Virtualization technology is not new; If you have ever read about mainframes like IBM CP/CMS, these were the first computers that implemented virtualization at the hardware level. In the early 1970s, the cost of computing resources was very high.

They were supposed to be shared for various operational purposes with application backward compatibility.

Then over a period of time, as the cost of computing decreased, it became distributed to personnel. This change helped virtualization to diversify its role in storage, network, and application execution environment and has become a well-matured technology.

Internet Accelerated Growth

Since 1990 the Internet became public; within 15-20 years, there has been exponential growth in the websites and applications hosted across the Internet. This growth has intense stress on the service providers regarding workload and operational and maintenance costs. Apart from this, increased use of computation requires intense cooling measures, leading to the indirect contribution of greenhouse gas emissions as most electricity is produced through burning natural resources.

The service providers like Google, Microsoft, and AWS worked around this problem. They used virtualization technology as a great tool for a sustained, comprehensive computing practice by integrating Virtualization into cloud computing.

Adoption of Virtualization

Virtualization technology becomes a fundamental component of a cloud computing stack because it allows the creation of a secure, customizable, and isolated abstract execution environment for running the application.

This abstract execution environment completely separates the underlined host through the inception of the hypervisor, also known as virtual machine managers. There have been various types of Virtualization in cloud computing based on the type of resource involved.

Additionally, virtualization provides a great opportunity to build elastically scalable systems capable of provisioning additional capacity with minimum cost, as the underlying host machines are shared among various virtual machines. Ultimately leading to the Infrastructure as a service that can be offered on a pay-as-you-go model.

Even though this technology has brought a huge difference to the real computing paradigm, but as a cloud computing researcher, it is really difficult to leverage such a commercial system, as its usage for testing the research hypothesis will cost you based on what resources require for how long. So without funding, testing on a real cloud is impossible.

Cloud Computing Simulation using Cloudsim

Dr, Raj Kumar Buyya and his team presented the cloudsim and addressed this problem, which supports the modeling and simulation of large-scale Cloud computing data centers.

This tool is an API developed using Java programming language.

The interesting point of this tool is that the various components of this real-world virtualization were modeled in the form of Java classes.

These classes produce similar behavior during the simulation using logical and mathematical models.

Let’s take an example of AWS EC2 instance “t4g.micro”, the configuration that it defines is vCPU 2 with consistent baseline compute availability of 10%, 1 GB RAM, up to 10 GB of EBS, and up to 5 Gbps of network bandwidth.

Like above, the virtual machine configuration can be defined using a few lines of Java code. The Virtual machine model behavior is implemented in Vm.java class.

This class defines all the attributes like processing elements, RAM, Storage Size, Bandwidth, computation capacity, etc.

The following snippet demonstrates how these attributes are initialized before the simulation process.

// VM description
int vmid = 0;
int mips = 1000;
long size = 10000; // image size (MB)
int ram = 512; // vm memory (MB)
long bw = 1000;
int pesNumber = 1; // number of cpus
String vmm = "Xen"; // VMM name

// create VM
Vm vm = new Vm(vmid, brokerId, mips, pesNumber, ram, bw, size, vmm, new CloudletSchedulerTimeShared());

Here, it is important to mention that just like a real cloud, cloudsim supports allocating more than one CPU to Vm through the attribute pesNumber.

But the allocation is only possible if the underlined simulating host has enough resources to support the requested resources.

In the real-world cloud-based system, every virtual machine contains an execution environment to accommodate the application processing, where the number of processes run on this abstract virtual machine and based on the execution of these processes, the output/ response is either displayed to the user or sent back to the requesting entity.

These process executions are intern managed through schedulers like Round-robin/ shortest job first etc.

On a similar note, to support the simulation of virtualization in the cloudsim simulation toolkit, there exist four classes CloudletScheduler.java, CloudletSchedulerTimeshared.java, CloudletSchedulerSpaceShared.java, CloudletSchedulerDynamicWorkload.java.

These class models facilitate the cloudsim simulation engine to imitate the real-life scheduling process and are state-of-the-art.

Virtual Machine Allocation Policy in Cloudsim

The real cloud-based virtual machine allocation engine allocates the created virtual machine to a suitable host.

Similarly, the cloudsim virtualization simulation engine has a model implementation in VmAllocationPolicy.java and VmAllocationPolicySimple.java.

The datacenter class uses these classes to find the right mapping of the VM to the Host. This task of mapping is performed through the processVmCreate() method of Datacenter.java class.

The logic is as follows:

protected void processVmCreate(SimEvent ev, boolean ack) {
		Vm vm = (Vm) ev.getData();

boolean result = getVmAllocationPolicy().allocateHostForVm(vm);

if (ack) {
	int[] data = new int[3];
	data[0] = getId();
	data[1] = vm.getId();

	if (result) {
		data[2] = CloudSimTags.TRUE;
	} else {
		data[2] = CloudSimTags.FALSE;
		}
	send(vm.getUserId(), CloudSim.getMinTimeBetweenEvents(), CloudSimTags.VM_CREATE_ACK, data);

	}

	if (result) {
		getVmList().add(vm);
		if (vm.isBeingInstantiated()) {
			vm.setBeingInstantiated(false);
			}

		vm.updateVmProcessing(CloudSim.clock(), getVmAllocationPolicy().getHost(vm).getVmScheduler()
					.getAllocatedMipsForVm(vm));
	}

}

Also, we know that real cloud-based virtual machines are just abstract machines requiring a host to fulfill their resource request. Also, a single host may manage more than one virtual machine. So to support the scheduling of Virtual machines, a VM Scheduler policy exists.

To support the same in simulation, the cloudsim simulation toolkit implements a VmScheduler.java, VmSchedulerTimeshare.java, VmSchedulerspaceShared.java, and VmSchedulerTimesharedoversubscription.java.

These VmScheduler classes are called every time the VM processing is updated means that whenever the cloudsim simulation engine is progressing, the cloudlet/task processing intern updates the VM processing using the updateVmsProcessing() method available in Host.java class.

Conclusion

Every individual action the real-world virtual machine manager requires is modeled within the cloudsim simulation toolkit using Java classes. These classes are exposed as an API and are highly extensible.

This allows the cloud computing researcher to test and benchmark all possible hypotheses on a state-of-the-art tool.