核心概念
Analyzing online non-convex optimization with memory and switching cost for capacity provisioning.
要約
The content discusses an online non-convex optimization problem for capacity provisioning, focusing on minimizing flow time with switching costs. It covers worst-case and stochastic inputs, competitive algorithms, and prior work on online convex optimization. The analysis includes linear and quadratic switching costs, memory features, and algorithmic approaches.
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Introduction
- Describes the capacity provisioning problem in data centers.
- Discusses the objective function and penalty for changing active servers.
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Prior Work
- Explores online convex optimization without memory.
- Discusses OCO-S and its variations with different switching costs.
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Speed Scaling
- Introduces speed scaling models for minimizing flow time and energy spent.
- Studies multi-server cases with homogenous and heterogeneous servers.
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Comparison with Prior Work
- Compares the time scale considerations between different optimization problems.
- Highlights the importance of memory features and non-convex cost functions.
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Our Contributions
- Presents algorithms for worst-case and stochastic models with small competitive ratios.
- Discusses the impact of linear and quadratic switching costs on algorithm performance.
統計
Compared to OCO-S, the competitive ratio of any algorithm for the considered problem grows linearly with memory length.
The competitive ratio of A𝐿𝐺 is at most 4𝛼1/4.
Algorithm A𝐿𝐺 has a competitive ratio of at most 4𝛼1/4.
引用
"An online non-convex optimization problem is considered where the goal is to minimize the flow time of a set of jobs by modulating the number of active servers." - Rahul Vaze, Jayakrishnan Nair