A Review of Cloud Computing Simulation Platforms and Related

Environments

James Byrne

, Sergej Svorobej

, Konstantinos M. Giannoutakis

, Dimitrios Tzovaras

, P. J. Byrne

Per-Olov Östberg

, Anna Gourinovitch

and Theo Lynn

Irish Centre for Cloud Computing and Commerce, Dublin City University, Glasnevin, Dublin 9, Ireland

Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Xarilaou-Thermi,

57001 Thessaloniki, Greece

Dept. Computing Science and HPC2N, Umeå University, SE-901 87 Umeå, Sweden

Keywords: Cloud Computing, Cloud Simulation Tools, Data Centre, Fog Computing.

Abstract: Recent years have seen an increasing trend towards the development of Discrete Event Simulation (DES)

platforms to support cloud computing related decision making and research. The complexity of cloud

environments is increasing with scale and heterogeneity posing a challenge for the efficient management of

cloud applications and data centre resources. The increasing ubiquity of social media, mobile and cloud

computing combined with the Internet of Things and emerging paradigms such as Edge and Fog Computing

is exacerbating this complexity. Given the scale, complexity and commercial sensitivity of hyperscale

computing environments, the opportunity for experimentation is limited and requires substantial investment

of resources both in terms of time and effort. DES provides a low risk technique for providing decision

support for complex hyperscale computing scenarios. In recent years, there has been a significant increase in

the development and extension of tools to support DES for cloud computing resulting in a wide range of

tools which vary in terms of their utility and features. Through a review and analysis of available literature,

this paper provides an overview and multi-level feature analysis of 33 DES tools for cloud computing

environments. This review updates and extends existing reviews to include not only autonomous simulation

platforms, but also on plugins and extensions for specific cloud computing use cases. This review identifies

the emergence of CloudSim as a de facto base platform for simulation research and shows a lack of tool

support for distributed execution (parallel execution on distributed memory systems).

1 INTRODUCTION

The definition of cloud computing is widely

accepted to be “…a model for enabling ubiquitous,

convenient, on-demand network access to a shared

pool of configurable computing resources (e.g.,

networks, servers, storage, applications, and

services) that can be rapidly provisioned and

released with minimal management effort or service

provider interaction” (Mell and Grance, 2009).

While the reference architecture for cloud

computing has evolved over time the essential

characteristics, service models and deployment

models have largely remained the same (Liu et al.,

2011). The broad cross-domain applicability of

cloud computing has led to the emergence of a

variety of resource profiles and technological

options, with a substantial degree of heterogeneity in

data centre resources and service offerings (Östberg

et al., 2014). Recently, this trend has also been

magnified by increasing demands for dependability

and real-time low latency communication, which has

driven integration of telecommunications and cloud

infrastructure (edge computing), as well as

development and integration of applications that

make increased use of the capabilities of end-user

devices and appliances (fog computing). A general

inability to control and process the network

environment and predict and control network

conditions in hyperscale computing environments

has necessitated the development of discrete event

simulation (DES) platforms capable of supporting

complex decision making within these environments

(Jiang et al., 2012), (Tian et al., 2015). IDC (2016)

predict rapid and substantial increases in enterprise

cloud and the Internet of Things (IOT) adoption with

Byrne, J., Svorobej, S., Giannoutakis, K., Tzovaras, D., Byrne, P., Östberg, P-O., Gourinovitch, A. and Lynn, T.

A Review of Cloud Computing Simulation Platforms and Related Environments.

DOI: 10.5220/0006373006790691

In Proceedings of the 7th International Conference on Cloud Computing and Services Science (CLOSER 2017), pages 651-663

ISBN: 978-989-758-243-1

651

at least 40% of IoT-created data being stored,

processed, analysed, and acted upon close to or at

the edge of the network by 2019. These trends are

increasing both the range of use cases and features

that DES tools are required to support. An overview

of the state of the art for such DES tools is presented

in this paper.

Earlier efforts for DES in this domain focused on

grid computing, whereby simulation tooling support

was provided for uniformly aggregating and sharing

distributed heterogeneous resources for solving

large-scale applications, such as in the fields of

science, engineering and commerce (Sulistio et al.,

2008). Various grid computing simulators have been

developed (Sulistio et al., 2008) and are presented in

literature, such as OptorSim (Bell et al., 2002),

MONARC (Legrand and Newman, 2000), SimGrid

(Legrand et al., 2003), GridSim (Buyya and

Murshed, 2002) and MicroGrid (Song et al., 2000).

