Resource Management In Cloud Computing Research Articles

Efficient cloud data center: An adaptive framework for dynamic Virtual Machine Consolidation

Cloud computing is a thriving and ever-expanding sector in the industry world. This growth has sparked increased interest from organizations seeking to harness its potential. However, the sheer volume of services and offerings in this field has resulted in a noticeable surge in related data. With the rapid evolution and growing demand, cloud computing resource management faces a fresh set of challenges. Resource limitations, such as high maintenance costs, elevated Energy Consumption (EC), and adherence to Service Level Agreements (SLA), are critical concerns for both the cloud computing industry and its user organizations. In this context, taking a proactive approach to resource management and Virtual Machine Consolidation (VMC) has become imperative. The logical management of resources and the consolidation of Virtual Machines (VMs) in a manner that aligns with the requirements and demands of service providers and users have garnered widespread attention. The goal of this proposed paper is to focus on addressing the VMC problem within a unified framework, divided into two main phases. The first phase deals with host workload detection and prediction, while the subsequent phase tackles the selection and allocation of appropriate VMs. In our proposed method, for the first time, we use a Granular Computing (GRC) model, which is an efficient, scalable, and human-centric computational approach. This model exhibits behaviors similar to intelligent human decision-making, as it can simultaneously consider all factors and criteria involved in the problems. We evaluated our proposed method through simulations using CloudSim on various types of workloads. Experimental results demonstrate that our proposed algorithm outperforms other algorithms in all measurement metrics.

Dynamic resource allocation for virtual machine migration optimization using machine learning

This article delves into the importance of applying machine learning and deep reinforcement learning techniques in cloud resource management and virtual machine migration optimization, highlighting the role of these advanced technologies in dealing with the dynamic changes and complexities of cloud computing environments. Through environment modeling, policy learning, and adaptive enhancement, machine learning methods, especially deep reinforcement learning, provide effective solutions for dynamic resource allocation and virtual intelligence migration. These technologies can help cloud service providers improve resource utilization, reduce energy consumption, and improve service reliability and performance. Effective strategies include simplifying state space and action space, reward shaping, model lightweight and acceleration, and accelerating the learning process through transfer learning and meta-learning techniques. With the continuous progress of machine learning and deep reinforcement learning technologies, combined with the rapid development of cloud computing technology, it is expected that the application of these technologies in cloud resource management and virtual machine migration optimization will be more extensive and in-depth. Researchers will continue to explore more efficient algorithms and models to further improve the accuracy and efficiency of decision making. In addition, with the integration of edge computing, Internet of Things and other technologies, cloud computing resource management will face more new challenges and opportunities, and the application scope and depth of machine learning and deep reinforcement learning technology will also expand, opening new possibilities for building a more intelligent, efficient and reliable cloud computing service system.

Distributed Resource Management in Cloud Computing: A Review of Allocation, Scheduling, and Provisioning Techniques

This review paper provides an in-depth examination of distributed resource management in cloud computing, focusing on the critical elements of allocation, scheduling, and provisioning. Cloud computing, characterized by its dynamic and scalable nature, necessitates efficient resource management techniques to optimize performance, cost, and service. The study encompasses a comprehensive analysis of various strategies in resource allocation, scheduling methodologies, and provisioning techniques within the cloud computing paradigm. Through comparative analysis, this paper aims to highlight the synergies and trade-offs inherent in these methods, offering a holistic view of distributed resource management. It contributes to the field by bridging the gap in existing literature, presenting a critical, comparative analysis of current strategies and their interplay in distributed cloud environments.

