Towards Media Inter-cloud Standardization – Evaluating Impact of Cloud Storage Heterogeneity

Aazam, Mohammad; Huh, Eui‐Nam; St-Hilaire, Marc

doi:10.1007/s10723-015-9356-5

Cited by 12 publications

(3 citation statements)

References 31 publications

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“…A requirements-driven interoperable architecture for fog is required, which may be customized according to the situation. Such architecture may also be implemented through multi-protocol translation [ 4 , 5 ].…”

Section: Challenges and Future Research Directionsmentioning

confidence: 99%

Floating Fog: extending fog computing to vast waters for aerial users

et al. 2022

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There are thousands of flights carrying millions of passengers each day, having three or more Internet-connected devices with them on average. Usually, onboard devices remain idle for most of the journey (which can be of several hours), therefore, we can tap on their underutilized potential. Although these devices are generally becoming more and more resourceful, for complex services (such as related to machine learning, augmented/virtual reality, smart healthcare, and so on) those devices do not suffice standalone. This makes a case for multi-device resource aggregation such as through femto-cloud. As our first contribution, we present the utility of femto-cloud for aerial users. But for that sake, a reliable and faster Internet is required (to access online services or cloud resources), which is currently not the case with satellite-based Internet. That is the second challenge we try to address in our paper, by presenting an adaptive beamforming-based solution for aerial Internet provisioning. However, on average, most of the flight path is above waters. Given that, we propose that beamforming transceivers can be docked on stationery ships deployed in the vast waters (such as the ocean). Nevertheless, certain services would be delay-sensitive, and accessing their on-ground servers or cloud may not be feasible (in terms of delay). Similarly, certain complex services may require resources in addition to the flight-local femto-cloud. That is the third challenge we try to tackle in this paper, by proposing that the traditional fog computing (which is a cloud-like but localized pool of resources) can also be extended to the waters on the ships harboring beamforming transceivers. We name it Floating Fog. In addition to that, Floating Fog will enable several new services such as live black-box. We also present a cost and bandwidth analysis to highlight the potentials of Floating Fog. Lastly, we identify some challenges to tackle the successful deployment of Floating Fog.

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Section: Challenges and Future Research Directionsmentioning

confidence: 99%

Floating Fog: extending fog computing to vast waters for aerial users

et al. 2022

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“…For example, personal information may be offloaded from smartphones to cloud servers, such as Dropbox, OneDrive, and Google Drive. Another example is that the information related to patients in a body area network is offloaded from medical machinery to a data center to keep the privacy [30].…”

Section: F Security Issuesmentioning

confidence: 99%

Improving IoT Services Using a Hybrid Fog-Cloud Offloading

Aljanabi

Chalechale

2021

IEEE Access

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With the rapid development of the internet of things (IoT) devices and applications, the necessity to provide these devices with high processing capabilities appears to run the applications more quickly and smoothly. Though the manufacturing companies try to provide IoT devices with the best technologies, some drawbacks related to run some sophisticated applications like virtual reality and smart healthcare-based are still there. To overcome these drawbacks, a hybrid fog-cloud offloading (HFCO) is introduced, where the tasks associated with the complex applications are offloaded to the cloud servers to be executed and sent back the results to the corresponding applications. In the HFCO, when an IoT node generates a high-requirement processing task that cannot handle itself, it must decide to offload the task to the cloud server or to the nearby fog nodes. The decision depends on the conditions of the task requirements and the nearby fog nodes. Considering many fog nodes and many IoT nodes that need to offload their tasks, the problem is to select the best fog node to offload each task. In this paper, we propose a novel solution to the problem, where the IoT node has the choice to offload tasks to the best fog node or to the cloud based on the requirements of the applications and the conditions of the nearby fog nodes. In addition, fog nodes can offload tasks to each other or to the cloud to balance the load and improve the current conditions allowing the tasks to be executed more efficiently. The problem is formulated as a Markov Decision Process (MDP). Besides, a Q-learning-based algorithm is presented to solve the model and select the optimal offload policy. Numerical simulation results show that the proposed approach has superiority over other methods regarding reducing delay, executing more tasks, and balance the load.

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“…Load balancing is another criterion when the server has reached its maximum limit of processing the tasks, and jobs can be dispersed among other servers using offloading. Offloading is beneficial in the above cases and also this may help in securing the privacy and security of data at edge, femto cloud, or at fog cloud [14][15][16][17].…”

Section: Offloading Criteriamentioning

confidence: 99%

Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

Bajaj

Sharma

Singh

2021

Complex Intell. Syst.

102

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The Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

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Towards Media Inter-cloud Standardization – Evaluating Impact of Cloud Storage Heterogeneity

Cited by 12 publications

References 31 publications

Floating Fog: extending fog computing to vast waters for aerial users

Floating Fog: extending fog computing to vast waters for aerial users

Improving IoT Services Using a Hybrid Fog-Cloud Offloading

Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

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