However, these alone do not provide an environment

that can be directly used by the cloud computing

community (W. Zhao et al., 2012); grid computing

simulators assume compute jobs to be deterministic,

non-interactive fixed duration whereas cloud

simulators typically aim to analyse the behaviour of

data centre resources that host virtual machines in

multi-tenancy scenarios over non-deterministic

timeframes, with highly variable user load taken into

consideration. The work presented in this paper

focuses on DES tools that support Infrastructure as a

Service (IAAS) cloud computing use cases and the

related Edge and Fog Computing paradigms.

There are a number of potential advantages to the

use and development of such DES tools to support

cloud computing. Experimentation in a simulated

environment is typically far less expensive

economically than using a real testbed. Furthermore,

such experimentation is repeatable and potentially

scalable in terms of addressing the simulation of

larger-scale systems. In addition, experimentations

can be performed in a timelier fashion, and risks

with respect to stochastic inputs can be taken into

account. However it is noted by (Sakellari and

Loukas, 2013) that while simulation offers a number

of advantages especially in terms of such scalability

and experiment repeatability, it is still based on

assumptions and simplifications that might not fully

represent an actual cloud. For this reason, it still

might be preferable in some circumstances to use

real cloud testbeds in place of simulation or to

validate results developed in simulated

environments. Sakellari and Loukas (2013) provide

an overview of such testbeds and software

frameworks for setting up such cloud testbeds.

This paper gives an overview of current work in

cloud computing simulation tool development. It

categorizes and reviews DES tools for cloud

computing, identifies application DES tools for

cloud computing environments, and provides a

multi-level feature comparison of identified

simulation tools plugins and extensions. This multi-

level comparison concerns a general high level

comparison as well as comparing high level

technical characteristics for classifying the tools.

The remainder of the paper is structured as

follows: Section 2 provides and overview related

research to position the contribution of this work.

Section 3 introduces the tools identified in the

review. Section 4 presents a multi-level feature

analysis of the tools. The paper concludes with a

discussion of key findings and areas for future

research.

2 RELATED WORK

There are a number of existing papers that provide

overviews of DES tools to support cloud computing.

Zhao et al. (2012) present a summary of tools to

model and simulate cloud computing systems,

including both software and hardware simulators.

They give a feature description for 11 tools, and

provide a comparison based on the underlying

platform, programming language, and whether they

are software or hardware-based. Sinha and Shekhar

(2015) present a high level overview of 15 cloud

simulation tools, and provide a tabular comparison

of these based on graphical user interface support,

platform used, language used, support of TCP/IP,

whether they are software or hardware-based, and

their availability (software license type). As part of

their work, Sakellari and Loukas (2013) provide an

overview of cloud simulation software. They present

an overview of eight tools, and provide a tabular

comparison based on whether they support energy

efficiency modelling, performance/quality of service

(QoS), programming language, availability (on the

web), and license type. Malhotra and Jain (2013)

provide an overview of five cloud simulation tools,

and compare them based on underlying platform,

programming language, networking support, the

type of simulator (event versus packet based), and

license type. Similarly, Mohana, Saroja, and

Venkatachalam (2014) provide an overview of six

cloud simulation tools and compares them by

underlying platform, simulator type, language,

networking, and availability. Ahmed and Sabyasachi

(2014) give an overview of 12 cloud simulators and

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compare these based on underlying platform,

availability, programming language, whether or not

they provide cost modelling, if they have a GUI, if

they have communication models or energy models,

the simulation time and whether they model

federation policies.

The work presented in this paper builds on these

previous related works by extending both the

breadth and depth of analysis. 33 platforms, plugins

and extensions are introduced and analysed

including many which have not been analysed and

compared previously e.g. CactoSim (Östberg et al.,

2014), DISSECT-CF (Kecskemeti et al., 2014),

iFogSim (Gupta et al., 2016) and CloudEXP

(Jararweh et al., 2014). For each tool, a multi-level

feature analysis is provided, for high-level

comparison of the frameworks.

3 PLATFORMS FOR CLOUD

COMPUTING SIMULATION

Table 1 lists current identified tools that support

DES for cloud computing, in alphabetical order.

Table 1: Identified cloud computing-related DES tools.

