[Paper Review] A Manifesto for Future Generation Cloud Computing: Research Directions for the Next Decade

Rajkumar Buyya, Satish Narayana Srirama+23 more|arXiv (Cornell University)|Nov 24, 2017

Cloud Computing and Resource ManagementComputer Science163 references69 citations

TL;DR

The paper outlines open challenges, emerging trends, and future research directions to realize Future Generation Cloud Computing over the next decade, addressing scalability, reliability, sustainability, heterogeneity, intercloud interoperation, and security.

ABSTRACT

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing.

Motivation & Objective

• Identify major open challenges in Cloud computing and assess state-of-the-art solutions and their limitations.
• Explore emerging trends (e.g., edge/fog, serverless, interCloud) and their impact on research directions.
• Propose a comprehensive set of future research directions to guide the next decade of Cloud computing.
• Highlight opportunities for sustainability, security, and interoperability across geographically distributed data centers.

Proposed method

• Synthesize state-of-the-art challenges and limitations across Cloud computing domains (scalability, elasticity, reliability, sustainability, heterogeneity, interCloud, IoT/edge).
• Analyze emerging trends and impact areas (edge/fog computing, serverless, virtualization, heterogeneous resources).
• Propose a structured set of future research directions that align with the identified trends and challenges.
• Discuss the role of novel hardware, programming models, and data management in enabling future-generation Clouds.

Experimental results

Research questions

• RQ1 What are the key open challenges in Cloud computing today that will shape research for the next decade?
• RQ2 How do emerging trends such as edge/fog computing, serverless architectures, and InterCloud affect research directions and infrastructure design?
• RQ3 What research directions are needed to address scalability, reliability, sustainability, heterogeneity, and security in future Cloud ecosystems?

Key findings

• Cloud computing faces ongoing challenges in scalability, elasticity, reliability, energy sustainability, heterogeneity, InterCloud interoperability, and security.
• Emerging paradigms like edge/fog computing and serverless architectures create new opportunities but also require rethinking resource management, programming models, and data governance.
• Heterogeneity at VM, vendor, and hardware levels necessitates hardware-agnostic software abstractions and middleware-based acceleration.
• InterCloud and federated/hybrid cloud models offer benefits but require standardized interfaces and portable data formats for true interoperability.
• Sustainability concerns demand autonomic, energy-aware management and machine learning-enabled optimization of QoS and energy use.