Wld: Workload of a High Performance Cluster model

In hpcwld: High Performance Cluster Models Based on Kiefer-Wolfowitz Recursion

Workload of a High Performance Cluster model

Description

This function computes the Kiefer-Wolfowitz modified vector for a HPC model. This vector contains the work left on each of 'm' servers of a cluster for the time of the arival of a task. Two methods are available, one for the case of concurrent server release (all the servers end a single task simultaneously), other for independent release (service times on each server are independent).

Usage

Wld(T, S, N, m, method = "concurrent")

Arguments

T	Interarrival times of tasks
S	Service times of customers (a vector of length n, or a matrix nrows=n, ncols='m').
N	Number of servers each customer needs
m	Number of servers for a supercomputer
method	Independent or concurrent

Value

A dataset is returned, containing 'delay' as a vector of delays exhibited by each task, 'total_cores' as the total busy CPUs in time of arrival of each task, and 'workload' as total work left at each CPU.

Examples

Wld(T=rexp(1000,1), S=rexp(1000,1), round(runif(1000,1,10)), 10)

hpcwld documentation built on March 19, 2024, 3:09 a.m.