Optimal resource management in cloud computing

In order to provide users timely and reliable services, resources and facilities in cloud data centers are often more than actual needs, which leads to low resource utilization and high operating costs. However, controling the quantity of resources and facilities may cause low throughput. Therefore, many researches are devoted to increasing resource utilization, improving jobs throughput and reducing operating costs through effective cloud resource management. The cloud resource management in cloud data center can be regarded as a multi-resource demand job allocation problem. This paper proposed an allocation algorithm of jobs based on deep reinforcement learning. The algorithm allocates waiting jobs to clusters respectively, so as to optimize throughput and improve resource utilization. The algorithm is implemented in cloud data center simulation model CloudSim. The experimental results show that the method based on Deep Reinforcement Learning is better than traditional method in multi-resource job allocation problem.

Cloud management faces with great challenges, due to the diversity of Cloud resources and ever-changing management requirements. For constructing a management system to satisfy a specific management requirement, a redevelopment solution based on existing management systems is usually more practicable than developing the system from scratch. However, the difficulty and workload of redevelopment are also very high. As the architecture-based runtime model is causally connected with the corresponding running system automatically, constructing an integrated Cloud management system based on the architecture-based runtime models of Cloud resources can benefit from the model-specific natures, and thus reduce the development workload. In this paper, we present an architecture-based approach to managing diverse Cloud resources. First, manageability of Cloud resources is abstracted as runtime models, which could automatically and immediately propagate any observable runtime changes of target resources to corresponding architecture models, and vice versa. Second, a customized model is constructed according to the personalized management requirement and the synchronization between the customized model and Cloud resource runtime models is ensured through model transformation. Thus, all the management tasks could be carried out through executing programs on the customized model. The experiment on a real-world cloud demonstrates the feasibility, effectiveness and benefits of the new approach to integrated management of Cloud resources

Abstract: Online resource management in cloud computing must respond to rapidly changing demand and system conditions, often exceeding the adaptability of static or periodically recomputed optimisation plans. This study formulates cloud resource management as a Markov decision process (MDP) and investigates deep reinforcement learning (DRL) as an approximate dynamic programming (ADP) approach for deriving adaptive control policies. The system state encompasses workload, capacity utilisation, and performance indicators, while actions govern virtual machine placement, scaling, and server power-state transitions. Reward functions combine energy and operating costs with penalties associated with service-level agreement violations. DRL algorithms, including deep Q-networks, advantage actor–critic, and proximal policy optimization, are implemented in PyTorch and trained through interaction with a CloudSim-based simulation environment calibrated to realistic load and power profiles. The learned policies are evaluated against deterministic and scenario-based stochastic optimisation baselines using metrics such as average cost, energy consumption, SLA violations, and robustness to non-stationary demand. Results show that DRL policies can approach or surpass the performance of stochastic optimisation solutions in highly dynamic scenarios, while maintaining feasible control actions. The study demonstrates how DRL can be positioned within an OR/ADP framework and highlights conditions under which learning-based methods offer significant advantages for online cloud control. Keywords: deep reinforcement learning, approximate dynamic programming, cloud computing, Markov decision processes, online resource management, energy-aware control, PyTorch, simulation

This study addresses the critical need for effective resource management through software container migration in cloud data centers. It emphasizes its role in avoiding resource shortages and reducing energy consumption in cloud environments. This study introduces a novel multi-objective integer linear programming (ILP) approach for software container replacements, complemented by a specialized algorithm designed to migrate software containers between over- and underutilized hosts to enhance efficiency compared to traditional optimization methods. The simulation results demonstrate the algorithm's effectiveness, validating its potential for optimizing energy usage and resource allocation in cloud environments. Statistical analyses confirm the proposed model's and algorithm's superiority over benchmark approaches, highlighting their potential for enhancing resource management in cloud computing systems.

The trend of adopting Internet of Things (IoT) in healthcare, smart cities, Industry 4.0, and so on is increasing by means of cloud computing, which provides on-demand storage and computation facilities over the Internet. To meet specific requirements of IoT applications, the cloud has also shifted its service offering platform to its next-generation models, such as fog, mist, and dew computing. As a result, the cloud and IoT have become part and parcel of smart applications that play significant roles in improving the quality of human life. In addition to the inherent advantages of advanced cloud models, to improve the performance of IoT applications further, it is essential to understand how the resources in the cloud and cloud-influenced platforms are managed to support various phases in the end-to-end IoT deployment. Considering this importance, in this article, we provide a brief description, a systematic review, and possible research directions on every aspect of resource management tasks, such as workload modeling, resource provisioning, workload scheduling, resource allocation, load balancing, energy management, and resource heterogeneity in such advanced platforms, from a cloud perspective. The primary objective of this article is to help early researchers gain insight into the underlying concepts of resource management tasks in the cloud for IoT applications.

Enterprise risk forecasting in human resource management in cloud computing using cascaded block visual modelling, artificial hummingbird guided graph attention networks

A novel bio-inspired approach for VM load balancing and efficient resource management in cloud

Enterprise application performance determines business success levels because these systems enable decisive operational functions and decision processes. Cloud computing is an innovative management solution that provides adaptable systems, scalable abilities, and affordable operational costs for enterprise applications. Maximizing cloud benefits requires proper management of cloud resources to take complete advantage. The paper evaluates significant cloud resource management approaches, including auto-scaling, load balancing, resource optimization, and AI technology to handle performance issues. Multiple best practices are delivered that help achieve top application performance, reduced costs, and reliable operation. This text investigates modern cloud resource management patterns through edge computing analysis, serverless architecture, and sustainable cloud environment approaches for future cloud resource management frameworks. The adopted strategies shield applications from interruptions while creating adaptability and efficiency, allowing enterprises to meet their upcoming operational requirements.

