Optimal Implementation Research Articles

Cloud Technologies in Healthcare Supply Chains: Ensuring Efficiency and Transparency

The integration of cloud technologies in healthcare supply chains has revolutionized the management of critical medical supplies and temperature-sensitive products. This transformation encompasses comprehensive solutions for cold chain management, supplier collaboration, and logistics optimization. Cloud-based systems have enabled healthcare organizations to achieve remarkable improvements in inventory accuracy, order processing efficiency, and regulatory compliance. Through advanced IoT sensor networks and real-time monitoring capabilities, healthcare facilities have enhanced their ability to maintain temperature-sensitive pharmaceuticals within critical thresholds. The implementation of predictive analytics and AI-driven optimization has transformed delivery logistics, particularly benefiting underserved areas. Modern supplier collaboration platforms featuring multi-tenant architectures have streamlined procurement processes and enhanced communication efficiency. Looking ahead, quantum computing integration and 5G network deployment are positioned to further transform healthcare supply chains, while robust security protocols and blockchain-based compliance automation ensure data protection. These technological advancements have resulted in substantial reductions in operational costs, improved inventory accuracy, decreased product wastage, and enhanced overall healthcare delivery efficiency.

Quality of Experience-Oriented Cloud-Edge Dynamic Adaptive Streaming: Recent Advances, Challenges, and Opportunities

The widespread adoption of dynamic adaptive streaming (DAS) has revolutionized the delivery of high-quality internet multimedia content by enabling dynamic streaming quality adjustments based on network conditions and playback capabilities. While numerous reviews have explored DAS technologies, this study differentiates itself by focusing on Quality of Experience (QoE)-oriented optimization in cloud-edge collaborative environments. Traditional DAS optimization often overlooks the asymmetry between cloud and edge nodes, where edge resources are typically constrained. This review emphasizes the importance of dynamic task and traffic allocation between cloud and edge nodes to optimize resource utilization and maintain system efficiency, ultimately improving QoE for end users. This comprehensive analysis explores recent advances in QoE-driven DAS optimization strategies, including streaming models, implementation mechanisms, and the integration of machine learning (ML) techniques. By contrasting ML-based DAS approaches with traditional methods, this study highlights the added value of intelligent algorithms in addressing modern streaming challenges. Furthermore, the review identifies emerging research directions, such as adaptive resource allocation and hybrid cloud-edge solutions, and underscores potential application areas for DAS in evolving multimedia systems. With the aim of serving as a valuable resource for researchers, practitioners, and decision-makers in addressing the challenges of resource-constrained edge environments and the need for QoE-centric solutions, this comprehensive analysis endeavors to promote the development, implementation, and application of DAS optimization. Acknowledging the crucial role of DAS optimization in improving the overall QoE for the end users, we hope to facilitate the continued advancement of video streaming experiences in the cloud-edge collaborated environment.

Evaluation of workload distribution and exposure factors in common use in ten general x-ray rooms in Ireland: An initial step towards protocol optimisation.

This study aimed to assess current practice in general x-ray rooms in three Irish hospitals. Evaluating the extent to which x-ray protocols are currently standardised can be considered as a preparatory step towards the larger goal of implementing optimisation in a diagnostic radiology department. Data on various aspects of general x-ray room exposures was manually collected and analysed to highlight variations with exposure factors in use and workload distribution across each of the ten hospital x-ray rooms included in the study. Workload distribution for the x-ray rooms surveyed demonstrated the most commonly performed x-ray, the chest x-ray, to account for 44% of exposures acquired, compared to the most recently published national statistic of 31%1, suggesting a potential change in referral practices over the past decade. A high degree of protocol variation for the same imaging indications exists across the x-ray rooms included in the study, for example there were 14 protocols in routine use for shoulder imaging across the 10 x-ray rooms, spanning a wide range of tube potentials from 55 to 81kVp. The lack of x-ray protocol standardisation (and associated image quality variation) prompts questions around the implementation of optimisation, as required by Irish legislation, for general x-ray protocols. National review of x-ray referral practices or most common general x-ray exposures in use would be welcomed, as deviations from the previously published dataset are locally evident. This body of work considers the variety of x-ray protocols in use for specific anatomical areas and outlines how similar analyses at a local level could provide a foundation for targeted optimisation exercises.

