Highly demanding services require an appropriate amount of resources to manage the fluctuating workload in cloud environment, which is a challenging task for cloud service provides over the Internet. Cloud providers offer these services to enduser with pay and use model, such as utility computing. The services are offered to end-user by a cloud provider in a shareable fashion over Infrastructure-as-a-Service. So, IaaS is a type of computing service on which third parties host their application on virtualized platforms, such as either VMs or Containers. Whenever some containers are overloaded or under-loaded, it may cause SLA violation, degrade performance, cosume maximum energy, and also cause minimum throughput and maximum response time. It also leads to minimizing the customer satisfaction level along with cloud providers, leading to the penalty. The services hosted on VMs or Containers are highly demanding services, and these highly demanding services are handled with the help of load balancing. Load balancing is a way to automatically transfer the incoming requests or load across a group of back-end containers. It improves the distribution of workload across multiple virtual machines. Traditionally, load balancing algorithms use one or two parameters to balance the load. In this paper, we used one of the popular optimization techniques, namely the Technique for Order of Preferences by Similarity to Ideal Solution (TOPSIS) algorithm to manage the incoming traffic with the multiplecriteria decision-making (MCDM) technique. When the proposed technique was compared with different other techniques, such as round robin, it was found that TOPSIS gives better performance in terms of efficient resources utilization. It also minimizes the average response time, which prevents the machine from getting overloaded.