Resource Allocation In Cloud Environment Research Articles

Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach

The cloud computing paradigm provides services to users in an on-demand fashion using high-speed Internet. This Internet-based computing paradigm provides resources on a rent basis without any fault. Virtual machine resource allocation is one of the challenging concerns in a cloud computing environment. The existing static, dynamic, and Meta-Heuristic approaches provide the solution to the virtual machine allocation problem. These techniques stuck with the local optimal solution. The slow convergence rate leads to the optimal solution locally and fails to provide the optimal solution Globally. This manuscript proposes a hybrid Spotted Hyena optimizer and artificial neural network, named the SHO-ANN technique, to provide a solution to the virtual machine assignment problem. The presented hybrid technique is evaluated and analyzed using performance metrics “Energy Consumption (Kwh) (8.54%), Host Utilization (24.8%), Average Execution Time(ms) (26.33%), SLA Violations (1.33%), and Number of Migrations (Counts) (19.73%)”. The spotted hyena optimizer is used to provide the vast data set to the ANN model for better accuracy. The hybrid approach provides an optimal solution globally with high convergence. The experimental results exhibit that the SHO-ANN outperforms the IqMc, SHO, and Genetic approaches using real workload scenarios and fabricated scenarios.

Optimization of containerized application deployment in virtualized environments: a novel mathematical framework for resource-efficient and energy-aware server infrastructure

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.

Joint optimization of load balancing and resource allocation in cloud environment using optimal container management strategy

SummaryDue to the high performance of cloud computing‐based microservices, a wide range of industries and fields rely on them. In a containerized cloud, traditional resource management strategies are typically used to allocate and migrate virtual machines. A major problem for cloud service providers is resource allocation for containers, which directly affects system performance and resource consumption. In this paper, we propose a joint optimization of load balancing and resource allocation in the cloud using an optimal container management strategy. We aim to enhance scheduling efficiency and reduce costs by improving the container's schedule requested digitally by users. An improved backtracking search optimization (IBSO) algorithm is used to allocate resources between end‐users/IoT devices and the cloud under the consideration of service‐level agreements. Mechanic quantum recurrent neural networks (MQ‐RNNs) are designed to allocate, consolidate, and migrate containers in cloud environments. The various simulation measures used to validate the proposed strategy are energy consumption, number of active servers, number of interruptions, total cost, runtime, and statistical measures.

Optimizing Resource Allocation in Cloud Environments using Fruit Fly Optimization and Convolutional Neural Networks

Optimizing Resource Allocation in Cloud Environments using Fruit Fly Optimization and Convolutional Neural Networks

Dynamic Load Balancing for Improved Resource Allocation in Cloud Environments

Cloud computing has emerged as a revolutionary paradigm in information technology, offering scalable and on-demand access to computing resources. Efficient resource allocation is a crucial aspect in ensuring optimal performance and cost-effectiveness of cloud environments. Load balancing algorithms play a vital role in distributing workloads across available resources, preventing resource overutilization and underutilization. This research focuses on optimizing resource allocation in cloud computing through the application of advanced load balancing algorithms. The primary objective is to enhance resource utilization, minimize response time, and improve overall system performance. Traditional load balancing techniques often fall short in addressing the dynamic and heterogeneous nature of cloud environments. Therefore, this study investigates novel algorithms that leverage real-time resource monitoring, predictive analytics, and adaptive decision-making to intelligently allocate workloads. The research involves the design, implementation, and evaluation of multiple load balancing algorithms. Comparative analysis is conducted to assess the efficiency of the proposed algorithms against existing methods. Performance metrics such as response time, throughput, resource utilization, and scalability are used to gauge the effectiveness of the algorithms under various workload scenarios. this research explores the impact of load balancing strategies on energy consumption and environmental sustainability. Energy-efficient load balancing algorithms are developed to align resource allocation with energy consumption patterns, contributing to the overall green computing initiative.

