Enhancing Constraint-Based Multi-Objective Combinatorial Optimization
Authors: Miguel Terra-Neves, Inês Lynce, Vasco Manquinho
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experimental results on the Virtual Machine Consolidation (VMC) problem show the effectiveness of the proposed techniques. In this section, the performance of the techniques proposed in sections 3 and 4 is evaluated on instances of the Virtual Machine Consolidation (VMC) problem. |
| Researcher Affiliation | Academia | Miguel Terra-Neves, Inês Lynce, Vasco Manquinho INESC-ID / Instituto Superior Técnico, Universidade de Lisboa, Portugal |
| Pseudocode | Yes | Algorithm 1: CLD algorithm for computing an MCS; Algorithm 2: Sample MCS algorithm for sampling a single MCS of a MOCO instance; Algorithm 3: MCSEnum PD algorithm for enumerating MCSs of a MOCO instance using path diversification |
| Open Source Code | Yes | The benchmark set and the prototype that implements the algorithms evaluated in this paper are publicly available online2. (2http://sat.inesc-id.pt/dome) |
| Open Datasets | Yes | The evaluation is performed on the VMC benchmarks used in the work of Terra-Neves, Lynce, and Man quinho(2017), which are based on subsets of workload traces randomly selected from the Google Cluster Data project1. (1http://code.google.com/p/googleclusterdata/) |
| Dataset Splits | No | The paper describes the benchmark instances and their characteristics, but it does not specify explicit train/validation/test dataset splits, percentages, or methodology for partitioning the data into these sets. |
| Hardware Specification | Yes | The evaluation was conducted on an AMD Opteron 6376 (2.3 GHz) with 128 GB of RAM. |
| Software Dependencies | Yes | All algorithms were implemented in Java and Sat4j-PB (Le Berre and Parrain 2010) (version 2.3.4) was used as the PBS solver. |
| Experiment Setup | Yes | Each algorithm was executed with a memory limit of 4 GB and a time limit of 1800 seconds. Randomized algorithms were executed with 10 different seeds for each instance, and the analysis is performed using the median values over all executions. The m (n P ) parameter of Sample MCS (MCSEnum PD) was set to 1 (4), as suggested by our empirical evaluation. MGGA was adapted to consider migration costs instead and was configured to use a population size of 12, and crossover rate and mutation rate as suggested by Xu and Fortes(2010). |