Cloud Computing Resource Management and Optimization
Summary
The growth of cloud computing has transformed the provision of on-demand computing services across industries. Effective management and optimisation of cloud resources encompass the provisioning, allocation, scheduling and consolidation of virtualised resources such as processing power, storage and network bandwidth. Key objectives include minimising operational costs and energy consumption, ensuring quality-of-service guarantees and dynamically adjusting capacity to meet fluctuating workloads. Modern approaches draw on meta-heuristic algorithms, control-theoretic feedback loops and machine-learning techniques to balance trade-offs between performance, cost and sustainability. For example, workload consolidation reduces energy use by co-locating virtual machines on fewer physical servers, while auto-scaling mechanisms ensure applications remain responsive under sudden demand spikes. Recent advances extend optimisation to edge-cloud environments, where latency-sensitive applications require fine-grained resource decisions across geographically distributed data centres. As cloud infrastructures underpin critical services from scientific research to global commerce, innovations in resource management and optimisation carry profound implications for economic efficiency, environmental impact and the resilience of digital systems worldwide.
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Cloud Computing Resource Management and Optimization publication trend
The graph below shows the total number of articles in cloud computing resource management and optimization across all publications each year (not limited to Nature Index journals).
Technical terms
Virtual machine (VM): an emulated computing instance that runs on a physical host, enabling multiple operating environments on a single server.
Resource scheduling: the process of assigning computational tasks to available resources to satisfy performance and cost objectives.
Elasticity: the capability of a cloud system to automatically scale resource allocation up or down in response to workload fluctuations.
Load balancing: the distribution of workloads across multiple computing resources to optimise utilisation and avoid overloading.
Reinforcement learning: a machine-learning paradigm in which an agent learns optimal actions through trial and error interactions with an environment.
References
- Seagull optimization algorithm based multi-objective VM placement in edge-cloud data centers. Internet of Things and Cyber-Physical Systems (2023).
- Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions. Artificial Intelligence Review (2024).
- Energy efficiency in cloud computing data centers: a survey on software technologies. Cluster Computing (2022).
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