Implementation of Chernobyl disaster optimizer based feature selection approach to predict software defects

Background Software Defect Prediction (SDP) enables developers to investigate unscrambled faults in the inaugural parts of the software progression mechanism. However, SDP faces the threat of high dimensionality. Feature selection (FS) selects the finest features while carefully discarding others. Several meta-heuristic algorithms, like Genetic Algorithm, Particle Swarm Optimization, Differential Evolution, and Ant Colony Optimization, have been used to develop defect prediction models. However, these models have drawbacks like high cost, local optima trap, lower convergence rate, and higher parameter tuning. This study applies an innovative FS technique (FSCOA) rooted in Chernobyl Disaster Optimizer (CDO) technique. The proposed procedure intends to unwrap the best features for a prediction model while minimizing errors. Methods The proposed FSCOA investigated twelve public NASA software datasets from the PROMISE archive on Decision Tree, K-nearest neighbor, Naive Bayes, and Quantitative Discriminant Analysis classifiers. Furthermore, the accuracy of the recommended FSCOA method was correlated with existing FS techniques, like FSDE, FSPSO, FSACO, and FSGA. The statistical merit of the proposed measure was verified using Friedman and Holm tests. Results The experiment indicated that the proposed FSCOA approach bettered the accuracy in majority of the instances and achieved an average rank of 1.75 among other studied FS approaches while applying the Friedman test. Furthermore, the Holm test showed that the p-value was lower than or equivalent to the value of α/(A-i), except for the FSCOA and FSGA and FSCOA and FSACO models. Conclusion The results illustrated the supremacy of the prospective FSCOA procedure over extant FS techniques with higher accuracy in almost all cases due to its advantages like enhanced accuracy, the ability to deal with convoluted, high-magnitude datasets not grounded in local optima, and a faster convergence rate. These advantages empower the suggested FSCOA method to overcome the challenges of the other studied FS techniques.

The Implementation of Bayesian Optimization for Automatic Parameter Selection in Convolutional Neural Network for Lung Nodule Classification

Lung cancer's high mortality rate makes early detection crucial. Machine learning techniques, especially convolutional neural networks (CNN), play a very important role in lung nodule detection. Traditional machine learning approaches often require manual feature extraction, while CNNs, as a specialized type of neural network, automatically learn features directly from the data. However, tuning CNN hyperparameters, such as network structure and training parameters, is computationally intensive. Bayesian Optimization offers a solution by efficiently selecting parameter values. This study develops a CNN classification model with hyperparameter tuning using Bayesian Optimization, achieving a 97.2% accuracy. Comparatively, Particle Swarm Optimization and Genetic Algorithm methods each resulted in 96.4% accuracy. The research concludes that Bayesian Optimization is an effective approach for CNN hyperparameter tuning in lung nodule classification.

Control of Tollmien–Schlichting waves using particle swarm optimization

The implementation of the Particle Swarm Optimization (PSO) algorithm is investigated to optimize the active attenuation of Tollmien–Schlichting (TS) waves developing in a two-dimensional zero pressure gradient boundary layer. This is done numerically, where the PSO algorithm optimizes the characteristics of harmonic suction and blowing jets, in a feedforward control framework. The PSO-based controller selects and modifies the phase and amplitude of the jets to minimize the pressure fluctuation amplitude downstream of the actuator. To allow for efficient simulation, the 2-dimensional incompressible Navier–Stokes equations are expanded in a harmonic perturbation form and solved in linear and nonlinear variants using harmonic balancing. This study explores the performance of control in both linear and nonlinear development regimes of TS waves through control of single and multi-frequency ensembles of instabilities. Respectively, linear and nonlinear controller design approaches are employed. The findings reveal that the integration of PSO into the control design produces an effective suppression of TS waves through opposition control. The linearly designed controller effectively attenuates single and multi-frequency disturbances. However, when applied in regions of strong nonlinear interactions among instability modes, performance degradation is observed. On the contrary, the nonlinearly designed controller proves effective in mitigating nonlinear multi-frequency instabilities dominating the later stages of growth. A near-complete elimination of TS waves is achieved by accounting for nonlinear interactions among harmonic modes detected by an input sensor. This highlights the benefit of integrating the PSO algorithm in control of TS waves, particularly in the nonlinear growth regime, where classical control methods are generally ineffective.