Bazaar Extension* DISSECT-CF

CACTOSim EMUSim*

CDOSim* GDCSim

CEPSim* GreenCloud

Cloud2Sim* GroudSim

CloudAnalyst* ICanCloud

CloudEXP* iFogSim*

CloudNetSim++ MDCSim

CloudReports* MR-CloudSim*

CloudSched NetworkCloudSim*

CloudSim SimGrid

CloudSimDisk* SimIC

CloudSimSDN* SPECI

CMCloudSimulator* TeachCloud*

DartCSim* Ucloud*

DCSim

WorkflowSim*

DCSim

*Derivatives or extensions of CloudSim

This refers to DCSim by Tighe, (2012)

This refers to DCSim by Chen et al., (2012)

Bazaar-Extension (Pittl et al., 2016) is a CloudSim

extension for simulating resource allocations by

negotiation processes. The negotiation process is

realised between provider and consumer using the

offer-counteroffer negotiation protocol for resource

allocation, while the authors simulate different

negotiation strategies. The architecture of Bazaar-

Extension (which is built on CloudSim) consists of

the Datacenter broker and the Negotiation Manager

that handles the auctioning process for forming

service level agreements.

CactoSim was developed as part of CACTOS, a

European Union Framework 7 project (CACTOS

Consortium, 2016). CACTOS aimed to deliver a set

of integrated tools for analysing application

behaviour and infrastructure performance data,

mathematical models and their realization to

determine the best fitting resource within a provider

context, and a prediction and simulation

environment for diverse application workloads

(Östberg et al., 2014). To this end, CactoSim is a

DES framework built on top of Palladio (Becker et

al., 2009), and SimuLizar (Becker et al., 2013)

which was developed as part of CloudScale (Brataas

et al., 2013). It is used to evaluate the effectiveness

of optimization strategies for the cloud, as well as

for iterative resource planning and operations

decision support.

CDOSim (Fittkau et al., 2012) is a simulation

framework based on CloudSim and focuses on

evaluating competing cloud deployment options. It

simulates response times, SLA violations and costs

of various deployment options. Its purpose is to

assist cloud users to find the best ratio between high

performance and low costs.

The CEPSim (Higashino et al., 2016) simulator

is also an extension to CloudSim that focuses on

supporting cloud-based Complex Event Processing

(CEP) and Stream Processing (SP) systems that

related to big data technologies. CEPSim transforms

user queries into directed acyclic graph

representations. The modelled queries can be

simulated on different deployment models including

private, public, hybrid and multi-clouds.

Cloud2Sim (Kathiravelu and Veiga, 2014) is a

concurrent and distributed cloud and MapReduce

simulator that is built on top of CloudSim, using the

distributed shared memory from Hazelcast and the

in-memory key-value data grid of Infinispan. The

motivation for the development of this simulator was

the long execution time and limited simulation size

on uniprocessor systems. It provides the

functionality to execute CloudSim in parallel and

thus scale up simulations.

CloudAnalyst (Wickremasinghe et al., 2010) is a

Cloud simulation tool developed on the Java

platform for the simulation of large-scale cloud

applications with the purpose to study and analyse

the behaviour of such applications under various

deployment scenarios. It extends the functionality of

the CloudSim toolkit through the introduction of

concepts that model the Internet and Internet

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application behaviour. It allows description of

application workloads including information on the

geographic location of users generating traffic, the

location of data centres, the number of users and

data centres, and the number of resources in each

data centre. Provided with this information, metrics

such as the response and processing time of requests

are generated. The main features of CloudAnalyst

are: the easy to use Graphical User Interface, the

ability to define a simulation with a high degree of

configurability and flexibility, the repeatability of

experiments, its graphical output, the use of

consolidated technology, and ease of extension.

The CloudExp framework is Java-based and

again is built on top of CloudSim (Jararweh et al.,

2014). CloudExp can be used to evaluate cloud

components such as processing elements, data

centers, storage, networking, SLA constraints, web-

based applications, Service Oriented Architecture

(SOA), virtualization, management and automation,

and Business Process Management (BPM)

components. In addition, CloudExp introduces the

Rain workload generator which emulates real

workloads in cloud environments.

CloudNetSim++ (Malik et al., 2014) is designed

to allow researchers to incorporate their custom

protocols and applications for analysis under

realistic data centre architectures with various

network traffic patterns. It provides a framework

that allows users to define SLA policies, scheduling

algorithms and models for different components of

data centres. The energy utilization is computed in

three components: servers, communication links and

data centre infrastructures (such as routers and

switches). It is built on top of OMNeT++ and

provides a rich GUI to simplify analysis, debugging

and addition of hardware components into the

simulation.

CloudReports (Teixeira Sá et al., 2014) is an

extensible simulation tool for energy-aware cloud

computing environments to enable researchers to

model multiple complex scenarios through a GUI. It

provides four layers on top of the CloudSim

simulation engine: Reports manager, Simulation

manager, Extensions and Core entities. The main

advantage of CloudReports is its modular

architecture that allows the extension of its API for

experimenting with new scheduling and

provisioning algorithms.

