Cloud Computing Revisited For E-Learning Systems with Some Improvements with Auditing

2022-02-03T11:39:58+00:00

E-learning has brought great dynamics and parameters in the field of education and research. It has revolutionized the way learning has imparted to students. The perks of e-learning include consistency, scalability, cost cut down, in contrast to the traditional learning method. It is equally well-being and doing great in every field of learning. During this period of COvid-19 e-learning is developing and has completely taken over and administrated the education system. But along with the advancement of the field there comes the challenges. One would always keen to look up for the technology to make the learning process reliable, faster, easier to use, and can enhance with more interactive features.
This is the spot where cloud computing stands with a bunch of its unmatchable capabilities. Cloud Computing refers to provide the computing assets on the internet which may include storage, servers, software systems, databases, online management systems, and online applications. Having a paradigm like Cloud Computing gives an edge for innovators and entrepreneurs to design and develop their mainstream projects and products with ease.
This paper provides an insight into how Cloud Computing has the potential and can be the primary driver of e-learning. How the field of e-learning can benefit from this revolutionary technology. This research discusses the cloud services, architecture nature as a service for the e-learning environment. Further, the comprehensive converses about the impact of Cloud Computing on e-learning along with the security fears and features can potentially get the most out of the combination of both fields with the introduction of auditing of the system .

A Comparison of Fair Sharing Algorithms for Regulating Search as a Service API

2021-02-11T17:44:58+00:00

Providers of a Search as a Service (SaaS) environment must ensure that their users will not monopolize the service or use more than their fair share of resources. Fair sharing algorithms have long been used in computer networking to balance access to a router or switch, and some of these algorithms have also been applied to the control of queries submitted to search engine APIs. If a search query’s execution cost can be reliably estimated, fair sharing algorithms can be applied to the input of a SaaS API to ensure everyone has equitable access to the search engine.

The novelty of this paper lies in presenting a Single-Server Max-Min Fair Deficit Round Robin algorithm, a modified version of the Multi-Server Max-Min Fair Deficit Round Robin algorithm. The Single-Server Max-Min Fair Deficit Round Robin algorithm is compared to three other fair sharing algorithms, token-bucket, Deficit Round Robin (DRR), and Peng and Plale’s [1] Modified Deficit Round Robin (MDRR) in terms of three different usage scenarios, balanced usage, unbalanced usage as well as an idle client usage, to determine which is the most suitable fair sharing algorithm for use in regulating traffic to a SaaS API. This research demonstrated that the Single-Server Max-Min Fair DRR algorithm provided the highest throughput of traffic to the search engine while also maintaining a fair balance of resources among clients by re-allocating unused throughput to clients with saturated queues so a max-min allocation was achieved.

Transactions on Networks and Communications

Cloud Computing Revisited For E-Learning Systems with Some Improvements with Auditing

A Comparison of Fair Sharing Algorithms for Regulating Search as a Service API