Modern Computing: Vision and Challenges
Sukhpal Singh Gill, Huaming Wu, Panos Patros, Carlo Ottaviani, Priyansh Arora, Victor Casamayor Pujol, David Haunschild, Ajith Kumar Parlikad, Oktay Cetinkaya, Hanan Lutfiyya, Vlado Stankovski, Ruidong Li, Yuemin Ding, Junaid Qadir, Ajith Abraham, Soumya K. Ghosh, Houbing Herbert Song, Rizos Sakellariou, Omer Rana, Joel J. P. C. Rodrigues, Salil S. Kanhere, Schahram Dustdar, Steve Uhlig, Kotagiri Ramamohanarao, Rajkumar Buyya
TL;DR
Modern Computing: Vision and Challenges surveys six decades of computing evolution, tracing the shift from centralized mainframes to distributed architectures including cloud, fog, edge, IoT, and serverless while highlighting emerging paradigms like quantum computing and edge AI. The authors introduce a three-axis taxonomy (Standalone vs Networked, General vs Specialized, Centralized vs Decentralized) and extend it with a parallel/sequential methodology framework, offering a comprehensive hype cycle and an openness-focused discussion of Societal impact, security, and economics. The work foregrounds core open challenges such as QoS/SLA guarantees, autoscaling under latency constraints, energy efficiency, data governance, and the social implications of AI, while outlining future directions through emergent technologies like distributed ledger systems, federated learning, and AI-driven resource management. Its significance lies in providing a structured, forward-looking lens to guide research and investment in cost-effective, scalable, and secure modern computing architectures as the ecosystem becomes increasingly heterogeneous and AI-centric.
Abstract
Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress.