A Lightweight Fluid Model for Mobile Ad hoc Distributed Systems

Abstract

Emerging edge applications introduced new computing timevariant topologies with mobile nodes connected via ad hoc networks. Such topologies are fundamentally different from cloud infrastructures due to the lack of hierarchy and clear network function separation. Sometimes, nodes that are sources are also destinations and routers, hence, creating dynamic flow patterns traversing the network. The latter, thus, can change the average performance of a distributed system, such as throughput and latency. This work introduces an analytical model based on fluid quantities to study data flows of distributed systems in mobile ad hoc networks. Using an approach based on a network of queues with evenly distributed bandwidth over concurrent flows, this lightweight model enables fast, coarse-grained analysis of different distributed systems configurations. They enable the analysis of different topologies, mobility and data flow models with a small footprint. The model was implemented, validated and evaluated with stress workloads to confirm its accuracy.

An Improved Simulation Based Method forSelection of Cell in Cellular Network

“…But if the user is doing a job in which he / she has to work at single place then in that situation the change in location as well as cell selection is comparatively less in numbers. Various models for Mobility patterns are given below (1) Memory less (Random Walk) Movement Model [18] (2) Markovian Movement Model [19] (3) Shortest Distance Model [20] (4) Gauss Markov Model [21] (5) Activity based model [22] (6) Mobility Trace [23] (7) Fluid-Flow Model [24] Call Arrival Pattern Time is the important factor to describe the Call Arrival Pattern or rate at which the user will receive the calls. User always gets more number of call during working hours as compared to non-working hours.…”