Modeling and Simulation of QoS-Aware Power Budgeting in Cloud Data Centers

Abstract

Power budgeting is a commonly employed solution to reduce the negative consequences of high power consumption of large scale data centers. While various power budgeting techniques and algorithms have been proposed at different levels of data center infrastructures to optimize the power allocation to servers and hosted applications, testing them has been challenging with no available simulation platform that enables such testing for different scenarios and configurations. To facilitate evaluation and comparison of such techniques and algorithms, we introduce a simulation model for Quality-of-Service aware power budgeting and its implementation in CloudSim. We validate the proposed simulation model against a deployment on a real testbed, showcase simulator capabilities, and evaluate its scalability.

Energy and Network Aware Workload Management for Geographically Distributed Data Centers

“…(b) Data Center Network Management: Many recent papers focus on intelligent workload scheduling among cloud centers to optimize the quality of service (QoS) while increasing cloud operating profits [21], [22]. As data migration among cloud data centers increases network costs, some efforts try to address this issue by proposing techniques that intelligently allocate workloads to reduce overall network costs [23], [24].…”

ML-driven classification scheme for dynamic interference-aware resource scheduling in cloud infrastructures

“…• Proportional is similar to [14] but categorizes the interference from each profiled resource through a proportional division of the interference levels ranges (1/3 for each level, low [1%-33%], medium [34%-66%], and high [67%-100%]). This technique was chosen because this strategy to define fixed thresholds is commonly adopted in the resource management field [58,59].…”