ADAPTIVE RESOURCE ALLOCATION METHOD FOR DATA PROCESSING AND SECURITY IN CLOUD ENVIRONMENT

Subject of research: methods of resource allocation of the cloud environment. The purpose of the research: to develop a method of resource allocation that will improve the security of the cloud environment. At the same time, effective data processing should be achieved. Method characteristics. The article discusses the method of adaptive resource allocation in cloud environments, focusing on its significance for data processing and enhanced security. A notable feature of the method is the consideration of external influences when calculating the characteristics of cloud resource requests and predicting resource requests based on a time series test. The main idea of this approach lies in the ability to intelligently distribute resources while considering real needs, which has the potential to optimize both productivity and confidentiality protection simultaneously. Integrating adaptive resource allocation methods not only improves data processing efficiency in cloud environments but also strengthens mechanisms against potential cyber threats. Research results. To ensure timely resource allocation, the NSGA-II algorithm has been enhanced. This allowed reducing the resolution time of multi-objective optimization tasks by 5%. Additionally, research results demonstrate that effective utilization of various types of resources on a physical machine reduces resource losses by 1.2 times compared to SPEA2 and NSGA-II methods.

POLSTM: Poplar optimization-based long short term memory model for resource allocation in cloud environment

Due to the evolution of Internet of Things (IoT) paradigm, the number of devices is growing day by day which arises the stringent requirements for various communications. The standard cloud computing model is not capable of efficiently hosting IoT tasks due to the high latency associated with it. Hence, this work utilizes the mobile edge computing and fog concept to process the input tasks independent of the cloud layer. Generally, the high computation costs and energy consumption resulted in the applications such as power-hungry and computation-intensive, which become massive challenges for an IoT device. Motivated by the previous research, we mainly plan to investigate the resource allocation issues that arise in the MEC-enabled IoT–Fog–cloud architecture. We propose a Poplar Optimization algorithm (POA) based on Attention 1DCNN-LSTM architecture (POA-A1DCNN-LSTM) for improving the total utility of the MEC servers by optimizing the energy consumption and task delay. Initially, the POA algorithm is implemented for cluster head (optimal fog node) selection using the node degree, node distance, and residual energy. Next, the A1DCNN-LSTM architecture is employed for task offloading by selecting the fog node with minimal task length and processing delay. The performance of the proposed method is validated by average latency, user satisfaction, network lifetime, energy consumption, average time delay, and normalized system utility. The experimentation results revealed that the proposed method attained better effectiveness in different metrics by achieving 1.5 ms, 0.65, 60 s, 0.45 j, 385 ms, and 0.98 compared to state-of-the-art methods.The experimentation outcomes demonstrate the effectiveness of the proposed POA-A1DCNN-LSTM architecture to decrease task completion delay and offer effective task scheduling as well as resource allocation performances in the MEC-enabled IoT–Fog–cloud network.

Dynamic Resource Allocation in Cloud Environments

Abstract: The provisioning and use of computing resources has been completely transformed by cloud computing, which provides previously unheard-of levels of scalability, flexibility, and cost-effectiveness. In order to maximize resource utilizatio and guarantee peak performance in cloud settings, dynamic resource allocation is essential. In order to provide a thorough grasp of the most recent developments in this subject, this survey paper offers an in-depth review of dynamic resource allocation methods in cloud computing. The survey includes a wide range of dynamic resource allocation techniques, including as cost-optimization methods, resource provisioning and scheduling techniques, and load-balancing algorithms. Each method is examined in terms of its advantages, drawbacks, and applicability to particular cloud deployment scenarios. The research also investigates evaluation approaches and performance indicators for evaluating the efficacy of dynamic resource allocation systems. It emphasizes the need for continued research and innovation to address the evolving demands of cloud computing environments.

Optimizing Resource Allocation in Cloud Infrastructure through AI Automation: A Comparative Study

Optimizing resource allocation in cloud infrastructure is paramount for ensuring efficient utilization of computing resources and minimizing operational costs. With the proliferation of diverse workloads and dynamic user demands, manual resource management becomes increasingly challenging. In this context, artificial intelligence (AI) automation emerges as a promising approach to enhance resource allocation efficiency. This paper presents a comparative study of various AI techniques applied to optimize resource allocation in cloud environments. We explore the efficacy of machine learning, evolutionary algorithms, and deep reinforcement learning methods in dynamically allocating resources to meet performance objectives while minimizing costs. Through a comprehensive evaluation of these approaches using real-world datasets and simulation experiments, we highlight their strengths, limitations, and comparative performance. Our findings provide valuable insights into the effectiveness of AI-driven resource allocation strategies, enabling cloud providers and practitioners to make informed decisions for enhancing cloud infrastructure management

An Improved Ant Colony Algorithm for New energy Industry Resource Allocation in Cloud Environment

An Improved Ant Colony Algorithm for New energy Industry Resource Allocation in Cloud Environment

