Reinforcing Cloud System Security and Sustainability with Rust-based Particle Swarm Optimization Task Scheduling

Presenter

David Gerard

Campus

UMass Amherst

Sponsor

Tim Richards, Department of Computer Science, UMass Amherst

Schedule

Session 2, 11:30 AM - 12:15 PM [Schedule by Time][Poster Grid for Time/Location]

Location

Poster Board A14, Campus Center Auditorium, Row 1 (A1-A20) [Poster Location Map]

Abstract

With the widespread popularity of cloud computing solutions in industry, the efficiency of data centers becomes critical in reducing carbon emissions. Cloud task scheduling algorithms such as Particle Swarm Optimization (PSO) are highly optimizable with influence on energy consumption and safety exposure. As such, efficient task scheduling is a barrier to the balance between sustainability, consumer interests, and provider interests. The growing popularity of the Rust programming language, boasting guaranteed safety without the cost of efficiency, poses a critical question: How does applying Rust to the PSO algorithm impact system-wide safety vulnerability, data exposure level, and runtime efficiency in cloud computing task scheduling? Java and Rust PSO algorithms are constructed and analyzed according to qualitative safety exposures and quantitative efficiency metrics. Following the same logic as the Java algorithm, the Rust algorithm patches vulnerabilities such as race conditions, discrete data leaks, and other concurrency-related issues. Rust constructs ultimately provoke deeper consideration of data flow design that is integral to the development of complex distributed systems. Although efficiency tends to be inversely related to safety guarantees in large-scale software systems, the Rust PSO algorithm exhibits a 2.65x efficiency increase over the Java version. These safety and performance improvements are catalysts for augmenting customer quality of service, business operation costs, and data center sustainability.

Keywords

Cloud Computing, Data Safety, Software Efficiency, Sustainability, Programming Languages

Research Area

Computer Science

SIMILAR ABSTRACTS (BY KEYWORD)