citation statements)

References 39 publications

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“…Many offloading policy proposed by researchers considers mobile edge paradigm [33]. The computation offloading issues in heterogeneous devices at the network edge and cloud layer was presented in [36]. They presented computation offloading method to predict the energy consumption and response time of IoT task by investigating edge devices or VMs at the cloud using regression models.…”

Section: Related Workmentioning

confidence: 99%

Investigation into the effect of data reduction in offloadable task for distributed IoT-fog-cloud computing

et al. 2021

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With the increasing level of IoT applications, computation offloading is now undoubtedly vital because of the IoT devices limitation of processing capability and energy. Computation offloading involves moving data from IoT devices to another processing layer with higher processing capability. However, the size of data offloaded is directly proportional to the delay incurred by the offloading. Therefore, introducing data reduction technique to reduce the offloadable data minimizes delay resulting from the offloading method. In this paper, two main strategies are proposed to address the enormous data volume that result to computation offloading delay. First, IoT Canonical Polyadic Decomposition for Deep Learning Algorithm is proposed. The main purpose of this strategy is to downsize the IoT offloadable data. In the study, the Kaggle-cat-and-dog dataset was used to evaluate the impact of the proposed data compression. The proposed method downsizes the data significantly and can reduce the delay due to network traffic. Secondly, Rank Accuracy Estimation Model is proposed for determining the Rank-1 value. The result of the proposed method proves that the proposed methods are better in terms of data compression compared to distributed deep learning layers. This method can be applied in smart city, vehicular networks, and telemedicine etc.

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Section: Related Workmentioning

confidence: 99%

Investigation into the effect of data reduction in offloadable task for distributed IoT-fog-cloud computing

et al. 2021

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“…Mobile devices need to measure a number of factors including resources, latency, security, and privacy when choosing an edge node or cloud server for workload offload. Meurisch et al [81] proposed an approach to make offloading decisions with knowledge of the disadvantages of the current service running status on the offloading system in advance for all current computation offloading methods. Firstly, they detect and query unknown available targeted offloading destinations, such as nearby edge nodes, cloudlets, or remote clouds, in an energy efficient manner at runtime to make better offloading decisions.…”

Section: Where To Offload: the Scheduling Of Offloaded Workloadsmentioning

confidence: 99%

Toward Computation Offloading in Edge Computing: A Survey

et al. 2019

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The explosive growth of massive data generation from Internet of Things in industrial, agricultural and scientific communities has led to a rapid increase for data analytics in cloud data centers. The ubiquitous and pervasive demand for near-data processing urges the edge computing paradigm in recent years. Edge computing is promising for less network backbone bandwidth usage and thus less data center side processing pressure, as well as enhanced service responsiveness and data privacy protection. Computation offloading plays a crucial role in edge computing in terms of network packets transmission and system responsiveness through dynamic task partitioning between cloud data centers and edge servers and edge devices. In this paper a thorough literature review is conducted to reveal the state-of-the-art of computation offloading in edge computing. Various aspects of computation offloading, including energy consumption minimization, Quality of Services guarantee, and Quality of Experiences enhancement are surveyed. Moreover, resource scheduling approaches, gaming and tradeoffing among system performance and overheads for computation offloading decision making are also reviewed.INDEX TERMS Edge computing, computation offloading, task partitioning, game theory, edge-cloud collaboration.

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“…The majority of edge offloading decision making mechanisms proposed in the literature refer to mobile edge computing [6]. For example, Meurisch et al [7] address the issue of heterogeneity of the edge or cloud infrastructures for mobile offloading, where the resource availability of the different edge or cloud devices can vary considerably, as might the resource requirements of the tasks to be offloaded. They propose an offloading decision support system that predicts the completion time and energy consumption by probing edge or cloud devices with micro tasks only lasting a few microseconds, and using regression models.…”

Section: Related Work On Decision Making In Iot Offloadingmentioning

confidence: 99%

Dynamic decision support for resource offloading in heterogeneous Internet of Things environments

Jaddoa

Sakellari

Panaousis

et al. 2020

Simulation Modelling Practice and Theory

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Computation offloading is one of the primary technological enablers of the Internet of Things (IoT), as it helps address individual devices' resource restrictions. In the past, offloading would always utilise remote cloud infrastructures, but the increasing size of IoT data traffic and the real-time response requirements of modern and future IoT applications have led to the adoption of the edge computing paradigm, where the data is processed at the edge of the network.

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Decision Support for Computational Offloading by Probing Unknown Services

Cited by 31 publications

References 39 publications

Investigation into the effect of data reduction in offloadable task for distributed IoT-fog-cloud computing

Investigation into the effect of data reduction in offloadable task for distributed IoT-fog-cloud computing

Toward Computation Offloading in Edge Computing: A Survey

Dynamic decision support for resource offloading in heterogeneous Internet of Things environments

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