BFS-SVM Classifier for QoS and Resource Allocation in Cloud Environment

In cloud computing Resource allocation is a very complex task. Handling the customer demand makes the challenges of on-demand resource allocation. Many challenges are faced by conventional methods for resource allocation in order to meet the Quality of Service (QoS) requirements of users. For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work. The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection (BFS) in the proposed work, this further reduces the inappropriate features from the data. The similarities that were hidden can be demoralized by the Support Vector Machine (SVM) classifier which is also determine the subspace vector and then a new feature vector can be predicted by using SVM. For an unexpected circumstance SVM model can make a resource allocation decision. The efficiency of proposed SVM classifier of resource allocation can be highlighted by using a single-cell multiuser massive Multiple-Input Multiple Output (MIMO) system, with beam allocation problem as an example. The proposed resource allocation based on SVM performs efficiently than the existing conventional methods; this has been proven by analysing its results.

Energy Efficient Resource Allocation in Cloud Environment Using Metaheuristic Algorithm

Energy Efficient Resource Allocation in Cloud Environment Using Metaheuristic Algorithm

A Review on Trust-Based Resource Allocation in Cloud Environment: Issues Toward Collaborative Cloud

Due to the exponential rise in the usage of the internet and smart devices, there is a demand for enhanced network efficiency and user satisfaction in a cloud computing environment. Moreover, moving to the cloud systems, it mainly focuses on storage, computation and resources. Due to copious growth, there exist more challenges as well. Among those, resource allocation in cloud computing is the main study, which is essential to determine the QoS and improved performance concerning reliability, confidentiality, trust, security, user satisfaction, profits, etc. This paper plans to prepare a detailed review on trust-based resource allocation in the collaborative cloud. The cloud industry has been assessed in terms of trust-based and other important factors to produce a road plan for resource allocation. Many papers are reviewed here and give a substantial evaluation of cloud resources and their resource allocation models using machine learning and optimization models. First, this survey provides an elaborated study concerning the various cloud resources considering the performance and QoS. Eventually, it extends the research based on trust-based approaches, with the intention of motivating the researchers to focus on trust-based resource allocation on collaborative cloud computing (CCC) atmosphere.

STATIC ALLOCATION METHOD IN A CLOUD ENVIRONMENT WITH A SERVICE MODEL IAAS

The article discusses a method that allows the allocation of the required computing resources for the initial launch of a virtual host. The method is focused on the functioning of a virtual host in a cloud environment focused on the service model "Infrastructure as a Service". The subject of research is the methods of static resource allocation in cloud environments. The object of study is the process of functioning of a virtual host in a cloud environment that provides all information technology resources for it. The purpose of the study is to develop a method for the static allocation of resources in the cloud environment, focused on the features of the service model "Infrastructure as a Service". Results: An approach has been developed for carrying out the decomposition of a cloud computing environment with the IAAS service model. The analysis of existing methods of static allocation of resources has been carried out. The application of the method of analysis of hierarchies for this problem is substantiated. A step-by-step algorithm for finding the most acceptable alternative from the set proposed has been developed. An example of the application of the developed method for initializing a virtual host in a cloud environment with the IAAS service model is given. Conclusion. The proposed method makes it possible to rationally use the computing resources of the cloud environment, which uses the "Infrastructure as a Service" service model. Direction for further research. The development of this direction is the development of a method for dynamic redistribution of resources in a cloud environment with the IAAS service model.

Multi-Objective Optimization Method for Task Scheduling and Resource Allocation in Cloud Environment

In the cloud environment, the process of task scheduling and resource allocation plays a vital role in cloud resource management. The unpredictable and uncertain behaviour of the task arrival rate poses significant challenges in the effective allocation of resources. An efficient scheduling technique is essential to avoid under or overutilization of resources. In order to increase the performance of scheduling and allocation, this paper presents multi-objective optimization method for optimal resource allocation and task scheduling based on a three-stage strategy. In the first stage, a description of tasks and virtual machines is prepared. At stage two, tasks are classified and labelled based on the resource demand and execution time. Finally, the modified-Grey Wolf optimization algorithm is used for the allocation and scheduling of tasks for a disparate scenario. The experimental results proved that the proposed method reduced the makespan time and cost with an improved utilization rate.

