Abstract In the last few years, the cloud computing becomes the most important developing platform for both scientific and commercial application. Clouds are rapidly becoming an important platform for scientific applications. In the Cloud environment with uncountable numeric nodes, resource is inevitably unreliable, which has a great effect on task execution and scheduling. In cloud computing, cloud providers can offer cloud consumers two provisioning plans for computing resources, namely reservation and on-demand plans. In general, cost of utilizing computing resources provisioned by reservation plan is cheaper than that provisioned by on-demand plan, since cloud consumer has to pay to provider in advance. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, the best advance reservation of resources is difficult to be achieved due to uncertainty of consumer&apos;s future demand and providers&apos; resource prices. To address this problem, The SLA can provision computing resources for being used in multiple provisioning stages as well as a long-term plan, The Service Level Agreement (SLA) based super scheduling approach promotes cooperative resource sharing. Super scheduling is facilitated between administratively and topologically distributed sites via resource schedulers such as Resource brokers and workflow engines

A Arun

J Udayakumar

M Manikkam

CiteSeerX

International Journal of Computing Science and Information Technology, 2013, Vol.1 (02) 13 - 16 
ISSN: 2278-9669,April 2013 (http://ijcsit.org)  
13                                        International Journal of Computing Science and Information www.ijcsit.org 
                             Vol. 01, issue 02, April 2013 
CLOUD-SLA: Service Level Agreement for Cloud 
Computing 
 
1
 J.UDAYAKUMAR     
2
 M.MANIKKAM  
3
A.ARUN 
 
1
 DEPT. OF CSE, P.K.I.E.T, KARAIKAL. 
Uday.ja@gmail.com 
 
2
 DEPT. OF CSE, P.K.I.E.T, KARAIKAL. 
manikkam11@gmail.com 
 
3
 DEPT. OF CSE, P.K.I.E.T, KARAIKAL. 
activearun1006@gmail.com 
 
 
Abstract  
In the last few years, the cloud computing becomes the most important developing platform for both scientific and 
commercial application. Clouds are rapidly becoming an important platform for scientific applications. In the Cloud 
environment with uncountable numeric nodes, resource is inevitably unreliable, which has a great effect on task execu-
tion and scheduling. In cloud computing, cloud providers can offer cloud consumers two provisioning plans for compu-
ting resources, namely reservation and on-demand plans. In general, cost of utilizing computing resources provisioned 
by reservation plan is cheaper than that provisioned by on-demand plan, since cloud consumer has to pay to provider in 
advance. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, the best 
advance reservation of resources is difficult to be achieved due to uncertainty of consumer’s future demand and provid-
ers’ resource prices. To address this problem, The SLA can provision computing resources for being used in multiple 
provisioning stages as well as a long-term plan, The Service Level Agreement (SLA) based super scheduling approach 
promotes cooperative resource sharing. Super scheduling is facilitated between administratively and topologically dis-
tributed sites via resource schedulers such as Resource brokers and workflow engines. 
 
Keywords: Service level agreement; Cloud Computing; Software as a service; Quality of Service; Resource allocation. 
 
1. Introduction  
Cloud computing is basically an Internet-based network 
made up of large numbers of servers - mostly based on 
open standards, modular and inexpensive.  Clouds con-
tain vast amounts of information and provide a variety of 
services to large numbers of people. The benefits of 
cloud computing are Reduced Data Leakage, Decrease 
evidence acquisition time, they eliminate or reduce ser-
vice downtime, they Forensic readiness, they Decrease 
evidence transfer time. The main factor to be discussed is 
security of cloud computing, which is a risk factor in-
volved in major computing fields. 
 