ADVANCED METHODS FOR MAINTAINING AND MANAGING THE LIFE CYCLE OF CLOUD ENVIRONMENTS: SURVEY

Resource management is a fundamental concept in cloud computing and virtualization, encompassing the allocation, release, coordination, and monitoring of cloud resources to optimize efficiency. The complexity arises from the virtualized, heterogeneous, and multi-user nature of these resources. Effective governance is challenging due to uncertainty, large-scale infrastructures, and unpredictable user states. This paper presents a comprehensive taxonomy of resource management technologies, offering a detailed analysis of design architecture, virtualization, and cloud deployment models, along with capabilities, objectives, methods, and mechanisms. In a cloud computing environment, deploying application-based resource management techniques necessitates understanding the system architecture and deployment model. This paper explores centralized and distributed resource management system architectures, providing a review of effective resource management techniques for both, accompanied by a comparative analysis. The evolution of cloud computing from a centralized to a distributed paradigm is examined, emphasizing the shift towards distributed cloud architectures to harness the computing power of smart connected devices at the network edge. These architectures address challenges like latency, energy consumption, and security, crucial for IoT-based applications. The literature proposes various methods for distributed resource management, aligning with the distributed nature of these architectures. Resource management in cloud computing involves discovery, provisioning, allocation, and monitoring functions, with sub-functions like mapping and scheduling. Integrated approaches to consolidation and resource management have been explored in numerous studies. This paper summarizes and analyzes existing research on resource management functions, focusing on identification, provisioning, allocation planning, and monitoring, based on their objectives and methods.

Application of cloud computing technology in enterprise resource management

The progress of intelligent manufacturing has made enterprise resource planning (ERP) an essential core technology in the field of intelligent manufacturing. In the process of technology development, enterprises have been looking for a more optimized ERP architecture, and cloud-based ERP is one of the practical and efficient solutions. However, for the application of cloud computing in ERP, the current summary analysis based on this field is not comprehensive enough. In this paper, a comprehensive examination of the real-world implementation of cloud computing in ERP is conducted. It delves into the practical utilization and transformation of service-oriented architecture (SOA) and Model View Controller (MVC) within the context of ERP, while also providing an overview of the merits and drawbacks of SOA technology. At the same time, it summarizes the concept of layering and the practical application of cloud computing in different layers for human resource management system of enterprises. In addition, this paper summarizes the practical applications and development prospects of cloud computing in enterprise resource management, such as resource sharing and virtualization. Finally, the paper summarizes the full text, and puts forward a summary and prospect of the combination and future development of cloud computing and enterprise resource management.

Dynamic Firewall Rule Building Engine for Hybrid Cloud

Growth in the cloud computing resource management aspects have also increases the risks associated with the services hosted on the cloud.The foundational challenges are firstly to accommodate the ease of access without violating the security aspects and the time constraints to provide service responses on time, to provide the security to the active services. A good number of researches are observed to achieve the best firewall security to the hosted services. Nonetheless, the existing methods are often criticised for higher complexity and the non-performing aspects for the newer attack types. Henceforth, this work aims to solve the existing research bottlenecks. The proposed work firstly aims to solve the higher complexity of the deployed strategy by reducing the attribute sets with a sense of accuracy, time complexity and information loss. Further, this work proposes a dynamic firewall rule engine design strategy for detection of the attacks using the thresholding method. The proposed algorithms are testing on the benchmarked KDD dataset and as outcome a nearly 99.7% accuracy is observed and the time complexity is reduced to nearly 40%. Hence, this proposed work demonstrates a state-of-the-art technology for firewall design for hybrid cloud and shall be considered as a new benchmark in this domain of the research.

Cloud Computing Value Chains: Research from the Operations Management Perspective

Problem definition: Cloud computing is recognized as a critical driver of information technology–enabled innovations. The operations management (OM) community, however, has not been exposed enough to the essential operations problems that arise from the management of cloud value chains. Academic/practical relevance: In this paper, we examine recent research on cloud value chains and explore future research opportunities from an OM perspective. In particular, we focus on major operations management challenges facing a cloud provider in three problem domains: (1) cloud computing resource management, (2) pricing in the cloud computing marketplaces, and (3) capacity planning and management of cloud supply chains. Methodology: We describe prevalent business models and management practices in the cloud value chains, discuss recent research from OM that falls into each of the three problem domains mentioned, and point out opportunities for future research. Results: We note that cloud computing operations are driven by demand that exhibits distinct characteristics, including complex workflow, demand redundancy, multifeatured characteristics, multidimensional resource requirement, and nonstationarity. On the supply side, cloud computing operations also exhibit distinct characteristics, including heterogeneous resources, packing constraints, preconfigured (“bundled”) supply, technology risks, and cost uncertainty. These characteristics of demand and supply are not all prevalent in other operations. Managerial implications: Cloud computing operations not only share many features with classic OM problems, but also bring new challenges and innovative business models. Thus, OM tools and research have the potential to provide vital insights into cloud computing operations and impact management practices in the cloud industry, which, in turn, can stimulate much innovative research from the OM perspective. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1178 .

