Online Learning for Load Balancing of Unknown Monotone Resource Allocation Games
Authors: Ilai Bistritz, Nicholas Bambos
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide numerical simulations that include the two applications discussed in Section 5. In the first two experiments, to simulate a vanishing estimation error (Definition 3), at iteration t, Gaussian noise with mean δt = 0.25 (t+1)0.4 and variance σ2 = 1 4 was added to the gradients of each player. To quantify the effectiveness of our algorithm, we compare it to the uncontrolled system where αk = 0 for all k. For all experiments, we used the step-size sequence ηt = η0 (t+1)p and the control step-size sequence εt = ε0 (t+1)q with different values of η0, ε0. We ran 100 realizations for each experiment and plotted the average result along with the standard deviation region, which was always small. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering, Stanford University. Correspondence to: Ilai Bistritz <bistritz@stanford.edu>. |
| Pseudocode | Yes | Algorithm 1 Online Load Balancing with Bandit Feedback Initialization: Let x0 X and α0 RK + . Let {ηt} , {εt} satisfy the conditions of Theorem 1. Input: Target total load vector l . For each turn t 1 do 1. Each player n updates its action using gn,t 1 and αt 1 to approximate xnun (x; α): xn,t = ΠXn xn,t 1 + ηt 1 gn,t 1 αt 1 (5) where ΠXn is the Euclidean projection into Xn. 2. The manager observes PN n=1 xk n,t for each k. 3. The manager updates the pricing coefficients using αt 1 + εt 1 PN n=1 xn,t l !#+ where [x]+ = max {x, 0} (element-wise). |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the methodology, nor does it include links to a code repository. |
| Open Datasets | No | The paper describes setting up simulations with randomly generated parameters and conditions (e.g., “The location y1 n of transmitter n was chosen uniformly at random on a 2D square of area 2N.”), rather than utilizing or providing access information for a publicly available dataset. |
| Dataset Splits | No | The paper performs numerical simulations and refers to “100 realizations” but does not specify any training, validation, or test dataset splits or percentages. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the simulations or experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | For all experiments, we used the step-size sequence ηt = η0 (t+1)p and the control step-size sequence εt = ε0 (t+1)q with different values of η0, ε0. |