Cloud Computing is a technology that uses the internet 
and central remote servers to maintain data and applica-
tions. Cloud computing allows consumers and businesses 
to use applications without installation and access their 
personal files at any computer with internet access. This 
technology allows for much more efficient computing by 
centralizing data storage, processing and bandwidth. 
 A service level agreement is a formal negotiated     
agreement which helps to identify expectations, clarify 
responsibilities, and facilitate communication between a 
service provider and its customers. The purpose of this 
Agreement is to ensure that the proper elements and 
commitments are in place to provide consistent IT service 
support and delivery to the Customer(s) by the Service 
Provider(s). 
J.UDAYAKUMAR et.Al.,                                               Copyright © 2013 IJCSIT. 
14                                        International Journal of Computing Science and Information www.ijcsit.org 
                             Vol. 01, issue 02, April 2013 
A service-level agreement (SLA) is a contract between a 
network service provider and a customer that specifies, 
usually in measurable terms, what services the network 
service provider will furnish. Many Internet service pro-
viders (ISP)s provide their customers with an SLA. More 
recently, IS departments in major enterprises have 
adopted the idea of writing a service level agreement so 
that services for their customers (users in other depart-
ments within the enterprise) can be measured, justified, 
and perhaps compared with those of outsourcing network 
providers. 
2. Related Work 
Though Cloud computing is a highly studied topic today 
and a large body of research has gone into studying spe-
cific standards of  interoperability amongst  clouds  
and  how  they are  to  be  achieved,  the  aspects  
of  brokering  services  to  the end  client  from  
amongst  those  available  is  finding  refereed sta-
tus  only  recently  [2]. A cloud broker has been de-
scribed as an entity that manages the use, performance 
and delivery of cloud services and negotiates relation-
ships between cloud providers and cloud consumers. 
Existing work in  literature  primarily  stress  on  
using SLAs  to guarantee  consumer  of  cloud  
services a  level  of performance,  that  is  defined  
by  abstract  metrics,  directly from  the cloud  ser-
vice  providers to  the  end  client  or  cloud con-
sumers  [3],  [4],  [5].  There is  an  apparent  
void  in research  on  SLA  formulation  strategies  
between  the  cloud service  broker  and  the  
cloud consumer and  between  the cloud  service  
broker  and  the  cloud  service  provider. This re-
search  is  an  attempt  to  highlight  the  re-
search  void  and recommend  a  framework  which  
can  be  developed  for creation  of  enforceable  
and  implementable  SLAs  in  the cloud paradigm.  
The  architecture  of  the  cloud,  whether public, 
private, community or hybrid, would  make  it non  
trivial  to propose and  implement a  framework  for  
creating  of such  binding frameworks  in  the  ab-
sence  of  accurate  measuring  and monitoring  
mechanisms  for  provision  of  services. This is 
especially true for a use case when the broker is aggre-
gating and arbitraging services from multiple cloud ser-
vice providers and packaging them as a service bundle 
for the end client. Previous  work  on  the subject 
include [6],  [7] and  [8] that pertains to  SLA  for-
mulation,  but  does  not  address  the aspects  of  
the  cloud  brokering  actor’s  role  in  the provi-
sioning  of  services.  Alhamad  [3]  [4] discusses  
the aspect  of  SLA  and  performance  measure-
ment  in  his  recent findings but does not address the 
issue in the perspective of how  a  broker  would  
become  a  party  to  the  SLA  agreement be-
tween  the  end  user or  the  cloud  consumer and  
the cloud service  provider. In  [9],  Alhamad  de-
scribes a  conceptual framework  for  SLA  in  the  
cloud  computing  paradigm,  but the  same  is  
silent  on  the aspect  pertaining  to  Brokers in the 
service model.  
This system  is in  charge  of  brokering the collec-
tion  of  Cloud  resources  from  different  pro-
viders  that fulfills  requirements  of  user's  appli-
cations  as  a  best  effort service.  The  user  is  
able  to  delegate  to  the  Agency  the necessary  
checks  of  the  agreement  fulfillment,  the moni-
toring  of  resource  utilization  and  eventually  
necessary re-negotiations.  In  [13],  Balakrishnan  
and  Somasundaram propose  a  broker framework  
where  SLA  enabled  broker evaluate  the  number  
of  resources  available  in  the environment and  
the  number  of  policies  per  resource  that need  
to  be  implemented.  The  results presented  in  
the  paper indicate  that  the  inclusion  of  SLA 
affects  the  resource selection behavior of the broker. 
The paper is however silent on the methods to control the 
affect using an SLA. It does however indicate that the 
overall performance of the system improves in terms of 
job throughput with an extra overhead in request pro-
cessing due to the presence of a broker. These results  
are shown on  a grid  sharing  environment and  
major differences  exist  in  the  business  model  
used  for  the  grid service provisioning and cloud 
service provisioning model. A number  of publications,  
post  2010 [14], [3], [11], [15], [16],  [9]  are  either  
addressing  the  aspect  of  SLA management for 
brokering services at the level of a resource scheduler, or  
abstractions  of  the  same  when  lifted  from  
the grid  computing  era.   
3. Existing System 
In cloud computing, a resource provisioning mechanism 
is required to supply cloud consumers a set of computing 
resources for processing the jobs and storing the data. 
Cloud providers can offer cloud consumers two resource 
provisioning plans, namely short-term on-demand and 
long-term reservation plans. Cloud providers which offer 
IaaS services with both plans. In general, pricing in 
on-demand plan is charged by pay-per-use basis (e.g., 1 
day). Therefore, purchasing this on-demand plan, the 
consumers can dynamically provision resources at the 
moment when the resources are needed to fit the fluctu-
ated and unpredictable demands. For reservation plan, 
pricing is charged by a onetime fee (e.g., 1 year) typical-
ly before the computing resource will be utilized by 
cloud consumer. With the reservation plan, the price to 
utilize resources is cheaper than that of the on-demand 
Copyright © 2013 IJCSIT                                                 J.UDAYAKUMAR et.Al., 
15                                        International Journal of Computing Science and Information www.ijcsit.org 
                             Vol. 01, issue 02, April 2013 
 
