An EMUSIM Technique and its Components in Cloud Computing- A Review

TL;DR

This paper reviews EMUSIM, an integrated architecture combining emulation and simulation to predict cloud service behavior and costs more accurately, aiding cloud resource management decisions.

Contribution

It introduces the architecture and components of EMUSIM, including AEF, QAppDeployer, and CloudSim, enhancing cloud performance prediction methods.

Findings

EMUSIM effectively combines emulation and simulation for better cloud service prediction.

The architecture automates extraction of application behavior for simulation modeling.

Proposed techniques improve accuracy of cloud resource management simulations.

Abstract

Recent efforts to design and develop Cloud technologies focus on defining novel methods, policies and mechanisms for efficiently managing Cloud infrastructures. One key challenge potential Cloud customers have before renting resources is to know how their services will behave in a set of resources and the costs involved when growing and shrinking their resource pool. Most of the studies in this area rely on simulation-based experiments, which consider simplified modeling of applications and computing environment. In order to better predict service's behavior on Cloud platforms, an integrated architecture that is based on both simulation and emulation. The proposed architecture, named EMUSIM, automatically extracts information from application behavior via emulation and then uses this information to generate the corresponding simulation model. This paper presents brief overview of the EMUSIM technique and its components. The work in this paper focuses on architecture and operation details of Automated Emulation Framework (AEF), QAppDeployer and proposes Cloud Sim Application for Simulation techniques.