Autonomic Cloud Computing: Research Perspective
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2015
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| _version_ | 1866908273709416448 |
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| author | Gill, Sukhpal Singh |
| author_facet | Gill, Sukhpal Singh |
| contents | As the cloud infrastructure grows, it becomes more challenging to manage resources in such a massive, diverse, and distributed setting, despite the fact that cloud computing provides computational capabilities on-demand. Due to resource variability and unpredictability, resource allocation issues arise in a cloud setting. A Quality of Service (QoS) based autonomic resource management strategy automates resource management, delivering trustworthy, dependable, and cost-effective cloud services that efficiently execute workloads. Autonomic cloud computing aims to understand how computing systems may autonomously accomplish user-specified "control" objectives without the need for an administrator and without violating the Service Level Agreement (SLA) in a dynamic cloud computing environments. This chapter presents a research perspective and analysis on autonomic resource allocation in cloud computing based on the last decade of conducted research with a focus on QoS and SLA-aware autonomic resource management. This study delves into the current state of autonomic resource management in the cloud and introduces a conceptual model for Artificial Intelligence (AI)-driven autonomic cloud computing. This model aims to optimise server load distribution and energy consumption, thus enhancing cost savings and environmental impact. Finally, it highlights key next-generation research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1507_01546 |
| institution | arXiv |
| publishDate | 2015 |
| record_format | arxiv |
| spellingShingle | Autonomic Cloud Computing: Research Perspective Gill, Sukhpal Singh Distributed, Parallel, and Cluster Computing As the cloud infrastructure grows, it becomes more challenging to manage resources in such a massive, diverse, and distributed setting, despite the fact that cloud computing provides computational capabilities on-demand. Due to resource variability and unpredictability, resource allocation issues arise in a cloud setting. A Quality of Service (QoS) based autonomic resource management strategy automates resource management, delivering trustworthy, dependable, and cost-effective cloud services that efficiently execute workloads. Autonomic cloud computing aims to understand how computing systems may autonomously accomplish user-specified "control" objectives without the need for an administrator and without violating the Service Level Agreement (SLA) in a dynamic cloud computing environments. This chapter presents a research perspective and analysis on autonomic resource allocation in cloud computing based on the last decade of conducted research with a focus on QoS and SLA-aware autonomic resource management. This study delves into the current state of autonomic resource management in the cloud and introduces a conceptual model for Artificial Intelligence (AI)-driven autonomic cloud computing. This model aims to optimise server load distribution and energy consumption, thus enhancing cost savings and environmental impact. Finally, it highlights key next-generation research directions. |
| title | Autonomic Cloud Computing: Research Perspective |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/1507.01546 |