Edge-cloud computing refers to a paradigm that combines the benefits of edge and cloud computing to optimize data processing and resource utilization. Edge-cloud computing plays a crucial role in resource allocation by optimizing the distribution of computational resources between edge devices and centralized cloud infrastructures. In the rapidly evolving landscape of edge-cloud computing, efficient VM allocation is critical for optimizing resource utilization, minimizing latency, and ensuring high SLA compliance. This paper introduces a novel heuristic VM allocation strategy, named LLCD, to enhance cloudlet or task processing in edge-cloud data centers. By employing a heuristic approach inspired by mixed-integer nonlinear programming models, this strategy dynamically assigns VMs based on their current load and the impending deadlines of tasks, significantly reducing overall system latency and enhancing SLA success rates. Simulation was conducted across various computational intensities. The findings reveal that the proposed approach substantially improves resource utilization and operational efficiency, adapting to dynamic workloads, by achieving an SLA success ratio as 74.26% and 83.7% in different deadline scenarios. The adaptive nature of the LLCD algorithm allows real-time task reallocation based on system feedback, which mirrors the operational principles of AI-driven orchestration in distributed IoT environments. The validation is achieved through a multi-iteration simulation model that emulates dynamic IoT workloads, demonstrating LLCD’s learning capability in maintaining SLA stability and consistent latency reduction across changing task distributions. Moreover, the proposed heuristic provides a foundation for latency-efficient and learning-based management in distributed computing environments.

ABSTRACT Containerization has transformed application deployment by offering a lightweight, scalable, and portable architecture for the deployment of container applications and their dependencies. In contemporary cloud computing data centers, where virtual machines (VMs) are frequently utilized to host containerized applications, the challenge of effective placement of the container has garnered significant attention. Container placement (CP) involves placing a container over the VM to execute a container. CP is a nontrivial problem in the container cloud data center (CCDC). Poor placement decisions can lead to decreased service performance or wastage of cloud resources. Efficient placement of containers within a virtual environment is critical while optimizing resource utilization and performance. This paper proposes a penalty‐based particle swarm optimization (PB‐PSO) CP algorithm. In the proposed algorithm, we have considered the makespan, cost, and load of the VM while making the CP decisions. We have proposed the concept of a load‐balancing penalty to prevent a VM from becoming overloaded. This algorithm solves various CP challenges by varying container application sizes in heterogeneous cloud environments. The primary goal of the proposed algorithm is to minimize the makespan and computational cost of containers through efficient resource utilization. We have performed extensive simulation studies to verify the efficacy of the proposed algorithm using the CloudSim 4.0 simulator. The proposed optimization algorithm (PB‐PSO) aims to minimize both the makespan and the execution monetary costs and maximize the resource utilization simultaneously. During the simulation, we observed a reduction of 10% to 15% in both execution cost and makespan. Furthermore, our algorithm achieved the most optimal cost‐makespan trade‐offs compared to other competing algorithms.

Abstract In contrast to a conventional virtual machine (VM), a container is a lightweight virtualization technology. Containers are becoming a prominent technology for cloud services because of their portable, scalable, and flexible deployments, especially in the Internet of Things (IoT), smart devices, and fog and edge computing. It is a type of operating system‐level virtualization in which the kernel allows multiple isolated containers to run independently. Container placement (CP) is a nontrivial problem in Container‐as‐a‐Service (CaaS). CP is mapping to a container over virtual machines (VMs) to execute an application. Designing an efficient CP strategy is complex due to several intertwined challenges. These challenges arise from a diverse spectrum of computing resources, like on‐demand and unpredictable fluctuations of IT resources by multiple tenants. In this article, we propose a modified sum‐based container placement algorithm called a multi‐objective optimization‐based container placement algorithm (MSBCPA). In the proposed algorithm, we have considered two metrics: makespan and monetary costs for optimizing available IT resources. We have conducted comprehensive simulation experiments to validate the effectiveness of the proposed algorithm over the CloudSim 4.0 simulator. The proposed optimization algorithm (MSBCPA) aims to minimize the makespan and the execution monetary costs simultaneously. In the simulation, we found that the execution cost and energy consumption cost reduce by 20% to 30% and achieve the best possible cost‐makespan trade‐offs compared to competing algorithms.

Reducing energy consumption in the fog computing environment is both a research and an operational challenge for the current research community and industry. There are several industries such as finance industry or healthcare industry that require a rich resource platform to process big data along with edge computing in fog architecture. As a result, sustainable computing in a fog server plays a key role in fog computing hierarchy. The energy consumption in fog servers depends on the allocation techniques of services (user requests) to a set of virtual machines (VMs). This service request allocation in a fog computing environment is a nondeterministic polynomial-time hard problem. In this paper, the scheduling of service requests to VMs is presented as a bi-objective minimization problem, where a tradeoff is maintained between the energy consumption and makespan. Specifically, this paper proposes a metaheuristic-based service allocation framework using three metaheuristic techniques, such as particle swarm optimization (PSO), binary PSO, and bat algorithm. These proposed techniques allow us to deal with the heterogeneity of resources in the fog computing environment. This paper has validated the performance of these metaheuristic-based service allocation algorithms by conducting a set of rigorous evaluations.