Deep Learning-Based Predictive Resource Allocation Framework for Energy-Efficient Cloud Systems
TL;DR
A new dynamic energy-efficient Deep Learning-Based Predictive Resource Allocation Framework (DLPRAF) is introduced for timely allocation of resources while upholding SLA adherence and is able to achieve 98.6% SLA compliance for this framework while providing cloud infrastructure with meaningful sustainability benefits.
Abstract
Energy efficiency and service provisioning are the two major challenges in current days cloud computing paradigm. In this article, a new dynamic energy-efficient Deep Learning-Based Predictive Resource Allocation Framework (DLPRAF) is introduced for timely allocation of resources while upholding SLA adherence. This framework incorporates several deep learning architectures—namely Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Convolutional Neural Networks (CNN)—to accurately forecast cloud workload trends. We incorporate temporal and spatial feature extraction ability to capture complex nonlinear dependencies in cloud workloads. By allowing for proactive resource provisioning instead of reactive approaches, the recommended system in better use of resources, lower energy consumption and improved QoS. We experimentally evaluate the effectiveness of DLPRAF and show on real-world cloud datasets (Google Cluster Data and Alibaba traces), that DLPRAF are 32.5% more resource utilization efficient, 43.3% timely and incur 26.6% lower operational costs than threshold-based approaches2. We are able to achieve 98.6% SLA compliance for our framework while providing cloud infrastructure with meaningful sustainability benefits.