Performance Gains from Web Performance Optimization - Case Including the Optimization of Webpage Resources in a Comprehensive Way

Drilling operations in the Gulf of Mexico (GoM) area, are characterized by high geological uncertainty, which requires optimum engineering, planning, and early risk detection. The expertise and technical knowledge must be complemented with a strategy that accomplishes a continuous and detailed risk evaluation and self-verification process, covering all drilling stages - design, execution, and evaluation. The objective of this paper is to describe how a risk management system was implemented within an integrated services model, resulting in a higher level of operational efficiency. The strategy is based on the technical risk evaluation and determination of initiatives to become effective and efficient during both planning and execution cycles. From the risk identification perspective, establishing different strategies and technologies to minimize the occurrence of undesirable events and defining mitigation measures to reduce the impact, with the inputs of Subject Matter Experts (SMEs) to determine the most appropriate technologies to de-risk operations. Project schedule management is part of the considerations, looking to facilitate the technology implementations and applications across the integrated project and align with the well scope and operational complexity. The implementation of the Risk Management strategy in projects operating under shared risks drilling modality, facilitates the definition of shared risk appetite, objectives, and key performance indicators. The time and cost estimation are the main input for the integrated services provider (ISP) and the Oil and Gas Operator in their performance partnership. This paper will describe different digital solutions that aid in risk management and performance optimization. Key initiatives employed were around resource optimization and dynamic update and reviews of a well’s critical path.

This paper analyzed self-similarity of Ad-hoc network traffic, and how the network performance was influenced by the self-similarity. Based on Fractional Brownian Motion model, paper proposed a self-similar traffic sequence generation method of RMD algorithm. RMD algorithm is one of the self-similar statistic models. Furthermore, simulation and modeling for Ad-hoc tactic network was implemented based on this kind of traffic model. According to the each performance indicator of curve contrast chart generated by simulating experiment, conclusion is made as follow: self-similarity has great influence to Ad-hoc network indicator and has great contrast to traditional flow models’ short-range dependency character. The research result of this thesis has significant meaning for precise modeling of Ad-hoc network flow, resource optimization and improvement of network performance.

Low Earth Orbit (LEO) satellite communication systems have evolved into a critical component of global broadband networks, enabling wide‐area connectivity, IoT services, and intelligent multilayer satellite–terrestrial integration. Despite rapid advancements in constellation deployment, routing mechanisms, resource management, and LEO–5G/6G convergence, the long‐term evolution of research themes and network architectures remains insufficiently mapped. This study analyzes 2420 Web of Science articles published between 1995 and 2025 and applies bibliometric techniques, main path analysis, and edge‐betweenness clustering to identify the core technological trajectories shaping the LEO communication domain. The results reveal five major phases of development: foundational constellation design, multilayer architecture integration, performance and QoS optimization, 6G‐driven intelligent connectivity, and secure collaborative networking. Four dominant research clusters emerge: communication and IoT applications, routing and topology management, satellite edge computing and task offloading, and LEO‐enhanced GNSS positioning, highlighting the field's structural diversification. Main path and key‐route analyses further show a gradual shift from traditional connectivity‐oriented engineering toward distributed, AI‐enabled, and security‐aware satellite communication frameworks. This study provides the first longitudinal mapping of LEO communication research, offering an integrated understanding of how architectural evolution, protocol innovation, and emerging intelligent services intersect. The findings support future advances in satellite networking, resource optimization, and the design of resilient and interoperable LEO systems.

The memorability of mobile ad-hoc network (MANET) is the precondition of its management, performance optimization and network resources re-allocations. The traditional network interior measurement technique performs measurement on the nodes or links directly, and obtains the node or link performance through analyzing the measurement sample, which usually is used in the wired networks measurement based on the solid infrastructure. However, MANET is an infrastructure-free, multihop, and self-organized temporary network, comprised of a group of mobile nodes with wireless communication devices. Not only does its topology structure vary with time, but also the communication protocol used in its network layer or data link layer is diverse and non-standard. Specially, with the limitation of node energy and wireless bandwidth, the traditional interior network measurement technique is not suited for the measurement requirement of MANET. In order to solve the problem of interior links performance (such as packet loss rate and delay) measurement in MANET, this dissertation has adopted an external measurement based on network tomography (NT). Being a new measurement technology, NT collects the sample of path performance based on end-to-end measurement to infer the probability distribution of the network logical links performance parameters by using mathematical statistics theory, which neither need any cooperation from internal network, nor dependence from communication protocols, and has the merit of being deployed exibly. Thus from our literature review it can be concluded that Network Tomography technique is adaptable for ad-hoc network measurement. We have the following contribution in the eld of ad-hoc network performance: PLE Algorithm: We developed the PLE algorithm based on EM model, which statistically infer the link performance. Stitching Algorithm: Stitching algorithm is based on the isomorphic properties of a directed graph. The proposed algorithm concatenates the links, which are common over various steady state period and carry forward the ones, which are not. Hence in the process it gives the network performance analysis of the entire network over the observation period. EM routing: EM routing is based on the statistical inference calculated by our PLE algorithm. EM routing provides multiple performance metric such as link delay and hops of all the possible path in various time period in a wireless mesh network.