CloudSched (Tian et al., 2015) is a simulation

tool for the evaluation and modelling of cloud

environments and applications with a focus on

comparing different resource scheduling algorithms

in IaaS with regards to both computing servers and

user workloads. CloudSched was introduced as a

means to provide better cloud performance

compared to CloudSim and CloudAnalyst. Unlike

traditional scheduling algorithms that consider only

one factor (such as CPU), which can cause hotspots

or bottlenecks in many cases, CloudSched treats

multi-dimensional resources such as CPU, memory

and network bandwidth integrated for both physical

machines and virtual machines for different

scheduling objectives. The main CloudSched

features are: its focus on infrastructure as a service

(IaaS) layer, the provision of a uniform view of all

resources, the lightweight design and scalability, its

high extensibility and ease of use.

CloudSim (Calheiros, 2011) is an open source

and extensible Java simulation platform for enabling

continuous modelling, simulation, and

experimentation of cloud computing and application

services. CloudSim is the de facto platform of choice

for open source simulation tool development; 18 of

the tools analysed were derivatives or extensions of

CloudSim. The

CloudSim architecture follows a

layered approach. At the fundamental layer,

management of applications, hosts of VMs, and

dynamic system states are provided. By extending

the core VM provisioning functionality, the

efficiency of different strategies at this layer can be

studied. At the top layer, the User Code represents

the basic entities for hosts, and through extending

entities at this layer, one can enable the application

to generate requests using a variety of approaches

and configurations, model cloud scenarios,

implement custom applications and so on. In the

CloudSim implementation, there are no actual

entities available for simulating network entities,

such as routers or switches. Instead, network latency

between two components is simulated based on the

information stored in a latency matrix. The event

management engine of CloudSim utilizes the inter-

entity network latency information for inducing

delays in transmitting message to entities. This delay

is expressed in simulation time units such as

milliseconds. The CloudSim framework provides

basic models and entities to validate and evaluate

energy-conscious provisioning of

techniques/algorithms.

CloudSimDisk (Louis et al., 2015) is a CloudSim

extension focusing on modelling and simulating

energy aware storage hardware components in cloud

infrastructures. The implementation of

CloudSimDisk is based on analytical models that

were tested against hard disk drive manufacturer

specifications. It includes HDD power models, disk

array management algorithms and energy-aware data

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center storage. Experimentation with CloudSimDisk

shows good results in terms of validation, while the

scalability of the extension allows future

implementations of more complex systems.

CloudSimSDN (Son et al., 2015) is a CloudSim

extension for Software Defined Networking (SDN)-

enabled cloud environments. It provides a

lightweight and scalable simulation environment to

evaluate the network allocation capacity policies. It

simulates cloud data centres, physical machines,

switches, network links and virtual topologies for

measuring performance metrics, and energy

consumption. It also provides a GUI for simplifying

the simulation configuration.

CMCloudSimulator (Alves et al., 2016) focuses

on simulating applications with various deployment

configurations. It incurs the cost it would require

when implemented in a cloud provider according to

the cost model of any service provider. It is built as a

CloudSim extension and supports various cost

models that can be designed using XML. With

CMCloudSimulator, one can estimate the total cost

of the resulting simulation and compare the results

with different cloud providers, by obtaining the best

price from them dynamically.

DartCSim (Li et al., 2012) is a GUI layer on top

of CloudSim providing a more user-friendly

interface. This allows the user to configure all the

simulation data easily including the configuration of

network cloudlets, network topology, and the

algorithms for managing the cloud data center.

DCSim (Tighe, 2012), (Keller et al., 2013) is an

extensible framework for simulating a multi-tenant,

virtualized data centre with special purpose of

dynamically managing hardware resources. DCSim

provides an application model that can simulate the

interactions and dependencies between many VMs

working together to provide a single service, such as

in the case of a multi-tiered web application. DCSim

simulates a virtualized data centre operating an

Infrastructure as a Service (IaaS) cloud. Virtual

machine management operations, such as VM live

migration and replication, are supported within

DCSim. The resource needs of each VM in DCSim

are driven dynamically by an application class

component, which varies the level of resources

required by the VM to simulate a real workload.

DCSim reports a number of metrics in order to help

determine the behaviour of the data centre during the

simulation such as SLA violations, data centre

utilization, active hosts, host-hours, active host

utilization, number of migrations, and power

consumption. A visualization tool is included with

DCSim which automatically generates a set of

graphs based on the simulation log files.

