TIMBER: On supporting data pipelines in Mobile Cloud Environments

TL;DR

TIMBER is a framework designed to efficiently support data pipelines in Mobile Edge Cloud environments, addressing real-time processing challenges in smart city applications with significant cost savings and high throughput.

Contribution

We introduce TIMBER, a novel framework that enhances real-time data pipeline support in mobile cloud environments, reducing costs and maintaining high throughput.

Findings

Reduces operating costs by 66.245% on average

Achieves up to 96.4% throughput performance

Effectively handles dynamic and bursty workloads

Abstract

The radical advances in mobile computing, the IoT technological evolution along with cyberphysical components (e.g., sensors, actuators, control centers) have led to the development of smart city applications that generate raw or pre-processed data, enabling workflows involving the city to better sense the urban environment and support citizens' everyday lives. Recently, a new era of Mobile Edge Cloud (MEC) infrastructures has emerged to support smart city applications that aim to address the challenges raised due to the spatio-temporal dynamics of the urban crowd as well as bring scalability and on-demand computing capacity to urban system applications for timely response. In these, resource capabilities are distributed at the edge of the network and in close proximity to end-users, making it possible to perform computation and data processing at the network edge. However, there are important challenges related to real-time execution, not only due to the highly dynamic and transient crowd, the bursty and highly unpredictable amount of requests but also due to the resource constraints imposed by the Mobile Edge Cloud environment. In this paper, we present TIMBER, our framework for efficiently supporting mobile daTa processing pIpelines in MoBile cloud EnviRonments that effectively addresses the aforementioned challenges. Our detailed experimental results illustrate that our approach can reduce the operating costs by 66.245% on average and achieve up to 96.4% similar throughput performance for agnostic workloads.