Scheduler in Cloud Computing using Open Source Technologies

Darshan Upadhyay et al ,Int.J.Computer Technology & Applications,Vol 3 (3), 1093-1098 

ISSN:2229-6093 

Scheduler in Cloud Computing using Open Source Technologies 

Darshan Upadhyay 

Prof. Chirag Patel 

Student of M.E.I.T 

Asst. Prof. Computer Department 

S. S. Engineering College, Bhavnagar L. D. College of Engineering, Ahemdabad 

Gujarat Technological University Gujarat Technological University 

darshanit7@gmail.com 

chirag.email@yahoo.com 

Abstract 

Cloud Computing utilities are coming to be 

omnipresent, and then are beginning to serve as the 

essential root of computing capacity for both 

undertakings and private computing requisitions. Any 

request comes to cloud will be provided by cloud in 

terms of Virtual Machine. On which basis, Virtual 

Machine will be allocated to particular host will be 

decided by Scheduler. We make an effort to establish 

private cloud using OpenNebula – an open source 

technology to establish cloud, and carried out tests 

regarding how scheduler behave with different 

requests. 

1. Introduction 

The flexibility associated with fog up computing has 

its origin withinside the mix of virtualization 

technologies along together using net providers. A 

definition is given in [1]: ”Building on compute and 

storage virtualization, and leveraging the modern 

Web, Cloud Computing provides scalable, networkcentric, 

abstracted IT infrastructure, platforms, and 

applications as on-demand services that are billed by 

consumption.” Cloud Computing is defined as a pool 

of virtualized computer resources. Based on this 

Virtualization the Cloud Computing paradigm allows 

workloads to be deployed and scaled-out quickly 

through the rapid provisioning of virtual machines or 

physical machines. Any request of resources will be 

delivered by Cloud in terms of Virtual Machine. So 

placement of Virtual machine is most important part 

in Cloud Computing. Resource Management is 

necessity in Cloud Computing. Because now a day’s 

multinational company have large number of 

resources. And using Cloud Computing through 

resource Management, we can manage those 

resources efficiently. Through which, we can assure 

effective use of resources, provides scalability and 

elasticity. The large scalability possibilities offered 

by Cloud platforms can be harnessed not only for 

services and applications hosting but also as a raw 

on-demand computing resource[2]. Ultimately, 

Service Providers are under pressure to architect their 

infrastructure to enable real-time end to end visibility 

and dynamic resource management with fine grained 

control to reduce total cost of ownership while also 

improving agility. Again cloud computing is defined 

as a pooled of virtualized computer resources. So, to 

define effective VM placement policy[3] is necessary 

for dynamic resource management. 

2. OpenNebula – Open Source technology to 

build Cloud 

OpenNebula was first established as a research 

project back in 2005 by lgnacio M. Llorente and 

Ruben S. Montero, releasing the first version of the 

toolkit and continuing as an open source project in 

march 2008[4]. OpenNebula is one of the key 

technologies of reservoir plan and the flagship 

research project in virtualization infrastructure and 

cloud computing of European Union. Like nimbus, 

OpenNebula is also an open source cloud service 

framework [4]. It allows user deploy and manage 

virtual machines on physical resources and it can set 

user’s data centers or clusters to flexible virtual 

infrastructure that can automatically adapt to the 

change of the service load. The main difference of 

OpenNebula and nimbus is that nimbus implements 

remote interface based on EC2 or WSRF through 

which user can process all security related issues, 

while OpenNebula does not. Using opennebula we 

IJCTA | MAY-JUNE 2012 

Available online@www.ijcta.com 

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ISSN:2229-6093 

can establish public cloud, private cloud and hybrid 

cloud. OpenNebula also allow to work with existing 

system or external modules. OpenNebula can also 

work with an open source technology Haizea – 

resource scheduler tool. 

The Match-making algorithm as described in [7] 

allocates resources with a higher RANK expression 

first to allocate VMs. This RANK expression is 

important in applying placement policies like 

Packing, Striping and Load-aware policy. Packing 

policy minimizes the number of cluster nodes in use 

by using those nodes with more VMs running first. 

Striping policy maximizes the resources available to 

VMs in a node by using those nodes with less VMs 

running first while Load-aware policy do the same 

job by using those nodes with more free CPU first. 