There is an additional simulation platform also

called DCSim (Data Centre Simulator) as referred to

by Chen et al., (2012). They use this to model a

small-scale operating system, HDD and SSD

towards achieving a multi-layer heterogeneous

system simulation.

DISSECT-CF (DIScrete event baSed Energy

Consumption simulator for clouds and Federations)

is a simulation framework capable of simulating the

internal components and processes of cloud

infrastructures allowing the evaluation of energy

consumption, network behaviour and the effects of

cross virtual machine CPU sharing (Kecskemeti et

al., 2014). In their paper, (Kecskemeti et al., 2014)

introduce techniques for unifying DISSECT-CF with

GroudSim, thereby providing GroudSim with the

ability to model the internals of infrastructure clouds

(such as energy models and more complex

networking), as DISSECT-CF is more focused on

the internal organization and behaviour of IaaS

systems. This improves the modelling of resource

usage, network usage, power consumption and data

centre configurations.

EMUSIM (Calheiros et al., 2013) is a tool built

on top of CloudSim that automatically extracts

information from application behaviour via

emulation, and uses this information to generate a

corresponding simulation model. This process is

performed order to better predict the service’s

behaviour on cloud platforms; increased accuracy in

an application behaviour model leads to higher

accuracy in simulated system resource utilization

estimation on cloud platforms.

GDCSim (Gupta et al., 2014) is a simulation tool

for studying the energy efficiency of data centres

under various data center geometries, workload

characteristics, platform power management

schemes, and scheduling algorithms. The main focus

of GDCSim simulator is the energy efficiency

analysis and its functional behaviour can be

characterised by: automated processing, online

analysis capability, iterative design analysis, thermal

analysis capability, workload and power

management and consideration of cyber-physical

interdependency.

GreenCloud (Kliazovich et al., 2012) is an open-

source cloud computing simulator, specifically

designed for data centre simulation by implementing

detailed modelling of communication aspects of the

data centre. It is classified as a packet-level

simulator, and, along with the workload distribution,

the simulator is designed to capture details of the

energy consumed by data centre components

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(servers, switches, routers, and connection links

between them) as well as packet-level

communication patterns in realistic setups.

GreenCloud also allows analysis of the load

distribution through the network, as well as

communication with high accuracy (TCP packet

level). It implements a simplistic application model

without any communicating tasks or limited network

model within the data centre. GreenCloud simulator

is an extension of ns-2 simulator, which is used in

computer networking. Using ns-2 as the foundation,

GreenCloud implements a full TCP/IP protocol

reference model, which allows seamless integration

of a wide variety of communication protocols

including IP, TCP, and UDP with the simulation.

GroudSim (Ostermann et al., 2011) is an event-

based Java-based simulation toolkit, mainly focused

on scientific applications running on combined Grid

and cloud infrastructures. GroudSim supports

modelling of cloud compute and network resources,

job submissions, file transfers, as well as integration

of failure, background load, and cost models.

iCanCloud (Núñez et al., 2012) is aimed at

simulating cloud resources as provided by the

Amazon Elastic Compute Cloud (EC2), although its

creators claim it can be extended to simulate other

environments using the provided API. Its primary

aim is to predict the trade-offs between cost and

performance of a given application executed in a

specific hardware. iCanCloud is based on various

platforms: OMNeT++, MPI, and C++. The

iCanCloud architecture follows a layered approach

with four layers: VMs repository, Application

repository, Cloud hypervisor and Cloud system. It

provides configurations for storage systems, which

include models for local storage systems, remote

storage systems and parallel storage systems.

iFogSim (Gupta et al., 2016) is a simulator built

on top of CloudSim specifically for supporting the

modelling of IoT and Fog computing environments,

in order to measure the impact of resource

management techniques in terms of latency, network

congestion, energy consumption and cost.

MDCSim (Lim, 2009) is a flexible and scalable

simulation platform for in-depth analysis of multi-

tier data centres. It implements all the important

design specifics of communications, kernel level

scheduling artefacts and application level

interactions among the tiers of a three-tier data

centre.

MR-CloudSim is primarily concerned with

designing and implementing the MapReduce

computing model on CloudSim (Jung and Kim,

2012), in order to provide an easier way to examine

a MapReduce model in a data centre.

