Due to limited resources, offloading certain parts of these applications (tasks) to connected MEC servers becomes essential. Many of these applications are generated by resource-constrained handheld or mobile UE. Multi-access Edge Computing (MEC) technology offers promising support for modern, computation-intensive, and time-sensitive applications. The number of container deployments in the proposed policy is reduced by 12.2–17.36% compared to the Spread policy and 6.13–10.57% compared to First-Fit and Best-Fit policies. We have compared the performance of the proposed technique with the existing state-of-the-art. The performance of the proposed algorithm is validated and justified through the extensive experimental results. This paper presents a heuristic algorithm for microservice allocation in a containerized cloud environment to achieve these objectives. In this scheduling, a few additional containers and VMs are kept in the available resource pool so that during peak demand for services, the users get their service at the earliest (preferably without any delay). This research aims to maximize the resource utilization of the hosts by effectively allocating the containers to the VMs and VMs to the hosts. Effective consolidation of the service requests to the containers may reduce the number of active hosts in a cloud environment, resulting in lesser power consumption of the cloud data centers. The containers are deployed in virtual machines (VMs), which in turn run in hosts. The microservices are developed as small independent modules and deployed in containers. Most developers consider that microservice-based application design and development can improve scalability and maintainability. Although Azure Batch Service is used in this paper to illustrate the proposed framework, our approach can also be implemented on other PaaS distributed computing platforms. The proposed framework improves the overall performance of the distributed computing workflow by optimizing task allocation and utilization of resources. To address this issue in this paper, we propose an Intelligent task scheduling framework that uses a classifier-based dynamic task scheduling approach to determine the best available node for each task. However, a significant challenge with cloud services is configuring them with only a single type of machine for performing all the tasks in the distributed workflow, although each task has diverse compute node requirements. These PaaS services, coupled with auto-provisioning and auto-scaling, reduce costs through a Pay-As-You-Go model. This transition is driven by the convenience of Platform-as-a-Service offerings scuh as Batch Services, Hadoop, and Spark. Industries that previously relied on on-premises setups for big data processing are shifting to cloud environments offered by providers such as Azure, Amazon, and Google. Numerous distributed computing algorithms have been suggested in the literature, focusing on efficiently utilizing compute nodes to handle tasks within a workflow on on-premises setups. Most of these use cases involve tasks executed in a sequence on different computers to arrive at the results. Examples of use cases requiring distributed computing are stream processing, batch processing, and client-server models. They enable huge computational power and data processing with a quick response time. This approach achieves an impressive 98.5% accuracy while comparing the utility of the secondary users to the optimal benchmark results.ĭistributed Computing platforms involve multiple processing systems connected through a network and support the parallel execution of applications. To tackle this computationally hard problem, a heuristic solution based on a joint time-and-power allocation strategy is proposed. The goal is to enhance the utility of the secondary user. To optimize the allocation of transmission time and harvested power, a challenging computational problem is formulated. In this setup, an energy-constrained secondary user shares the spectrum and simultaneously harvests energy while assisting with the primary transmission. This study focuses on a wireless energy harvesting and information transfer protocol within cognitive radio relay networks. By utilizing conventional radio frequency transmissions, this technology can extend the battery life of mobile devices and improve the operational period of energy-constrained wireless networks. To address this, wireless energy harvesting technology has emerged as a potential solution. The increasing number of wireless devices has raised significant global concerns regarding energy efficiency and spectrum scarcity.
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