Resource Allocation In Cloud Computing Research Articles

Optimizing Resource Allocation in Cloud Computing Using Machine Learning

Purpose: A key component of large-scale distributed computing is the allocation of resources, as computer networks cooperate to address complex optimization problems. To get the most out of computers in general, or throughput, is the goal of resource allocation in this case. When it comes to distributed computing, there are two main varieties: grid computing and cloud computing. In grid computing, many geographically dispersed clusters are linked and made available to the general public. Materials and Methods: We looked at Monte Carlo Tree Search and Long-Short Term Memory and examined how efficient they were. By maintaining consistent traffic patterns, the simulation demonstrated that MCTS performance was improved. However, such a strategy is difficult to implement due to the potential speed with which traffic patterns may alter. A successful service level agreement (SLA) was achieved, and the issue was shown to be fixable using LSTM. We compare the suggested model to different load-balancing algorithms to find the one that best allocates resources. Findings: The results show that compared to the state-of-the-art models, the suggested model achieves an accuracy rate that is 10-15% higher. The suggested model lowers the error percentage rate of the average request blocking likelihood of traffic load by around 9.5-10.2% when compared to the predictions of existing models. Therefore, the proposed method has the potential to enhance network utilization by reducing the amount of time required by memory and the central processing unit. Implications to Theory, Practice and Policy: One advantage of the new method is a more robust forecasting strategy in comparison to earlier models. Using firefly algorithms, future research will construct a cloud data center that employs a variety of heuristics and machine learning methodologies to load balance the energy cloud (Oshawa et al., 2022).

Joint Offloading and Resource Allocation for Hybrid Cloud and Edge Computing in SAGINs: A Decision Assisted Hybrid Action Space Deep Reinforcement Learning Approach

Joint Offloading and Resource Allocation for Hybrid Cloud and Edge Computing in SAGINs: A Decision Assisted Hybrid Action Space Deep Reinforcement Learning Approach

Achieving Agility through Auto-Scaling: Strategies for Dynamic Resource Allocation in Cloud Computing

Abstract: Auto-scaling is a crucial aspect of cloud computing, allowing for the efficient allocation of computational resources in response to immediate demand. This article delves into the concept of auto-scaling, its key components, and the strategies used to effectively manage resources in cloud environments. This study emphasizes the importance of auto-scaling in the cloud computing landscape by exploring its benefits, including cost efficiency, performance optimization, high availability, and scalability [1]. The article explores the various factors to consider when implementing scaling policies, such as selecting the right approach for scaling, whether it be predictive or reactive and the availability of auto-scaling services provided by major cloud platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure [2, 3]. In addition, the paper addresses the challenges and complexities related to configuring auto-scaling systems, cost management, and latency in resource provisioning [4]. The article also showcases case studies that illustrate the successful implementation of auto-scaling in different industries, along with valuable insights and recommended approaches [5]. Lastly, this paper delves into future trends and research directions in auto-scaling techniques, integration with emerging technologies, and potential research areas [6].

Key Challenges of Cloud Computing Resource Allocation in Small and Medium Enterprises

Although cloud computing offers many benefits, such as flexibility, scalability, and profitability, some small and medium enterprises (SMEs) are still unable to fully utilize cloud resources, such as memory, computing power, storage, and network bandwidth. This reduces their productivity and increases their expenses. Therefore, the central objective of this paper was to examine the key challenges related to the allocation of cloud computing resources in small and medium enterprises. The method used for this study is based upon qualitative research using 12 interviews with 12 owners, managers, and experts in cloud computing in four countries: the United States of America, the United Kingdom, India, and Pakistan. Our results, based on our empirical data, show 11 key barriers to resource allocation in cloud computing that are classified based on the technology, organization, and environment (TOE) framework. Theoretically, this research contributes to the body of knowledge concerning cloud computing technology and offers valuable understanding of the cloud computing resource allocation approaches employed by small and medium enterprises (SMEs). In practice, this research is useful to aid SMEs in implementing successful and sustainable strategies for allocating cloud computing resources.