 
 
plan. In this way, the consumer can reduce the cost of 
computing resource provisioning by using the reserva-
tion plan 
Disadvantages- The reservation plan, the cloud consum-
ers a priori reserve the resources in advance. As a result, 
the under provisioning problem can occur when the re-
served resources are unable to fully meet the demand due 
to its uncertainty. Although this problem can be solved 
by provisioning more resources with on-demand plan to 
fit the extra demand, the high cost will be incurred due to 
more expensive price of resource provisioning with 
on-demand plan. On the other hand, the over provision-
ing problem can occur if the reserved resources are more 
than the actual demand in which part of a resource pool 
will be underutilized. It is important for the cloud con-
sumer to minimize the total cost of resource provisioning 
by reducing the on-demand cost and oversubscribed cost 
of under provisioning and over provisioning. To achieve 
this goal, the optimal computing resource management is 
the critical issue. 
4. Proposed System 
In this paper, minimizing both under provisioning and 
over provisioning problems under the demand and price 
uncertainty in cloud computing environments is our mo-
tivation to explore a resource provisioning strategy for 
cloud consumers. In particular, an optimal cloud Service 
Level Agreement (SLA) algorithm is proposed to mini-
mize the total cost for provisioning resources in a certain 
time period. To make an optimal decision, the demand 
uncertainty from cloud consumer side and price uncer-
tainty from cloud providers are taken into account to 
adjust the tradeoff between on-demand and oversub-
scribed costs.   
Using our SLA users  can share our memory to any 
other users through pay per basic or open source  and 
user can use unused memory’s to use other applications, 
and the results show that SLA can minimize the total cost 
under uncertainty. In this paper, cloud consumer can suc-
cessfully minimize total cost of resource provisioning in 
cloud computing environments using SLA. 
 
5. Approach 
The key advantages of our approach are that it allows:  
 
i. Resource owners to have new degree of control 
over the resource allocation which is something 
that is not possible with traditional mechanisms; 
and  
 
ii. Schedulers to bid for SLA contracts with focus 
on completing a job within a user specified 
deadline. In this work, we use simulation to 
show the effectiveness of our proposed 
approach. 
 
iii. And SLA will provide only authorized users to 
access our Application. 
 
Here, we present the algorithm that is based on a pro-
portional allocation of bandwidth per traffic class as that 
provides the be sterilizations of available resources. To 
fully support SLAs, bandwidth constraints agreed in a 
service contract are used to ensure that the just amount 
of bandwidth is assigned to a particular class. 
Let Qi(j) be the amount of the bandwidth requested 
for queue j by ONU I and βk
i (j) is the bandwidth al-
located to this queue in step k of the algorithm. Let γ
i
min 
(j) and γ
i
max (j) be the minimum and maximum of the 
bandwidth guaranteed to the particular queue. 
 