EMPOWERING ISLAMIC BOARDING SCHOOLS' BUSINESS UNITS: A DIGITAL MARKETING OPTIMIZATION MODEL

Introduction: This study aims to develop a comprehensive digital marketing optimization model specifically designed for Islamic boarding schools in East Java, a province with a high concentration of Islamic boarding schools and their associated business units. The intense competition among these schools necessitates the adoption of digital transformation to enhance the effectiveness and competitiveness of their business operations. Methods: Utilizing qualitative methods and the Analytic Network Process (ANP) for data analysis, this study identifies critical internal challenges, such as low product quality, and external threats, including competition from larger enterprises. The research also explores alternative strategies to address these challenges. Results: The findings indicate that the primary barriers to effective digital marketing stem from both internal and external factors. Strategic solutions are proposed, including the implementation of Search Engine Optimization (SEO), social media marketing, and targeted advertising through platforms like Google Ads. Moreover, the study highlights the importance of continuous mentoring and counseling to support the successful application of these strategies. Conclusion and suggestion: This research provides valuable insights into leveraging digital marketing to enhance human resource potential and improve product quality, thereby fostering economic empowerment within Islamic boarding schools and increasing their competitiveness.

Interactive multiobjective evolutionary optimization model for dam management support

Dam management optimization is a complex multiobjective problem, whose current solutions struggle to spread in normal practice. Here, an interactive evolutionary multiobjective optimization method is proposed, which involves stakeholders by using multi-criteria decision analysis to embed a parsimonious elicitation of their preferences and expertise in the optimization process. Specifically: (i) one or more decision-makers are asked to rank a set of representative dam management strategies in order of preference; (ii) the ranking is used to build a preference model; and (iii) the preference model is used in an evolutionary multiobjective optimization algorithm to converge to the part of the Pareto front most preferred by the decision-makers. The inclusion of a user-friendly interaction in the optimization methodology makes the process more understandable for stakeholders and policy-makers, and it allows to address some of the key obstacles to the practical implementation of multiobjective dam management optimization, even when a large number of objectives is considered. The proposed methodology is applied to a case study (Lake Pozzillo, Sicily, Italy) with five objectives, where flood control is in contrast with irrigation water demand and hydropower production. Five simple and easy to implement optimal strategies are obtained by interacting with five decision-makers. The results are compared to the management practice currently used, indicating that the proposed approach can satisfy water demands while greatly improving flood attenuation.

The implementation of preoperative optimization in British Columbia: a quality improvement initiative.

Surgical patient optimization is a proactive approach to improve postoperative outcomes. This article reviews the development of the Surgical Patient Optimization Collaborative, an initiative supporting preoperative optimization in British Columbia, Canada. The Collaborative facilitated optimization programs over two cohorts between 2019 and 2024. A "Change Package" offered screening, intervention, and measurement tools for the following 13 surgical risk factors: anemia, anxiety, body mass index (cohort 2 only), cardiac status, frailty, glycemic control, nutrition, sleep apnea, pain management, physical activity, smoking, social support, substance use, and venous thromboembolism (cohort 1 only). Monthly data submissions from participating sites included the number of patients undergoing optimization, National Surgical Quality Improvement Program-defined 30-day outcomes, length of stay, and patient-reported measures. Run charts were used to analyze the progress of optimization implementation across both cohorts. Fourteen sites participated in each cohort. In total, 9,686 patients were screened for optimization, with 7,100/7,505 (95%) patients receiving at least one optimization intervention. Improvement shifts in the number of patients screened were identified in the run charts across both cohorts. Most patients felt that their optimization improved their surgical experience and outcomes. Data for clinical outcomes were inconsistently reported from sites and precluded analyses. Barriers to implementation included project complexity and structural characteristics, and facilitators were knowledge and beliefs about the intervention, reflection, and evaluation. Preoperative optimization programs were successfully implemented across multiple sites in British Columbia. High-quality clinical outcome analyses are still needed to determine the impact of preoperative optimization on postoperative outcomes. The insight gained from the Collaborative's implementation process may help inform future multicentre preoperative optimization efforts.

