This document summarizes a thesis submitted to Punjabi University for a Master of Technology degree in Computer Sciences and Engineering. The thesis, written by Kirandeep Kaur and supervised by Dr. Rajesh K. Bawa, proposes a novel flexible resource scheduling model for public clouds to avoid resource starvation. It introduces the problem of resource starvation in cloud computing environments and reviews existing resource scheduling mechanisms. The proposed model aims to simulate a public cloud environment using CloudSim and demonstrate that no user request will starve without proper resource allocation through a flexible scheduling approach that considers server load.

1. EFFICIENT RESOURCE MANAGEMENT IN CLOUD
COMPUTING ENVIRONMENT
Master of Technology
In
Computer Sciences and Engineering
Submitted By
Kirandeep Kaur
Under Supervision of
Dr. RAJESH . K . BAWA
(HEAD OF DEPARTMENT)
Department of Computer Science, Punjabi University, Patiala.
Department of Computer Science,
Punjabi University, Patiala – 147002
January, 2015

2. 1. Introduction
2. Literature review
3. Problem formulation
4. Problem statement
5. Objectives
6. Proposed work
7. References

3.  Cloud Computing
 Cloud computing promises a new era of service delivery
and deployment in such a way that every person can
access any kind of services like storage, application,
operating system and so on from anywhere any time
using any device having internet connection. Cloud
computing opens new possibilities approaching
sustainable solutions to deploy and advance their services
upon that platform. Cloud computing is an on demand
service in which shared resources, information, software
and other devices are provided according to the clients
requirement at specific time.

4.  The trend toward cloud computing started in the late
1980s with the concept of grid computing when, for the
first time, a large number of systems were applied to a
single problem, usually scientific in nature and
requiring exceptionally high levels of parallel
computation. Grid computing provided a virtual pool of
computation resources but it's different than cloud
computing. In grid computing, the focus is on moving a
workload to the location of the needed computing
resources, which are mostly remote and are readily
available for use.

5.  In a cloud environment, computing and extended IT
and business resources, such as servers, storage,
network, applications and processes, can be
dynamically shaped out from the hardware
infrastructure and made available to a workload.

6.  In 1961, John McCarthy suggested that computing can
be sold like a utility, but due to lack of technology this
idea was not implemented.
 In 1999 Salesforce started delivering applications to
users using a simple website.
 In 2002 Amazon started Amazon Web Services,
providing services like storage, computation and even
human intelligence.
 In 2006 a truly commercial service open to everybody
came into existence with the launch of Elastic Compute
Cloud called EC2.

7.  In 2009 arrival of browser based cloud enterprise
applications known as Google Apps.
 In 2010 Sales force introduced the cloud-based
database at Database.com for developers.

8.  Cloud Application
 Cloud Platform
 Cloud Infrastructure

9.  Cloud User
 Cloud Provider
 Cloud Broker
 Cloud Carrier

10.  Software as a service (SaaS)
 Platform as a service (PaaS)
 Infrastructure as a service (IaaS)

11.  Public Cloud
 Private Cloud
 Community Cloud
 Hybrid Cloud

12.  Time to Market
 Economic
 Flexibility
 Scalability
 Simplicity
 Rapid elasticity

13.  Resource management is a core function required of
any man-made system. It affects the three basic criteria
for system evaluation: performance, functionality and
cost. Inefficient resource management has a direct
negative effect on performance and cost. It can also
indirectly affect system functionality. Optimal resource
scheduling has been a great challenge in IaaS cloud
computing environment. Cloud Computing is emerging
as a replacement for traditional physical hardware
computing.

14.  Infrastructure-as-a-Service (IaaS) is one of the
fundamental cloud computing models, where users can
request virtual resources with various capabilities
whenever needed. Also users request various resources
at the same time for the completion and execution of
various processes. Here, comes the term Resource
Management where we need to manage the resources
because it is quite possible that a particular resource is
requested by many processes at the same time.

15. Sr. no. Author Outcome
1 Mayank
Mishra et al.
[7]
author told that, the users of cloud services pay only for the amount of
resources (a pay-as-use model) used by them. Traditional data centers
are provisioned to meet the peak demand, which results in wastage of
resources during non-peak periods. To alleviate the above problem,
modern-day data centers are shifting to the cloud. The important
characteristics of cloud-based data centers are making resources
available on demand. The operation and maintenance of the data center
lies with the cloud provider.
2 Vijindra and
Sudhir
shenai. A [8]
Author, have presented an algorithm for a cloud computing
environment that could automatically allocate resources based
on energy optimization methods. Then, prove the effectiveness
of our algorithm. In the experiments and results analysis, we
find that in a practical Cloud Computing Environment, using
one whole Cloud node to calculate a single task or job will
waste a lot of energy

16. Sr. no. Author Outcome
3 Qiang Li and
Yike Guo [9]
proposed a model for optimization of SLA-based resource
schedule in cloud computing based on stochastic integer
programming technique. The performance evaluation has
been performed by numerical studies and simulation.
4 Xin Lu, Zilong
GU [10]
discussed that, by monitoring performance parameters of
virtual machines in real time, the overloaded is easily
detected once these parameters exceeded the threshold.
Quickly finding the nearest idle node by the ant colony
algorithm from the resources and starting the virtual
machine can bears part of the load and meets these
performance and resource requirements of the load. This
realizes the load adaptive dynamic resource scheduling in
the cloud services platform and achieves the goal of load
balancing.