Fig. 2 shows the comparison of various toolkits on 

the basis of Virtual Machine placement policy. 

Cloud 

toolkits 

Amazon 

EC2 

Nimbus 

VM placement 

policies 

Proprietary 

Round robin and 

static greedy 

Support for 

hybrid 

cloud 

No 

Yes 

Fig. 1 OpenNebula Architecture 

By default, OpenNebula comes with match making 

scheduler. You can also work with external scheduler 

like Haizea with OpenNebula. The toolkit includes 

features for integration, management, scalability, 

security and accounting. It also emphasizes 

standardization, interoperability and portability, 

providing cloud users and administrators with a 

choice of several cloud interfaces (EC2 Query, OGF 

OCCI and vCloud) and hypervisors (Xen, KVM and 

VMware), and a flexible architecture that can 

accommodate multiple hardware and software 

combinations in a data center[3]. 

As shown in fig.1, OpenNebula architecture can be 

divided into three layers: 1. tools – developed using 

interfaces provided by the OpenNebula core. 2. core 

– which is the main part of OpenNebula architecture, 

consists like virtual machine(VM), virtual 

network(VN) and host management components. 3. 

drivers – useful for the support to different 

virtualization and technologies like monitoring. 

3. Scheduler in Cloud Computing 

On which basis Virtual Machine will be allocated to 

particular host will be decided by Scheduler using 

various policies. By default, OpenNebula Comes 

with match making Scheduler[4][6]. OpenNebula 

uses only Immediate lease provisioning to schedule 

IaaS cloud resources using Match-making Algorithm. 

Eucalyptus 

Static greedy and 

round robin 

OpenNebula Match Making - 

Initial placement 

based on rank policy 

OpenNebula 

and Haizea 

Dynamic placement 

to support advance 

reservation leases 

No 

Yes 

Yes 

Fig. 2 Comparison of various cloud toolkits one the 

basis of VM placement policy. 

4. Experiments 

To build a private cloud, i have used OpenNebula 3.0 

– an open source technology and CentOS 5.5 as an 

operating system. 

Experiment – 1 

A. Experiment goal 

Goal of this experiment is to visualize different states 

of VM and analyze behavior of scheduler. 

B. Experiment setup 

For this experiment, it is necessary to have one host 

connected with Cloud Front-end. You also need the 

operating system’s image which you want to run in 



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Virtual Machine as os. Secondly, you have to prepare 

Virtual machine’s template file. A template file 

consists of a set of attributes that defines a Virtual 

Machine. For operating system of Virtual Machine, 

there are 2 – ways to define it. 1) You can use image 

template file(same as Virtual Machine’s template) 

which consists of a set of attributes that defines a 

image and then using oneimage command you can 

register the image in OpenNebula. From now 

onwards, you can use this image by image id or by 

image name. 2) You can use image directly in Virtual 

Machine’s template file and set necessary attributes. 

In this experiment two machines were used. Their 

hardware details and software details are given in fig. 

3. Host and Cloud Front-end are connected. 

CentOS 5.5 

IP 192.169.1.16 

Virtual Machine 3 

CentOS 5.5 

IP 192.169.1.15 


CentOS 5.5 

IP 192.169.1.14 


CentOS 5.5 

Lenovo core 2 duo + 

1 GB RAM 

IP 192.169.1.11 

Cloud Front-end 

Fig. 3 Hardware/Software setup 

CentOS 5.5 

Lenovo core 2 duo + 

1 GB RAM 

IP 192.169.1.10 

Host1 

C. Experiment method & Result 

In this experiment, we can see the different states of 

Virtual Machines with respect to time. At same time, 

procedure of VM creation will be started, but from 

the graph fig.5 we can show that all Virtual Machines 

time of “Active” state is different and time of 

“running” state of VM is almost same. Here 

scheduler will try to discover all possible hosts, but 

due to only one host scheduler will allocate all VM to 

host1. When we create VM it will follow the VM 

life-cycle. 