In phase I, the OLT assigns bandwidth proportionally to 
reported queue length Q
i
 (j). 
 
β
i
I (j) =1/Σi,jQ
i
(j).Q
i
(j) 
 
In the second phase the constraints given in the SLA are 
applied. Three distinct situation have to be considered: 
 
1) β
i
I (j) ≥ γ
i
max(j) – Assigned bandwidth has exceeded 
the amount promised in the SLA. The bandwidth allo-
cated to a particular queue is thus reduced to β
i
II (j) = 
γ
i
max (j). 
 
2) β
i
I (j) ≥ γ
i
min(j) and β
i
I(j) < γ
i
max(j) – Requested band-
width is within the limits of the SLA. No changes are 
made and β
i
II (j) = β
i
I (j) 
 
3) β
i
I (j) < γ
i
min(j) – In a situation where Q
i
(j) > β
i
(j) 
bandwidth assigned is equal to β
i
II(j) = Q
i
(j) as it is 
smaller than agreed in the SLA. Otherwise no changes 
are made and β
i
II (j) = β
i
I (j) 
 
Bandwidth that is not allocated during the second step is 
shared among all queues in phase three. The amount of 
bandwidth assigned to a queue could be thus expressed 
as: 
 
β
i
III (j) =β
i
II(j)+βex/β
i
I(j)   [18] 
 
6. Conclusion 
The number of users that can be served simultaneously. 
The proposed mathematical analysis will be useful to the 
cloud consumers (e.g., organization and company) for 
the management of virtual machines in cloud computing 
J.UDAYAKUMAR et.Al.,                                               Copyright © 2013 IJCSIT. 
16                                        International Journal of Computing Science and Information www.ijcsit.org 
                             Vol. 01, issue 02, April 2013 
environment. Then proposed SLA will facilitate the 
adoption of cloud computing of the users as it can reduce 
the traffic and cost of using computing resource 
significantly. Then increase the trustworthiness and 
Quality of Service (QOS).Cloud computing is one of the 
important domain in today’s technology world and it is 
accessible from anywhere so in our future work security 
constraints are implemented to provide highly secured 
resource provisioning. 
 
REFERENCE 
 
[1] Wei Wang, Guosun Zeng, “Cloud-DLS: Dynamic trusted 
scheduling for Cloud computing”. 
 
[2] C. A.  Yfoulis and A. Gounaris, “Honoring SLAs on 
cloud computing services:  a control perspective”, Proc.  
2nd Workshop on Bio-inspired Algorithms for Distributed 
Systems, Jan. 2010, pp. 29-38. 
 
[3] M.  Alhamad, T.  Dillon, and E.  Chang, “SLA-Based 
Trust Model for Cloud Computing,” Proc.  13th Interna-
tional Conference on Network-Based Information Systems 
(NBIS '10).IEEE Computer Society, Dec.  2010, pp.  
321-324. doi:10.1109/NBiS.2010.67 
 
[4] M. Alhamad, T. Dillon, and E. Chang, “A survey on SLA 
and performance measurement  in  cloud  computing,”  
Proc. Confederated International Conference  on  On  
the Move  to Meaningful Internet Systems -  Vol  
II(OTM'11),  Springer-Verlag, Dec. 2011, pp. 469-477. 
 
[5] A.  Lawrence, K.  Djemame, O.  Wäldrich, W.  Zieg-
ler, C. Zsigri, “Using service level agreements for opti-
mising cloud infrastructure services,” Proc.  International 
Conference on Towards a Service-based Internet 
(ServiceWave'10), Springer-Verlag Berlin, 2011, pp. 
38-49. 
 
[6] D.D. Lamanna, J.  Skene, and W.  Emmerich, “Slang:  
A language for defining service level agreements,” Proc.  
Nnth IEEE  workshop  of  Future  Trends  of  
Computing  2003,  IEEE Press,  May  2003,  pp.  
100-106, doi:10.1109/FTDCS.2003.1204317. 
 
