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

Bajaj, Karan; Sharma, Bhisham; Singh, Raman

doi:10.1007/s40747-021-00434-6

Cited by 104 publications

(25 citation statements)

References 61 publications

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“…Simulations were run using the cloudsim-3.0.3 toolkit simulator to test the performance of the proposed technique [54][55][56][57][58]. The cloud environment was characterized by the heterogeneity of tasks and virtual machines.…”

Section: Simulation and Resultsmentioning

confidence: 99%

A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling

Zubair

Razak

Ngadi

et al. 2022

Sensors

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The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The task scheduling problem is NP complete, which makes it hard to obtain a correct solution, especially for large-scale tasks. This paper proposes a modified symbiotic organisms search-based scheduling algorithm for the efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm’s mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aims to minimize the task execution time (makespan), cost, response time, and degree of imbalance, and improve the convergence speed for an optimal solution in an IaaS cloud. The performance of the proposed technique was evaluated using a CloudSim toolkit simulator, and the percentage of improvement of the proposed G_SOS over classical SOS and PSO-SA in terms of makespan minimization ranges between 0.61–20.08% and 1.92–25.68% over a large-scale task that spans between 100 to 1000 Million Instructions (MI). The solutions are found to be better than the existing standard (SOS) technique and PSO.

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Section: Simulation and Resultsmentioning

confidence: 99%

A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling

Zubair

Razak

Ngadi

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Afterward, it is demonstrated the TDO issue has NP-hard, and G-TDO technique has been developed for solving it with carefully planned utility function. In [14], the role of context or condition for performing the offloading has been considered and drawn to end which for meeting the efficacy requirement of IoT enabled service, context-based offloading is role an important play. In several existing structures EMCO, MobiCOP-IoT, Autonomic Management structure, CSOS, Fog Computing structure dependent upon its novelty and optimal efficiency are taken to execution analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Hierarchical Data Aggregation with Data Offloading Scheme for Fog Enabled IoT Environment

Nalayini¹,

Prakash²

2023

Computer Systems Science and Engineering

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Fog computing is a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and location-awareness Internet of Things (IoT) services. After the emergence of IoT-based services, the industry of internet-based devices has grown. The number of these devices has raised from millions to billions, and it is expected to increase further in the near future. Thus, additional challenges will be added to the traditional centralized cloud-based architecture as it will not be able to handle that growth and to support all connected devices in real-time without affecting the user experience. Conventional data aggregation models for Fog enabled IoT environments possess high computational complexity and communication cost. Therefore, in order to resolve the issues and improve the lifetime of the network, this study develops an effective hierarchical data aggregation with chaotic barnacles mating optimizer (HDAG-CBMO) technique. The HDAG-CBMO technique derives a fitness function from many relational matrices, like residual energy, average distance to neighbors, and centroid degree of target area. Besides, a chaotic theory based population initialization technique is derived for the optimal initial position of barnacles. Moreover, a learning based data offloading method has been developed for reducing the response time to IoT user requests. A wide range of simulation analyses demonstrated that the HDAG-CBMO technique has resulted in balanced energy utilization and prolonged lifetime of the Fog assisted IoT networks.

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“…A comparative analysis 4 by grouping the simulators into three categories—cloud, edge, and fog simulator—offered a quick analysis of the various most popular simulators in the research community for data‐intensive applications. A study by Bajaj et al 5 about the significance of context or scenario in performing offloading leads to the conclusion that context‐based offloading can be extremely important for meeting the performance needs of IoT‐enabled applications. Based on their novelty and best performance, some of the existing frameworks—such as Evidence‐aware Mobile Computational Offloading (EMCO), MobiCOP‐IoT, Autonomic Management Framework, Context‐Sensitive Offloading System (CSOS), and Fog Computing Framework—are chosen for implementation analysis and contrasted with the frameworks from MAUI, Any Run Computing (ARC), AutoScaler, Edge computing, and Context‐Sensitive Model for Offloading System (CoSMOS).…”

Section: Related Workmentioning

confidence: 99%

A novel data size‐aware offloading technique for resource provisioning in mobile cloud computing

Manukumar

Muthuswamy

2022

Int J Communication

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Mobile device users are involved in social networking, gaming, learning, and even some office work, so the end users expect mobile devices with highresponse computing capacities, storage, and high battery power consumption. The data-intensive applications, such as text search, online gaming, and face recognition usage, have tremendously increased. With such high complex applications, there are many issues in mobile devices, namely, fast battery draining, limited power, low storage capacity, and increased energy consumption. The novelty of this work is to strike a balance between time and energy consumption of mobile devices while using data-intensive applications by finding the optimal offloading decisions. This paper proposes a novel efficient Data Size-Aware Offloading Model (DSAOM) for data-intensive applications and to predict the appropriate resource provider for dynamic resource allocation in mobile cloud computing. Based on the data size, the tasks are separated and gradually allocated to the appropriate resource providers for execution. The task is placed into the appropriate resource provider by considering the availability services in the fog nodes or the cloud. The tasks are split into smaller portions for execution in the neighbor fog nodes. To execute the task in the remote side, the offloading decision is made by using the min-cut algorithm by considering the monetary cost of the mobile device. This proposed system achieves low-latency time 13.2% and low response time 14.1% and minimizes 24% of the energy consumption over the existing model. Finally, according to experimental findings, this framework efficiently lowers energy use and improves performance for data-intensive demanding application activities, and the task offloading strategy is effective for intensive offloading requests.

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

Cited by 104 publications

References 61 publications

A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling

A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling

Hierarchical Data Aggregation with Data Offloading Scheme for Fog Enabled IoT Environment

A novel data size‐aware offloading technique for resource provisioning in mobile cloud computing

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