NetworkCloudSim (Garg and Buyya, 2011) is an

extension of CloudSim that supports a scalable

network model of a data centre and generalized

applications such as high-performance computing

(HPC), e-commerce, social networks, and web

applications. NetworkCloudSim can simulate a cloud

data centre network and applications with

communicating tasks with accuracy. It provides

models to support realistic, multi-tier applications

that comprise several tasks that communicate with

each other. In the original

CloudSim

implementation, it was assumed subtly that each VM

is connected with all other VMs. The drawback of

this is that it fails to model a realistic data centre

environment. To tackle this issue, NetworkCloudSim

provides three types of switches in the

corresponding levels: root, aggregate and edge level.

Users can design customized types of switches and

their ports according to the data centre environment

they want to simulate.

SimGrid (Casanova et al., 2008) is a simulation

toolkit for the study of scheduling algorithms for

distributed applications. Originally designed for

simulating grid computing, it has been extended to

support a variety of cloud computing use cases

including multi-purpose network representation

(Bobelin et al., 2012); VM abstraction (Hirofuchi

and Lebre, 2013); live migration (Hirofuchi et al.,

2013); virtual machine support (Hirofuchi et al.,

2015), and storage simulation (Lebre et al., 2015).

Sotiriadis et al. (2013) present SimIC (Simulating

the Inter-Cloud) which is a DES toolkit based on the

process oriented simulation package of SimJava

(Howell and McNab, 1998). It aims to replicate an

inter-cloud facility wherein multiple clouds

collaborate with each other for distributing service

requests with regard to the desired setup of the

simulation.

According to (Sriram, 2009) SPECI (Simulation

Program for Elastic Cloud Infrastructures) is a

simulation tool that allows exploration of aspects of

scaling as well as performance properties of future

data centres. SPECI simulates the performance and

behaviour of data centres given the size and

middleware design policy as an input.

TeachCloud is a tool designed to overcome

challenges in teaching cloud computing (Jararweh et

al., 2013). Based on CloudSim, the authors

developed a GUI for the toolkit. They also integrated

the MapReduce framework, and added a rain cloud

workload generator, modules relating to SLA and

BPM, cloud network models, a monitoring outlet for

most of the cloud system components, and an action

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model to enable students to reconfigure the system

and study impact on the total system performance.

UCloud (Sqalli et al., 2012) was also developed

for educational purposes. Built on CloudSim,

UCloud’s architecture is based on the hybrid cloud

model and therefore supports both public and private

clouds. It comprises two parts, the Cloud

Management System and the Hybrid Cloud.

WorkflowSim (Chen and Deelman, 2012)

extends CloudSim through the provision of a higher

layer of workflow management. This enables

researchers to evaluate their workflow optimisation

techniques with more accuracy and support.

4 CLOUD COMPUTING DES

FEATURE MATRICES

In order to compare the identified DES platforms

presented in Section 3, a multi-level approach is

employed. Two feature matrices have been

produced: Table 2 presents a general high level

feature matrix, whereas Table 3 presents a high level

technical feature matrix.

Table 2 presents the following key features for

comparison of general high level aspects:

 Underlying Software Stack. Any major 3rd party

dependencies that are required for software to

function.

 License(s). The software license type of the

simulation platform and the underlying software

stack.

 Initial Publication Year. The year when the first

academic publication became available

describing features and usage scenarios of

simulation platform.

 Lines of Code (LOC). The number of lines of

code, determined by using Cloc v1.64.

Comments and empty lines are not included in

this calculation. Also, the authors made the best

judgement to exclude any 3rd party source code

that also was distributed in a bundle. For

example, for all CloudSim based simulators the

actual CloudSim code (usually located in

src\org\cloudbus) was removed from

calculations.

 Last Code Update. The identified year that the

last commit of the source code was carried out.

 User Documentation Availability. The identified

availability of separate documentation that

explains how to install and use the relative DES

platform.

 Source Code Availability. The identified

availability of an online repository with the latest

source code that can be downloaded and used by

anyone.

 Binary availability. The availability of pre-

compiled executable code.

Table 3 summarises the high level technical features

as follows:

 Language(s). The major identified programming

language(s) that were used in the development of

the simulation platform.

 Platform Portability. The ability to use the

simulation platform under multiple operation

systems (e.g. MS Windows, Linux) without

significant effort and performance difference.

 Distributed Architecture. The ability of software

to be executed on more than one host. This

category includes a single simulation run being

distributed among multiple hosts as well as

scaling up for load balancing if the multiple

simulation runs need to be executed at the same

time.

 Model Persistence Type. The identified

persistence format of the experiment scenarios

that the simulation platform requires in order to

execute simulation runs.

 Web API Availability. The identified availability

of a web-based API for controlling the

simulation platform remotely.

 User Documentation Availability. The identified

availability of separate documentation that

explains how to install and use the relative DES

platform.