Implementation of Trusted Traceability Query Using Blockchain and Deep Reinforcement Learning in Resource Management.

To better track the source of goods and maintain the quality of goods, the present work uses blockchain technology to establish a system for trusted traceability queries and information management. Primarily, the analysis is made on the shortcomings of the traceability system in the field of agricultural products at the present stage; the study is conducted on the application of the traceability system to blockchain technology, and a new model of agricultural product traceability system is established based on the blockchain technology. Then, a study is carried out on the task scheduling problem of resource clusters in cloud computing resource management. The present work expands the task model and uses the deep Q network algorithm in deep reinforcement learning to solve various optimization objectives preset in the task scheduling problem. Next, a resource management algorithm based on a deep Q network is proposed. Finally, the performance of the algorithm is analyzed from the aspects of parameters, structure, and task load. Experiments show that the algorithm is better than Shortest Job First (SJF), Tetris∗, Packer, and other classic task scheduling algorithms in different optimization objectives. In the traceability system test, the traceability accuracy is 99% for the constructed system in the first group of samples. In the second group, the traceability accuracy reaches 98% for the constructed system. In general, the traceability accuracy of the system proposed here is above 98% in 8 groups of experimental samples, and the traceability accuracy is close for each experimental group. The resource management approach of the traceability system constructed here provides some ideas for the application of reinforcement learning technology in the construction of traceability systems.

Intelligent resource management in cloud computing and networking

Intelligent resource management in cloud computing and networking

A Survey of Game-Theoretic Approach for Resource Management in Cloud Computing

Cloud computing is a groundbreaking technique that provides a whole lot of facilities such as storage, memory, and CPU as well as facilities such as servers and web service. It allows businesses and individuals to subcontract their computing needs as well as trust a network provider with its data warehousing and processing. The fact remains that cloud computing is a resource-finite domain where cloud users contend for available resources to carry out desired tasks. Resource management (RM) is a process that deals with the procurement and release of resources. The management of cloud resources is desirable for improved usage and service delivery. In this paper, we reviewed various resource management techniques embraced in literature. We concentrated majorly on investigating game-theoretic submission for the management of required resources, as a potential solution in modeling the resource allocation, scheduling, provisioning, and load balancing problems in cloud computing. This paper presents a survey of several game-theoretic techniques implemented in cloud computing resource management. Based on this survey, we presented a guideline to aid the adoption and utilization of game-theoretic resource management strategy.

Improving the Mean-Field Fluid Model of Processor Sharing Queueing Networks for Dynamic Performance Models in Cloud Computing

Resource management in cloud computing is a difficult problem, as one needs to balance between adequate service to clients and cost minimization in a dynamic environment of interconnected components. To make correct decisions in such an environment, good performance models are necessary. A common modeling methodology is to use networks of queues, but as these are prohibitively expensive to evaluate for many applications, approximation methods for key metrics are frequently employed. One such method-that provides both transient solutions and short, scalable computation times-is the fluid model, which approximates the dynamics of the mean queue lengths using a system of ordinary differential equations. However, finding a fluid model that can adequately approximate an arbitrary queueing network is in general difficult. In the paper, we extend the state of the art with the following three contributions. First, we show that for any mixed multiclass queueing network of processor sharing and delay queues with phase-type service time distributions, such a fluid model can be found via the meanfield approximation. Furthermore, we propose an improved model based on smoothing of the processor share function that improves the performance of certain systems. Finally, using the smoothed mean-field model, we introduce an accurate closed-form approximation of the response time CDF over any subset of classes and queues.