Spider Monkey Optimization based Energy-Efficient Resource Allocation in Cloud Environment

The origin of Cloud computing is from the principle of utility computing, which is characterized as a broadband service providing storage and computational resources. It provides a large variety of processing options and heterogeneous tools, allowing it to meet the needs of a wide range of applications at different levels. As a result, resource allocation and management are critical in cloud computing. In this work, the Spider Monkey Optimization (SMO) is used for attaining an optimized resource allocation. The key parameters considered to regulate the performance of SMO are its application time, migration time, and resource utilization. Energy consumption is another key factor in cloud computation which is also considered in this work. The Green Cloud Scheduling Model (GCSM) is followed in this work for the energy utilization of the resources. This is done by scheduling the heterogeneity tasks with the support of a scheduler unit which schedules and allocates the tasks which are deadline-constrained enclosed to nodes which are only energy-conscious. Assessing these methods is formulated using the cloud simulator programming process. The parameter used to determine the energy efficiency of this method is its energy consumption. The simulated outcome of the proposed approach proves to be effective in response time, makespan, energy consumption along with resource utility corresponding to the existing algorithms.

An Enhanced Load Balancing Approach for Dynamic Resource Allocation in Cloud Environments

An Enhanced Load Balancing Approach for Dynamic Resource Allocation in Cloud Environments

Hybrid Evolutionary Algorithm based Task Scheduling Mechanism for Resource Allocation in Cloud Environment

Distributed computing, grid computing, and virtualization have all resulted in the growth of cloud computing.. The existing research work, introduced a strategic theory (ST) based Improved Elephant Herd Optimization (IEHO) for feasible allotment procedure where the result function confess a straight payment system to meet the users requirements. However total users is huge in cloud computing, total tasks and total data are as well vast. Because the cost of every work on cloud services varies, client task scheduling in the cloud is not like conventional scheduling approaches. In a cloud computing system, it's critical to figure out how to effectively schedule work. As a results, the research proposes an unique Task Scheduling Mechanism (TSM) was proposed, which may suit users' needs while also improving resource usage and so improving the overall performance of the cloud cloud computing. The goal of this project is to plan task groups in a cloud computing platform with varying resource costs and computational performance. The suggested cloud scheduling solution uses an enhanced cost-based scheduling scheme to efficiently map workloads to existing cloud services. The scheduling technique, that is founded on Hybrid Evolutionary (HE)algorithms, increases the computation/communication proportion by combining user jobs as per a cloud resource's processing capacity and sending the aggregated activities to the resource. The simulation experiments show that the proposed Hybrid Evolutionary based Task Scheduling Mechanism (HE-TSM) is efficiently schedule the tasks along with both costs and performance in the cloud computing environment.

Algorithm and model for improve the avoiding of deadlock with increasing efficiency of resource allocation in cloud environment

Recently, cloud computing has been the most popular method among all other promising technologies that provides the service for many users at the same time. all users can reach and use the resource that is provided by the Virtual Machines (VMs). Hence users process, data must arrive to the resources to perform as a disturbed system, which provides dynamical scale services and virtualized resources. As a result of limitation of resources, the deadlock status may occur in such type of systems. In this paper, we have designed an algorithm that could increase the efficiency of deadlock avoidance. Our technique will use the attribute of execution time in the process to get better resource allocation in additional to the checking system to stay in safe state and free of deadlock.

A Robust Strategic Theory based Load Balancing and Resource Allocation in Cloud Environment

An on demand elastic service is cloud computing. At particular time, based on clients requirements, software, information, shared resources and other devices are provided in this. For supporting cost effective usage of computing resources, it is designed using distributed computing and virtualization advances and it enhances resource scalability. Based on requirement, business outcomes can scale up or down its resources. On-demand resource allocation challenges are created by customer demand management. To minimize energy, Dynamic Particle Swarm Optimization (DPSO) model is developed in available research works, where CPU utilization are regulated while operating at maximum frequency. Geographically distributed resources like computers, storage etc. are owned by self interested organizations or agents are available in large-scale computing systems of cloud computing. In their own benefit, this resource allocation algorithm can be manipulated by these agents and severe degradation in performance are produced due to its selfish behaviour and its efficiency is also very poor. To solve this kind of problem, a strategy is designed for developing a resource allocation protocols with load balancer in first phase of this research work. In this agents are forced to follow the rules and tell truth. In heterogeneous distributed systems, to solve load balancing problem, a truthful strategy is designed using this strategic theory. Optimal allocation algorithm based on Improved Elephant Herd Optimization (IEHO) is proposed where a truthful payment scheme is admitted by output function which satisfies voluntary participation. Good performance is exhibited by proposed approach as indicated in experimentation results.