Cost-Aware Resource Allocation for Fog-Cloud Computing Systems
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Summary
A distributed heuristic algorithm is designed based on Proximal Jacobian Alternating Direction Method of Multipliers (ADMM), which determines the number of active fog devices, workload allocation, and thenumber of active servers in each cloud data center.
- Type
- article
- Published
- 2017-01-25
- Cited by
- 3
- References
- 21
- Access
- Open access
- OpenAlex
- https://openalex.org/W2581091208
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17525292
Keywords
Cloud computing, Resource allocation, Computer science, Resource (disambiguation), Fog computing
References
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- It's not easy being green
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- Energy and Performance Management of Green Data Centers: A Profit Maximization Approach
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- Towards Optimal Electric Demand Management for Internet Data Centers
- Renewable and cooling aware workload management for sustainable data centers
- Managing distributed UPS energy for effective power capping in data centers
- Fog computing and its role in the internet of things
- Safe bounds in linear and mixed-integer linear programming
- Cutting the electric bill for internet-scale systems
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- The cost of a cloud: research problems in data center networks
- Parallel Multi-Block ADMM with o(1 / k) Convergence
- Joint Energy Management Strategy for Geo-Distributed Data Centers and Electric Vehicles in Smart Grid Environment
- Fog Computing May Help to Save Energy in Cloud Computing
- Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption
- Fog and IoT: An Overview of Research Opportunities
- Distributed Real-Time Energy Management in Data Center Microgrids
Cited by
- A cost- and performance-effective approach for task scheduling based on collaboration between cloud and fog computing
- Fuzzy clustering-based task allocation approach using bipartite graph in cloud-fog environment
- Cost Minimization in Multi-Path Communication under Throughput and Maximum Delay Constraints
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