Multi-User Task Offloading to Heterogeneous Processors With Communication Delay and Budget Constraints
Bibliographic record
Abstract
We study task scheduling and offloading in a cloud computing system with multiple users where tasks have different processing times, release times, communication times, and weights. Each user may schedule a task locally or offload it to a shared cloud with heterogeneous processors by paying a price for the resource usage. We consider four different models in this article: (i) zero task release and communication times; (ii) non-zero task release times and zero communication times; (iii) non-zero task release times and fixed communication times; and (iv) non-zero task release times and sequence-dependent communication times. Our article aims at identifying a task scheduling decision that minimizes the weighted sum completion time of all tasks, while satisfying the users’ budget constraints. We propose an efficient solution framework for this NP-hard problem. As a first step, we use a relaxation and a rounding technique to obtain an integer solution that is a constant factor approximation to the minimum weighted sum completion time. This solution violates the budget constraints, but the average budget violation decreases as the number of users increases. Thus, we develop a scalable algorithm termed Single-Task Unload for Budget Resolution (STUBR), which resolves budget violations and orders the tasks to obtain robust solutions. We prove performance bounds for the rounded solution as well as for the budget-resolved solution, for all four models considered. Via extensive trace-driven simulation for both chess and compute-intensive applications, we observe that STUBR exhibits robust performance under practical scenarios and outperforms existing alternatives. We also use simulation to study the scalability of STUBR algorithm as the number of tasks and the number of users in the system increases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".