17. Sr. no. Author Outcome
5 Liang Luo et
al. [11]
discussed about, a new VM Load Balancing Algorithm is
proposed and then implemented in Cloud Computing
environment using CloudSim toolkit, in java language. In this
algorithm, the VM assigns a varying (different) amount of the
available processing power to the individual application
services. These VMs of different processing powers, the
tasks/requests (application services) are assigned or allocated
to the most powerful VM and then to the lowest and so on.
6 Gulati et al.
[12]
describe the particular CPU and memory related utilization
metrics used by VMware's Distributed Power Management
(DPM) to trigger management actions including VM
migration and PM power-on. Recently, researchers have
started to address the issue of estimating the utilization of
micro-architectural resources such as shared processor
caches, contention for which have been shown to negatively
impact the performance of consolidated VMs

18.  “A novel flexible resource scheduling model for public
clouds to avoid starvation”
 Cloud computing is a pay-per-use third party based
service delivery method which provides all the required
features as a services. Main service offerings of cloud
computing model are:
 IaaS: Infrastructure as a Service
 PaaS: Platform as a Service
 SaaS: Software as a Service

19.  There are various scheduling mechanisms available in
cloud computing architecture. As heizea model
provides a set of Immediate, Best-Effort, Dead Line
Sensitive, and Advance Reservation scheduling
mechanisms. Any of these scheduling mechanism can
be used as per the client needs.
 To show the proof of concept of our scheduling
mechanism we will simulate the public cloud
environment on Cloud Sim simulator. It is a java based
simulator which supports eclipse IDE for development
environment.

20.  We will simulate our flexible scheduling mechanism
for resource utilization like VMs, and processing
elements and will show that no user request starve
longer in the lack of proper resource allocation.

21.  The existing mechanism is prone to resource starvation
for Best-Effort scheduled process if resources are busy
in other time constrained policies.
 Starvation may need in increase in service delivery
time which in turn may lead to customer un-
satisfaction.
 Some improved anti-starvation algorithm are corrective
measures which still don't avoid the occurance of
starvation.

22.  To simulate public cloud environment and initiating
user request for resources and giving it to scheduler to
grant access for resources.
 Designing flexible scheduler to avoid any possibility of
starvation in cloud environment.
 Verifying our proposed flexible scheduling mechanism
on diverse range of user requests.

23.  To deal with problem of efficient resource scheduling
in cloud environment we provide a flexible scheduling
mechanism based on the load on the server. As the load
or the client request on the server will increase our
model will switch for equal resource sharing
mechanism. With the application of this scheduling
model no client request will be prone to starvation even
in case of heavy load from various clients.

24.  [1] Hitoshi Matsumoto, Yutaka Ezaki,” Dynamic Resource Management in
Cloud Environment”, July 2011, FUJITSU science & Tech journal, Volume 47,
No: 3, page no: 270-276.
 [2] B. Sotomayor, R.S. Montero, I.M. Llorente, I. Foster. Capacity leasing in
cloud systems using the opennebula engine. In: Cloud Computing and
Applications; 2008, pp. 1–5.
 [3] Vaquero, Luis M., et al. A break in the clouds: towards a cloud definition.
ACM SIGCOMM Computer Communication Review 2008; 50-55.
 [4] Kurdi, Heba, Madeeha Enazi, and Auhood Al Faries. Evaluating Firewall
Models for Hybrid Clouds. Modelling Symposium European. IEEE,2013.
 [5] Nathani, S. Chaudhary, and G. Somani. Policy based resource allocation in
IaaS cloud. Future Generation Computer Systems 2011.

25.  [6] B. Sotomayor, K. Keahey, and I. Foster. Combining Batch Execution and
Leasing Using Virtual Machines. 17th International Symposium on High
Performance Distributed Computing (HPDC’08:), ACM, Boston Massachussets
2008;page no: 87-96.
 [7] Mayank Mishra, Anwesha Das, Purushottam Kulkarni, and Anirudha Sahoo,
“Dynamic Resource Management Using Virtual Machine Migrations”, Sep 2012,
0163-6804/12, IEEE Communications Magazine, page no: 34-40.
 [8] Vijindra and Sudhir Shenai. A, “Survey of Scheduling Issues in Cloud
Computing”, 2012, ICMOC-2012, 1877-7058, Elsevier Ltd, Doi:
10.1016/j.proeng.2012.06.337, page no: 2881 – 2888.
 [9] Qiang Li and Yike Guo, “Optimization of Resource Scheduling in Cloud
Computing”, 2010, 12th International Symposium on Symbolic and Numeric
Algorithms for Scientific Computing, 978-0-7695-4324-6/10, IEEE, DOI
10.1109/SYNASC.2010.8, page no: 315 – 320

26.  [10] Xin Lu, Zilong Gu, “A Load-adapative cloud resource scheduling model based
on ant colony algorithm”, 2011, 978-1-61284-204-2/11, Proceedings of IEEE
CCIS2011, Page no: 296-300
 [11] Liang Luo, Wenjun Wu, Dichen Di, Fei Zhang, Yizhou Yan, Yaokuan Mao, “A
Resource Scheduling Algorithm of Cloud Computing based on Energy Efficient
Optimization Methods”, 2012, 978-1-4673-2154-9/12, IEEE.
 [12] Gulati, A., Holler, A., Ji, M., Shanmuganathan, G., Waldspurger, C., Zhu, X.:
VMware distributed resource management: design, implementation, and lessons
learned. VMware Technical Journal 1(1), 45-64 (2012). URL
http://labs.vmware.com/publications/gulati-vmtj-spring2012

27. THANK YOU