Dynamic workload management in heterogeneous Cloud computing environments
Bibliographic record
Abstract
Cloud computing is a paradigm that harnesses massive resource capacity of data centers to support applications in a scalable, flexible, reliable and cost-effective manner. Despite its recent success and rapid adoption in the IT industry, recent literature has shown that effective workload management in cloud computing environments remains to be a difficult challenge. A key reason behind this difficulty is that resources and workloads in production environments are both heterogeneous and dynamic. In particular, large cloud data centers often consist of machines with heterogeneous capacities and performance characteristics. At the same time, cloud workloads often show significant diversity in terms of priority, resource requirements, arrival rate and performance objectives. Consequently, it is difficult to devise heterogeneity and dynamicity-aware management scheme that satisfy diverse application performance objectives, while reducing operational expenses such as energy consumption. This work addresses several key challenges pertaining to dynamic workload management in heterogenous Cloud environments. Specifically, we first present a scheme that place service application across geographically distributed data centers to meet service demand while minimizing total resource usage cost. Then, we design a heterogeneity-aware dynamic application provisioning technique to minimize energy consumption while satisfying performance objectives. Finally, we study the problem of MapReduce scheduling and present a novel scheme that leverages heterogenous run-time task usage characteristics. Through experiments and simulations, we show our proposed solutions can significantly reduce data center energy consumption, while achieving better application performance in terms of service response time and job completion time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".