## 5 Experiments
### Experimental Results
#### 5.2.2 Assessment of the Processable Workload  
In 2010, we published two papers addressing the issue of the processable workload for replanning. In the first publication, this topic was investigated for pure GLEAM and the two benchmark classes based on large dependencies [59]. In the second paper, this investigation was extended to cover the other two benchmark classes and two new sizes (5000 and 7000 grid jobs) [60], but unfortunately, the newly generated benchmarks were faulty due to a software error. For this reason and because of the introduction of the two new benchmark parameters, we generated a complete new set of benchmarks for all four classes based on the rescheduling situation of 10% finished grid jobs and a set of new grid jobs of the same size. The basic scheduling task of each benchmark class consisting of 200 grid jobs and 20 resources was constructed with homogeneous tight budgets and time frames as described in Section 5.1. The basic tasks were scaled up to 7000 grid jobs and 700 resources in steps shown in Figure 6. This resulted in 23 sizes per benchmark class, totaling 92 single benchmarks. To obtain replanning benchmarks, every benchmark was firstly processed by GORBA using the heuristics and GLEAM and its execution was started. Then, the described replanning event occurred, resulting in a rescheduling task as described in detail in [58]. These tasks served as benchmarks for the comparison of GLEAM and the MA variants. For each benchmark, seven measurement series were done to compare the basic EA GLEAM and the following MA variants: The AMMA based on all four local searchers and the two ASMAs using the two best performing local searches from the AMMA runs. Together with the manually controlled choice between _best_- and _adaptive all-improvement_ (see Section 4.4. ), this resulted in six MA variants. Thus, the investigation reported here is based on the total of 644 single measurements. Each of them is performed with different population sizes and the best run is the result of the measurement. Firstly, the results of the GLEAM runs are presented. Figure 6 shows the success rates and fitness improvements for all 92 benchmarks. The large variations of the improvement rates correspond to weak and good heuristic solutions. The better the heuristics of the first planning phase solve the rescheduling task, the smaller is the room for a subsequent improvement. A detailed analysis of the  
Figure 6: Success rates and fitness gains compared to the best heuristic result for all benchmarks and the basic EA GLEAM. The fitness gains are given with their confidence intervals. In some cases, these intervals are so small that they merge with their marks. The scale was reduced in the lower diagram for a better visibility. sources of the final fitness values, which is given in [58], shows that the heuristics and GLEAM complement each other well: weak heuristic results are cured by the subsequent GLEAM run and good heuristic outcomes receive a comparably small improvement only. The best heuristic solution for the _IRID_-200-benchmark is an exception in so far, as the investigated replanning task meets the budget and time constraints, although its basic new planning solution did not. Therefore, an improvement of only 11.6% was achieved. The large improvements in the range of more than 1000% result from poor heuristic replanning solutions, e.g., the normalized fitness of the _sRID-200-_benchmark was improved from a value of 2.35 to 61.0, a growth of 2496%. Figure 6 also shows that runs with a success rate of 100% show little deviations in the fitness and, hence, have small confidence intervals. All fitness gains are a significant improvement of the best heuristic results (99.9% confidence). Another question is the further course of the improvement rate beyond the scale of budget and time frame violation-free runs, _i.e._, for more than 2200 grid jobs. The lower diagram of Figure 6 shows that the improvement rates decrease with increasing load as expected.