Projects per year
Abstract
Edge Computing (EC) enables a new kind of caching system in close geographic proximity to end-users by allowing app vendors to cache popular data on edge servers deployed at base stations. This edge cache system can better support latency-sensitive applications. However, transmitting data from the centralized cloud to the edge servers without proper transmission strategies may cost app vendors dearly. Cost-effective data distribution strategies are of particular importance for applications, whose data to be cached at the edge often changes dynamically. In this paper, we study this <italic>Online Edge Data Distribution</italic> (OEDD) problem, aiming to minimize app vendors’ total transmission cost, while ensuring low transmission latency in the long term. We first model this problem and prove its <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula>-hardness. We then combine Lyapunov optimization and game theory to propose a novel Latency-Aware Online (LAO) approach for solving this OEDD problem over time in a distributed manner with provable performance guarantees. The evaluation of LAO based on a real-world dataset demonstrates that it can help app vendors formulate cost-effective edge data distribution strategies in an online manner.
Original language | English |
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Pages (from-to) | 4270-4281 |
Number of pages | 12 |
Journal | IEEE Transactions on Parallel and Distributed Systems |
Volume | 33 |
Issue number | 12 |
DOIs | |
Publication status | Published - 1 Dec 2022 |
Keywords
- Australia
- Cloud computing
- Costs
- Data communication
- data distribution
- edge cache system
- online algorithm
- optimization
- Optimization
- Servers
- Videos
Projects
- 1 Finished
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HCMDSE: Human-centric Model-driven Software Engineering
Australian Research Council (ARC)
3/02/20 → 2/02/25
Project: Research