Cost-Effective Edge Data Distribution with End-To-End Delay Guarantees in Edge Computing
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909597522984960 |
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| author | Shankar, Ravi Sahu, Aryabartta |
| author_facet | Shankar, Ravi Sahu, Aryabartta |
| contents | Cloud Computing is the delivery of computing resources which includes servers, storage, databases, networking, software, analytics, and intelligence over the internet to offer faster innovation, flexible resources, and economies of scale. Since these computing resources are hosted centrally, the data transactions from the cloud to its users can get very expensive. Edge Computing plays a crucial role in minimizing these costs by shifting the data from the cloud to the edge servers located closer to the user's geographical location, thereby providing low-latency app-functionalities to the users of that area. However, the data transaction from the cloud to each of these edge servers can still be expensive both in time and cost. Thus, we need an application data distribution strategy that minimizes these penalities. In this research, we attempt to formulate this Edge Data Distribution as a constrained optimization problem with end-to-end delay guarantees. We then provide an optimal approach to solve this problem using the Integer Programming (IP) technique. Since the IP approach has an exponential time complexity, we also then provide a modified implementation of the EDD-NSTE algorithm, for estimating solutions to large-scale EDD problems. These algorithms are then evaluated on standard real-world datasets named EUA and SLNDC and the result demonstrates that EDD-NSTE significantly outperformed, with a performance margin of 80.35\% over the other representative approaches in comparison. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21070 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cost-Effective Edge Data Distribution with End-To-End Delay Guarantees in Edge Computing Shankar, Ravi Sahu, Aryabartta Distributed, Parallel, and Cluster Computing Cloud Computing is the delivery of computing resources which includes servers, storage, databases, networking, software, analytics, and intelligence over the internet to offer faster innovation, flexible resources, and economies of scale. Since these computing resources are hosted centrally, the data transactions from the cloud to its users can get very expensive. Edge Computing plays a crucial role in minimizing these costs by shifting the data from the cloud to the edge servers located closer to the user's geographical location, thereby providing low-latency app-functionalities to the users of that area. However, the data transaction from the cloud to each of these edge servers can still be expensive both in time and cost. Thus, we need an application data distribution strategy that minimizes these penalities. In this research, we attempt to formulate this Edge Data Distribution as a constrained optimization problem with end-to-end delay guarantees. We then provide an optimal approach to solve this problem using the Integer Programming (IP) technique. Since the IP approach has an exponential time complexity, we also then provide a modified implementation of the EDD-NSTE algorithm, for estimating solutions to large-scale EDD problems. These algorithms are then evaluated on standard real-world datasets named EUA and SLNDC and the result demonstrates that EDD-NSTE significantly outperformed, with a performance margin of 80.35\% over the other representative approaches in comparison. |
| title | Cost-Effective Edge Data Distribution with End-To-End Delay Guarantees in Edge Computing |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2504.21070 |