Next-Gen Modern Computing: The Ultimate Vision and Challenge

Modern Computing: Vision and Challenges

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.

Introduction

The Internet, the expansive computational backbone of interactive machines, is largely responsible for the 21st century’s social, financial, and technological growth [1]. The growing reliance on the computing resources it encapsulates has pushed the complexity and scope of such platforms, leading to the development of innovative computing systems.
These systems have genuinely improved the capabilities and expectations of computing equipment driven by rapid technical and user-driven evolution

[2]. For instance, vintage mainframes combined centralized data processing and storage with transmission interfaces for user input. Due to advancements in clusters and packet-switching technologies, microchip gadgets, and graphical user interfaces, technology originally shifted from big, centrally-run mainframe computers to Personal Computers (PCs). The globalization of network standards made it possible for interconnected networks worldwide to communicate and share data

[3]. Businesses slowly combined sensor and actuator goals with built-in network connectivity by creating architectures and standards that submit tasks to remote pools of computing resources, such as memory, storage, and data processing

[4]. As a result, newer models like the Internet of Things (IoT) and edge computing are now beginning to expand the reach of technology outside the confines of traditional network nodes

[5]. Over the past six decades, computing models have fundamentally shifted to address the problems posed by the ever-evolving nature of our civilization and its associated computer system architectures

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[6]. The evolution of computing from mainframes to workstations to the cloud to autonomous and decentralized architectures, such as edge computing and IoT technologies, however, maintains identical core parts and traits that characterize their function

[7]. Research in computing underpins all of them! Advancements in areas like security, computer hardware acceleration, edge computing, and energy efficiency typically serve as catalysts for innovation and entrepreneurship that span across various business domains

[8]. While computing systems and other forms of system integration create new problems/opportunities, software frameworks have been developed to address them. Thus, middleware, network protocols, and safe segregation techniques must be continually developed and refined to support novel computing systems—and their innovative use cases.

Motivation

By tracking the effect of computing systems on the community, this comprehensive study seeks to (a) establish the essential features and components of modern computing systems, (b) thoroughly assess the development of innovations and behavioral patterns that inspired the invention of these paradigms, and (c) recognize significant developments throughout the models, such as the integration of system design, the shifting between centralization and decentralization, and lags in model conceptualization and development. This investigation suggests that next-generation computing systems will facilitate the decentralization of computational services. This will be achieved via the composition of decentralized calculation tools with workload-specific targets for performance to create dramatically more complex structures. These will satisfy holistic operational demands, such as improved capacity and power accessibility.

Related Surveys and Our Contributions

Computing being a rapidly growing topic, the time is
right for a novel, forward-thinking study to summarize,
improve, and integrate the existing and newly-generated
information, and to explore possible trends and future viewpoints. Previously, Pujol et al. [9] provided a survey on

distributed computing continuum systems that focused on
business models. Further back in 2018, Buyya et al. [1]
presented a manifesto on fundamental issues, developments,
and impacts in cloud computing research. Meanwhile, Gill
et al. [4] offered a visionary survey of advances in computing paradigms for fog, edge, and serverless computing.
Further, Shalf [10] summarized the 2020 state of the art of
technological roadmaps and their implications for the future
of systems, including what a post-exascale system would
entail. Finally, in 2021, Angel et al. [11] reviewed leading

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computational frameworks for cloud and edge computing,
and showcased breakthroughs that had been brought about
via the merging of Machine Learning (ML) with these
models.
In order to evaluate and identify the most pressing research issues of modern computing, we have developed the
very first taxonomy of its type. We performed a gap analysis
of the current surveys using several criteria, as shown in

Table 1, which underpinned the design of our work. Hence,
our study uniquely contributes by (a) exploring the history
of computing paradigm shifts with a focus on technology
drivers, (b) providing a thorough taxonomy of computing
systems, (c) introducing the hype cycle for modern computing systems with a focus on new trends, and (d) discussing
the effects and cost-effective performance requirements of
modern computing.

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The key contributions of this article are summarized as
follows:

• It offers a concise overview of the transition from early
  to modern computing.
• The study explores the evolution of computing paradigms,
  focusing on technological drivers (1960–2023).
• Following a novel methodology, the article produces
  a taxonomy of modern computing based on traits of
  computing such as 1) focus or paradigms; 2) technologies or impact areas; and 3) trends or observations.
• It presents a comprehensive classification of computing: 1) Standalone vs. Networked Computing; 2)
  General Purpose vs. Specialized Computing, 3) Centralized vs. Decentralized Computing, 4) Computing
  Trends and Emerging Technologies; and 5) Computational Methodologies: Parallel vs. Sequential Computing.
• The study identifies the impact and performance criteria of modern computing in terms of performance
  metrics, efficiency metrics, social impact, security and
  compliance, and economics and management.
• It provides an in-depth summary of computing traits
  and resources for further research.
• The article identifies open challenges and research
  directions for the traits of computing.
• Finally, it introduces the hype cycle for modern computing systems, spotlighting emerging trends.

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