Due to the diversity of resources and different management requirements, Cloud management is faced with great challenges in complexity and difficulty. For constructing a management system to satisfy a specific management requirement, a redevelopment solution based on existing system is usually more practicable than developing the system from scratch. However, the difficulty and workload of redevelopment are very high. In this paper, we present a runtime model based approach to managing diverse Cloud resources. First, we construct the runtime model of each kind of Cloud resources. Second, we construct the composite runtime model of all managed resources through model merge. Third, we make Cloud management meet personalized requirements through model transformation from the composite model to the customized models. Finally, all the management tasks can be carried out through executing operating programs on the customized model. The feasibility and efficiency of the approach are validated through a real case study.

Recent advancements in the automotive industry have led to the design of smart vehicles with high capacity resources for communication, sensing, processing, and storage of data. In the near future, it is envisaged that these resources will be harnessed and utilized to provide cloud services such as storage as a service, computation as a service, and sensing as a service. This paradigm of computing termed vehicular cloud, presents a lot of opportunities for the deployment of delay-sensitive applications in vehicular environment. However, high mobility of vehicles and rapidly changing topology of vehicular networks introduce new challenges such as instability of resources which make the management of resources in vehicular cloud complicated. Therefore, vehicular cloud computing require robust and mobility-aware resource management solutions to be designed. In order to achieve this goal, researchers and engineers need to be abreast with the techniques of resource management in vehicular cloud. This article presents a detailed survey of resource management tasks in vehicular clouds. A thorough introduction to vehicular cloud is presented initially. Then we identify and examine all the resource management tasks that are carried out in vehicular cloud by classifying them into three different phases: pre-resource assignment phase, resource-assignment phase, and post resource assignment phase. Proposed solutions to resource management challenges in literature at each phase are reviewed in detail. Unlike the existing surveys on vehicular clouds, this study covers all aspects of resource management such as resource brokering, resource demand prediction, resource allocation, and resource scheduling. Following the extensive survey, we also propose frameworks for resource monitoring and resource brokering for vehicular cloud. Finally, open issues and research directions for vehicular cloud resource management are presented.

Dual-timescale resource management for multi-type caching placement and multi-user computation offloading in Internet of Vehicle

A cloud resource management framework for multiple online scientific workflows using cooperative reinforcement learning agents

This paper investigates the optimal resource management in a microgrid under various operating conditions. A two-level optimization system is proposed for the distributed optimal resource management based on a multi-agent system framework. The proposed strategy generates a reference of the optimal power output at the top level through local communication. This strategy only requires the information among neighboring participants without a central control coordination, and simultaneously accomplishes resource optimization in a finite time while maintaining the supply-demand balance. The bottom-level control is responsible for the reference tracking of each corresponding participant in a microgrid. The convergent rate of the proposed algorithm is compared with other consensus-based algorithms through simulation studies. Simulation results in the IEEE 14-bus system and an actual islanded system are also presented to demonstrate the overall effectiveness of the proposed strategy.

We propose a framework that combines simulation optimization with Bayesian decision analysis to evaluate the worth of hydraulic conductivity data for optimal groundwater resources management in ecologically sensitive areas. A stochastic simulation optimization management model is employed to plan regionally distributed groundwater pumping while preserving the hydroecological balance in wetland areas. Because predictions made by an aquifer model are uncertain, groundwater supply systems operate below maximum yield. Collecting data from the groundwater system can potentially reduce predictive uncertainty and increase safe water production. The price paid for improvement in water management is the cost of collecting the additional data. Efficient data collection using Bayesian decision analysis proceeds in three stages: (1) The prior analysis determines the optimal pumping scheme and profit from water sales on the basis of known information. (2) The preposterior analysis estimates the optimal measurement locations and evaluates whether each sequential measurement will be cost‐effective before it is taken. (3) The posterior analysis then revises the prior optimal pumping scheme and consequent profit, given the new information. Stochastic simulation optimization employing a multiple‐realization approach is used to determine the optimal pumping scheme in each of the three stages. The cost of new data must not exceed the expected increase in benefit obtained in optimal groundwater exploitation. An example based on groundwater management practices in Florida aimed at wetland protection showed that the cost of data collection more than paid for itself by enabling a safe and reliable increase in production.

Cloud computing is currently playing an essential role in supporting emerging sectors such as smart energy, intelligent transportation, and large-scale distributed systems. The scalability and adaptability features of cloud computing are efficiently enable resource utilization and continuous data exchange in dynamic and heterogeneous environments. However, the increasing demand for cloud services has raised the energy consumption of cloud data centers, which has a critical environmental impact. Therefore, sustainable resource management tactics have become crucial in today's world. Dynamic Virtual Machine (VM) consolidation is one of the major tactics for sustainable resource management in the green cloud computing environment. Herein, a novel dynamic Energy-Aware Cosine Similarity Learning Network (ECSLN) is proposed to predict the overutilized host. Further, an Impact Factor-Based VM Selection (IFBVMS) method is proposed to select VMs for migration, and an ECSLN-Packed Placement method for VM placement into hosts. The main aim of the proposed VM consolidation is to maximize energy efficiency while preserving compliance with Service Level Agreements (SLAs) for sustainable environments. The experimental evaluation using real-world traces like PlanetLab, Bitbrains, and Alibaba Cluster 2020 workload validates that the proposed VM consolidation methods significantly reduce energy consumption and SLA violation compared to the existing methods for sustainable environments. The evaluation shows that the proposed ECSLN-based consolidation framework enables green cloud infrastructure by intelligently balancing energy consumption, SLA violation, and performance of cloud data centers.