What is Cloudsim?

CloudSim is a simulation toolkit that supports the modeling and simulation of the core functionality of cloud, like job/task queue, processing of events, creation of cloud entities(datacenter, datacenter brokers, etc), communication between different entities, implementation of broker policies, etc. This toolkit allows to:

  • Test application services in a repeatable and controllable environment.
  • Tune the system bottlenecks before deploying apps in an actual cloud.
  • Experiment with different workload mix and resource performance scenarios on simulated infrastructure for developing and testing adaptive application provisioning techniques

Core features of CloudSim are:

  • The Support of modeling and simulation of large scale computing environment as federated cloud data centers, virtualized server hosts, with customizable policies for provisioning host resources to virtual machines and energy-aware computational resources
  • It is a self-contained platform for modeling cloud’s service brokers, provisioning, and allocation policies.
  • It supports the simulation of network connections among simulated system elements.
  • Support for simulation of federated cloud environment, that inter-networks resources from both private and public domains.
  • Availability of a virtualization engine that aids in the creation and management of multiple independent and co-hosted virtual services on a data center node.
  • Flexibility to switch between space shared and time shared allocation of processing cores to virtualized services.

Is Cloudsim suitable for my research work?

This simulation toolkit is an API that allows a developer to run any server hardware model as a software simulation, to analyze its behavior for real-world workloads. Therefore it is clear that it allows as a researcher to simulate the Infrastructure as a Service(IaaS) layer. This includes software models of data centers, hosts, storage, virtual machines, Cloud datacenter brokers, allocation & scheduling policies for the virtual machines as well as tasks, power management policies including migrations and consolidation of the virtual machines over different hosts, defining the workload attributes for its execution simulation over the cloud systems, etc.

It is not applicable/suitable where you are looking to analyze any service related to Platform as a Service(PaaS) or Software as a Service(SaaS) for example real-time applications, security algorithms, platform implementations, etc

Does it require a High computing system?

No, not at all. The cloudsim is a software simulation toolkit and is developed using Java programming language, Therefore, any computer system with a dual-core processor, 2 GB RAM, and 1 GB storage is good enough to simulate the cloud-based systems using the cloudsim as this is required to support the JRE working. Also, the hardware requirements may differ on the basis of which IDE you are using for JAVA development.

How to download Cloudsim?

Cloudsim project source code, as well as compiled jars files, are published through the GitHub project page: By default, the project page displays the source code(based on maven build tool) of the current release, which is currently released version 5.0(beta) and the page displays the basic information about the project along with the publication details. Now if you are interested in using the previous versions of this project then you have click on releases link option available on project main page or otherwise, you may access it directly from the following links:

Every version except 5.0(still under development) contains 4 asset files example description for each asset file is as follows:

  • cloudsim-4.0.tar.gz: This file contains the compiled JAR file that can be directly used into the custom simulation implementation where there is no need to change in the source code of cloudsim simulation engine. This version is Linux specific.
  • This is the same as above the only difference is that it is windows specific.
  • Source code(zip): This file contains the complete source code of the cloudsim simulation framework project. As the cloudsim uses the maven build tool for its DevOps. Therefore to set up this project you require to use an IDE that supports maven project imports. How do you identify that its a maven based project? you will find a “pom.xml” file in the project root folder. This zip is windows operating system specific file.
  • Source code(tar.gz): This file contains a similar structure as mentioned above, but it is specific to Linux

How to install?

Cloudsim setup is very easy, for this you may follow the following link: cloudsim-setup-using-eclipse/. This link describes in detail all the steps that are required to successfully configure the Cloudsim 3.0.3 version. The cloudsim 3.0.3 version is best to start with that once you understand the basic working and architecture then you can move to any latest version.

How cloudsim work?

As it is already mentioned that the cloudsim allows to model and simulate the cloud system components, therefore to support its function different set of classes has been developed by its developers like:

  • To simulating the regions and datacenters the class named “” is available in org.cloudbus.cloudsim package.
  • To simulate the workloads for cloud, the class named as “” is available in org.cloudbus.cloudsim package.
  • To simulate the load balancing and policy-related implementation the classes named “”, “”, “”, etc are available under org.cloudbus.cloudsim package.
  • Now because all the different simulated hardware models are required to communicate with each other to share the simulation work updates for this cloudsim has implemented a discrete event simulation engine that keeps track of all the task assignments among the different simulated cloud components.

To read understand more on the architecture, you should definitely read the article titled “CloudSim Simulation Toolkit: An Introduction” as well as “Beginners Guide to Cloudsim Project Structure

How to run my first cloudsim simulation scenario?