Reducing measurement costs by recycling the Hessian in adaptive variational quantum algorithms

Abstract Adaptive protocols enable the construction of more efficient state preparation circuits in variational quantum algorithms (VQAs) by utilizing data obtained from the quantum processor during the execution of the algorithm. This idea originated with Adaptive Derivative-Assembled Problem-Tailored variational quantum eigensolver (ADAPT-VQE), an algorithm that iteratively grows the state preparation circuit operator by operator, with each new operator accompanied by a new variational parameter, and where all parameters acquired thus far are optimized in each iteration. In ADAPT-VQE and other adaptive VQAs that followed it, it has been shown that initializing parameters to their optimal values from the previous iteration speeds up convergence and avoids shallow local traps in the parameter landscape. However, no other data from the optimization performed at one iteration is carried over to the next. In this work, we propose an improved quasi-Newton optimization protocol specifically tailored to adaptive VQAs. The distinctive feature in our proposal is that approximate second derivatives of the cost function are recycled across iterations in addition to optimal parameter values. We implement a quasi-Newton optimizer where an approximation to the inverse Hessian matrix is continuously built and grown across the iterations of an adaptive VQA. The resulting algorithm has the flavor of a continuous optimization where the dimension of the search space is augmented when the gradient norm falls below a given threshold. We show that this inter-optimization exchange of second-order information leads the approximate Hessian in the state of the optimizer to be consistently closer to the exact Hessian. As a result, our method achieves a superlinear convergence rate even in situations where the typical implementation of a quasi-Newton optimizer converges only linearly. Our protocol decreases the measurement costs in implementing adaptive VQAs on quantum hardware as well as the runtime of their classical simulation.

Building an AI Portfolio: A Technical Guide for Modern Logistics Specialists

This technical article provides a comprehensive framework for logistics specialists to build an effective AI portfolio in modern supply chain management. As the industry processes massive volumes of data annually, the integration of artificial intelligence has revolutionized logistics operations. The article addresses critical aspects, including demand forecasting, route optimization, inventory management, and security implementations. With AI implementations demonstrating significant performance improvements across cycle time reduction, transportation expense optimization, and inventory accuracy enhancement, the need for structured portfolio development has become increasingly important. The article outlines detailed technical requirements, implementation frameworks, and best practices for developing AI solutions in logistics, supported by real-world performance metrics and industry standards. Special attention is given to security considerations, scalability architecture, and professional development strategies, providing logistics professionals with a holistic approach to AI integration. The comprehensive coverage encompasses technical depth and practical implementation guidelines, making it a valuable resource for professionals seeking to advance their careers in AI-driven logistics operations.

Cooling layout optimization for a turbine blade squealer tip with the application of oval holes

For the purpose to enhance the cooling performance of the squealer tip under stage conditions, an automatic optimization framework was constructed to optimize the cooling holes on a squealer tip, including the utilization of oval-shaped holes. The analysis of the optimization results indicates that the configuration of the cooling holes with positive axial inclination and the reduction in the arrangement interval of holes that are assembled in the front cavity can effectively enhance the film attachment, resulting in augmented film coverage and cooling effectiveness. The coefficient of heat transfer in the region between and downstream the holes is observed to decrease in accordance with the film coverage. Meanwhile, the positive axial inclination guides the jets to accumulate towards the rear of the cavity, enhancing the blocking effect to leakage flow. The film attachment is further improved as the jets outflow along the long axis edge of oval holes, which exhibit low curvature. In general, the implementation of round hole optimization has led to an increase in cooling effectiveness by 54.85 % in comparison to the benchmark. Furthermore, the use of oval holes has resulted in a greater improvement of 67.65 %. The aerodynamic performance has remained uncompromised throughout these modifications.

Event-Driven Architecture: Harnessing Kafka and Spring Boot for Scalable, Real-Time Applications

This comprehensive article explores the transformative impact of Event-Driven Architecture (EDA) in modern financial services, focusing on its implementation using Apache Kafka and Spring Boot. The article examines how financial institutions have revolutionized their transaction processing capabilities through EDA adoption, achieving significant improvements in system performance, reliability, and customer experience. Through a detailed examination of real-world implementations, the analysis demonstrates how EDA has enabled banks to handle unprecedented transaction volumes while substantially reducing system latency and improving resource utilization. The integration of Kafka and Spring Boot has proven particularly effective, with major financial institutions leveraging these technologies to achieve superior performance metrics and system availability. The article further explores critical patterns, including event sourcing, CQRS, and saga patterns, alongside cloud-based best practices for monitoring, performance optimization, and security implementations. By examining various implementation strategies and their outcomes, this article provides a comprehensive framework for building scalable, resilient financial systems that meet the demanding requirements of modern banking operations while ensuring regulatory compliance and maintaining high security and reliability standards.