[7] H. Ludwig, A. Keller, A. Dan, R. King, and R. Franck, 
“Web service  level  agreement  (WSLA)  language  
specification,” IBM  System  Journal,  vol.  43, Jan.  
2004, pp.  136-158, doi:10.1147/sj.431.0136. 
 
[8] A.  Paschke, “Rbsla - A declarative rule-based service 
level agreement language based on rulem,” Proc.  Inter-
national Conference  on  Computational  Intelligence  
for  Modelling, Control  and  Automation  and  In-
ternational  Conference  on Intelligent Agents, Web 
Technologies and Internet Commerce Vol-2  
(CIMCA-IAWTIC'06),  IEEE  Computer  Society  
Dec. 2005,  vol.  2, pp.  308-314, 28-30, 
doi:10.1109/CIMCA.2005.1631486. 
 
[9] M.  Alhamad, T.  Dillon, and E.  Chang,  "Conceptu-
al  SLA framework  for  cloud  computing,"  4th  
IEEE  International Conference  on  Digital Ecosys-
tems  and  Technologies (DEST, Apr. 2010, pp. 
606-610, doi: 10.1109/DEST.2010.5610586. 
 
[10] D. M. Parrilli, “The determination of jurisdiction in grid 
and cloud service level agreements,” Proc.  6th Interna-
tional Workshop on Grid Economics and Business Models 
(GECON '09), Springer-Verlag, 2009, pp.  128-139, doi: 
10.1007/978-3-642-03864-8_10. 
 
[11] M Wang, X Wu, W. Zhang, F. Ding, J. Zhou, and G. Pei , 
"A Conceptual  Platform  of  SLA  in  Cloud  
Computing," Proc. IEEE  Ninth  International  Con-
ference  on  Dependable, Autonomic and Secure Com-
puting (DASC), 2011, Dec 2011, pp.1131-1135, 12-14, 
doi: 10.1109/DASC.2011.184. 
 
[12]   S.  Venticinque, R.  Aversa, B. Martino, M.  Rak,  
and  Dana Petcu,  “A  cloud  agency  for  SLA  
negotiation  and management,” Proc. Conference on par-
allel processing (Euro-Par 2010), Springer-Verlag, Aug. 
2010, pp.587-594. 
 
[13] P.  Balakrishnan and T.  S.  Somasundaram, “SLA 
enabled CARE resource broker,” Proc. Future Gener.  
Comput.  Syst, vol.  23, Mar 2011, pp.  265-279, 
doi:10.1016/j.future.2010.09.006. 
 
[14] H. Boley, S. Tabet, and G. Wagner, “Design rationale of 
ruleml: A markup language for semantic web rules” 2001, 
pp. 380-401 [retrieved: May, 2012]. 
 
[15] R. N.  Calheiros, R. Ranjan, A Beloglazov, D. Rose, A.  
F. César, and R. Buyya, “CloudSim: a toolkit for modeling 
and simulation  of  cloud  computing  environments  
and  evaluation of  resource provisioning algorithms,” 
Journal of Software Practice  and  Experience,  vol  
41,  Aug  2010,  pp.  23-50, DOI: 10.1002/spe.995.   
 
[16] M.  Mac as, J.  O.  Fit, and J.  Guitart,  "Rule-based  
SLA management  for  revenue  maximization  in  
Cloud  Computing Markets," International  Conference  
on Network  and  Service Management  (CNSM),  
Oct.  2010, pp.  354-357, 
doi:10.1109/CNSM.2010.5691226. 
 
[17] V.  C.  Emeakaroha, I.  Brandic, M.  Maurer, I.  
Breskovic, “SLA-Aware Application Deployment and 
Resource Allocation in Clouds,” Proc.  Computer  
Software  and Applications  Conference  Workshops  
(COMPSACW  2011), IEEE  Press,  July  2011,    
pp.  298-303, doi:10.1109/COMPSACW.2011.97. 
 
[18] Dawid Nowak, Philip Perry, John Murphy “A novel Ser-
vice Level Agreement based algorithm for Differentiated 
Services enabled Ethernet PONs”. 


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