CloudSIM

Cloud computing aims to power the next generation data centers and enables application service providers to lease data center capabilities for deploying applications depending on user QoS (Quality of Service) requirements. Cloud applications have different composition, configuration, and deployment requirements. Quantifying the performance of resource allocation policies and application scheduling algorithms at finer details in Cloud computing environments for different application and service models under varying load, energy performance (power consumption, heat dissipation), and system size is a challenging problem to tackle. To simplify this process, in this paper we propose CloudSim: an extensible simulation toolkit that enables modelling and simulation of Cloud computing environments. The CloudSim toolkit supports modelling and creation of one or more virtual machines (VMs) on a simulated node of a Data Center, jobs, and their mapping to suitable VMs. It also allows simulation of multiple Data Centers to enable a study on federation and associated policies for migration of VMs for reliability and automatic scaling of applications.

Cloud computing can be termed as provisioning of computing power, storage, Infrastructure and other resources over the internet as an on-demand pay service. Recent developments in the technological advancement are in the cloud storage, security, fault tolerances, load balancing and optimization. Cloud simulator is a tool that assists in modelling different cloud applications. Cloudism offers a simulation framework for modelling and simulation that emphasis more on exploring design issues without factoring cloud infrastructure and services.Load balancing in cloud is a method of allocation of workload across multiple nodes to obtain resource optimization with minimum response time. In this paper the state of the art of the cloud simulations along with an approach implemented using CloudSim3.0 is illustrated.

Cloud computing based applications are beneficial for businesses of all sizes and industries as they don’t have to invest a huge amount on initial setup. This way, businesses can opt for Cloud services and can implement innovative ideas. But evaluating the performance of provisioning (e.g. CPU scheduling and resource allocation) policies in a real Cloud computing environment for different application techniques is challenging because clouds show dynamic demands, workloads, supply patterns, VM sizes, and resources (hardware, software, and network). User’s requests and services requirements are heterogeneous and dynamic. Applications models have unpredictable performance, workloads, and dynamic scaling requirements. So a demand for a Simulation toolkit for Cloud is there. Cloudsim is self-contained simulation framework that provides simulation and modeling of Cloud-based application in lesser time with lesser efforts. In this paper we tried to simulate the task performance of a cloudlet using one data center, one VM. We also developed a Graphical User Interface to dynamically change the simulation parameters and show simulation results.

Delivery of reliable services in cloud environment is a major issue. Reliability may be achieved by implementing the fault tolerance. Due to the abundant growth of traffic and service request on cloud datacenters, balancing the load in cloud environment is one of the serious challenges as failure may occur due to increase in power consumption, node failure, machine failure etc. Therefore there is a needof a policy for balancing the load among the datacenters and various solutions to balance the load have been proposed by researchers. Load distributionis the mechanism of dispersal the load between different nodes based on certain parameters such as underloaded(node) and overloaded (node). In this research articlewe have discussed the concept of dispersal of load and then perform a comparative analysis of various task-scheduling policies such as First Come First Serve, Shortest Job First and Round Robin onCloudsim.The simulation results on Cloudsim depicted that RR task-scheduling is much better than the FCFS and SJF whether we are using the Time shared policy or Space shared policy for execution of cloudlet.

Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end-users under a usage-based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter-networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy-efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns.

Cloud computing is a paradigm of large scale distributed computing which is a repackaging of various existing concepts/technologies such as utility computing, Grid computing, Autonomic computing, Virtualization and Internet technologies. Cloud computing use the Internet technologies for delivery of resources ‘as a service’ to users on demand. As it is a developing technology, various issues such as security, energy management, resource provisioning, reliability need to be addressed. The objective of the work is to study a simulation toolkit known as CloudSim and some of the research works done to address some of the above mentioned issues using this toolkit. Most of the studies in Cloud rely on simulation based experiments, since using real cloud infrastructures such as Amazon EC2, Google are cost and time consuming tasks and also large number of cloud resources is required to achieve realistic results which is impractical for many researchers.

Cloud computing has become a major force for change in how web design, configure, provision, and manage IT infrastructure. Instead of customprovisioning individual systems or clusters, an architect or administrator is expected to have hundreds, or even thousands of resources under their control! A variety of approaches have emerged to do this. CloudSim enables seamless modeling, simulation, and experimentation of emerging Cloud computing infrastructures and application services. In the CloudSim simulator there are two fundamental problems: i) Lack of links between Datacenters, this lack of links will lead necessarily to a lack of communication between them and therefore no exchange or shared of any service or information with other datacenters. ii) No possibility to create a virtual machine in more Datacenters. In a first time, we propose to use a ring topology to allow the exchange and the and sharing of information and services between different Datacenter, and in the second time improving the method of creating virtual machines, and by consequence to allow the creation of a virtual machine in several Datacenter, which improves fault tolerance in this type of environment.

