Service Migration for Delay-Sensitive IoT Applications in Edge Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The proliferation of <i>I</i>nternet <i>o</i>f <i>T</i>hings (<i>IoT</i>) applications prompts extraordinary demands for the collaboration of large amounts of computational resources provided by <i>IoT</i> devices in edge networks, and these applications are mostly delay-sensitive. Generally, these resources are encapsulated as <i>IoT</i> services. Thereafter, <i>IoT</i> applications can be performed, such that the collaboration of their sub-tasks is achieved through the composition of functionally complementary and geographically contiguous <i>IoT</i> services. The status of computational resources in <i>IoT</i> devices may change continuously along with their occupancy and release by <i>IoT</i> services. Considering the resource-scarceness of <i>IoT</i> devices, when the workload of <i>IoT</i> devices increases due to more services to be processed, certain <i>IoT</i> devices may hardly have enough remaining resources to co-host more instances of certain <i>IoT</i> services prescribed by forthcoming <i>IoT</i> applications with strict constraints. As a result, the delay satisfaction of both on-running and forthcoming <i>IoT</i> applications may be negatively impacted, or even hardly be satisfied any longer. To solve this issue, this paper proposes a r<i>E</i>source-<i>E</i>fficient se<i>r</i>vice <i>C</i>onfiguration (<inline-formula><tex-math notation="LaTeX">$E^{2}$</tex-math></inline-formula><i>rC</i>) mechanism, which aims to optimize the configuration of computational resources provided by <i>IoT</i> devices with respect to complex requirements prescribed by <i>IoT</i> applications, through service migration techniques. This service migration problem is formulated as markov multi-phases decisions, which is solved through our enhanced <i>D</i>eep <i>R</i>einforcement <i>L</i>earning (<i>DRL</i>) approach with a two-layer <i>Q</i>-network. Extensive experiments have been conducted upon the dataset of our testbed system. Evaluation results show that our <inline-formula><tex-math notation="LaTeX">$E^{2}$</tex-math></inline-formula><i>rC</i> is more efficient than the state-of-art counterparts in satisfying delay constraints of <i>IoT</i> applications, while reducing the energy consumption and improving the resource utilization efficiency of <i>IoT</i> devices.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it