Once you have completed your installation/setup and understand the basic working of the cloudsim, the next step is to implement your own custom scenario. Any simulation will go through the following steps:

  • Initialize the CloudSim with the current clock time and this will also initialize the core CloudInformationService entity.
  • Create Datacenter(s) as Datacenters are the resource providers in CloudSim. We need at list one of them to run a CloudSim simulation.
  • Create Broker to simulate the user workload scheduling as well as virtual machine allocation and placements.
  • Create one/more virtual machine and submit to the broker for further submitting it to the respective DataCenters for its placement and execution management during the simulation run.
  • Create one/more Cloudlet and submit the cloudlet list to the broker for further task scheduling on the active virtual machines for its processing during the simulation run.
  • Starts the simulation, this will initiate all the entities and components created above and put them into execution for supporting various simulation operations.
  • Stop the simulation, concludes simulation and flush all the entities & components before the exit of a simulation run.
  • Print results when the simulation is over, where you will be able to display which cloudlet executed on which virtual machine along with how much time it spent in execution, its start time as well as its finish time.

The step by step detailed description about each step can be read in the article “Guide to simulation workflow

It is frequently used for?

  • Load Balancing of resources and tasks
  • Task scheduling and its migrations
  • Optimizing the Virtual machine allocation and placement policies
  • Energy-aware Consolidations or Migrations of virtual machines
  • Optimizing schemes for Network latencies for various cloud scenarios
  • and many more.

Other simulators based on cloudsim

The Cloudsim 3.x.x version was a very important release and become the base for many other extensions which in turn supports a very specific type of use cases:

Learning Resources

Sample Source Codes

There are various researchers who have published their cloudsim project code on their GitHub handles, following are the quick links for your reference:

As a beginner, you may refer to these pieces of code to understand, how other fellow researchers have worked upon the cloudsim based simulation to implement their work. And definitely, this will help you to accelerate your by forking these projects and working on your own additions or a complete overhaul of these sets of codes.

References used for this post

CloudSim Simulation Toolkit: An Introduction

Cloud computing is a pay as you use model, which delivers infrastructure (IaaS), platform (PaaS) and software (SaaS) as services to users as per their requirements. Cloud computing exposes data centers capabilities as network virtual services, which may include the set of required hardware, application with support of the database as well as the user interface. This allows the users to deploy and access applications across the internet which is based on demand and QoS requirements.

As Cloud computing is a new concept and is still in a very early stage of its evolution, so researchers and system developers are working on improving the technology to deliver better on processing, quality & cost parameters. But most of the research is focused on improving the performance of provisioning policies and to test such research on real cloud environment like Amazon EC2, Microsoft Azure, Google App Engine for different applications models under variable conditions is extremely challenging as:

  1. Clouds exhibit varying demands, supply patterns, system sizes, and resources (hardware, software, and network).
  2. Users have heterogeneous, dynamic, and competing QoS requirements.
  3. Applications have varying performance, workload, and dynamic application scaling requirements.

Benchmarking the application performance on the real public cloud infrastructure like google cloud, Microsoft Azure, etc., are not suitable due to their multi-tenant nature as well as variable workload fulfillment.

Also, there are very few admin level configurations that a user/researcher could be able to change. Hence, this makes the reproduction of results that can be relied upon, an extremely difficult undertaking.

Further, even if we do, it is a tedious and time-consuming effort to re-configure benchmarking parameters across a massive-scale Cloud computing infrastructure over multiple test runs. Therefore, it is impossible on real-world public Cloud systems to undertake benchmarking experimentations as repeatable, dependable, and scalable environments.

Thus the need to use simulation tool(s) arises, which may become a viable alternative to evaluate/benchmark the test workloads in a controlled and fully configurable environment that can repeatable over multiple iterations and reproduce the results for analysis.

This simulation-based approach can provide various benefits across the researcher’s community as it allows them to:

  1. Test services in a repeatable and controllable environment.
  2. Tuning the system bottlenecks (performance issues) before deploying on real clouds.
  3. Simulating the required infrastructure(small or large scale) to evaluate different sets of workload as well as resource performance, which facilitates for developing, testing and deployment of adaptive application provisioning techniques.

This article provides a beginners guide to cloudsim simulation toolkit and helps then to get an insight into the role of its various components.

Why use Cloudsim?

Several simulators can be used to simulate the working of new services, among them CloudSim Simulation Toolkit is the more generalized and effective simulator for testing Cloud computing-related hypothesis. CloudSim is an extensible simulation framework developed by a team of researchers(under the guidance of Dr. Raj Kumar Buyya) at Cloud Computing and Distributed Systems (CLOUDS) Laboratory, University of Melbourne. This toolkit allows seamless modeling, simulation, and Experimentation related to cloud-based infrastructures and application services and its various released versions are published on Cloudsim’s GitHub project page.

