Currently, e-learning is one of the most prevalent educational methods because of its need in today’s world. Virtual classrooms and web-based learning are becoming the new method of teaching remotely. The students experience a lack of access to resources commonly the educational material. In remote locations, educational institutions face significant challenges in accessing various web-based materials due to bandwidth and network infrastructure limitations. The objective of this study is to demonstrate an optimization and queueing technique for allocating optimal servers and slots for users to access cloud-based e-learning applications. The proposed method provides the optimization and queueing algorithm for multi-server and multi-city constraints and considers where to locate the best servers. For optimal server selection, the Rider Optimization Algorithm (ROA) is utilized. A performance analysis based on time, memory and delay was carried out for the proposed methodology in comparison with the existing techniques. The proposed Rider Optimization Algorithm is compared to Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Firefly Algorithm (FFA), the proposed method is more suitable and effective because the other three algorithms drop in local optima and are only suitable for small numbers of user requests. Thus the proposed method outweighs the conventional techniques by its enhanced performance over them.
Computers and computing devices-based e-learning technologies play a significant role in the educational, business and industrial sectors. In India, all the educational institutions could not provide quality education uniformly. Few top-class institutions such as the Indian Institutes of Technology (IIT), Indian Institute of Science (IISc), and Indian Institute of Management (IIM’s), National Institutes of Technology (NITs) have good teaching and computing facilities to avail high-quality education [
The need for online education is growing daily [
The organizational flow of the paper includes the following section that contributes to the review based on conventional cloud-based e-learning techniques. Consequently in the proposed work, the elaborative demonstration of the proposed work is presented. In the fourth section, the result and simulation were performed and the final section concludes the overall work done.
This section deals with the evaluation of related works based on the conventional cloud computing-related e-learning methods and their drawbacks Masud et al. [
Elmasry et al. [
Ssemugabi et al. [
Ray [
Barbierato et al. [
Kalushkov et al. [
Vanjulavalli et al. [
The following section contributes a collaborative explanation of the proposed methodology.
This study focusing the implementation of a cloud-based e-learning educational platform in India. For example, we can use the same methodology for online-based e-learning educational platforms like Massive Open Online Courses (MOOC), National Programme on Technology Enhanced Learning, online degree programs via IIT Madras. MOOC and NPTEL help deliver quality education to students, irrespective of their geographical location. In this regard, the IITs have configured five centralized servers initially with network optimization models which are placed in IIT Bombay, IIT Guwahati, IIT Madras, IIT Delhi, and IIT Roorkee. This project implementation provides rural educational institutions have special significance/remote area students. It can be possible to establish the same cloud models in major cities in India and server availability can be increased based on the requirements in near future. The above-mentioned IITs are the prime locations to host the center and connect with all places in India. In any cloud computing online platform, server selection is an important issue, and high-end configuration servers are required. To balance data bandwidth utilization, Cloud servers must be linked to load balancers simultaneously. If at all feasible, the servers should be connected in a mirror arrangement, which will increase operational stability and eliminate hazards. A cloud computing system is depicted in
The attacker, overtaker, follower, and bypass rider are the four different groups adapted in ROA. Each group has a different approach to achieving the goal. The bypass rider’s goal is to reach the target by avoiding the leader’s path. The following group tries to keep up with the leader in axis. The over-taker group determines its position to reach the target via evaluating the leader’s closest places. The attacker group adapts the position of the leader to accomplish the destination with the use of utmost speed. The main objective of ROA is to select an optimal server based on the nearest distance and server usage for online education. In India, due to Covid-19, all the educational institutions were closed and most of them are providing classes in online mode. In the future, most of the education institutes will motivate for online classes; because this mode of study is cost-effective and also have the flexibility to learn anytime anywhere around the world.
Hence, the mandate for web communication propagates continuosly, however the accessible assets do not enhance at the similar level. High-end server system and web arrangement are main and instant essentials for educational institutes that are offering numerous webbased uses to their pupils.
The current study uses optimization and queuing techniques to allocate optimal servers and slots to users for accessing cloud applications [
To avoid traffic, several mirror servers can be placed. To establish their judgment, the ROA evaluates a few riding groups that travel to the same location to win the race, to formulate their opinion [
Identifiaction of ideal server via ROA: Data communication is a essential practice in online courses. To achieve this, the ideal server is needed to be chosen via ROA. The succeeding steps are involved in the server chosen method.
Step 1: Result encrypting: For all optimization techniques, result encrypting is a significant process. The result contains the amount of servers and the amount of user appeals. During the early phase, the server appeal is selected arbitrarily. The span of the answer is identical as the amount of user appeals. The result is deliberated as the rider. Subsequent to the initialization of cluster sets, rider factors for instance routing, gear, accelerator, and brake are modified.
Cloud server | CS1 | CS2 | CS3 | CS4 |
---|---|---|---|---|
User request | ||||
U1 | 1 | 0 | 0 | 0 |
U2 | 0 | 1 | 0 | 0 |
U3 | 0 | 0 | 1 | 0 |
U4 | 0 | 0 | 0 | 1 |
U5 | 0 | 1 | 0 | 0 |
U6 | 1 | 0 | 0 | 0 |
In
where I represent a rider’s number, Q represents an optimization problem, t represents a time instant,
Step 2: Fitness Estimation: The fitness of every rider is estimated while the group riders were primed. Here the multi-objective function is operated with the objectives such as time, distance, and server usage.
