Heterogeneous cloud data centers faced severe server bottlenecks and soaring electricity costs from unbalanced workloads; intelligent task scheduling algorithms optimize makespan and resource utilization across virtualized clusters.

Global cloud computing platforms process billions of concurrent user transactions, streaming videos, and computational jobs across vast warehouses of networked servers.
Scheduling diverse tasks across multi-core processors, memory banks, and storage tiers is an NP-hard optimization problem: simple first-come-first-served queues leave expensive hardware idling while critical jobs stall in backlogs.
This comprehensive review categorizes heuristic, meta-heuristic, and machine learning task scheduling algorithms in cloud environments. The analysis evaluates genetic algorithms, particle swarm optimization, and ant colony approaches against key metrics: execution makespan, energy efficiency, quality of service (QoS), and load balancing.
These scheduling taxonomy benchmarks guide enterprise cloud architects to build autonomous hypervisor schedulers that slash data center carbon footprints while maintaining ultra-low latency for end users.
Task Scheduling Algorithms in Cloud Computing: A Review
Cloud computing is the requirement based on clients and provides many resources that aim to share it as a service through the internet. For optimal use, Cloud computing resources such as storage, application, and other services need managing and scheduling these services. The principal idea behind the scheduling is to minimize loss time, workload, and maximize throughput. So, the scheduling task is essential to achieve accuracy and correctness on task completion. This paper gives an idea about various task scheduling algorithms in the cloud computing environment used by researchers. Finally, many authors applied different parameters like completion time, throughput, and cost to evaluate the system.
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