Bibliographic record
Abstract
Mobile edge computing (MEC) literally pushes cloud computing to the life radius of end users.The state-of-the-art mobile edge applications (MEApps) are posing rigorous latency requirements to service providers, as well as the need of security and high availability.However, vulnerabilities exist for both cloud and MEC environments.In this dissertation, our goal is to create a secure MEC environment for hosting MEApps with high availability, and to encourage resource sharing by both users for their Customer-premises equipment (CPE) and service providers (SPs) for their MEC hosts.While there is a number of challenges to achieve the goal, we present four parts of work to address these challenges.We first investigate the security issues in offloading applications and analyze vulnerabilities by modules of an application.A model called EdgePlace is formulated as a stochastic programming problem with a heuristic algorithm to leverage affinity and anti-affinity host placement rules for higher availability and lower cost.We then present IoT-B&B, an architecture featuring resource sharing of physical CPE nodes, with the goal to leverage unused resources at the network edge and to share them with users across the network edge.At last, we propose EdgeChain, a model for making fair MEApps placement decisions for multiple SPs and a heuristic placement algorithm for MEApps across different SPs.The algorithm is intended to run by multiple service providers for consensus and the placement decisions can be recorded onto a blockchain for the fairness of the results.iii Symbols used in Chapter 6H is the set of all MEHosts.E is the set of all HostLinks.A MEHost is denoted by h ∈ H, and a link between two MEHosts is denoted by e ∈ E. V h is the set of all MEApps placed on h.u, m, c s u is an end user.m is a MECSP.c s is the cost of deploying s.the total number of users requesting s.P m is a random variable denoting the percentage of the users of MECSP m. h m is an edge host of m. γ m , δ m γ m is the unit price of serving m's own subscribers.δ m is the extra charge for m serving users of other MECSPs.C v , M v CPU and memory requirement of the MEApp v. B(e ij ), ζ e ij B(e ij ) is the total bandwidth capacity of HostLink e ij .ζ e ij is the unit price of the bandwidth of e ij .B V (e ij ) B V (e ij ) is the total bandwidth used by MEApps deployed on h i and h j .B(v h i , v h j ) Bandwidth used between MEHosts h i and h j .C h , M h CPU and memory capacity of the MEHost h.t e ij , t s , T s t e ij is the latency incurred on HostLink e ij .t s is the latency of the service chain s.T s is the max latency allowed by s.xxiii
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".