Step 3: Modernise the bypass rider: The Bypass Riders evade the public route, however they will not adopt the prominent riders, therefore the eminence modernise for this is allotted to every base at arbitrary as follows
where
Step 4: Modernise the location of the follower: By chasing the chief rider’s position, a follower modernises its location so as to help the chief rider in attaining the endpoint as fast as probable. The organise selector is employed by a follower to alter the position reliant on the Q values.
where K is the sector coordinate and
Step 5: Modernise the location of the overtaker: The over-taker is modernized according with 3 vital strictures: route pointer, comparative success level and coordinate sector. The present and the chief rider positions are utilized to modernise the location of the overtaker according with the subsequent equation,
At this point
where
Step 6: Modernise the attacker location: The attacker try to proceed the location of the chief drives via the identical informing procedure as the follower. Though, instead of modernising the stated values, the attacker modernises all the values in the coordinate. The attacker location is updated as
where
Step 7: Closure conditions: The procedure is performed untill the finest server is identified. When the ideal server is identified, at that point the technique will be concluded. A user is allotted to the servers that were selected.
initialize the population |
initialize the rider parameters |
Find fitness calculation using |
While |
update the position of bypass rider with |
update the position of follower using |
using the equation, update the attacker’s position of the overtaker |
Using the equation, update the position of attacker position |
rank the rider based on the fitness function |
Return XL |
t = t + 1 |
end for |
end while |
The present study focuses on the effective usage of servers based on the demand from the user. The method combines optimization and queuing concepts to allocate the slots of the server to the respective user. Network optimization is a complex issue in the current network framework. One of the major issues is a balanced allocation of the server to avoid network traffic which slows down the overall performance. In the optimization framework, the algorithm searches the server which is nearer to the user. For optimization, ROA is implemented. In addition, there could be a chance that multiple users may be closer to a particular single server. In this condition, the algorithm has been devised to route the user to the next nearest server where the slots are free to use. In this way simultaneously, the algorithm searches optimal slots based on the distance as well it equally distributes the slots. Most of the time, it is inevitable that several slots in the server are less than the available user. In those circumstances, the algorithm implements the queuing theory concepts. In which users are allowed to stand in a queue where the slots are being allotted based on the first cum first serve bases.
To demonstrate the proposed methodology’s effectiveness, it was compared to other algorithms. In this process, Rider Optimization Algorithm (ROA) is utilized for the optimal server selection process. For comparison, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Firefly Algorithms (FA) are utilized. In this comparative analysis, 100 servers are utilized. The proposed methodology performance is analyzed in terms of memory usage, time and delay.
In
In
It is clear that utilized only minimum memory compared to the existing approach and also in
The overall wordone based on the memory usage, delay and running time the proposed method provides efficient result by utilizing less running time, memory usage and delay in comparison with the conventional techniques.
In this section, the server is allocated to the user based on the distance and server usage and for optimal server selection, the ROA algorithm is utilized.
User No. | Distance to Chennai Server | Distance to Bombay |
Distance to Delhi |
Distance to Guwahati |
Distance to Roorkee Server | Server Usage Time (in s) | Server allotted for User | Slot allotted for User |
---|---|---|---|---|---|---|---|---|
1 | 741 | 474 | 587 | 378 | 306 | 12118.9227855207 | 5 | 1 |
2 | 129 | 267 | 521 | 368 | 501 | 22475.9886411207 | 1 | 1 |
3 | 194 | 748 | 8 | 658 | 700 | 7501.73789970716 | 3 | 1 |
4 | 298 | 169 | 633 | 266 | 608 | 37100.5005268765 | 2 | 1 |
5 | 56 | 490 | 692 | 338 | 364 | 22229.9753213311 | 4 | 1 |
6 | 513 | 454 | 578 | 723 | 568 | 25709.4876352725 | 5 | 2 |
7 | 302 | 291 | 33 | 38 | 313 | 36708.3419128680 | 1 | 2 |
8 | 737 | 107 | 284 | 730 | 729 | 10300.3880067398 | 3 | 2 |
9 | 302 | 20 | 529 | 143 | 741 | 42774.2871494359 | 2 | 2 |
10 | 466 | 316 | 547 | 501 | 648 | 61033.8780520177 | 4 | 2 |
The network optimization approach based on data transmission in the cloud has been outlined effectively in this article by utilizing ROA. The Rider Optimization Algorithm (ROA) is used for selecting the optimal server with the least delay and memory utilization. The bypass rider’s goal is to reach the target by avoiding the leader’s path. There are several applications of the rider optimization algorithm, notably document clustering, enhanced video super-resolution, webpage re-raking, and resource allocation, all of which are utilized to gain access to materials to facilitate e-learning. Based on various requests and metrics, we have evaluated the performance of this methodology. Compared to PSO, GA, and Firefly, the present algorithm utilizes minimum memory usage. According to this ROA, the server-side will gradually share the load, as suggested earlier, to prevent overloading and idleness on a particular server. Based on the availability and comparison logic, it ensures new users are assigned for the maximum processing capacity of the server.
This work can be further enhanced by inducing ensemble of ROA with other machine learning algorithm for further performance improvement.
The authors would like to express their gratitude to Anna University, as well as anonymous reviewers for their “insights.”