Report on a Service Level Agreement for AWS Confidential Data

This report details a Service Level Agreement (SLA) designed for Amazon Web Services (AWS), specifically addressing the handling of confidential data. The SLA outlines the services included, such as Amazon Elastic Block Store, Amazon Fargate, Amazon Elastic Compute Cloud, and Amazon Elastic Container Service. It specifies a 99.99% uptime guarantee with corresponding service credits for any downtime exceeding the commitment. The agreement covers cost recovery, information security practices to maintain data confidentiality, and change management processes. Responsibilities of both Information and Educational Technology (IET) and AWS are clearly defined, ensuring diligence in service provision. The report references several academic sources supporting the principles and practices of SLAs in cloud computing environments.

Running head: SERVICE LEVEL AGREEMENT
SERVICE LEVEL AGREEMENT
Name of Student
Name of University
Author’s Note

1SERVICE LEVEL AGREEMENT
Service level agreement that I would sign if I were the process confidential data using AWS.
Service: Amazon Web Services (AWS)
Version: 2020. 08. 33
Valid till: 20/ 06/ 2019 – 30/ 07/ 2021
Details of service:
Amazon Web Services (AWS) which is introduced by the organization named
Amazon.com have been an on demand resources for computing as well as services that are
hosted on a specific cloud platform. The pricing of the services is done with the usage of pay-
as-you-go policy which does not include any sort of investment (Serrano, Bouchenak &
Kouki, 2016). This agreement would use the Included Services as well as applies to every
account that makes use of included services. In case of conflict between numerous terms used
in this service level agreement and the terms used in AWS customer agreement or any other
agreement proposed by the organization, the terms described in the SLA would be applicable
evenly.
Services that would be included
The list of services and products are mentioned below
 Amazon Elastic Block Store.
 Amazon Fargate for the Amazon ECS.
 Amazon Elastic Compute Cloud
 Amazon Elastic Container Service
Availability

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The services would be available for 24 hours and 7 days a week along with ensuring no
disruption in the quality of services.
Agreement:
General Service commitment
AWS would make use of numerous reasonable efforts which result in making the
services accessible for every AWS range along with an Uptime Percentage of minimum
99.99 percent at the time of the monthly billing cycle (Sens, 2016). In this specific event any
service would not fulfill the Service Commitment, users would be entitled for receiving a
Service Credit. Service level would not entitle the users with any refund or any other form of
payments from the AWS.
Service credits
Service credits would be calculated form the percentage obtained from the overall
charges that would be paid for by the users for the service that are used by them. The
schedule had been provided below.
Scheduled Uptime Percentage
Monthly Uptime
Percentage
Service Credit Percentage
Less than 99.99
percent and
equal or more
than 99 percent
10 %
Less than 99.0
percent and
30 %

3SERVICE LEVEL AGREEMENT
equal or more
than 95.0
percent
Lesser than 95.0
%
100 %
Table 1: Monthly Uptime Percentage
(Source: Wolski, Brevik & Chard, 2017)
According to the agreement AWS would apply any kind of service credits against the
payments that are carried out in future for applied included service that had been otherwise
due from the organization (Zeng, Ranjan & Strazdins, 2015). At the discretion of the
organization, AWS might issue Service Credit to the users’ credit cards for billing cycle
where the unavailability had taken place.
Recovery of cost
IET needs various costs that are paid directly by the customers with the help of
numerous mechanisms as well as methods or they have been paid in a central manner. In the
cases where the customers makes the payment, the campus rates that are established would be
used (Sidhanta, Golab & Mukhopadhyay, 2017). Various changes in the organizational rates
would be applied by following a notice of 30 days.
Security of information
Practices of IET information security management, regulatory compliance as well as
policies requirements have been aimed in ensuring the fact that the confidentiality of the data
would be maintained (Scheid & Stiller, 2018). Besides the confidentiality, availability of
information related to customer and their integrity would also be maintained.

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The cyber safety policy of UC Davis as well as the security standard policy of UC
Davis would be adopted by the organization along with IET for the purpose of defining the
responsibilities as well as vital practices for the purpose of assuring that the computing
systems as well as electronic information of UC Davis is secured.
Change management
The managed systems, services as well as applications that are used throughout the
entire organization might prove to be a subject of formal change management of IET
processes. The changes in services might be necessary by the maintenance tools of the
systems, corrective actions for the purpose of resolving the incidents, improvement projects
for the services (Al-Sayyed, Hijawi & Bashiti, 2019). Various changes might require the
adherence to the policies of change management such as formal authorization, peer reviews,
approvals, lead times for the notifying customers and assessment of detected risks.
Responsibilities of parties
Information and educational technology
The information as well as educational technology would be provided the
responsibility of ensuring the fact that skills, diligence and care have been exercised in the
process of carrying out the property of services as well as their efficiency in the accordance
with this particular service level agreement.

5SERVICE LEVEL AGREEMENT
References
Serrano, D., Bouchenak, S., Kouki, Y., de Oliveira Jr, F. A., Ledoux, T., Lejeune, J., ... &
Sens, P. (2016). SLA guarantees for cloud services. Future Generation Computer
Systems, 54, 233-246.
Sidhanta, S., Golab, W., Mukhopadhyay, S., & Basu, S. (2017). Adaptable sla-aware
consistency tuning for quorum-replicated datastores. IEEE Transactions on Big
Data, 3(3), 248-261.
Scheid, E. J., & Stiller, B. (2018). Automatic SLA Compensation based on Smart Contracts.
Technical Report IFI-2018.02 https://files. ifi. uzh.
ch/CSG/staff/scheid/extern/publications/IFI-2018.02. pdf, Universität Zürich, Zürich,
Switzerland (April 2018).
Al-Sayyed, R. M., Hijawi, W. A., Bashiti, A. M., AlJarah, I., Obeid, N., & Adwan, O. Y.
(2019). An Investigation of Microsoft Azure and Amazon Web Services from Users'
Perspectives. International Journal of Emerging Technologies in Learning, 14(10).
Zeng, X., Ranjan, R., Strazdins, P., Garg, S. K., & Wang, L. (2015, May). Cross-layer SLA
management for cloud-hosted big data analytics applications. In 2015 15th
IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (pp.
765-768). IEEE.
Wolski, R., Brevik, J., Chard, R., & Chard, K. (2017, November). Probabilistic guarantees of
execution duration for amazon spot instances. In Proceedings of the International
Conference for High Performance Computing, Networking, Storage and Analysis (p.
18). ACM.