Controlling Data Gravity and Data Friction: From Metrics to Multidimensional Elasticity Strategies

Abstract

The growing amount of data generated at the edge of the network, e.g., by Internet of Things (IoT) devices, made it indispensable to relocate computational power close to the data source. Meanwhile, data tends to accumulate in chunks and is frequently subject to resource-intensive transformations, such as privacy enforcement. These phenomena, which are summed up as “data gravity” and “data friction”, have an impact on data processing and the overall system. However, whereas cloud centers are able to dynamically adapt services, e.g., by provisioning additional resources, edge devices provide fewer options to react to changing workloads. To retain the option to process data locally, we present the idea of controlling data gravity and friction with Service Level Objectives (SLOs). We introduce Markov SLO Configurations (MSCs) as a novel approach to organizing performance metrics and elasticity strategies. MSCs, in conjunction with our presented architecture, enable the evaluation of SLOs, the context-based selection of elasticity strategy (i.e., corrective measures), and the execution of strategies directly on edge devices. Thus, we lay the foundation for a new generation of SLOs that can operate across multiple elasticity dimensions, e.g., by scaling quality of service (QoS).