Ant Colony Optimization-based Light Weight Container (ACO-LWC) Algorithm for Efficient Load Balancing

Abstract

Container technology is the latest lightweight virtualization technology which is an alternate solution for virtual machines. Docker is the most popular container technology for creating and managing Linux containers. Containers appear to be the most suitable medium for use in dynamic development, packaging, shipping and many other information technology environments. The portability of the software through the movement of containers is appreciated by businesses and IT professionals. In the docker container, one or more processes may run simultaneously. The main objective of this work is to propose a new algorithm called Ant Colony Optimization-based Light Weight Container (ACO-LWC) load balancing scheduling algorithm for scheduling various process requests. This algorithm is designed such that it shows best performance in terms of load balancing. The proposed algorithm is validated by comparison with two existing load balancing scheduling algorithms namely, least connection algorithm and round robin algorithm. The proposed algorithm is validated using metrics like response time (ms), mean square error (MSE), node load, largest Transactions Per Second (TPS) of cluster (fetches/sec), average response time for each request (ms) and run time (s). Quantitative analysis show that the proposed ACO-LWC scheme achieves best performance in terms of all the metrics compared to the existing algorithms. In particular, the response time for least connection, round robin and the proposed ACO-LWC algorithm are 58, 60 and 48 ms respectively when 95% requests are finished. Similarly, the error for scheduling 120 requests using least connection, round robin and the proposed ACO-LWC algorithm are 0.15, 0.11 and 0.06 respectively.

Contention-aware Greedy Heuristic Method and Learning based Method for Load Balancing through Scheduling for Containers in Cloud Computing Environments

“…𝜉 𝑖,𝑗 = (𝜓 𝑖,𝑗 + 𝜑 𝑖,𝑗 ) × 𝑃 𝑗 (14) The waiting time represented by 𝜑 𝑖,𝑗 is part of the job 𝑒 𝑖 given to 𝑣 𝑗 . 𝑃 𝑗 represents the unit price of each virtual machine.…”

“…Many bio-inspired algorithms came into existence for container scheduling. For instance, Ant Colony Optimization (ACO) is one such approach which is inspired by ant behaviour [4], [14]. GWO based approach found in [15] is another bio-inspired methodology.…”

An Ant-colony Based Model for Load Balancing in Fog Environments

“…In this research [7], they have proposed a new algorithm called Ant-colony Optimizationbased Light Weight Container (ACO-LWC) load balancing scheduling algorithm for scheduling various process requests. Initially, the task allocation was done in a round-robin fashion.…”

“…It affects the probability of choosing a particular path. L is a random variable obtained from the probability distribution of equations (7). q is a uniform random number in the range [0, 1] and q O is a parameter between zero and one, which controls the balance between discovering routes traveled so far and searching for unmet routes.…”