Machine Learning-Based Predictive Model to Improve Cloud Application Performance in Cloud SaaS

Abstract

Researchers have long touted a vision of the future enabled by a proliferation of internet-of-things devices, including smart sensors, homes, and cities. Increasingly, embedding intelligence in such devices involves the use of deep neural networks. However, their storage and processing requirements make them prohibitive for cheap, off-the-shelf platforms. Overcoming those requirements is necessary for enabling widely-applicable smart devices. While many ways of making models smaller and more efficient have been developed, there is a lack of understanding of which ones are best suited for particular scenarios. More importantly for edge platforms, those choices cannot be analyzed in isolation from cost and user experience. In this work, we holistically explore how quantization, model scaling, and multi-modality interact with system components such as memory, sensors, and processors. We perform this hardware/software co-design from the cost, latency, and user-experience perspective, and develop a set of guidelines for optimal system design and model deployment for the most cost-constrained platforms. We demonstrate our approach using an end-to-end, on-device, biometric user authentication system using a $20 ESP-EYE board.

Adaptive Threshold Tuning-based Load Balancing (ATTLB) for Cost Minimization in Cloud Computing

“…The key capability they provide allows multiple VMs to coexist independently on a single physical machine. Hypervisors effectively partition and mediate access to underlying physical resources, such as CPU, memory, storage, and networking between virtualized environments [39]. This allows the efficient sharing and allocation of these resources from the host to individual VMs.…”