A Survey of Game-Theoretic Approach for Resource Management in Cloud Computing

Table 4

A comparative analysis of game-theoretic models for load balancing in cloud architecture.

Author	Techniques	Usage	Comments
Ramya et al. [53]	Using the Nash bargaining solution (NBS), cooperative game theory delivers the Pareto optimal allocation of load to the user	Auto scaling-load balancing allows cloud users to make effective use of network capacity while also lowering provisioning costs
Ramya et al. [53]	Clustering scheduling	To improve performance
Ramya et al. [53]	Duplication (replication) based scheduling	To achieve a directed acyclic graph (DAG) scheduling with minimized makespan time of the task and high efficiency of the task in the cloud service
Subrata et al. [54]	Defined the problem as a non-cooperative game, whereby the objective is to reach the Nash equilibrium	The proportional-scheme algorithm	Tasks are allocated to processors in proportion to their computing power
Abdeyazdan et al. [55]	Prescheduling algorithms	Task graph scheduling	It minimizes their earliest start time while reducing the overall completion time
Swathy et al. [50]	Stackelberg model	Effective utilization of resources	It is a centralized load balancing