Deep Learning Modified Reinforcement Learning with Virtual Machine Consolidation for Energy-Efficient Resource Allocation in Cloud Computing

Cloud computing has attracted significant attention because of the growing service demands of businesses that outsource computationally intensive tasks to the data center. Meanwhile, the infrastructure of a data center is comprised of hardware resources that consume a great deal of energy and release harmful levels of carbon dioxide. Cloud data centers demand massive amounts of electrical power as modern applications and organizations grow. To prevent resource waste and promote energy efficiency, virtual machines (VMs) must be dispersed over numerous physical machines (PMs) in a data center in the cloud. The actual allocation of VMs to PMs can involve more complex decision-making processes, such as considering the resource utilization, load balancing, performance requirements, and constraints of the system. Advanced techniques, like intelligent placement algorithms or dynamic resource allocation, may be employed to optimize resource utilization and achieve efficient VM distribution across multiple PMs. Cloud service suppliers aim to lower operational expenses by reducing energy consumption while offering clients competitive services. Minimizing large-scale data center power usage while maintaining the quality of service (QoS), especially for social media-based cloud computing systems, is crucial. Consolidating VMs has been highlighted as a promising method for improving resource efficiency and saving energy in data centers. This research provides deep learning augmented reinforcement learning (RL)-based energy efficient and QoS-aware virtual machine consolidation (VMC) approach to meet the difficulties. The proposed deep learning modified reinforcement learning-virtual machine consolidation (DLMRL-VMC) model can motivate both cloud providers and customers to distribute cloud infrastructure resources to achieve high CPU utilization and good energy efficiency as measured by power usage effectiveness (PUE) and data center infrastructure efficiency (DCiE). The suggested model, DLMRL-VMC, offers a VM placement approach based on resource usage and dynamic energy consumption to determine the best-matched host and VM selection strategy, Average Utilization Migration Time (AUMT). Based on AUMT, deep learning modified reinforcement learning (DLMRL) will choose a VM with a low average CPU utilization and a short migration time. The DLMRL-VMC Energy-efficient, Resource Allocation strategy is evaluated on the trace of the CloudSim VM to attain good PUE and CPU utilization.

An Empirical Study of Different Reinforcement Learning Algorithms for Resource Allocation in Cloud Computing

Regarding the increasing importance of cloud computing in modern IT architecture, it is crucial to create highly efficient resource allocation algorithms. This work conducts an empirical investigation into the utilisation of several reinforcement learning methods for optimising resource allocation in cloud computing environments. Our objective is to assess the efficiency of RL algorithms in a dynamic environment with fluctuating workloads, focusing on resource utilisation, cost effectiveness, and optimality. This research examines the effects of altering cloud settings in order to integrate theoretical reinforcement learning concepts into a practical resource management system. In the literature review, we consider the traditional resource allocation techniques and their inability to accommodate the changing demand. We additionally analyse the available research employing machine learning approaches, paying special attention to RL in cloud computing resource distribution. The methodology describes the research design, detailing the employed RL algorithms Q-learning, Deep Q Networks (DQN), and Proximal Policy Optimization (PPO). We explain the data collection procedure that involves different workloads and also situations to mimic real environments. Performance of each RL algorithm is presented in the experimental results based on the resource utilization, cost efficiency and also system responsiveness. Q-learning, DQN and PPO are being tested which provides a better understanding of their pros and cons. Discussion that follows the Interprets results of these findings bringing to light many challenges along the way as well as possible directions for future inquiry. Therefore, this research fills in the evolving landscape of cloud computing by demonstrating RL algorithms’ adaptability and effectiveness regarding resource allocation challenges under the dynamic environments.

Optimized resource allocation in cloud computing for enhanced performance with modified particle swarm optimization

Cloud Computing (CC) offers abundant resources and diverse services for running a wide range of consumer applications, although it faces specific issues that need attention. Cloud customers aim to choose the most suitable resource that fulfills the requirements of consumers at a fair cost and within an acceptable timeframe; however, at times, they wind up paying more for a shorter duration. Many advanced algorithms focus on optimizing a single variable individually. Hence, an Optimized Resource Allocation in Cloud Computing (ORA-CC) Model is required to achieve equilibrium between opposing aims in Cloud Computing. The ORA-CC study aims to create a task processing structure with the decision-making ability to choose the best resource in real-time for handling diverse and complicated uses on Virtual Computers (VC). It will utilize a Modified Particle Swarm Optimization (MoPSO) method to meet a deadline set by the user. The fitness value is calculated by combining a base value with the enhanced estimation of resources based on the ORA-CC algorithm to create a robust arrangement. The ORA-CC technique's effectiveness is evaluated by contrasting it with a few current multi-objective restrictions applied to machine scheduling strategies utilizing the Cloudsim simulation. The comparison demonstrates that the suggested ORA-CC strategy offers more efficient resource allocation than other techniques.