This simulation toolkit allows the researchers as well as cloud developers to test the performance of the potential cloud application for performance testing in a controlled and easy to setup environment. And Also allows finetuning the overall service performance even before it is deployed in the production environment.

Features of CloudSim Simulation Toolkit

  1. Support for modeling and simulation of large-scale Cloud computing environments, including data centers, on a single physical computing node(could be a desktop, laptop, or server machine).
  2. A self-contained platform for modeling Clouds, service brokers, provisioning, and allocation policies.
  3. Facilitates the simulation of network connections across the simulated system elements.
  4. Facility for simulation of federated Cloud environment that inter-networks resources from both private and public domains, a feature critical for research studies related to Cloudbursts and automatic application scaling.
  5. Availability of a virtualization engine that facilitates the creation and management of multiple, independent, and co-hosted virtualized services on a data center node.
  6. Flexibility to switch between space-shared and time-shared allocation of processing cores to virtualized services.

All these features would help in accelerating the development, testing and deployment of potential resource/application provisioning policies/algorithms for Cloud Computing based systems.

CloudSim Architecture

Cloudsim Architecture

The above diagram demonstrates the layered architecture of CloudSim Simulation Toolkit. The CloudSim Core simulation engine provides support for modeling and simulation of virtualized Cloud-based data center environments including queuing and processing of events, creation of cloud system entities (like data center, host, virtual machines, brokers, services, etc.) communication between components and management of the simulation clock.

The CloudSim layer provides dedicated management interfaces for Virtual Machines, memory, storage, and bandwidth. Also, it manages the other fundamental issues, such as provisioning of hosts to Virtual Machines, managing application execution, and monitoring dynamic system state(e.g. Network topology, sensors, storage characteristics, etc), etc.

The User Code layer is a custom layer where the user writes their own code to redefine the characteristics of the stimulating environment as per their new research findings.

Design and Implementation of CloudSim

In this section, we provide finer details related to the fundamental classes of CloudSim Simulation Toolkit(Version 3.0.3), which are also the building blocks of the simulator. The overall class design diagram for CloudSim is as(to better understand read- “Guide to simulation workflow“):

Detailed Class diagram of cloudsim.

The description of all the major classes mentioned in the class diagram above is described below:

