Strategies for optimizing edge computing workloads– Creating Applications on the Edge

We will discuss three main strategies to optimize edge computing workloads: load balancing, resource allocation, and data management.

Load balancing

Load balancing is a technique to distribute workloads across multiple edge devices or gateways to better the performance of each and ensure that all resources are utilized efficiently. Several algorithms can be used for edge computing for this purpose, which include round-robin and least connections. Round-robin load balancing is the simplest approach and operates by distributing requests to edge devices cyclically, which is best suited for applications that have relatively uniform traffic patterns. Least connections works by distributing requests that are coming to the edge device with the fewest possible active connections, which is useful for applications that have variable traffic patterns, making it effective in preventing the overloading of specific devices.

Another great technique to test the proportion of traffic to allocate to a particular device is to perform load testing. Load testing is a way of simulating test loads to each of your devices to see how much they can handle for you to allocate loads appropriately to each of the devices. This is good for you to understand the behavior of your loads and the amount that can be handled by each of the devices, enabling you to configure the algorithms to allocate loads accordingly.

Resource allocation

Resource allocation is a strategy that is used to optimize edge computing workloads by assigning resources to specific tasks and applications in the most effective and efficient way possible. These resources include CPU, memory, and storage. This can be done through several resource allocation algorithms such as first-fit, best-fit, and worst-fit algorithms.

The first-fit algorithm allocates the first available resource that is large enough to handle the workload. The best-fit algorithm allocates the resource that best matches the size of the workload. The worst-fit algorithm allocates the resource that is the least suited for the workload. It is also possible to use a combination of the algorithms, such as having a first-fit algorithm for initial resource allocation and a best-fit algorithm for fine-tuning.

Data management

Data management is a strategy that is used to optimize edge computing workloads through techniques such as data reduction, data compression, data caching, or data replication to minimize the amount of data that is required to be transmitted over networks, along with edge computing techniques such as utilizing lightweight data processing algorithms and models within the edge devices themselves. This can help to reduce requirements for network bandwidth, promoting the efficiency and responsiveness of the edge computing workloads.

We can now complete our strategy forming with an understanding of how to evaluate edge networks.