 Graphical User Interface Availability. The

availability of a graphical user interface that

enables the graphical modelling of experiments,

simulation execution and the presentation of

simulation results.

 Headless Execution. The identified ability to run

the simulation platform without a user interface,

using only command line arguments.

 Format of Result Output. The format which is

used by the simulation platform to save

simulation results once a simulation run(s) has

been completed.

5 DISCUSSION AND

CONCLUSIONS

This work provides an overview of 33 cloud

simulation tools through an analysis of the available

literature. This analysis not only focused on

autonomous simulation platforms, but also includes

plugins and extensions that many researchers have

A Review of Cloud Computing Simulation Platforms and Related Environments

657

Table 2: Identified cloud computing DES platform high-level feature matrix.

Simulation

Platform

Underlying

Stack

License(s)

Initial

Publication

Year

Lines of Code

Last Update

Year

Documentation

Available

Source Code

Available

Binary

Bazaar-Extension CloudSim, F(X)yz Apache 2, BSD 2015 N/A N/A No No No

CACTOSim DESMO-J, Palladio,

Simulizar, EMF,

Eclipse, CDO

GPL, Apache 2,

EPL 2014 46914 2016 Yes Yes Yes

CDOSim CloudSim,

CloudMIG Xpress,

Eclipse, EMF EPL, Apache 2 2012 15619 2012 Yes Yes Yes

CEPSim CloudSim MIT 2015 5564 2015 No Yes Yes

Cloud2Sim CloudSim,

Hazelcast,

Infinispan GPL, Apache 2 2014 2994 2015 Yes Yes Yes

CloudAnalyst CloudSim No data, Apache 2 2009 3277 2010 Yes Yes Yes

CloudEXP CloudSim No data, Apache 2 2014 N/A N/A No No No

CloudNetSim++ Inet, Omnet++ GNU, Academic,

GPL, LGPL 2014 2276 2014 No Yes No

CloudReports CloudSim GPL 3, Apache2 2011 19274 2015 Yes Yes Yes

CloudSched None No data 2015 16681 2015 No Yes Yes

CloudSim None Apache 2 2009 28450 2016 Yes Yes Yes

CloudSimDisk CloudSim LGPL3, Apache 2 2015 1901 2015 Yes Yes No

CloudSimSDN CloudSim GPL 2, Apache 2 2015 4006 2015 Yes Yes No

CMCloudSimulator CloudSim No data, Apache 2 2016 566 2016 No Yes No

DartCSim CloudSim No data, Apache 2 2012 N/A N/A No No No

DCSim

None GPL 3 2012 7369 2014 Yes Yes No

DCSim

MicroC/os-II No data, Comm. 2012 N/A N/A No No No

DISSECT-CF Trove, Apache

commons

GPL 3, LGPL,

Apache 2 2015 9153 2016 Yes Yes No

EMUSim CloudSim, Xen GPL, Apache 2 2012 1369 2012 Yes Yes No

GDCSim None GPL 2 2011 3061 2001 No Yes No

GreenCloud NS2 GPL 2010 6543 2016 Yes Yes Yes

GroudSim SSJ, DISSECT-CF GPL,

Apache,GPL 3 2010 8714 2010 Yes Yes No

iCanCloud Inet, Omnet++ GPL 3, GNU,

Academic 2011 38708 2015 No Yes No

iFogSim CloudSim No data, Apache 2 2016 8397 2016 No Yes No

MDCSim CSIM Commercial/Educ

ational 2009 N/A N/A No No No

MR-CloudSim CloudSim No data, Apache 2 2012 N/A N/A No No No

NetworkCloudSim - - 2011 ~~ ~~ ~~ ~~ ~~

SimGrid None GPL 2001 94951 2016 Yes Yes Yes

SimIC SimJava, jFreeChart Uni. Of Ed. Acad.

Non-Comm.,

LGPL 2013 N/A N/A No No No

SPECI No data No data 2009 N/A N/A No No No

TeachCloud CloudSim, Rain GNU, Apache 2 2013 9891 2014 No Yes No

Ucloud CloudSim No data, Apache 2 2012 N/A N/A No No No

WorkflowSim CloudSim LGPL3, Apache 2 2015 5269 2015 Yes Yes No

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Table 3: Identified cloud computing DES platform high level technical feature matrix.