Task Classification for Improving Scheduling and Resource Management in Cloud Computing

Task Classification for Improving Scheduling and Resource Management in Cloud Computing

Downtime Distribution, Risk Volatility, and Optimal Backup VMs Allocation in Cloud Services

Service Level Agreement (SLA) is an important basis to ensure the availability of cloud service. Cloud service providers seek to not only fulfill the SLA, but also simultaneously minimize the risk uncertainty. Under such a mechanism, service providers look forward to determine the optimal number of backup virtual machines (VMs) to minimize operational costs. In this paper, we propose a data-driven risk measurement method under multi-stage SLA and empirically portray the likelihood and risk values of compensation events at different stages under several service periods. Furthermore, a multi-objective optimization function is established to balance the provisioning cost and risk volatility for cloud service providers with different risk preferences. This paper provides a practical method to cloud service providers by addressing the risk uncertainty in multi-stage SLA from cloud computing resource management perspective.

Improving the mean-field fluid model of processor sharing queueing networks for dynamic performance models in cloud computing

Resource management in cloud computing is a difficult problem, as one is often tasked with balancing between adequate service to clients and cost minimization in dynamic environments of many interconnected components. To make correct decisions in these environments, good performance models are necessary. A common modeling methodology is to use networks of queues, but as these are prohibitively expensive to evaluate for many real-time applications, different approximation methods for important metrics are frequently employed. One such method—that provides both transient solutions and short, scalable computation times—is the fluid model, which approximates the dynamics of the mean queue lengths using a system of ordinary differential equations. However, finding a fluid model that can adequately approximate an arbitrary queueing network is in general difficult. In this paper, we extend the state of the art with the following three contributions. First, we show that for any mixed multiclass queueing network of processor sharing and delay queues with phase-type service time distributions, such a fluid model can be found via the mean-field approximation. Furthermore, we propose an improved model based on smoothing of the processor share function that improves the performance of certain systems. Finally, using the smoothed mean-field model, we introduce an accurate closed-form approximation of the response time CDF over any subset of classes and queues. The contributions are further evaluated in a large simulation experiment, which shows that they can be used to accurately predict performance metrics under some system perturbations common in cloud computing.

Cloud computing and human resource management: systematic literature review and future research agenda

PurposeThis study aims to provide an overview of what characterizes the current state of research in the field of cloud computing use in human resource management (HRM) with the identification, analysis and classification of the existing literature and lines of research addressed and to provide guidance for future research.Design/methodology/approachThe systematic literature review (SLR) technique has been used to identify, select, analyze and evaluate the existing publications on cloud computing and HRM. A total of 35 papers published up to December 2020 have been obtained from the Web of Science (WoS) scientific database. The research design has allowed us to determine what characterizes the current state of research on the use of cloud computing in HRM and obtain a novel classification of the literature that identifies four lines of research and the contributions in each line and has allowed us to define the future research agenda.FindingsThe four groups into which the papers on the cloud computing-HRM relationship have been classified are: (1) studies focused on the development of cloud platforms for HRM that highlight technical aspects, (2) papers that focus on the concept of human resource elasticity, (3) papers on the adoption and/or implantation of cloud platforms for HRM and (4) studies that highlight the effects or implications of cloud platforms for HRM. This paper proposes some new opportunities for future research and presents some helpful implications from the theoretical and management perspectives.Research limitations/implicationsThis study uses only scientific articles in the WoS database with a Journal Citation Report (JCR) or SCImago Journal Rank (SJR) impact.Originality/valueThis paper provides an overview of the knowledge on cloud computing and HRM research and offers recommendations for future research.

Rationalizing Resource Utilization in Cloud Computing Using Coalition Formation Strategy

