Architecture of a time‐sensitive provisioning system for cloud‐native software. (9th December 2021)

• Record Type: Journal Article
• Authors: Venkateswaran, Sreekrishnan; Bauskar, Adwait; Sarkar, Santonu
• Is Part Of: Software, practice & experience. Volume 52:Number 5(2022)
• Page Start: 1170
• Page End: 1198
• Publication Date: 2021-12-09
• Subjects: bare metal servers -- cloud‐native applications -- committed use instances -- hyperscalers -- multiclouds -- public clouds -- reliable software architecture -- reserved instances -- time‐sensitive provisioning; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.3059
• Languages: English
• ISSNs: 0038-0644

Abstract

Abstract: Application development paradigms and composition of technology services are decisively moving in the direction of hybrid and multi clouds. Enterprises are stitching new cloud‐native business models that leverage containerized multi‐tier microservice architecture, heterogeneity of cloud deployment models, and diversity of cloud providers. Scalability and resiliency are key components of this new world architecture, but these are also functions of the predictability of provisioning the underpinning compute instances on cloud. Thus, a major challenge to surmount before complex multicloud aware applications can be designed is the problem of unpredictable latencies associated with the provisioning of compute services on cloud. In the first part of this article, we develop a technique for time‐sensitive provisioning of virtual compute on demand, while also allowing deprovisioning on demand. Using the technique we propose, a cloud broker will be able to operate on a pool of reserved instances sourced from cloud providers and multiplex them profitably across cloud customers with associated provisioning time guarantees, but without usage commitment restrictions. We articulate this challenge in the form of the Reserved Instance Allocation Problem (RIAP), which we first prove to be NP‐hard. We then design a heuristic‐based method to solve the intractable RIAP in polynomial time. We evaluate the effectiveness of our heuristic‐based mechanism through a combination of deep simulations and practical validation on mainstream public clouds. We demonstrate that our algorithm consistently yields a high profit‐to‐investment ratio for a broker who seeks to operate a commerce of virtual machines with time‐sensitive provisioning. In the second part of this article, we tackle the problem of unpredictable provisioning latencies in bare metal commerce. We build and evaluate an allocation model to calculate optimal supporting bare metal inventory to maximize cost‐sensitive fulfillment of bare metal provisioning requests in a time‐sensitive manner.