On Improving the Performance of Mobile Applications using the Cloud
Bibliographic record
Abstract
As a more efficient means of supplying computing resources in the form of a utility, cloud computing platforms have been increasingly used to meet the insatiable demand from mobile applications. The research problems we study in this thesis are in the general research area of mobile cloud computing, as we seek to design and implement new algorithms and protocols that straddle the boundary between mobile applications and cloud computing systems, so that their performance can be jointly optimized to provide the best possible user experience, yet operating within the constraints of available resources and operational costs. From the perspective of mobile applications, we show that interactive applications have the need to stream multi-touch gestures among multiple users. Tailored to the nature of multi-touch gesture streams, we propose a new protocol that uses inter-session network coding to reduce the gesture recognizing delays. Towards supporting mobile applications using the cloud resources, we believe that multi-party video conferencing service can benefit from the inter-datacenter networks in the cloud. We apply intra-session network coding to design a new protocol to maximize the total throughput of all conferencing sessions in the cloud, subject to a latency constraint imposed by the nature of video conferencing. Our real-world experiments have shown that the inter-datacenter networks help to achieve substantially improved throughput, with very similar delays compared to traditional peer-to-peer solutions. From the perspective of cloud service providers, we study the challenges involved when resource utilization is to be maximized and when operational costs are to be minimized. To maximize resource utilization, we propose a virtual machine (VM) migration algorithm based on Nash bargaining solutions. To minimize operational costs, we present optimal routing and flow assignment algorithms for the inter-datacenter traffic, with and without store-and-forward capabilities in intermediate datacenters. With efficient and cost-effective utilization of resources in the cloud, and by designing new protocols that are applicable to both mobile applications and cloud computing systems, achieving an optimized level of user experience with interactive mobile applications will become a reality.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".