Implementation and clinical impact of an interdisciplinary tool to promote skin integrity after flap surgery in Veterans with spinal cord injury

Context/Objective Pressure injury (PrI) recurrence is common among persons with spinal cord injury and disorders (SCI/D) who undergo reconstructive flap surgery for pelvic stage 4 PrI (S4PrI). This paper describes the development and implementation of the Minneapolis Spinal Cord Optimization, Rehabilitation and Empowerment (SCORE), a preoperative interdisciplinary tool for risk assessment and mitigation, and reports its effect on the one-year flap failure rate (significant breakdown). Design Retrospective review. Setting Minneapolis Veterans Affairs Health Care System (MVAHCS) SCI/D Center, a tertiary care center. Participants 141 Veterans with SCI/D and pelvic S4PrI underwent 204 interdisciplinary assessments for flap surgery. Interventions The Minneapolis SCORE was developed by the MVAHCS SCI/D Center and first implemented in 2012. The eight domains of the tool and continuous improvements in tool implementation for risk assessment and mitigation are described. Outcome Measures Per-year incidence rate of flap failures within one year of surgery from 2009 to 2019. Results 48.3% (28/58) of S4PrI assessments during 2009–2011 (pre-SCORE) led to flap surgery the same year, increasing to 59.6% (87/146) of assessments after SCORE implementation during 2012–2019. The one-year flap failure rate abruptly decreased from 40.5% (15/37) of the 2009–2012 surgeries to 7.7% (6/78) of the 2013–2019 surgeries (P < .0001). Characteristics of patients by time period, operative status, and flap outcome are presented. Conclusion After an initial learning curve in tool implementation and subsequent tool refinement, the use of the Minneapolis SCORE before flap surgeries was associated with improved flap integrity at one year. Successful use of the tool requires collaborative problem-solving between the patient and interdisciplinary team.

OPTIMIZATION OF RADIATION PROTECTION IN PATIENTS THROUGH IMPLEMENTATION OF LOCAL DIAGNOSTIC REFERENCE LEVEL (DRL) IN PANORAMIC DENTAL EXAMINATION IN RADIOLOGY INSTALLATION OF PROF. DR. MARGONO SOEKARJO HOSPITAL

One of the principles of radiation protection is optimization. Optimization is done to make the dose received as low as reasonably achievable (ALARA) by considering economic and social factors. Implementation of radiation protection optimization can be done with dose management through the Local Diagnostic Reference Level (DRL). In recent years, the Nuclear Energy Regulatory Agency (BAPETEN) has set the Indonesian DRL value for several X-ray modalities, including dental panoramic. This study was motivated by panoramic patient dose audit data using the auto mode exposure factor, which operators often use. The method for determining the manual mode exposure factor is carried out by comparing the image quality of the current variations of 6.3 mA, 8 mA, 10 mA, 13 mA, and 16 mA with a fixed voltage of 66 kV and a time of 14.1 seconds. The local DRL value was obtained by comparing the dental panoramic auto mode measurements with manual mode using certain exposure factors according to the recommendations in the initial study using a head phantom. This study used 100 patient data with adult human criteria aged over 15. The results obtained from the initial study and questionnaires in manual mode were the values that had the highest CNR and SNR at a current variation of 10 mA. The exposure factor was then used to collect patient data. The local DRL value obtained for auto mode was 83.35 mGy cm2; in manual mode, it was 57.7 mGy cm2. Manual mode exposure factors in dental panoramic examinations affected the implementation of local DRL values with a decrease of 30.77% from auto mode. The use of manual mode in dental panoramic examinations significantly reduces the radiation dose received by patients, so the use of manual mode is one of the steps to optimize radiation protection. Keywords: Diagnostic Reference Level (DRL), optimization of radiation protection in patients, dental panoramic.

Multi-objective planning of distribution network based on distributionally robust model predictive control

The uncoordinated integration of numerous distributed resources poses significant challenges to the safe and stable operation of distribution networks. To address the uncertainties associated with the intermittent output of distributed power sources, we propose a multi-objective planning strategy for distribution networks based on distributionally robust model predictive control (MPC). Initially, an error fuzzy set is established on a Wasserstein sphere using historical data to enhance out-of-sample performance. Next, a multi-objective optimization framework is constructed, balancing returns and risks, and is subsequently converted into a single-objective solution using value-at-risk conditions. This is followed by the implementation of multi-step rolling optimization within the model predictive control framework. We have linearized the proposed model using the linearized power flow method and conducted a thorough validation on an enhanced IEEE 37-node test system. Distributionally robust optimization (DRO) has been shown to reduce costs by a significant 29.16% when compared to an RO method. Moreover, the energy storage capacity required has been notably reduced by 33.33% on the 29-node system and by 20% on the 35-node system. These quantified results not only demonstrate the substantial economic efficiency gains but also the enhanced robustness of our proposed planning under the uncertainties associated with renewable energy integration.

