T-COMS: A Time-Slot-Aware and Cost-Effective Data Transfer Method for Geo-Distributed Data Centers

Abstract

With the increasing demands placed on geographically distributed Data Centers (DCs), recent studies have focused on optimizing performance from the perspective of both cloud providers and customers. These studies address a variety of goals, such as minimizing transmission time, reducing resource usage, and optimizing network costs. However, many existing models for workload transfers operate using a uniform time-slot approach, which limits their flexibility in handling variable data transfer requests with different deadline requirements. This lack of adaptability can negatively impact the quality of service for users. Additionally, these models often overlook the potential benefits of incorporating multiple data sources, which can lead to sub-optimal transmission times. To overcome these limitations, this paper introduces T-COMS, a Time-slot-aware, COst-effective, and Multi-Source-aware method for file transfers tailored specifically for geo-distributed DCs, leveraging a multi-source and dynamic time-slot strategy to accelerate transmission and enhance service quality. The proposed model identifies the optimal sources, paths, and time slot lengths required to efficiently transmit workloads to their destinations while minimizing costs. Initially, we introduced a Mixed Integer Non-Linear Programming (MINLP) model and subsequently linearized it within our framework. Given the NP-hard nature of the proposed model, its applicability is limited in large-scale environments. To address this issue, we developed an efficient heuristic algorithm that can derive near-optimal solutions in polynomial time. The simulation results demonstrate the effectiveness of the proposed T-COMS model and the heuristic algorithm in terms of the reduction in cost and transmission time for file transfers between geographically distributed DCs.