Exploring Micro-Services for Enhancing Internet QoS

TL;DR

This paper investigates micro-service deployment, discovery, and scheduling across multiple clouds to improve internet service quality, proposing a novel heuristic that outperforms existing algorithms in reducing latency and costs.

Contribution

It introduces a new affinity-based fair weighted scheduling heuristic for micro-services, addressing deployment and scheduling challenges across multiple clouds with SLA considerations.

Findings

Significant reduction in turnaround time and costs with the proposed heuristic.

Improved service chain formation and resource utilization.

Outperforms standard greedy algorithms in experiments.

Abstract

With the enhancements in the field of software-defined networking and virtualization technologies, novel networking paradigms such as network function virtualization (NFV) and the Internet of things (IoT) are rapidly gaining ground. Development of IoT as well as 5G networks and explosion in online services has resulted in an exponential growth of devices connected to the network. As a result, application service providers (ASPs) and Internet service providers (ISPs) are being confronted with the unprecedented challenge of accommodating increasing service and traffic demands from the geographically distributed users. To tackle this problem, many ASPs and ISPs, such as Netflix, Facebook, AT&T and others are increasingly adopting micro-services (MS) application architecture. Despite the success of MS in the industry, there is no specific standard or research work for service providers as guidelines, especially from the perspective of basic micro-service operations. In this work, we aim to bridge this gap between industry and academia and discuss different micro-service deployment, discovery and communication options for service providers as a means to forming complete service chains. In addition, we address the problem of scheduling micro-services across multiple clouds, including micro-clouds. We consider different user-level SLAs, such as latency and cost, while scheduling such services. We aim to reduce overall turnaround time as well as costs for the deployment of complete end-to-end service. In this work, we present a novel affinity-based fair weighted scheduling heuristic to solve this problem. We also compare the results of proposed solution with standard greedy scheduling algorithms presented in the literature and observe significant improvements.