Mobile Edge Computing (MEC) is an emerging network architecture designed to enhance Quality of Service (QoS) by bringing computational resources and storage closer to end users. In MEC environments, strategic placement of edge servers is crucial to minimize costs and optimize QoS. The Edge Server Placement (ESP) problem has been effectively addressed by using Multi-Armed Bandit (MAB) algorithms, known for their efficiency and adaptability, with the Upper Confidence Bound (UCB) algorithm being particularly prominent due to its stable performance and low dependency on parameters. However, UCB suffers excessive exploration and high uncertainty in reward estimation during its initial phase, leading to slow convergence. To overcome these challenges, we propose a novel Preference-based UCB (P-UCB) algorithm, which integrates the preference function into the UCB framework, drawing inspiration from the Gradient Bandit (GB) method. This modification not only accelerates convergence, but also improves overall efficiency. Furthermore, to address the Base Station Allocation (BSA) issue within the ESP context, we introduce a Weighted Base Station Allocation (WBSA) algorithm, which helps better manage access delay and workload balance. The P-UCB algorithm is evaluated through a comprehensive metric that based on access delay and workload balance, showing significant improvements over existing methods such as Multiple Choice (MC)-UCB, Q- Particle Swarm Optimization (QPSO), and Genetic Algorithm (GA). Experimental results on a real-world dataset show that P-UCB achieves a notable performance increase of at least 12.9%, effectively optimizing access delay and workload balance across various experimental settings, including the number of base stations, edge servers, and other relevant system parameters.

Virtual machine (VM) consolidation strategies are widely deployed in Cloud Data Centers (CDCs) to optimize resource utilization and improve the Quality of Service (QoS). However, the host overload detection algorithms in current VM consolidation strategies are static. That means, once the overload threshold is calculated, it will not change until the next recalculation. The current algorithms are not suitable for the environment of highly dynamic workloads which results in additional energy consumption and potential Service Level Agreement Violations (SLAVs) which will affect the QoS of CDC. In PMPHD, a novel host dynamic threshold adjustment algorithm is proposed. In the proposed algorithm, the PMs are classified into mildly overloaded, normal, and severely overloaded based on the resource utilization. If the PM is predicted to be severely overloaded in the next moment, the threshold of this PM will be proactively reduced. The PM is determined to be overloaded, and some VMs in this PM will be migrated in advance. Thus, this PM will be in normal in the next moment, and the VM performance degradation resulting from SLAV and VM migration overlap in the next moment will be avoided. If the PM is predicted to be mildly overloaded, the threshold will be appropriately increased to transit it to be in normal state in the next moment, and the VM in the PM will not be migrated. Since the PMs’ workloads are dynamic, the PMPHD overload algorithm predicts the resource utilization rate of PM continuously, and adjusts the overload threshold of PM. Compared with other algorithms, PMPHD maintains high efficiency while having lower ESV (a combination metric for balancing energy consumption and SLAV).