Cloud Computing Resource Management and Optimization

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.

Research from Nature Portfolio

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Research from all publishers

Recent studies have demonstrated the promise of deep reinforcement learning (DRL) methods for resource scheduling in cloud environments. By formulating scheduling as a sequential decision-making problem, DRL agents learn policies that adapt to dynamic workloads and heterogeneous resource capabilities, yielding improved makespan and resource utilisation compared with traditional heuristic and meta-heuristic approaches.

Efforts to enhance energy efficiency in data centres have focused on software-level techniques for green cloud computing. Surveys highlight novel power-management strategies at the virtualisation, operating-system and application layers—such as dynamic voltage scaling, container consolidation and adaptive middleware—that collectively reduce carbon footprints while meeting service-level constraints.

In edge-cloud data centres, multi-objective optimisation techniques have been applied to virtual machine placement. Bio-inspired algorithms, such as seagull optimisation, concurrently minimise energy usage and network traffic by clustering inter-communicating VMs onto the same physical hosts, achieving significant reductions in power consumption and latency under realistic network topologies.

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

1. Seagull optimization algorithm based multi-objective VM placement in edge-cloud data centers. Internet of Things and Cyber-Physical Systems (2023).
2. Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions. Artificial Intelligence Review (2024).
3. Energy efficiency in cloud computing data centers: a survey on software technologies. Cluster Computing (2022).