Ben\'e: On Demand Cost-Effective Scaling at the Edge

TL;DR

This paper proposes a hybrid edge computing model combining dedicated edge resources with on-demand micro-data centers to improve resource utilization, scalability, and security while reducing costs.

Contribution

It introduces a novel hybrid approach that leverages shared micro-data centers alongside dedicated edge resources for efficient, scalable, and secure edge computing.

Findings

Hybrid approach improves resource utilization and scalability.

Reduces over-provisioning costs for enterprises.

Enhances data privacy, security, and availability.

Abstract

Edge computing has become increasingly popular across many domains and enterprises. However, given the locality constraint of edges (i.e., only close-by edges are useful), multiplexing diverse workloads becomes challenging. This results in poor resource utilization in edge resources that are provisioned for peak demand. A simple way to allow multiplexing is through micro-data centers, that bring computation close to the users while supporting diverse workloads throughout the data, along with edges. In this paper, we argue for a hybrid approach of dedicated edge resources within an enterprise and on demand resources in micro-data centers that are shared across entities. We show that this hybrid approach is an effective and cost-efficient way for scaling workloads and removes the need for over-provisioning dedicated resources per enterprise. Moreover, compared to a scaling approach that uses only the edges across enterprises, micro-data centers also form a trusted third party that can maintain important quality of service guarantees such as data privacy, security, and availability.