States/VM VM - 1 VM - 2 VM - 3 

ACTIVE 0.00 0.30 1.00 

PROLOG 0.00 0.30 1.00 

BOOT 2.17 2.18 3.31 

RUNNING 3.30 3.33 3.37 

Fig. 4 Table shows time with respect to different 

states 

Fig. 5 Graph shows all VM with respect to different 

states & time 

So, we can conclude from this experiment that by 

default, OpenNebula’s scheduler works like First 

Come First Served. 



Goal of this experiment is how many VM can be 

placed on single host and what scheduler will do after 

host doesn’t have enough resources to run VM. 


For this experiment, we have taken one host 

connected with Cloud Front end. We will create 

fifteen Virtual Machine using Cloud Front end. 

Procedure for creating Virtual Machine will remain 

same as mention in previous experiment. The 

hardware and software details are as shown in fig. 6. 




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C. Experiment Method & Result 

In this experiment first we had created two Virtual 

Machines which will be allocated to host1 by 

scheduler. Each and every time scheduler will try to 

find whether the RANK has been defined in 

particular VM or not. In this experiment, we haven’t 

defined any RANK in any VM. Then after we had 

created thirteen Virtual Machines from which, again 

two Virtual Machines will be allocated to host1. 

Remaining eleven Virtual Machines will be in 

pending states due to less resources(insufficient 

memory) as shown in fig. 7. 

Stage 

Pending 

VM 

queue 

Status 

Total 

VM 

Initial 0 - 0 

After 

executing 

command to 

create VM 

twice 

0 VM Allocated 

to host 

2 

After 


command to 

create VM 

twice 

After 


command to 

create VM, 

eleven 

times 

After 

deletion of 

first two 

running VM 

0 VM Allocated 

to host 

11 Host filtered 

out due to less 

resources to run 

VM, all eleven 

VM remains in 

pending VM 

queue 

9 Due to deletion 

of two running 

VM, Now host 

have capacity 

to run two new 

VM. First, 2- 

VM from 

pending queue 

will be 

allocated to 

host 

Fig. 7 Scheduler log(auto generated) 

Now, we have deleted first 2-running Virtual 

Machines. So, again host1 have capacity to run two 

more Virtual Machines. So from the pending Virtual 

Machine queue first 2-VM will be allocated to host1. 

And remaining VM will be in pending queue. Fig. 8 

shows the graph of host1’s CPU, host1’s MEMORY 

and total VM. This graph is generated by sunstone 

which provides the GUI to OpenNebula’s cloud. 

4 

15 

13 

Fig. 8 Sunstone’s graph for Host1’s CPU, Memory 

and Total VM 

So, from this experiment we can conclude that, 

OpenNebula’s scheduler will filtered out the host if it 

doesn’t have enough capacity to run more VM. And 

Virtual Machine will remain in pending Virtual 

Machine queue if there is no host available. 



The goal of this experiment to analyze that how 

scheduler will work when there are more than one 

host. 


For this experiment, we have connected three hosts 

with Cloud Front end. Procedure to create VM is 

same as mentioned in Experiment – 1. The 

hardware/software setup is shown in fig. 9. 




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C. Experiment Method & Result 

In this experiment, we have created three VM. And 

we have three host connected with Cloud Front end. 

And to see how scheduler will allocate hosts to VM. 

Whether scheduler will allocate all VMs to one host 

only? Or scheduler will allocate 2 VMs to one host 

and 1 VM to another host and one host remains 

empty? Or scheduler will allocate 1 VM to each host? 

Thu Mar 15 13:37:41 2012 [HOST][D]: Discovered 

Hosts (enabled): 13 15 17 

Thu Mar 15 13:37:41 2012 [VM][D]: Pending 

virtual machines : 104 105 106 

Thu Mar 15 13:37:41 2012 [RANK][W]: No rank 

defined for VM 





Thu Mar 15 13:37:41 2012 [SCHED][I]: Select 

hosts 

PRI HID 

------------------- 

Virtual Machine: 104 

0 17 

0 15 

0 13 


0 17 

0 15 

0 13 


0 17 

0 15 

0 13 

Thu Mar 15 13:37:41 2012 [VM][I]: Dispatching 

virtual machine 104 to HID: 17 





Fig. 10 Scheduler log 

Here, as shown in fig. 10 Scheduler had allocated one 

VM to each host. So we can say that scheduler 

distribute VM equally between hosts when there are 

multiple hosts are available. 



