A platform to simulate cloud enviornment using simpy as simulation engine
To learn more about usage please see example.py
you can clone the repository and run exmple using following
git clone git@github.com:cloudsim-py/cloudsim.git
pip install simpy
python example.pyCloudsi-py has the following entitiy to build your cloud enviornment
A Processing Element (PE) is a fundamental unit of computation within a computer system, executing instructions to perform operations on data. An individual host can have one or more PEs, collectively contributing to its processing capabilities.
Example of creating PE
mips = 1000 # MIPS (Million Instructions Per Second) rating of the computing resource
pe = PE(mips)A host is a computing entity that provides resources, such as processing power, memory, storage, and bandwidth, within a computer system. It serves as a platform for running VM. A datacenter can have one or more hosts, each contributing to its computational capacity. A host can run multiple VM
Example of host properties:
pe_list = [] # List to store processing elements (PEs) in a host
ram = 2048 # Host memory capacity in megabytes (MB)
storage = 1000000 # Host storage capacity in megabytes (MB)
bw = 10000 # Host bandwidth capacity in megabits per second (Mbps)Example for creating host:
pe_list = [Pe(1000),Pe(1000),Pe(1000)] # Assuming mips value is 1000
ram = 2048 # host memory (MB)
storage = 1000000 # host storage
bw = 10000
$host = Host(ram, bw, storage, pe_list)A datacenter is a central facility that stores, manages, and processes data. It houses hosts with resources like processing power and storage.
Example of datcenter properties:
host_list = [], # List to store host machines in the datacenter
arch = "x86" # Architecture of the datacenter (e.g., x86)
os = "Linux" # Operating system of the datacenter (e.g., Linux)
vmm = "Xen" # Virtual machine monitor (e.g., Xen)
time_zone = 10.0 # Time zone of the datacenter
cost = 3.0 # Overall cost of the datacenter
cost_per_mem = 0.05 # Cost per unit of memory in the datacenter
cost_per_storage = 0.001 # Cost per unit of storage in the datacenter
cost_per_bw = 0.0 # Cost per unit of bandwidth in the datacenter
storage_list = [] # List to store storage devices in the datacenterExample for creating datacenter:
def create_datacenter(name):
# 1. Create a list to store our machine
host_list = []
# 2. Create PEs and add these into a list.
pe_list = [Pe(1000),Pe(1000),Pe(1000)] # Assuming mips value is 1000
# 3. Create Host with its id and list of PEs and add them to the list of machines
ram = 2048 # host memory (MB)
storage = 1000000 # host storage
bw = 10000
host_list.append(
Host(
ram,
bw,
storage,
pe_list
)
) # This is our machine
# 5. Create a DatacenterCharacteristics object
arch = "x86"
os = "Linux"
vmm = "Xen"
time_zone = 10.0
cost = 3.0
cost_per_mem = 0.05
cost_per_storage = 0.001
cost_per_bw = 0.0
storage_list = deque() # we are not adding SAN devices by now
characteristics = DatacenterCharacteristics(
arch, os, vmm, host_list, time_zone, cost, cost_per_mem, cost_per_storage, cost_per_bw
)
# 6. Finally, create a Datacenter object.
datacenter = None
datacenter = Datacenter(name, characteristics, None, storage_list, 0)
return datacenterVirtual Machine (VM) acts as a virtualized computing instance for running applications (cloudlets). It emulates the behavior of a physical machine, facilitating efficient resource allocation and management within the simulated cloud environment.
