|
Author
Year | Method | Purpose | Description |
|
| Erana Veerappa Dinesh Subramaniam | Gray wolf optimization | In the current time, task offloading from smartphones to cloud servers has proven to be a promising technique for increasing smartphone functionality and battery life. The cost of communication and energy usage are used to determine the effectiveness of task offloading [17] | Strength
We offer a new framework that uses a task scheduler to reduce energy usage during HMCC task offloading [10]. The suggested system uses a multiobjective function to model the scheduler, which takes into account network metrics, cloud parameters, and other system data
Weakness
The outcome in tangling also not accurate result in global optimization. Low precision or poor local optimization |
| Kaiyang Liu (2016) [18] | An iterative decoupling algorithm | Internet of things offers a new concept to improve the abilities through offloading the computational resources to consume less energy from smart phones; we offload resources to cloud server. | Strength
To use less energy, the users of smart devices offload work with an appropriate data connection and contact efficiency, and offloading decision strategy is studied
Weakness
It takes large computation period |
| Kai Lin (2018) [23] | Fruit fly algorithm | An offloading algorithm fruit fly is suggested to enhance the distribution by offloading and utilizes its resources to achieve less energy usage under responsibilities assigned. Energy consumption, time, and cost efficiency, relative to cooperative multitask, allocated total on an ant colony optimization algorithm and algorithm based on the heuristic server. The findings further suggest the efficiency of the algorithm suggested by contrasting that with current algorithms | Strength
The simulation outcomes show that the average FOTO algorithm promises better energy efficiency, reduced processing times, and also reduced datacenter costs which are used for edge computing in advanced applications. For further study into mobile offloading across numerous mobile devices, a cooperative device-to-device communication system will be established, allowing mobile devices to assist each other in offloading duties. This method can increase channel capacity while maintaining high bandwidth efficiency, allowing task offloading to be better utilized
Weakness
Less accuracy or bad optimal solution |
| Jing Zhang, Weiwei Xia (2018) [3] | Subalgorithms | Low power consumption and low computing capacities restrict the installation of high computational programs on smart devices | Strength
The appropriate approach that impedes efficiency that analyzes the characteristics will transfer the intensive apps as on a cloud
Weakness
Problem not solving independently |
| Men his chin, Ben Liang, Min dong (2016) | Heuristic algorithm | Each smartphone user requires several independent tasks that share the resource while offloads workload to the cloud network for less computational power through other methods [33] | Strength
Currently, we work on improving the offloading and allocating contact tools for all projects, to eliminate electricity expenses, computing costs, and delays for all consumers. Our approach can be applied to several users and activities where even the machine sophistication of thorough search becomes costly
Weakness
This method is not providing optimal solution |
| Rahul Yadav (2020) [34] | Heuristic approach | To solve the energy consumption and resource allocation problem in this paper used an energy efficient computation. | Strength
It is better for the energy saving, latency, and energy latency cost than overall schemes [34] |
| Muhammad Shafiq (2021) [35] | RL based | Computation Offloading using Reinforcement Learning (CORL)
For more energy, less battery time, and delay in portable machines, there are not enough resources for allocate these, so in this research, we proposed some work to handle this situation | Strength
Better offloading decisions in a quick time [35] |
|
