Server placement in mobile cloud computing: A comprehensive survey for edge computing, fog computing and cloudlet

Asghari, Ali; Sohrabi, Mohammad Karim

doi:10.1016/j.cosrev.2023.100616

Cited by 20 publications

(2 citation statements)

References 87 publications

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“…Experimental verification using Alibaba’s task dataset and benchmark algorithms demonstrate improved task scheduling performance in supply chain management. Similarly, in [ 30 ], the authors provide an elite-preserving genetic algorithm (ETS_GA)-based blockchain-assisted safe ES placement technique. Using niche sharing and tabu search, it addresses the classic GA’s premature issue.…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics

Umer,

Ali,

Jehangiri

et al. 2024

Sensors

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IoT-based smart transportation monitors vehicles, cargo, and driver statuses for safe movement. Due to the limited computational capabilities of the sensors, the IoT devices require powerful remote servers to execute their tasks, and this phenomenon is called task offloading. Researchers have developed efficient task offloading and scheduling mechanisms for IoT devices to reduce energy consumption and response time. However, most research has not considered fault-tolerance-based job allocation for IoT logistics trucks, task and data-aware scheduling, priority-based task offloading, or multiple-parameter-based fog node selection. To overcome the limitations, we proposed a Multi-Objective Task-Aware Offloading and Scheduling Framework for IoT Logistics (MT-OSF). The proposed model prioritizes the tasks into delay-sensitive and computation-intensive tasks using a priority-based offloader and forwards the two lists to the Task-Aware Scheduler (TAS) for further processing on fog and cloud nodes. The Task-Aware Scheduler (TAS) uses a multi-criterion decision-making process, i.e., the analytical hierarchy process (AHP), to calculate the fog nodes’ priority for task allocation and scheduling. The AHP decides the fog nodes’ priority based on node energy, bandwidth, RAM, and MIPS power. Similarly, the TAS also calculates the shortest distance between the IoT-enabled vehicle and the fog node to which the IoT tasks are assigned for execution. A task-aware scheduler schedules delay-sensitive tasks on nearby fog nodes while allocating computation-intensive tasks to cloud data centers using the FCFS algorithm. Fault-tolerant manager is used to check task failure; if any task fails, the proposed system re-executes the tasks, and if any fog node fails, the proposed system allocates the tasks to another fog node to reduce the task failure ratio. The proposed model is simulated in iFogSim2 and demonstrates a 7% reduction in response time, 16% reduction in energy consumption, and 22% reduction in task failure ratio in comparison to Ant Colony Optimization and Round Robin.

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Section: Related Workmentioning

confidence: 99%

Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics

Umer,

Ali,

Jehangiri

et al. 2024

Sensors

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“…Furthermore, many of these review papers have limitations in terms of the factors used to analyze the performance parameters of load balancing. These QoS parameters include response time, migration cost, energy conservation, throughput, convergence, and scalability [40][41][42][43][44][45]. None of the existing reviews has provided an appropriate classification for classifying various load balancing strategies.…”

Section: Research Motivationmentioning

confidence: 99%

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Syed,

Muhammad,

Rizvi

2024

IASC

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Cloud Computing has the ability to provide on-demand access to a shared resource pool. It has completely changed the way businesses are managed, implement applications, and provide services. The rise in popularity has led to a significant increase in the user demand for services. However, in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization. This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms. Specifically, metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic. In a cloud-based context, this paper describes load balancing measurements, including the benefits and drawbacks associated with the selected load balancing techniques. It also summarizes the algorithms based on implementation, time complexity, adaptability, associated issue(s), and targeted QoS parameters. Additionally, the analysis evaluates the tools and instruments utilized in each investigated study. Moreover, comparative analysis among static, traditional dynamic and metaheuristic algorithms based on response time by using the CloudSim simulation tool is also performed. Finally, the key open problems and potential directions for the state-of-the-art metaheuristic-based approaches are also addressed.

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Robotic Precision Fitness: Accurate Pose Training for Elderly Rehabilitation

Rincon,

Marco-Detchart

2024

Lecture Notes in Computer Science

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Server placement in mobile cloud computing: A comprehensive survey for edge computing, fog computing and cloudlet

Cited by 20 publications

References 87 publications

Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics

Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Robotic Precision Fitness: Accurate Pose Training for Elderly Rehabilitation

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