Cloud and Edge-Based Distributed Computing: A Survey of Architectures and Resource Management Approaches
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V8I3P106Keywords:
Cloud Computing, Edge Computing, Distributed Systems, Resource Management, Task Scheduling, Qos and SLA Optimization, Data and Network ManagementAbstract
The swift development of data-intensive applications and Internet of Things (IoT) systems has increased the rate of change in the distributed computing paradigms, especially cloud and edge computing. Although cloud computing provides scalable and on-demand services with centralized infrastructures, it has limitations in terms of high latency, bandwidth, and supporting real-time applications. To address these challenges, edge and fog computing have become complementary paradigms that can allow processing data nearer to the source, thus, decreasing the latency and enhancing responsiveness. This paper provides a wide overview of the architectures and resource management strategies in cloud and edge-based distributed computing systems. It discusses the centralized cloud architecture, edge, and fog computing models, the cloud-edge continuum that incorporates these paradigms into a single framework. Moreover, it discusses important resource management methods, including resource allocation and provisioning, task scheduling and load balancing, and QoS- and SLA-aware optimization. Data placement, communication protocols, network optimization and real-time constraints are also discussed in the paper as the data and network management strategies. Finally, it identifies the current challenges, research gaps and future directions for developing efficient, scalable and intelligent distributed computing systems.
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