Modelling and Analysis of A Novel Deadline-Aware Scheduling Scheme for Cloud Computing Data Centers

Abstract

User request (UR) service scheduling is a process that significantly impacts the performance of a cloud data center. This is especially true since essential quality-of-service (QoS) performance metrics such as the UR blocking probability as well as the data center's response time are tightly coupled to such a process. This paper revolves around the proposal of a novel Deadline-Aware UR Scheduling Scheme (DASS) that has the objective of improving the data center's QoS performance in term of the above-mentioned metrics. A minority of existing work in the literature targets the formulation of mathematical models for the purpose of characterizing a cloud data center's performance. As a contribution to covering this gap, this paper presents an analytical model, which is developed for the purpose of capturing the system's dynamics and evaluating its performance when operating under DASS. The model's results’ accuracy are verified through simulations. Also, the performance of the data center achieved under DASS is compared to its counterpart achieved under the more generic First-In-First-Out (FIFO) scheme. The reported results indicate that DASS outperforms FIFO by $11$ to $58$ percent in terms of the blocking probability and by $82$ to $89$ percent in terms of the system's response time.