Virtualized Fog Network with Load Balancing for IoT based Fog-to-Cloud

Al-Joboury, Istabraq M.; Al-Hemiary, Emad H.

doi:10.30630/joiv.4.3.374

Cited by 6 publications

(3 citation statements)

References 25 publications

(24 reference statements)

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“…The FC architecture facilitates a dynamic transition to the fog node, the cloud, and the IoT continuum 47 for transmission, networking, and stocking services. However, fog interfaces to connect to the cloud, other fogs, and information or users must make mobility simpler with this different computing, storage, or control roles and complicated transfer.…”

Section: System Architecturementioning

confidence: 99%

Energy‐efficient fog computing in Internet of Things based on Routing Protocol for Low‐Power and Lossy Network with Contiki

Kumar

Sharma

Goyal

et al. 2021

Int J Communication

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Summary The traditional centralized cloud computing (CC) model faces a range of problems with the exponential growth of the Internet of Things (IoT) applications, like high latency, reduced bandwidth, and network instability. Fog computing (FC) takes the cloud closer to IoT computers to overcome these problems. Rather than moving them to the cloud, the FC provides local IoT data processing and storage on IoT computers. This paper focuses on the Routing Protocol for Low‐Power and Lossy Network (RPL), a universally routing protocol for a static environment that is used for reducing energy consumption, delay, and packet loss with data aggregation at the border router using a fog simulation model in Contiki Cooja. The objective function (OF) choice has an impact on network topology, as each node selects a set of potential parents to send to the destination. However, no systematic analysis of the effects of OF behavior in the RPL environment has been undertaken. Here, three different OFs, Objective Function Zero (OF0), Advanced Objective Function Zero (AOF0), and Minimum Rank with Hysteresis Objective Function (MRHOF) for RPL in the static environment for different node numbers, have been compared. The findings demonstrate that altering all three OFs has a significant impact on RPL. The energy consumption is reduced in the case of the AOF0 in the fog node by 50.86%, which is less than the case of the OF0 and MRHOF function. Extensive simulations show that AOF0 outperforms the existing OFs.

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Section: System Architecturementioning

confidence: 99%

Energy‐efficient fog computing in Internet of Things based on Routing Protocol for Low‐Power and Lossy Network with Contiki

Kumar

Sharma

Goyal

et al. 2021

Int J Communication

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“…The users should be able to share the documents with any authorized users. To provide a hassle-free sharing, the users should be able to use a single click UX to share data [36], [37].…”

Section: ) Sharingmentioning

confidence: 99%

Cloud Computing Issues, Challenges, and Needs: A Survey

Aljanabi

Abd-Alwahab

Saedudin

et al. 2021

JOIV : Int. J. Inform. Visualization

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Cloud computing represents a kind of computing that is based on the sharing of computing resources instead of possessing personal devices or local servers for handling several applications and tasks. This kind of computing includes three distinguished kinds of services provided remotely for clients that can be accessed by using the Internet. Typically, clients work on paying annual or monthly service fees for suppliers, in order to gain access to systems that work on delivering infrastructure as a service, platforms as a service, and software as a service for any subscriber. In this paper, the usefulness and the abuse of the cloud computing are briefly discussed and presented by highlighting the influences of cloud computing in different areas. Moreover, this paper also presents the kinds and services of cloud. In addition, the security issues that cover the cloud security solution requirements, and the cloud security issues, which is one of the biggest issues in recent years in cloud computing were presented in this paper. The security requirement that needs by the cloud computing covers privacy, lack of user control, unauthorized secondary usage, and finally data proliferation and data flow. Meanwhile, the security issues cover including ownership of device, the trust issue and legel aspects. To overcome the security issues, this paper also presents the solution at the end of this paper.

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“…Typically, energy optimization is one of the important goals that need to be addressed in the cloud system, hence this paper intends to provide a new solution for solving the load balancing problem with the help of a hybrid GAACO-based heuristic model. Here, the energy-performance tradeoff [23,24] can be evaluated for a physical machine with computational capability at its peak. When compared to the other conventional techniques, the proposed hybrid GAACO technique can efficiently balance the loads across the machines in the cloud with reduced energy consumption, makespan time, response time, and degree of imbalance.…”

Section: Introductionmentioning

confidence: 99%

An Energy-Aware QoS Load Balance Scheduling Using Hybrid GAACO Algorithm for Cloud

Ilankumaran,

Narayanan

2023

Cybernetics and Information Technologies

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In recent days, resource allocation is considered to be a complex task in cloud systems. The heuristics models will allocate the resources efficiently in different machines. Then, the fitness function estimation plays a vital role in cloud load balancing, which is mainly used to minimize power consumption. The optimization technique is one of the most suitable options for solving load-balancing problems. This work mainly focuses on analyzing the impacts of using the Genetic Algorithm and Ant Colony Optimization (GAACO) technique for obtaining the optimal solution to efficiently balance the loads across the cloud systems. In addition to that, the GA and ACO are the kinds of object heuristic algorithms being proposed in the work to increase the number of servers that are operated with better energy efficiency. In this work, the main contribution of the GAACO algorithm is to reduce energy consumption, makespan time, response time, and degree of imbalance.

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Virtualized Fog Network with Load Balancing for IoT based Fog-to-Cloud

Cited by 6 publications

References 25 publications

Energy‐efficient fog computing in Internet of Things based on Routing Protocol for Low‐Power and Lossy Network with Contiki

Energy‐efficient fog computing in Internet of Things based on Routing Protocol for Low‐Power and Lossy Network with Contiki

Cloud Computing Issues, Challenges, and Needs: A Survey

An Energy-Aware QoS Load Balance Scheduling Using Hybrid GAACO Algorithm for Cloud

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