The goal of this experiment is to implement rank 

policy and find out how scheduler will allocate host 

to VMs on basis of rank. 


For this experiment, we have connected three hosts 

with Cloud Front end. In this experiment we had 

created six VM. All VM created one after another, 

not at a time. Procedure to create VM is same as 

mentioned in Experiment – 1. The hardware/ 

software setup is shown in fig. 11. 

C. Experiment method 

In this experiment, we had created VM one by one. 

And in all VM we had specify “ – FREEMEMORY” 

as RANK. Scheduler will sort all hosts according to 

rank and set priority of all hosts to particular VM. So, 

scheduler will allocate VM to those hosts first whose 

free memory is less. When scheduler allocated one 

VM to particular host, scheduler will update the data 

of all hosts before allocating another VM to 

particular VM. 


Fig. 12 shows the initial free memory of each three 

hosts. 

Fig. 12 three hosts and their free memory 

From above figure we can say that, new VM will be 

allocate to darshh2. Now, we had created one VM, 

scheduler had allocated this VM to darshh2, again 

scheduler will pool all information related to host and 

find out the host with less free memory and assign 

new VM to that particular host. Fig. 13 shows the 

states of all VM with respect to time. Fig. 14 shows 

the free memory of hosts with respect to time. Fig. 15 

& 16 shows the graph which indicates when VM 

allocated to particular host, free memory of that 

particular host will decrease. 



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State 108 110 111 112 114 115 

s/VM 

Initia 0.00 6.30 12.30 15.30 18.30 21.30 

l 

ACT 0.00 6.30 12.30 15.30 18.30 21.30 

IVE 

PRO 0.00 6.30 12.30 15.30 18.30 21.30 

LOG 

BOO 1.20 7.46 12.57 15.59 19.02 22.20 

T 

RUN 1.26 7.56 13.45 16.38 19.42 22.40 

NIN 

G 

Fig. 13 VM states with respect to time 

Minute/Host darshhost darshh1 darshh2 

0 296 149 113 

5 296 149 54 

10 296 149 1 

15 296 89 1 

20 236 30 1 

25 177 30 1 

Fig. 14 host free memory(in MB) with respect to time 

Fig. 15 States Vs Time 

So, from this experiment we can conclude that 

scheduler will successfully work with rank policy. 

According to Rank policy scheduler will sort all hosts 

and set priorities of hosts for particular VM. 

5. Conclusion & Future work 

From above experiments we can conclude that 

scheduler is the most important thing in cloud which 

works on various policies and on the basis of that 

scheduler allocate VM to particular host. 

In these match making scheduler there is no rank to 

give priority to particular VM or there is no way to 

give priority to particular VM. So the future direction 

will be to improve this match making scheduler and 

defined new rank through which we can give priority 

to VM. 

6. References 

[1] Patrícia Takako Endo, Glauco Estácio Gonçalves, 

Judith Kelner, Djamel Sadok, “A A Survey on Opensource 

Cloud Computing Solutions”, VIII workshop 

on cloud. 

[2] DSA – Research, “The Open-Source Toolkit for 

building cloud infrastructure NEBULA”, July – 

2009. 

[3] B. Sotomayor, R. S. Montero, I. M. Llorente, I. 

Foster. "Virtual Infrastructure Management in Private 

and Hybrid Clouds", IEEE Internet Computing, vol. 

13, no. 5, pp. 14-22, Sep./Oct. 2009. 

[4] Vivek Shrivastava, D.S. Bhilare, “Algorithms to 

Improve Resource Utilization and Request 

Acceptance rate in IaaS Cloud Scheduling”, 

International Journal Advanced networking and 

Application, vol. 3, issue 5, pp 1367-1374, 2012. 

[5] OpenNebula Pro, OpenNebulaPro White Paper, 

Rev20110126,https://support.opennebula.pro/attachm 

ents/token/coiuzlpxct7oyvq/?name=OpenNebulaPro_ 

White_Paper_Rev20110126.pdf. 

[6] OpenNebula Virtual Machine,http://opennebula. 

org/documentation:rel2.2:vm_guide.. 

[7] OpenNebula Scheduler, http://opennebula.org/ 

documentation:archives:rel2.0:schg. 

Fig. 16 Free Memory Vs Time 



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