In this study, a microgrid in Matlab/Simulink environment controlled by artificial intelligence algorithm was designed for the campus area of a public institution. In addition, the performance analysis and optimization of this microgrid was realized by using Hybrid Optimization of Multiple Energy Resources (HOMER) software. In both simulation studies, the solar energy potential of the institution and the actual electricity consumption data collected for one year were used. With the HOMER simulation study, feasibility of renewable energy integration for the public institution was demonstrated. In addition, all combinations of microgrids were optimized and the results were evaluated. Simulink analysis was performed using artificial intelligence algorithms for 24 h energy flow analysis. According to the simulation results, it was seen that renewable energy sources can feed the existing loads and even supply energy to the grid when necessary. As a result, it has been proved that integrating the designed microgrid into the campus energy network will be profitable and efficient for the public institution in question.KeywordsArtificial intelligenceMicrogridMatlab/SimulinkHOMERSolar energySystem optimization

This research work aims at evaluating the possibilities of using artificial intelligence in dynamic resource management and optimization of software system performance. In today’s complex world of application usage, normal methods of resource management are unable to cater to these dynamic needs and fulfill its usage potential. In this work, an assessment of three mainstream AI techniques – reinforcement learning, neural network, and genetic algorithm – is performed based on performance indicators such as resource utilization and consumption, average response time, throughput, costs, prediction capability, stability, and time taken to converge. The results show that the neural networks have the best resource acquisition performance as well as response rates, while the reinforcement learning has the best cost management and flexibility rates. As it has been pointed out, genetic algorithms are quite useful in finding optimization solutions, however real-time responsiveness is lack. Thus, the results provide significant understating of how to choose the proper AI technique depending on the specific application needs which in turn will be useful for organizations willing to improve their resource management using AI-based solutions.

Formalisation and use of competencies for industrial performance optimisation: A survey

Delay/Disruption Tolerant Networking is an internetworking architecture for reliable data delivery. It combined with the actual application scenarios of M/G/1 queuing model and based on the data delivery mechanism of store-and-forward and retransmission under Bundle Protocol/Licklider Transmission Protocol in DTN. The data delivery time can be divided into two parts by the last round of LTP data segments transmission process, the custody queue length model of BP data unit (i.e. bundle) in relay nodes memory space is determined by calculating the retransmission time of LTP data segments spent in different delivery parts, which can directly measure the consumption and use of relay nodes memory resources. In order to obtain a more perfect memory resources allocation strategy, the optimized length of LTP data segment and the optimized number of bundles aggregated by per LTP block are proposed as the joint optimization scheme. The simulation results show that in deep-space channel environment where bundle arrival rate and bit error rate constantly change, the joint optimization scheme proposed in this paper can always maintain the shortest average queue length, which means, occupy less memory space. Compared with the queue without optimization, the queue length of the joint optimization scheme is reduced by 76.65%, which demonstrates the superior performance of this scheme, and realizes the reasonable optimization and utilization of the memory resources of the relay nodes in DTN.

Conventional struggles to achieve optimal design for transverse flux machines with many design parameters is still computationally an expensive task. By employing surrogate assisted indirect optimization methods, the task at hand is accelerated. However, for large design parameters vector, initial finite element analysis samples required to generate those surrogate models is high. Coupled with the fact, that use of three dimensional FEM is indispensable for a transverse flux machine, optimal design demands high computational resources and time. To reduce design time without loss of accuracy, this paper proposes to exploit the correlation between a low fidelity (Equivalent 2D-FEM) and a high fidelity (3D-FEM) model and generate a co-kriging based multi-fidelity surrogate which is then employed for optimization. The method is applied to a hybrid transverse flux PMLSM. Compared to conventional surrogate based methods, considerable reduction in design time is achieved.

The challenge of multi-dimensional performance optimization has been extensively addressed in the literature based on deterministic parameters. Since resources in Cloud Computing platforms are geographically separated and heterogeneous, it is rather difficult to apply a uniform distribution algorithm for achieving various optimization goals. Based on the analysis of cloud service performance measures, this paper proposes an approach for optimal network resource distribution managed by the multi-agent system (MAS), which is aimed to satisfy both the users’ and the service providers’ requirements. Moreover, a communication algorithm that uses the universal generating function technique is proposed to obtain the service time distribution efficiently.

