Energy and SLA-driven MapReduce Job Scheduling Framework for Cloud-based Cyber-Physical Systems
Bibliographic record
Abstract
Energy consumption minimization of cloud data centers (DCs) has attracted much attention from the research community in the recent years; particularly due to the increasing dependence of emerging Cyber-Physical Systems on them. An effective way to improve the energy efficiency of DCs is by using efficient job scheduling strategies. However, the most challenging issue in selection of efficient job scheduling strategy is to ensure service-level agreement (SLA) bindings of the scheduled tasks. Hence, an energy-aware and SLA-driven job scheduling framework based on MapReduce is presented in this article. The primary aim of the proposed framework is to explore task-to-slot/container mapping problem as a special case of energy-aware scheduling in deadline-constrained scenario. Thus, this problem can be viewed as a complex multi-objective problem comprised of different constraints. To address this problem efficiently, it is segregated into three major subproblems (SPs), namely, deadline segregation, map and reduce phase energy-aware scheduling. These SPs are individually formulated using Integer Linear Programming. To solve these SPs effectively, heuristics based on Greedy strategy along with classical Hungarian algorithm for serial and serial-parallel systems are used. Moreover, the proposed scheme also explores the potential of splitting Map/Reduce phase(s) into multiple stages to achieve higher energy reductions. This is achieved by leveraging the concepts of classical Greedy approach and priority queues. The proposed scheme has been validated using real-time data traces acquired from OpenCloud. Moreover, the performance of the proposed scheme is compared with the existing schemes using different evaluation metrics, namely, number of stages, total energy consumption, total makespan, and SLA violated. The results obtained prove the efficacy of the proposed scheme in comparison to the other schemes under different workload scenarios.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".