  1. Cloudlet: This class model(define specific attributes such as length of instruction, input/output filesize, no of processor required, etc) the Cloud-based application services (program based tasks) such as content delivery, social networking, etc. CloudSim implements the complexity of an application in terms of its computational requirements. As a developer, we know that every individual executable application/service workload has a pre-defined instruction length and requires certain network data flow (both pre and post fetches) overhead that it needs to undertake during its life cycle. this class allows modeling all the above-said requirements
  2. CloudletScheduler: This is responsible for the implementation of different policies that determine the share of processing power among Cloudlets in a VM. There are two types of provisioning policies offered: space-shared (using CloudetSchedulerSpaceShared class) and time-shared (using CloudletSchedulerTimeShared class).
  3. Datacenter: This class model the core infrastructure-level services (i.e. hardware) that are offered by Cloud providers (Amazon, Azure, and App Engine). It encapsulates a set of hosts(resembling server machine model) instances that can either be homogeneous or heterogeneous concerning their hardware configurations (memory, cores, capacity, and storage). Also, every Datacenter component takes care of generalized application provisioning that enforces a set of policies for the allocation of bandwidth, memory, and storage devices to hosts and its related VMs.
  4. DatacenterBroker or Cloud Broker: This class model a broker, which is responsible for mediating negotiations between SaaS and Cloud providers and such negotiations are driven by QoS requirements. The broker class acts on behalf of applications. Its prime role is to query the CIS to discover suitable resources/services and undertakes negotiations for the allocation of resources/services that can fulfill the application’s QoS needs. This class must be extended for evaluating and testing custom brokering policies.
  5. DatacenterCharacteristics: This class contains configuration information of data center resources like the available host list, the fine-grained cost for each resource type, etc.
  6. Host: This class model a physical resource such as a computer or storage server. It encapsulates important information such as the amount of memory and storage, a list and type of processing cores (if it is a multi-core machine), an allocation of policy for provisioning the compute, memory and bandwidth to the VMs.
  7. NetworkTopology: This class contains the information for inducing network behavior (latencies) in the simulation. It stores the topology information, which is generated using the BRITE topology generator.
  8. RamProvisioner: This is an abstract class that represents the provisioning policy for allocating primary memory (RAM) to Virtual Machines. The execution and deployment of VM on a host are feasible only if the RamProvisioner component approves that the host has the required amount of free memory. The RamProvisionerSimple does not enforce any limitation on the amount of memory that a VM may request. The RAM resource request will be rejected if it is beyond the available capacity.
  9. BwProvisioner: The main role of this component is to undertake the allocation of network bandwidths to a set of competing VMs that are deployed across the data center. Cloud system developers and researchers can extend this class with their policies (priority, QoS) to reflect the needs of their applications.
  10. Vm: This class model a Virtual Machine (VM), which is managed and hosted by a Cloud host component. Every VM component has access to a component that stores the following characteristics related to a VM (i.e.) accessible memory, processor, storage size, and the VM’s internal provisioning policy that is extended from an abstract class called the CloudletScheduler.
  11. VmAllocationPolicy: This is an abstract class that represents a provisioning policy to be utilized by VM Monitor for mapping VMs to hosts. The primary role is to select the best fit host in a data center that meets the memory, storage, and availability requirement for VM deployment mapping.
  12. VmScheduler: This is an abstract class implemented by a Host component that models the allocation policies (space-shared, time-shared) defining the rules for processor cores allocations to VMs. To accommodate application-specific processor sharing policies, the class functionality can be extended to define a new set of provisioning rules.
  13. CloudSim: This is the prime class, with the role of managing entity event queues and controlling the sequential execution of simulation events. Every event that is generated by the CloudSim entity at run-time is stored in the queue called future events. These events are sorted by their time parameters and are enqueued into the future queue. Next, the events that are scheduled at each step of the simulation are removed from the future events queue and transferred to the deferred event queue. Following this, an event processing method is invoked for each entity, which chooses events from the deferred event queue and performs appropriate actions. Such an organization allows flexible management of simulation and provides the following powerful capabilities:
    1. Deactivation (hold/pause) of entities.
    2. Context switching of entities between different states (e.g. waiting to active). Pause and resume the process of simulation.
    3. Creation of new entities at run-time.
    4. Aborting and restarting simulation at run-time.
  14. FutureQueue: This class implements the future event queue accessed by CloudSim and acts as a ready queue to the simulation engine.
  15. DeferredQueue: This class implements the deferred event queue used by CloudSim and hold such events which are failed or paused. It acts as a wait queue of the simulation engine, where preempted resource requests are kept.
  16. CloudInformationService: A CIS is an entity that provides resource registration, indexing, and discovering capabilities. CIS supports two basic primitives:
    1. publish(), on the start of simulation it allows entities to register themselves with CIS
    2. search(), allows Brokers in discovering resources status and endpoint addresses of other entities. This entity also acts as a notification service to the other entities about the end of the simulation.
  17. SimEntity: This is an abstract class, which represents a simulation entity (such as DataCenter, DatacenterBroker, etc) ) that is able to send messages to other entities and processes received messages as well as fire and handle events. SimEntity class provides the ability to schedule new events and send messages to other entities, where network delay is calculated according to the BRITE model. Once created, entities automatically register with CIS. All entities must extend this class and override its three core methods:
    1. startEntity(), which define actions for entity initialization.
    2. processEvent(), which defines actions processing of each called event(s) with respect to the entity.
    3. shutdownEntity(),which define actions for entity destruction.
  18. CloudSimTags. This class contains various static event/command tags that indicate the type of action that needs to be undertaken by CloudSim entities when they receive or send events.
  19. SimEvent: This entity represents a simulation event that is passed between two or more entities. SimEvent stores the following information about an event:
    1. type,
    2. init time,
    3. time at which the event should occur,
    4. finish time,
    5. time at which the event should be delivered to its destination entity,
    6. IDs of the source and destination entities,
    7. the tag of the event, and
    8. Data that have to be passed to the destination entity.
  20. CloudSimShutdown: This is an entity class that waits for the termination of all end-user submitted cloudlets(tasks) and broker entities events, and once detected, then signals the end of simulation to CIS.

Following is a very specific and important call hierarchy of classes which enables the CloudSim Simulation Toolkit to simulate the cloud computing environment, as demonstrated below:

Simulation flow to core cloudsim simulation core package

This hierarchy during the simulation iteratively calls a sequence of events to be performed with respect to the submitted Cloudlets, Datacenterborker policies and other relevant entities. The iteration stops when there are no more events left in FutureQueue/DeferredQueue instances.

Learn More

You may subscribe to an online self-paced course named Learn Basics of Cloudsim, The content for this course which will be updated weekly till August 2021. Click here to get the applicable discount coupon codes.

References used are:

  1. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A. and Buyya, R., 2011. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience41(1), pp.23-50.
  2. CloudSim: A Framework For Modeling And Simulation Of Cloud Computing Infrastructures And Services, Available at 31 May 2019).