With the rise of the Industrial Internet of Things (IIoT), there is an intense pressure on resource and performance optimization leveraging on existing technologies, such as Software Defined Networking (SDN), edge computing, and container orchestration. Industry 4.0 emphasizes the importance of lean and efficient operations for sustainable manufacturing. Achieving this goal would require engineers to consider all layers of the system, from hardware to software, and optimizing for resource efficiency at all levels. This emphasizes the need for container-based virtualization tools such as Docker and Kubernetes, offering Platform as a Service (PaaS), while simultaneously leveraging on edge technologies to reduce related latencies. For network management, SDN is poised to offer a cost-effective and dynamic scalability solution by customizing packet handling for various edge applications and services. In this paper, we investigate the energy and latency trade-offs involved in combining these technologies for industrial applications. As a use case, we emulate a 3D-drone-based monitoring system aimed at providing real-time visual monitoring of industrial automation. We compare a native implementation to a containerized implementation where video processing is orchestrated while streaming is handled by an external UE representing the IIoT device. We compare these two scenarios for energy utilization, latency, and responsiveness. Our test results show that only roughly 16 percent of the total power consumption happens on the mobile node when orchestrated. Virtualization adds up about 4.5 percent of the total power consumption while the latency difference between the two approaches becomes negligible after the streaming session is initialized.

This chapter studies mobile edge computing (MEC) networks assisted by unmanned aerial vehicles (UAVs). According to the application scenarios, we consider three roles for UAVs in MEC networks: exploiting MEC computing capabilities, serving as a computing server, and serving as a relay for computation offloading. Furthermore, the details for resource allocation and optimization are presented in the three scenarios of UAV-assisted MEC networks. In addition, we focus on the situation in which a UAV not only functions as an MEC server to inspect turbines on a wind farm, but also performs task computation. To facilitate wide applications of UAV-assisted MEC in practice, this chapter highlights the main implementation issues of UAV-assisted MEC, including optimal UAV deployment, wind models, and joint trajectory–computation performance optimization.

Contrivers produce modular microservice operations with Spring Boot that precisely match business capabilities by breaking monolithic structures into singly deployable services, thereby enhancing scalability and ensuring fault insulation across the system. It excels at this corruption process, using its expansive experience to align services with business disciplines like client operation or order processing. Deployment on Amazon Web Services through nonstop integration and nonstop deployment channels delivers automated testing, flawless operations, and rapid-fire rollouts across multiple environments like development, staging, and production. Core Amazon Web Services such as Elastic Compute Cloud for virtual waiters, Elastic Container Service for vessel applications, Lambda for serverless functions, and Relational Database Service for managed databases, form the foundation of a scalable and flexible platform structure that supports high availability and dynamic resource allocation. structure- as- law tools like Terraform and Ansible automate resource provisioning, drastically reducing deployment times from days to twinkles while minimising mortal error through interpretation-controlled delineations and drift discovery. Monitoring with CloudWatch provides comprehensive observability by adding up criteria, logs, and traces, enabling visionary incident response and performance optimisation. Containerization via Docker packages operations into featherlight, movable images, while Kubernetes unity handles scaling, cargo balancing, and tone-mending for optimal resource application and uptime. These practices enable businesses to introduce fleetly, maintain functional stability, achieve a cost edge, and secure their infrastructures against failures, delivering palpable issues in dexterity and trustworthiness for ultramodern enterprise operations.

Organizations must perform cloud migration to achieve scalability, cost-efficiency, and improved performance. The migration process of workloads, security protection, and resource optimization creates significant challenges for organizations. Artificial Intelligence functions as a transformative tool that optimizes and facilitates cloud migration operations. This paper examines the application of AI in cloud migration, focusing on its capabilities for data analysis, performance optimization, security improvements, and automation. The research investigates how AI algorithms enhance workload distribution, resource allocation, and compliance management in cloud computing systems. The paper examines how AI algorithms enhance workload distribution, resource allocation, and compliance in cloud environments. The paper discusses how AI-powered Infrastructure as Code (IaC) enables automated cloud deployment and evaluates the effects of AI-assisted migration on telecommunications and other industries. The advantages of AI migration come with ongoing privacy risks and integration complexities. The paper outlines research directions for future work to address current limitations and enhance AI-based cloud migration approaches.

Cloud computing is formally known as an Internet-centered computing technique used for computing purposes in the cloud network. It must compute on a system where an application may simultaneously run on many connected computers. Cloud computing uses computing resources to achieve the efficiency of data centres using the virtualization concept in the cloud. The load balancers consistently allocate the workloads to all the virtual machines in the cloud to avoid an overload situation. The virtualization process implements the instances from the physical state machines to fully utilize servers. Then the dynamic data centres encompass a stochastic modelling approach for resource optimization for high performance in a cloud computing environment. This paper defines the virtualization process for obtaining energy productivity in cloud data centres. The algorithm proposed involves a stochastic modelling approach in cloud data centres for resource optimization. The load balancing method is applied in the cloud data centres to obtain the appropriate efficiency.