Queue Analysis for Probabilistic Cloud Workflows

Abdullah Alenizi; Reda Ammar; Raafat Elfouly; Mohammad Alsulami; IEEE

doi:10.1109/ISSPIT51521.2020.9408967

Back

Conference proceeding

Queue Analysis for Probabilistic Cloud Workflows

Abdullah Alenizi, Reda Ammar, Raafat Elfouly, Mohammad Alsulami and IEEE

2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp.1-6

09/12/2020

DOI: https://doi.org/10.1109/ISSPIT51521.2020.9408967

Abstract

Cloud Computing

CSM

Directed acyclic graph

Information technology

non-DAG

Probabilistic logic

Queueing analysis

Queuing System

Scheduling algorithms

Signal processing

Task analysis

Workflow

Cloud applications can be modeled as workflows. These workflows are represented by Directed Acyclic Graphs (DAGs) or non-DAGs. The graph shows the relationship between tasks that compose a workflow and the dependencies between these tasks. in our previous work, we presented a method for transforming a workflow into an equivalent graph that shows all possible paths that a workflow will take. In this paper, we use the results of that method for multiple workflows coming to a queue and use the famous pollaczek-khintchine formula to estimate the average waiting and completion time for submitted workflows. Then, we use different scheduling algorithms, namely, Shortest Job First (SJF) and Longest Job First (LJF) and compare them with First Come First Serve (FCFS).

Metrics

1 Record Views

Details

Title: Queue Analysis for Probabilistic Cloud Workflows
Creators - without role: Abdullah Alenizi - University of Connecticut
Reda Ammar - University of Connecticut
Raafat Elfouly - Rhode Island College
Mohammad Alsulami - University of Connecticut
IEEE
Publication Details: 2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp.1-6
Publisher: IEEE
Identifiers: 9924788408331
Academic Unit: Najran University
Language: English
Resource Type: Conference proceeding