The main issues in a cloud based environment are security, process fail rate and performance. Fault tolerance plays a key role in ensuring high serviceability and reliability in cloud. Nowadays, demands for high fault tolerance, high serviceability and high reliability are becoming unprecedentedly strong, building a high fault tolerance, high serviceability and high reliability cloud is a critical, challenging, and urgently required task. A lot of research is currently underway to analyze how clouds can provide fault tolerance for an application. When the number of processes are too many and the virtual machine is overloaded then the processes are failed causing lot of rework and annoyance for the users. The major cause of the failure of the processes at the virtual machine level are overloading of virtual machines, extra resource requirements of the existing processes etc. This paper introduces dynamic load balancing techniques for cloud environment in which RAM/Broker (resource awareness module) proactively decides whether the process can be applied on an existing virtual machine or it should be assigned to a different virtual machine created a fresh or any other existing virtual machine. So, in this way it can tackle the occurrence of the fault. This paper also proposed a mechanism which proactively decides the load on virtual machines and according to the requirement either creates a new virtual machine or uses an existing virtual machine for assigning the process. Once a process is complete, it will update the virtual machine status on the broker service so that other processes can be assigned to it.

The evolution of ICT systems in the way data is accessed and used is very fast nowadays. Cloud computing is an innovative way of using and providing computing resources to businesses and individuals and it has gained a faster popularity in the last years. In this context, the user’s expectations are increasing and cloud providers are facing huge challenges. One of these challenges is fault tolerance and both researchers and companies have focused on finding and developing strong fault tolerance models. To validate these models, cloud simulation tools are used as an easy, flexible and fast solution. This paper proposes a Fault Injector Module for CloudSim tool (FIM-SIM) for helping the cloud developers to test and validate their infrastructure. FIM-SIM follows the event-driven model and inserts faults in CloudSim based on statistical distributions. The authors have tested and validated it by conducting several experiments designed to highlight the statistical distribution influence on the failures generated and to observe the CloudSim behavior in its current state and implementation.

The main issues in a cloud based environment are security, process fail rate and performance. Fault tolerance plays a key role in ensuring high serviceability and reliability in cloud. Nowadays, demands for high fault tolerance, high serviceability and high reliability are becoming unprecedentedly strong, building a high fault tolerance, high serviceability and high reliability cloud is a critical, challenging, and urgently required task. A lot of research is currently underway to analyze how clouds can provide fault tolerance for an application. When the number of processes are too many and the virtual machine is overloaded then the processes are failed causing lot of rework and annoyance for the users. The major cause of the failure of the processes at the virtual machine level are overloading of virtual machines, extra resource requirements of the existing processes etc. This paper introduces dynamic load balancing techniques for cloud environment in which RAM/Broker (resource awareness module) proactively decides whether the process can be applied on an existing virtual machine or it should be assigned to a different virtual machine created a fresh or any other existing virtual machine. So, in this way it can tackle the occurrence of the fault. This paper also proposed a mechanism which proactively decides the load on virtual machines and according to the requirement either creates a new virtual machine or uses an existing virtual machine for assigning the process. Once a process is complete, it will update the virtual machine status on the broker service so that other processes can be assigned to it

As Cloud Computing offers support to more and more complex applications, the need to verify and validate computing models under fault constraints becomes more important, aiming to ensure applications performance. Doing this experimental validation in the early development phase and with small costs require a cloud simulation tool. An extensible framework for Cloud simulation and modeling is CloudSim. This paper proposes a new module for CloudSim consisting of a fault injector based on a specification language. The aim is to assist simulation to be more realistic and includes concrete conditions and constraints. The impact is on testing Cloud applications and help test fault tolerant applications by specifying defect patterns and failing components. The evaluation of the fault injection module is done by measuring the behavior and performance of a tool based on CloudSim, named CloudAnalyst. Several metrics are determined and measured for experimental validation, and conclusions are drawn.

In this paper a smart grid cloud has been simulated using CloudSim. Various parameters like number of virtual machines (VM), VM Image size, VM RAM, VM bandwidth, cloudlet length, and their effect on cost and cloudlet completion time in time-shared and space-shared resource allocation policy have been studied. As the number of cloudlets increased from 68 to 178, greater number of cloudlets completed their execution with high cloudlet completion time in time-shared allocation policy as compared to space-shared allocation policy. Similar trend has been observed when VM bandwidth is increased from 1Gbps to 10Gbps and VM RAM is increased from 512MB to 5120MB. The cost of processing increased linearly with respect to increase in number of VMs, VM Image size and cloudlet length.

This paper aims to explore Cloud simulation tools comprehensively. Specifically, it is to propose which simulator will fit in one’s preferences since each simulator has its purpose. Gathering data from research papers along with the simulation processes of four cloud simulators provides a comprehensive approach for identifying the parameters in percentage, characteristics and important features of each cloud simulator. Utilizing cloud simulation tools during testing and modeling the real cloud datacenters provide a test environment which gives a repeatable and controllable environment promptly. The said tools offer the possibility to determine quickly whether the wise guess is true or false. Possibly, the stakeholder can map according to the algorithm used, and give various workloads, tasks, the number of hosts, and virtual machines. Also, the inexpensive way to study how the real cloud datacenters work brings more flexibility and scalability. Cloud simulation tools should be the primary instrument for any cloud computing testing, modeling, and technique.