Preface – This post is part of the Edge Computing series.

Introduction

Edge computing has changed the world of devices by deploying a gate over the server. But many challenges are coming in between the path of Edge computing to process the applications properly. Therefore to resolve the challenges, different types of algorithms are adopted. Here in this writing, we will learn about different types of algorithms with their features.

Offloading Algorithm

The offloading algorithm is an algorithm that is used to upload the data to the edge server through a network. It is basically user equipment that processes some applications over the edge server. It will allocate the resources in such a way to be replaced in any disaster and provides a good user experience. Before the offloading, the decision part gets activated, where the critical decision is taken regarding allocating the resources.

The execution is divided such as

Local execution: when it is a situation to execute the resource locally, then there are low requirements of computing power.

Full offloading: this decision is taken when the whole system has to be connected to the base station through any wireless communication over the edge server to migrate the data whenever in need. This problem assumes that the application cannot be split and only has to perform the local computing to the edge server.

Partial offloading: In this, it is assumed that the application of the server can be split in such a way the calculation can also be divided over the server for processing of data.

In the model for n number of users, the loading is represented as m(t)=1 for the m edge servers. So any tasks can be uploaded over the server, which can be allocated by the base station, and the upload rate can be calculated by Rn.

EBRS Time synchronization algorithm

It is also known as clothes synchronization, where a computer has a biological clock to count the oscillations of a crystal and also tracks the current time. The different main features from the other watches are IT acts as a drift. The frequency of this clock varies with the time and give slightly different time. It can lose up to 40 microseconds in each second. There are different types of synchronization, such as external synchronization and internal synchronization. External synchronization synchronizes with the external source of time, whereas internal synchronization synchronizes with one another in the computer system. The transmission is calculated as the upper and lower bound for a message where messages are sent between another process with the actual time of t, and another clock is set with the time of t + (max+min)/2. Hair Max and minimum are represented by the upper and the lower bounds in the transmission time of the message. With the need for different systems, sometimes it also uses network time protocol where the top level is of hierarchy architecture connected to the historical time source.

The criteria to measure the performance matrix is based on energy cost utilized by the system, memory requirements, scalability, synchronization error, and fault tolerance.

Multi-Node Task Scheduling Algorithm

The connecting nodes are dynamic. When there is a need for a response to a task for a user, then task scheduling is required. Due to this, on any server failure, the resources will remain available and push to the target known for a better result. It has in establishing a multi-objective optimization model that has helped to complete the task over time and consumes less energy when the task scheduling process is occurring. This algorithm helps in locating the sub-task in real-time to meet the requirements of the user and obtain the availability of Technology for multi-node scheduling.

In this algorithm, firstly, service traffic is managed in the first layer, which is then transmitted to the scheduling center. The traffic information is scheduled and categorized in a series where resource poles are allocated in a pipeline represented by R1, R2,…..Rn.

Stimulated Annealing Algorithm

It is an optimization method that can be characterized by progressive reduction. It helps in lining the network to resolve the competition problem by identifying the known response to the request of the users. It makes multiple network comparisons to detect the typical structures that must be shared across real-world network sizes efficiently. The algorithm uses a pheromone-based perturbation strategy to implement multiple local search strategies.

Conclusion

Here we have examined the different types of algorithms used in edge computing for efficiently processing the data over the need of the user. Didi algorithms have made the process very easy and have increased user satisfaction by improving the result of each request made by the client. Each algorithm has its specification for enhancing the quality of data processing.