Simulation

Platform

Language(s)

Platform

Portability

Distributed

Architecture

Model

Persistence

Type

Web API

Availability

GUI

Availability

Headless

Execution

Result

Output

Format

Bazaar-Extension

Java Yes No Java classes No Yes No data

Dashboard

plots, F(X)yz

3D renders

CACTOSim Java Yes No Ecore No Yes Yes EDP2, CSV

CDOSim Java Yes No Ecore No Yes No PNG export

CEPSim Scala, Java Yes No Java classes No No Yes Text

Cloud2Sim Java Yes Yes Java classes No No Yes Text

CloudAnalyst Java Yes No XML No Yes No PDF

CloudEXP Java Yes No No data No Yes No data No data

CloudNetSim++ C++ Yes No NED No Yes Yes Text

CloudReports Java, JS Yes No SQLite DB No Yes No Javascript, text

CloudSched Java Yes No Text No Yes No XLS,Text

CloudSim Java Yes No Yaml No No Yes Text

CloudSimDisk Java Yes No Java classes No No Yes XLS,Text

CloudSimSDN Java Yes No CSV No No Yes CSV, JSON

CMCloudSimulator

Java Yes No

XML, Java

classes No No Yes Text

DartCSim

Java, C++

data No XML No Yes No data XML

DCSim

Java Yes No Java classes No No Yes Text

DCSim

No data

data No No data No No No data Text

DISSECT-CF Java Yes No Java classes No No Yes Text

EMUSim Java No No XML No No Yes Text

GDCSim C/C++, Shell No No C code No No Yes Text

GreenCloud C++, TCL, JS,

CSS, Shell No No TCL Yes Yes Yes Dashboard plots

GroudSim

Java Yes No XML No No Yes

Java API,

Tracer handlers,

Filters

iCanCloud C/C++, Shell Yes No NED No Yes Yes Text

iFogSim Java Yes No JSON No Yes No data XLSX, PDF

MDCSim

No data

data No No data No No No No data

MR-CloudSim

No data

data No No data No N/A No No data

NetworkCloudSim - - - - - - - -

SimGrid

C/C++ Yes No

XML, Java

C++ classes No Yes Yes Text

SimIC

Java Yes No

text, Java

classes No No Yes Text

SPECI

No data

data No No data

data

data No data No data

TeachCloud Java Yes No Java classes No Yes No Java graphs

Ucloud Java Yes No No data No No No data No data

WorkflowSim Java Yes No Java Classes No No Yes Text

proposed and target to solve and support different

aspects of cloud, edge and fog computing. These

features have been presented and compared across

these tools with respect to two main categories:

general high-level features and high-level technical

features of the simulation platforms.

This review identifies the emergence of

CloudSim as a de facto base platform for simulation

A Review of Cloud Computing Simulation Platforms and Related Environments

659

development and research. 18 of the platforms

analysed were derivatives or extensions of

CloudSim. This is not surprising given the early

mover advantage CloudSim had, the eminence of the

researchers involved, and the quality and timeliness

of the release of the simulator platform. There are

advantages and disadvantages to such dominance

including code reuse, resource efficiencies and

development of a wider knowledge base in the use

of CloudSim. However, one might also argue that

dominance of CloudSim may result in inherited

limitations from drawbacks in the CloudSim design.

The multi-level analysis presented identifies

some apparent gaps in the features of existing

simulation tools. For example, the analysis

highlights a gap in the capability of the simulators

identified to support distributed execution, i.e.

parallel execution on distributed memory systems.

Due to the nature of the problem that simulators

have to solve, execution and scalability are crucial

and are limited by the sequential execution.

Similarly, with a number of notable exceptions there

are few simulators focussing on emerging cloud use

cases e.g. HPC in the cloud, Edge and Fog

computing, and IoT. This is unsurprising given the

nascent level of these use cases compared to the

public cloud IAAS use case.

This review is a significant extension of existing

reviews of simulation tools for cloud computing

both in terms of breadth and depth however it is not

without limitations. Future work is recommended

towards a deeper analysis of the tools against

alternative real cloud computing scenarios with a

focus towards heavy validation of simulated results.

Moreover, further analysis can be performed by

reviewing simulation models, VM allocation

policies, supported cloud services and levels, and in

general more cloud oriented specific characteristics.

Similarly, whereas this review focuses on simulation

tools for cloud computing, an additional survey on

the uses to which such tools are employed is

warranted and is worthy of investigation.

ACKNOWLEDGEMENTS

This work is partially funded by the European

Union’s Horizon 2020 and FP7 Research and

Innovation Programmes through CloudLightning

(http://www.cloudlightning.eu) under Grant

Agreement No. 643946, RECAP (http://www.recap-

project.eu) under Grant Agreement No. 732667 and

CACTOS (http://www.cactosfp7.eu) under Grant

Agreement No. 610711.

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