Example of VM properties:
mips = 250
size = 10000 # image size (MB)
ram = 1024 # vm memory (MB)
bw = 2000
pes_number = 1 # number of cpus
vmm = "Xen" Example for creating vm:
broker_id = datacenter_instance.get_broker_id()
vmlist = []
# VM description
mips = 250
size = 10000 # image size (MB)
ram = 1024 # vm memory (MB)
bw = 2000
pes_number = 1 # number of cpus
vmm = "Xen" # VMM name
# create two VMs
vm1 = Vm(broker_id, mips, pes_number, ram, bw, size, vmm) # Replace None with the appropriate CloudletScheduler
vm2 = Vm(broker_id, mips * 2, pes_number*2, ram, bw, size, vmm) # Replace None with the appropriate CloudletScheduler
# add the VMs to the vmList
vmlist.append(vm1)
vmlist.append(vm2)A cloudlet is akin to an application that runs on a virtual machine (VM). It represents a specific computing task or set of tasks offloaded to the cloud for execution.
Example of cloudlet properties:
length = 10 # Duration of the cloudlet's execution in simulation time units.
pes_number = 1 # Number of Processing Elements (PEs) required for the cloudlet.
file_size = 50 # Size of the input file associated with the cloudlet (e.g., in megabytes).
output_size = 50 # Size of the output file produced by the cloudlet (e.g., in megabytes).Example for creating datacenter:
cloudlet1 = Cloudlet( length=10, pes_number=1, file_size=50, output_size=50)
cloudlet2 = Cloudlet( length=15, pes_number=2, file_size=100, output_size=100)
cloudlet3 = Cloudlet( length=8, pes_number=1, file_size=30, output_size=30)simcloud-py provides following simulations / Feature
Example usage :
datacenter_instance.get_details()Example output:
Datacenter name: MyDatacenter
Number of hosts: 1
Number of VMs: 2
Host ID: 0c05847d-d7b1-4685-875b-8297bd480830
RAM: 2048
BW: 10000
Storage: 1000000
PEs: 3)
VM ID: 4bcbc66d-4d22-4eda-8a1c-63e12399da58
MIPS: 250
PEs: 1
RAM: 1024
BW: 2000
Size: 10000
VMM: Xen
VM ID: 94244787-3503-4981-8ff6-8e8dd47d8693
MIPS: 500
PEs: 2
RAM: 1024
BW: 2000
Size: 10000
VMM: Xen
Example usage:
datacenter_instance.get_total_cost()Example output:
Datacenter cost: 3.0
Memory cost: 102.4
Storage cost: 1000.0
Bandwidth cost: 0.0
Total cost: 1105.4
# Create a list of Cloudlets
cloudlet1 = Cloudlet(length=10, pes_number=1, file_size=50, output_size=50)
cloudlet2 = Cloudlet(length=15, pes_number=2, file_size=100, output_size=100)
cloudlet3 = Cloudlet(length=8, pes_number=1, file_size=30, output_size=30)
cloudlet_list = [cloudlet1, cloudlet2, cloudlet3]
# Execute the Cloudlets in the datacenter
cloudlet_execution = CloudletExecution(cloudlet_list, datacenter_instance)
cloudlet_execution.execute()Example output:
Cloudlet 39e06124-919f-41b5-956a-d028033ebb8f starts execution on VM 10078ab6-c844-4e4a-beab-cffd823b16eb at 0
Cloudlet ba8da363-5a1d-42a6-9fe7-bef2fa58bac7 starts execution on VM 348469dd-f98a-47e5-9892-bf5c460573a1 at 0
Cloudlet 39e06124-919f-41b5-956a-d028033ebb8f completes execution on VM 10078ab6-c844-4e4a-beab-cffd823b16eb at 10
Cloudlet 4eff8fdd-b3e8-4cd2-ab3c-44cfa8b1b4ac starts execution on VM 10078ab6-c844-4e4a-beab-cffd823b16eb at 10
Cloudlet ba8da363-5a1d-42a6-9fe7-bef2fa58bac7 completes execution on VM 348469dd-f98a-47e5-9892-bf5c460573a1 at 15
Cloudlet 4eff8fdd-b3e8-4cd2-ab3c-44cfa8b1b4ac completes execution on VM 10078ab6-c844-4e4a-beab-cffd823b16eb at 18