Paper detail

Disaggregation for Energy Efficient Fog in Future 6G Networks

We study the benefits of adopting server disaggregation in the fog computing tier by evaluating energy efficient placement of interactive apps in a future fog 6G network. Using a mixed integer linear programming (MILP) model, we compare the adoption of traditional server (TS) and disaggregated server (DS) architectures in a fog network comprising of selected fog computing sites in the metro and access networks. Relative to the use of TSs, our results show that the adoption of DS improves the energy efficiency of the fog network and enables up to 18% reduction in total fog computing power consumption. More instances of interactive fog apps are provisioned in a fog network that is implemented over a network topology with high delay penalty. This ensures that minimal delay is experienced by distributed users. Our result also shows that the proximity of fog computing sites such as metro-central offices and radio cell sites to geo-distributed users of interactive fog applications make them important edge locations for provisioning moderately delay sensitive fog apps. However, fog applications with more stringent delay thresholds require in situ processing at directly connected radio cell sites or at the location of the requesting users. Finally, we propose a heuristic for energy efficient and delay aware placement of interactive fog apps in a fog network which replicates the trends observed during comprehensive analysis of the exact results obtained by solving the MILP model formulated in this paper. Our results and proposed MILP and heuristic provide a good reference and tool for fog network design and deployment.

preprint2021arXivOpen access
0citations
0reviews
0saves
Nocode
Nodataset
0institutions

Next steps

Decide what to do with this paper

Use like or dislike for the fast social read. The more specific scholarly feedback stays available below when needed.

Log in to curate

Reading frame

Keep the important context close to the paper

Keep the important signals around this paper in one place: votes, save state, collection context, reviews and the metadata you need before deciding what to do next.

Institutions

Add specific reaction

Move through the context

Research map

Open full explorer

Move through nearby people, institutions, topics and adjacent work without leaving the paper page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Structured reviews

0 review(s)

ContributeLeave structured feedbackUse the review template when you have a concrete strength, concern or method question.Open review form

No structured reviews yet. High-signal critique starts here.

Work discussion

0 comment(s)

DiscussAdd a high-signal commentKeep quick notes, caveats and replication pointers separate from formal reviews.Open comment form

No discussion yet. The first strong comment sets the tone.