Table 1 Summary of related works

Ipsita Kar et.al. (2016) [22]

Genetic Algorithm and Darwin Theory

Global Search solution and self- managing scheme

Maximum Flowtime and resource utilization

Pan Yi.et.al (2016) [23]

Tabu Search based on techniques

Execution time and Throughput

Resource allocation time

X. -F. et.al (2018) [24]

Ant Colony Optimization

Randomization Techniques

Robustness and Effective Virtual Machine utility

M.Kaur.et.al (2016) [25]

Bacterial Foraging Algorithm

Makespan and Throughput

Minimum Flow time and Resource usage

S. Belgian (2014).[26]

Cat Swarm Optimization Algorithm

Optimal Resource utilization and Minimum iterations

In-secure Data access and throughput

Natesan.et.al (2019) [27]

grey wolf Algorithm

Randomization and parameter-based resource allocation

Multimodality support and convergence speed

Hariharan et. al. (2019) [28]

Whale and BAT optimization algorithm (WBAT)

Multi-objective job scheduling using hybridization

Complicated algorithm compared to other algorithms

K.Devi et. al. (2020) [29]

Deep Learning Based Cloud based Task Scheduling

Limits resource starvation and to guarantee fairness among the parties using the resources

As the number of cloud users increases, the scheduling becomes limited

Essam H et. al. (2021) [30]

Meta-heuristic task scheduling

Distribute complex tasks (cloudlets) to limited resources, within a reasonable time

The dilemmas of resources being underutilized (underloaded) and overutilized (overloaded)

Wanneng Shu et. al. (2021) [31]

Agile response task scheduling optimization

Explore the probability density function of the task request to avoid the timeout

Queue overflow happens, leads to network congestion

AK Reshmy et. al. (2019) [32]

Multilevel Fault-Tolerance Aware Scheduling Technique

Overcomes the real-time failure in the system

Mostly rely on the reactive scheme of checkpoint mechanism