Even though the contribution of cloud computing towards the Sustainable Development (SD) of communities is still under research investigation, cloud computing has become an integral part of many ICT solutions that shape our daily lives. Thus, some researchers recommend taking considerable actions to point cloud computing development towards supporting SD. In this research, an approach to designing energy efficient cloud architecture as a way of supporting SD is proposed. Resource allocation is a challenging process in cloud management, the goal is to allocate the exact amount of resources needed throughout the service duration; tight enough to avoid unnecessarily wasting resources and loose enough to prevent any degradation in Quality of Service (QoS) that may lead to the violation of the Service Level Agreement (SLA) between the service provider and the cloud user. This study aims to achieve the desired balance by benefiting from the history of the user’s behaviour and from sharing resources – more specifically Virtual Machines (VM) – among a coalition of users. Coalition formation strategy is used to build groups of cloud users based on their cloud behaviour history. Users are grouped in a way that their usage patterns complement each other, either to avoid the loss stemming from VM excess reserved space or from idle times. A type of architecture that fulfils this improvement process is proposed and implemented on Google Compute Engine (GCE). The contribution of this research is that it applies the Coalition formation strategy in cloud computing resource management in a novel way and experiments show that there are scenarios where the efficiency of resource management has improved. Evaluation of the performance of the proposed architecture is done by comparing resource utilization for both the cloud following this architecture and the cloud that runs the basic GCE strategy. In conclusion, it is observed that improvements depend on accuracy of the prediction of usage pattern of the user. Results show that in certain scenarios, improvements can be made to up to 24% of VM usage and, in other scenarios, it can minimize the number of required VMs, thus contributing to green computing.

Efficient Computing Resource Sharing for Mobile Edge-Cloud Computing Networks

Both the edge and the cloud can provide computing services for mobile devices to enhance their performance. The edge can reduce the conveying delay by providing local computing services while the cloud can support enormous computing requirements. Their cooperation can improve the utilization of computing resources and ensure the QoS, and thus is critical to edge-cloud computing business models. This paper proposes an efficient framework for mobile edge-cloud computing networks, which enables the edge and the cloud to share their computing resources in the form of wholesale and buyback. To optimize the computing resource sharing process, we formulate the computing resource management problems for the edge servers to manage their wholesale and buyback scheme and the cloud to determine the wholesale price and its local computing resources. Then, we solve these problems from two perspectives: i) social welfare maximization and ii) profit maximization for the edge and the cloud. For i), we have proved the concavity of the social welfare and proposed an optimal cloud computing resource management to maximize the social welfare. For ii), since it is difficult to directly prove the convexity of the primal problem, we first proved the concavity of the wholesaled computing resources with respect to the wholesale price and designed an optimal pricing and cloud computing resource management to maximize their profits. Numerical evaluations show that the total profit can be maximized by social welfare maximization while the respective profits can be maximized by the optimal pricing and cloud computing resource management.

Policies and mechanisms for enhancing the resource management in cloud computing: a performance perspective

Resource management is among the critical challenges in cloud computing since it can affect its performance, cost, and functionality. In this paper, a survey on policies and mechanisms for enhancing the resource management in cloud computing is proposed. From a performance perspective, several resource management schemes for cloud computing are investigated and qualitatively compared in terms of various different parameters such as performance, response time, scalability, pricing factor, throughput, and accuracy, providing some fundamental knowledge based for researchers in the cloud computing area. We also classified various cloud computing techniques based on various policies like capacity allocation, admission control, load balancing and energy optimisation. Further, we divided defined techniques on the basis of various parameters like low, medium, high time span time techniques, reliability, performance, availability to name a few.

Policies and mechanisms for enhancing the resource management in cloud computing: a performance perspective

Policies and mechanisms for enhancing the resource management in cloud computing: a performance perspective

A survey and classification of the workload forecasting methods in cloud computing

Workload prediction is one of the important parts of proactive resource management and auto-scaling in cloud computing. Accurate prediction of workload in cloud computing is of high importance for improving cloud performance, mitigate energy consumptions, meeting the required quality of service (QoS) level, predicting the energy consumption of data centers (DCs), and improving the cloud service providers’ scalability. However, in cloud computing context workload prediction is a challenging issue and various schemes using machine learning, data mining, and mathematical methods to deal with this issue. This scheme presents an extensive literature review of the workload prediction schemes proposed in the literature to improve resource management in the cloud DCs. It first provides the required knowledge regarding the workload prediction context and presents a taxonomy of the workload prediction schemes according to their applied prediction algorithm. Moreover, the main contributions of these schemes are illustrated and their major advantages and limitation are specified. At last, the open research opportunities in the workload prediction field are focused and the concluding remarks are presented.