Search for: All records

This paper studies a cloud datacenter (DC) consisting of two types of tasks with different priority levels. While non-priority tasks generally request the use of a single virtual machine (VM), priority tasks may utilize multiple available VMs to accelerate processing. We focus on determining whether to accept or reject non-priority tasks to maximize overall system benefits. By formulating the problem as a stochastic dynamic program, it is verified that the best approach for handling nonpriority tasks adheres to a control-limit framework. Both experimental outcomes and numerical evaluations highlight the efficacy of the proposed method, leading to the identification of the optimal threshold. The key contribution of this paper is the development of a stochastic dynamic program for DC resource management and the explicit derivation of an optimal control-limit policy. Both value iteration and linear programming methods are utilized to solve optimization problems. These results offer essential understanding for assessing the performance of various DC models, optimizing both rewards and resources efficiently. 

Cloud computing (CC), often necessitates dynamic adjustments due to its inherently fluid nature. In this paper, we introduce a novel dynamic task scheduling model that incorporates reward and holding cost considerations, leveraging the Continuous-Time Markov Decision Process (CTMDP) framework in heterogeneous CC systems. The primary goal of this model is to maximize the overall system reward for the Cloud Service Provider. By solving the Bellman Optimality Equation using the value-iteration method, we can derive an optimal scheduling policy for the dynamic task scheduling model. Additionally, to enhance its practicality in real-world scenarios, we incorporate a model-free reinforcement learning algorithm to obtain the optimal policy for our proposed model without requiring explicit knowledge of the system environment. Simulation results demonstrate that our proposed model outperforms two common static scheduling methods.