Name
Title | ||
|---|---|---|
Evolutionary Scheduling of Dynamic Multitasking Workloads for Big-Data Analytics in Elastic Cloud |
Abstract | ||
|---|---|---|
Scheduling of dynamic and multitasking workloads for big-data analytics is a challenging issue, as it requires a significant amount of parameter sweeping and iterations. Therefore, real-time scheduling becomes essential to increase the throughput of many-task computing. The difficulty lies in obtaining a series of optimal yet responsive schedules. In dynamic scenarios, such as virtual clusters in cloud, scheduling must be processed fast enough to keep pace with the unpredictable fluctuations in the workloads to optimize the overall system performance. In this paper, ordinal optimization using rough models and fast simulation is introduced to obtain suboptimal solutions in a much shorter timeframe. While the scheduling solution for each period may not be the best, ordinal optimization can be processed fast in an iterative and evolutionary way to capture the details of big-data workload dynamism. Experimental results show that our evolutionary approach compared with existing methods, such as Monte Carlo and Blind Pick, can achieve higher overall average scheduling performance, such as throughput, in real-world applications with dynamic workloads. Furthermore, performance improvement is seen by implementing an optimal computing budget allocating method that smartly allocates computing cycles to the most promising schedules. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TETC.2014.2348196 | IEEE Trans. Emerging Topics Comput. |
Keywords | Field | DocType |
optimisation,elastic cloud,computing cycles,rough models,scheduling,evolutionary computation,parameter sweeping,many-task computing,virtual clusters,blind pick,big-data workload dynamism,monte carlo,ordinal optimization,average scheduling performance,big-data analytics,big-data,multitasking workload,monte carlo methods,evolutionary ordinal optimization,dynamic multitasking workloads,evolutionary scheduling,big data,iterations,cloud computing,evolutionary approach,optimal computing budget allocating method,iterative methods,dynamic scheduling,optimization,throughput,schedules | Fixed-priority pre-emptive scheduling,Fair-share scheduling,Computer science,Two-level scheduling,Scheduling (production processes),Real-time computing,Rate-monotonic scheduling,Dynamic priority scheduling,Earliest deadline first scheduling,Round-robin scheduling,Distributed computing | Journal |
Volume | Issue | ISSN |
2 | 3 | 2168-6750 |
Citations | PageRank | References |
16 | 0.67 | 31 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Zhang | 1 | 41 | 2.19 |
| Junwei Cao | 2 | 935 | 70.95 |
| Wei Tan | 3 | 1317 | 78.90 |
| Samee Ullah Khan | 4 | 16 | 0.67 |
| Keqin Li | 5 | 2778 | 242.13 |
| Albert Y. Zomaya | 6 | 5709 | 454.84 |