QoS-Aware Efficient Tasks Scheduling in Heterogeneous Cloud Computing Environment
Keywords:
Cloud Computing, Task Scheduling, Genetic Algorithm, Quality of Service, BandwidthAbstract
The management and prioritization of network traffic to ensure the efficient transmission of important data is achieved through Quality of Service (QoS) technologies and techniques. QoS facilitates allocating necessary resources and bandwidth to critical applications and services while de-prioritizing less important traffic. This is accomplished by classifying and marking packets. Task scheduling involves coordinating and managing the execution of tasks in a computer system or network, including allocating resources and determining the order in which tasks are executed. Task scheduling algorithms use priority, resource requirements, and task dependencies to determine the most efficient way to execute tasks. A heterogeneous cloud environment utilizes multiple cloud computing platforms from different vendors such as IaaS, PaaS, and SaaS to deliver services and optimize cost, performance, and scalability. The task scheduling problem in cloud computing involves effectively mapping workloads to virtual resources. The study introduces the genetic-based algorithm BGA to increase makespan and resource consumption, while SGA focuses on convergence speed. These strategies are compared with current meta-heuristic and heuristic techniques.
Keywords: Cloud Computing; Task Scheduling; Genetic Algorithm; Quality of Service; Bandwidth
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