scholarly journals Service Level Agreement Based Fault Tolerant Workload Scheduling in Cloud Computing Environment

2016 ◽  
Vol 7 (4) ◽  
pp. 01-08
Author(s):  
Manpreet Singh Gill ◽  
R.K. Bawa
Author(s):  
Suvendu Chandan Nayak ◽  
Sasmita Parida ◽  
Chitaranjan Tripathy ◽  
Prasant Kumar Pattnaik

The basic concept of cloud computing is based on “Pay per Use”. The user can use the remote resources on demand for computing on payment basis. The on-demand resources of the user are provided according to a Service Level Agreement (SLA). In real time, the tasks are associated with a time constraint for which they are called deadline based tasks. The huge number of deadline based task coming to a cloud datacenter should be scheduled. The scheduling of this task with an efficient algorithm provides better resource utilization without violating SLA. In this chapter, we discussed the backfilling algorithm and its different types. Moreover, the backfilling algorithm was proposed for scheduling tasks in parallel. Whenever the application environment is changed the performance of the backfilling algorithm is changed. The chapter aims implementation of different types of backfilling algorithms. Finally, the reader can be able to get some idea about the different backfilling scheduling algorithms that are used for scheduling deadline based task in cloud computing environment at the end.


2020 ◽  
Vol 178 ◽  
pp. 375-385
Author(s):  
Ismail Zahraddeen Yakubu ◽  
Zainab Aliyu Musa ◽  
Lele Muhammed ◽  
Badamasi Ja’afaru ◽  
Fatima Shittu ◽  
...  

Author(s):  
Sugandh Bhatia ◽  
Jyoteesh Malhotra

The privacy, handling, management and security of information in a cloud environment are complex and tedious tasks to achieve. With minimum investment and reduced cost of operations an organization can avail and apply the benefits of cloud computing into its business. This computing paradigm is based upon a pay as per your usage model. Moreover, security, privacy, compliance, risk management and service level agreement are critical issues in cloud computing environment. In fact, there is dire need of a model which can tackle and handle all the security and privacy issues. Therefore, we suggest a CSPCR model for evaluating the preparation of an organization to handle or to counter the threats, hazards in cloud computing environment. CSPCR discusses rules and regulations which are considered as pre-requisites in migrating or shifting to cloud computing services.


Author(s):  
Afaf Edinat ◽  
Rizik M. H. Al-Sayyed ◽  
Amjad Hudaib

Cloud computing is considered one of the most important techniques in the field of distributed computing which contributes to maintain increased scalability and flexibility in computer processing. This is achieved because it, using the Internet, provides different resources and shared services with minimum costs. Cloud service providers (CSPs) offer many different services to their customers, where the customers’ needs are met seeking the highest levels of quality at the lowest considerate prices. The relationship between CSPs and customers must be determined in a formal agreement, and to ensure how the QoS between them will be fulfilled, a clear Service Level Agreement (SLA) must be called for. Several previously-proposed models used in the literature to improve the QoS in the SLA for cloud computing and to face many of the challenges in the SLA are reviewed in this paper. We also addressed the challenges that are related to the violations of SLAs, and how overcoming them will enhance customers’ satisfaction. Furthermore, we proposed a model based on Deep Reinforcement Learning (DRL) and an enhanced DRL agent (EDRLA). In this model, and by optimizing the learning process in EDRLA, proposed agents would be able to have optimal CSPs by improving the learning process in EDRLA. This improvement will be reflected in the agent's performance and considerably affect it, especially in identifying cloud computing requirements based on the QoS metrics.


2020 ◽  
Vol 12 (2) ◽  
pp. 47-63
Author(s):  
Sathiyamoorthy E. ◽  
Karthikeyan P

Cloud computing is a trending area of information technology (IT). In a cloud environment, the Cloud service provider (CSP) provides all the functionalities to the users or customers in terms of services. With the rapid development of cloud computing, the performance of any cloud environment relies on the quality of services (QoS) at the time of providing the services. A service level agreement (SLA) increases the confidence of the user or customer to use the cloud services in a cloud environment. There should be negotiation between the CSP and users to achieve a strong SLA. Many existing SLA models are already developed. However, these models do not concentrate to maintain the quality in a long-time duration period. To solve this issue, a novel SLA model has been proposed in this article by using Fuzzy logic. Both the theoretical and simulation results show the proficiency of the proposed scheme over the existing schemes in a cloud computing environment.


Author(s):  
Bahar Asgari ◽  
Mostafa Ghobaei Arani ◽  
Sam Jabbehdari

<p>Cloud services have become more popular among users these days. Automatic resource provisioning for cloud services is one of the important challenges in cloud environments. In the cloud computing environment, resource providers shall offer required resources to users automatically without any limitations. It means whenever a user needs more resources, the required resources should be dedicated to the users without any problems. On the other hand, if resources are more than user’s needs extra resources should be turn off temporarily and turn back on whenever they needed. In this paper, we propose an automatic resource provisioning approach based on reinforcement learning for auto-scaling resources according to Markov Decision Process (MDP). Simulation Results show that the rate of Service Level Agreement (SLA) violation and stability that the proposed approach better performance compared to the similar approaches.</p>


Sign in / Sign up

Export Citation Format

Share Document