No Agent Left Behind: Dynamic Fair Division of Multiple Resources
Authors: I. Kash, A. D. Procaccia, N. Shah
JAIR 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Indeed, in Section 6, we test our mechanisms on real data obtained from a trace of workloads on a Google cluster, and obtain encouraging results. Our next goal is to analyze the performance of both mechanisms on real data, for two natural objectives: the sum of dominant shares (the maxsum objective) and the minimum dominant share (the maxmin objective) of the agents present in the system. |
| Researcher Affiliation | Collaboration | Ian Kash EMAIL Microsoft Research Cambridge, UK Ariel D. Procaccia EMAIL Carnegie Mellon University, USA Nisarg Shah EMAIL Carnegie Mellon University, USA |
| Pseudocode | Yes | ALGORITHM 1: DYNAMIC DRF ALGORITHM 2: CAUTIOUS LP |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | As our data we use traces of real workloads on a Google compute cell, from a 7 hour period in 2011 (Hellerstein, 2010). The workload consists of tasks, where each task ran on a single machine, and consumed memory and one or more cores; the demands fit our model with two resources. (Hellerstein, J. L. (2010). Google cluster data. Google research blog. Posted at http://googleresearch.blogspot.com/2010/01/google-cluster-data.html.) |
| Dataset Splits | No | The paper mentions sampling strategies and averaging over simulations but does not provide specific training/test/validation dataset splits or cross-validation setups. It states: "For various values of n, we sampled n random positive demand vectors from the traces and analyzed the value of the two objective functions under DYNAMIC DRF and CAUTIOUS LP along with the corresponding lower and upper bounds. We averaged over 1000 such simulations to obtain data points." |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software components with version numbers used in the experiments. |
| Experiment Setup | Yes | For various values of n, we sampled n random positive demand vectors from the traces and analyzed the value of the two objective functions under DYNAMIC DRF and CAUTIOUS LP along with the corresponding lower and upper bounds. We averaged over 1000 such simulations to obtain data points. Figures 4(a) and 4(b) show the maxsum values achieved by the different mechanisms, for 20 agents and 100 agents respectively. Figures 4(c) and 4(d) show the maxmin values achieved for 20 agents and 100 agents, respectively. |