SCHEDULING ALGORITHMS IN MULTI CLOUD ENVIRONMENTS – A SHORT REVIEW
DOI:
https://doi.org/10.58973/CTRJ.221133Keywords:
Scheduling, task, Workflow, Multi-cloudAbstract
Background: Cloud computing is based on a pay-as-you-go model in which efficient allocation and management of resources plays a major role, since users are billed according to their actual usage; proper utilization of resources therefore remains an active research focus, and whether a single cloud or a multi-cloud environment is used, incoming jobs or tasks must be scheduled according to available resources and needs.
Objective: This short review examines the scheduling algorithms available for single- and multi-cloud environments and organizes them according to their categories.
Methods: The review surveys provider-specific schedulers (AWS Batch, Azure Scheduler, Kubernetes-based scheduling as used in Alibaba Cloud, and the Salesforce Apex scheduler) together with the general cloud scheduling framework, and classifies scheduling algorithms reported in the literature into task, resource, and workflow scheduling categories, including a comparison of eight representative task-scheduling schemes.
Results: Task scheduling algorithms (PRISM, throughput-optimal scheduling, hyper-heuristic scheduling, ACTS, SLPSO, EARH, agent-based scheduling, and EMO workflow scheduling) are compared in terms of advantages, drawbacks, and simulation platform; resource scheduling algorithms are grouped into dynamic, agent-based, and cost-optimization categories; and workflow scheduling algorithms are grouped into heuristic, metaheuristic, and hybrid categories.
Conclusion: Scheduling remains a central cloud-computing issue since it directly affects execution time, cost, and resource utilization; no single algorithm suits every requirement, and this review gives researchers a structured starting point for selecting scheduling strategies in single- or multi-cloud systems.
Keywords: Scheduling, task, Workflow, Resource, Multi-cloud
HIGHLIGHTS:
1. This short review on scheduling algorithms will help understand the fundamentals of cloud scheduling.
2. The review will help researchers getting started with Scheduling Algorithms in multi-cloud environments.
3. Eight task-scheduling schemes and the resource- and workflow-scheduling categories are classified and compared for quick reference.
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