Optimization and Resource Allocation in Cloud and Edge Computing

Dear Colleagues,

In the past decade, advances in cloud computing, the Internet of Things, big data, and next-generation communication networks have transformed the delivery of digital services. Edge devices, such as sensors, smartphones, and cameras now generate large volumes of real-time data and requests. Sending them all to remote clouds can cause significant delays, making it difficult to meet the low-latency requirements of many applications.

Edge computing addresses these challenges by extending the cloud model. Rather than replacing cloud centers, it deploys edge nodes near end devices, shifting some computation, storage, and control functions to them. In this cloud–edge–end structure, local edge nodes process latency- or location-sensitive tasks, while remote clouds manage cross-regional aggregation, long-term storage, and large-scale analytics. Designing effective task-scheduling approaches and resource allocation mechanisms that satisfy a variety of constraints on latency, energy consumption, and other costs has become one of the most important tasks within the field. Over the past decade, substantial research has addressed optimization and resource allocation issues in cloud and edge computing, yielding many exciting results that are both theoretically grounded and practical in the real world.

Emerging technologies over the past few years have posed new challenges for cloud and edge computing. For instance, the remarkable development of artificial intelligence (AI) and increasing concerns over data security and privacy have complicated the design of large-scale edge systems. AI training and inference require more computing capacity and system throughput than traditional edge nodes, and deploying AI inference closer to end users can enhance privacy and the user experience by reducing the transmission of less sensitive information to remote clouds. As cloud–edge–end models grow more complex, new scheduling and resource-management challenges arise. These trends underscore the growing importance of optimization and resource allocation for new applications in the cloud and edge computing domain.

We warmly invite researchers and practitioners to submit work on optimization and resource allocation in cloud and edge computing. Topics of interest include, but are not limited to, the following: (1) convex and nonconvex optimizations for task scheduling and resource allocation in cloud and edge computing; (2) game-theoretic approaches for incentive mechanisms design; (3) graph- and network-based methods for task routing and placement; (4) learning-assisted optimization, such as reinforcement or federated learning; (5) online and adaptive algorithms for dynamic workload allocation, and (6) stochastic and queueing models for latency and reliability. Relevant mathematical tools span convex and combinatorial optimization, stochastic processes, approximation algorithms, distributed optimization, and statistical learning methods. We hope this Special Issue will foster new ideas and practical solutions that advance cloud–edge systems for the next generation of digital services.

Dr. Wei Chang
Guest Editor

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• cloud computing
• convex optimization
• combinatorial optimization
• distributed optimization
• edge computing
• resource allocation
• scheduling
• statistical methods
• stochastic processes