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Advances, Systems and Applications

Table 1 Summary of research gaps

From: DSTS: A hybrid optimal and deep learning for dynamic scalable task scheduling on container cloud environment

Ref Proposed Methodology Parameters Future work
[21] Diego Heuristic algorithms Execution time The prototypes described were extending to wider environment; integrated into planned cloud services.
[22] Multiopt Virtual machine Response time To move containers without affecting or reducing the use of cloud services.
[23] MOO-ACA GA_MOCA algorithm Network transmission overhead Use scheduling methods in cloud containers to reduce the problem of algorithm time.
[24] EECS APSO Temperature Create a cloud environment for IoT applications that is dynamic and container-based, and allocate apps to the most appropriate containers.
[25] Container-based virtualized model VM Execution time Analyze the impact of post-failure work restructuring, interruptions due to work proximity in multiple cloud environments
[26] Adaptive fair-share method GPU memory allocation algorithm GPU memory utilization Improved Tensor Flow multi-container processing allows to securely share a GPU
[27] ECSched MCFP algorithm Fraction of containers To embrace more intricate circumstances, consider container dependencies and resource dynamics in the scheduler.
[28] SRPSM VM Sensitivity Searching multiple containers on same VM to perform multiple tasks in parallel
[29] KCSS Machine learning Computing time KCSS to identify residential containers and improve global performance.
[30] CANSS Naive Bayes Cache hit ratio Use artificial intelligence algorithms to compute if cache localization can be achieved
[31] State-of-the art scheduling algorithm Optimization algorithm Throughput Create a security alert table to avoid security issues related to the use of containers in your cloud infrastructure.
[32] Skippy scheduling container MCDM algorithm function execution time By implementing high-level operational goals, customize key planning parameters to explore specific aspects.