Implementation of Search Engine Optimization (SEO) with RankMath Plugin to Increase WordPress-Based Website Visits

Implementation of Search Engine Optimization (SEO) with RankMath Plugin to Increase WordPress-Based Website Visits

DC Motor Speed Control using Particle Swarm Optimization based on Labview

In Industrial applications, DC motors are commonly applied because of high reliability, ease of control and ability to provide accurate speed. However, to get accurate speed control under several operation conditions such as disturbances and changes in the load is significant challenge. This research explores the implementation of particle swarm optimization (PSO) to tune parameters of proportional-integral (PI) controller. PSO that is a population-based optimization technique, is inspired by the social behavior of swarms. It is a population-based optimization technique. By automation process in the algorithm. Using the tuning process of PSO, it can effectively obtain the parameters of PI controller. experimental hardware using DIGIAC 1750 is used to assess the performance of the proposed method. The parameters of and are 0.7492 and 0.2007, respectively. The results show that the performance of the DC motor using PSO tuned by PI for , , and are 0.3687 s, 0.5106 s, and 0.6051 s, respectively. Furthermore, when the system is given a disturbance, the response can come back again following the setpoint and when the setpoint is changed, the response can follow the setpoint quickly as well. The proposed method can address the challenges associated with DC motor speed control.

Theoretical foundations and practical implementation of supply chain optimization a metal fabrication business model

In the dynamic landscape of modern business, effective management of supply chains stands as a cornerstone of organizational success and competitive edge. This study investigates the theoretical principles and practical implementations of optimization models aimed at refining supply chain operations in the metal fabrication business, giving the maximum profit. It begins with a comprehensive survey of optimization theory, encompassing linear programming, integer programming, and stochastic optimization. It proceeds to their application in addressing diverse challenges encountered in supply chain management. The discussion includes deterministic models, such as those optimizing facility location under capacity constraints and transportation logistics, alongside stochastic models designed to manage uncertainties inherent in demand forecasting and inventory control. Advanced methodologies like metaheuristic algorithms and multi-objective optimization techniques are examined for their capacity to navigate the intricate and often conflicting objectives within supply chain networks. Through comprehensive theoretical analysis and a study of the supply chain model developed for the metal fabrication business, this paper contributes to advancing knowledge in mechanical engineering and supply chain management, offering insights pertinent to practitioners and researchers alike. Ultimately, it emphasizes the critical role of optimization models in optimizing efficiency, reducing costs, improving profit margin, reducing CO2 emission, and enhancing competitiveness in modern supply chain operations.

Cloud migration and microservices optimization framework for large-scale enterprises

As large-scale enterprises increasingly adopt cloud computing, the need for a structured framework to guide seamless cloud migration and optimize microservices architecture becomes critical. This review presents a comprehensive designed to address the challenges and opportunities associated with transitioning to cloud environments and optimizing microservices for performance, scalability, and security. The review begins with an assessment of the enterprise’s current infrastructure, emphasizing a phased approach to cloud migration. It outlines key strategies such as lift-and-shift, re-platforming, and re-architecting, each tailored to specific organizational needs. The selection of the appropriate cloud model (public, private, or hybrid) and the use of multi-cloud strategies are discussed to ensure flexibility and risk mitigation during migration. For microservices optimization, focuses on designing services that are loosely coupled and highly scalable. Key elements include the use of service discovery, load balancing, API gateways, and database management strategies to improve the efficiency and reliability of microservices. Additionally, this introduces security best practices, including identity and access management (IAM), encryption protocols, and microservices-specific security measures such as Zero Trust Architecture. The review also highlights optimization strategies involving containerization (Docker) and orchestration (Kubernetes), alongside the integration of Continuous Integration/Continuous Delivery (CI/CD) pipelines for streamlined development and deployment. Performance monitoring, real-time logging, and proactive maintenance are emphasized for sustained efficiency and cost control in cloud environments. Case studies demonstrate the successful implementation of cloud migration and microservices optimization in large enterprises, showcasing both the benefits and common challenges. The review concludes by exploring future trends, such as serverless computing and AI-driven optimization, that will continue to shape cloud migration and microservices architecture.