Resource Allocation in Cloud Computing with Economical Strategic Setting

Resource Allocation in Cloud Computing with Economical Strategic Setting

Optimized Deep Learning Schemes for Secured Resource Allocation and Task Scheduling in Cloud Computing - A Survey

Scheduling involves allocating shared resources gradually so that tasks can be completed within a predetermined time frame. In Task Scheduling (TS) and Resource Allocation (RA), the phrase is used independently for tasks and resources. Scheduling is widely used for Cloud Computing (CC), computer science, and operational management. Effective scheduling ensures that systems operate efficiently, decisions are made effectively, resources are used efficiently, costs are kept to a minimum, and productivity is increased. High energy consumption, lower CPU utilization, time consumption, and low robustness are the most frequent problems in TS and RA in CC. In this survey, RA and TS based on deep learning (DL) and machine learning (ML) were discussed. Additionally, look into the methods employed by DL-based RA and TS-based CC. Additionally, the benefits, drawbacks, advantages, disadvantages, and merits are explored. The work's primary contribution is an analysis and assessment of DL-based RA and TS methodologies that pinpoint problems with cloud computing.

A Deep Reinforcement Learning-Based Model for Optimal Resource Allocation and Task Scheduling in Cloud Computing

The advent of cloud computing has dramatically altered how information is stored and retrieved. However, the effectiveness and speed of cloud-based applications can be significantly impacted by inefficiencies in the distribution of resources and task scheduling. Such issues have been challenging, but machine and deep learning methods have shown great potential in recent years. This paper suggests a new technique called Deep Q-Networks and Actor-Critic (DQNAC) models that enhance cloud computing efficiency by optimizing resource allocation and task scheduling. We evaluate our approach using a dataset of real-world cloud workload traces and demonstrate that it can significantly improve resource utilization and overall performance compared to traditional approaches. Furthermore, our findings indicate that deep reinforcement learning (DRL)-based methods can be potent and effective for optimizing cloud computing, leading to improved cloud-based application efficiency and flexibility.

Task scheduling and VM placement to resource allocation in Cloud computing: challenges and opportunities

Task scheduling and VM placement to resource allocation in Cloud computing: challenges and opportunities

A Universal Fairness Evaluation Framework for Resource Allocation in Cloud Computing

Abstract: Machine learning is nowadays ubiquitous, providing mechanisms for supporting decision making that leverages big data analytics. However, this recent rise in importance of machine learning also raises societal concerns about the dependability and trustworthiness of systems which depend on such automated predictions. In cloud computing, fairness is one of the most significant indicators to evaluate resource allocation algorithms, which reveals whether each user is allocated as much as that of all other users having the same bottleneck. However, how fair an allocation algorithm is remains an urgent issue. In this paper, we propose Dynamic Evaluation Framework for Fairness (DEFF), a framework to evaluate the fairness of an resource allocation algorithm. In our framework, two sub-models, Dynamic Demand Model (DDM) and Dynamic Node Model (DNM), are proposed to describe the dynamic characteristics of resource demand and the computing node number under cloud computing environment. Combining Fairness on Dominant Shares and the two sub-models above, we finally obtain DEFF. In our experiment, we adopt several typical resource allocation algorithms to prove the effectiveness on fairness evaluation by using the DEFF framework.

A Universal Fairness Evaluation Framework for Resource Allocation in Cloud Computing

A Universal Fairness Evaluation Framework for Resource Allocation in Cloud Computing

Adaptive Resource Allocation in Cloud Data Centers using Actor-Critical Deep Reinforcement Learning for Optimized Load Balancing

This paper proposes a deep reinforcement learning-based actor-critic method for efficient resource allocation in cloud computing. The proposed method uses an actor network to generate the allocation strategy and a critic network to evaluate the quality of the allocation. The actor and critic networks are trained using a deep reinforcement learning algorithm to optimize the allocation strategy. The proposed method is evaluated using a simulation-based experimental study, and the results show that it outperforms several existing allocation methods in terms of resource utilization, energy efficiency and overall cost. Some algorithms for managing workloads or virtual machines have been developed in previous works in an effort to reduce energy consumption; however, these solutions often fail to take into account the high dynamic nature of server states and are not implemented at a sufficiently enough scale. In order to guarantee the QoS of workloads while simultaneously lowering the computational energy consumption of physical servers, this study proposes the Actor Critic based Compute-Intensive Workload Allocation Scheme (AC-CIWAS). AC-CIWAS captures the dynamic feature of server states in a continuous manner, and considers the influence of different workloads on energy consumption, to accomplish logical task allocation. In order to determine how best to allocate workloads in terms of energy efficiency, AC-CIWAS uses a Deep Reinforcement Learning (DRL)-based Actor Critic (AC) algorithm to calculate the projected cumulative return over time. Through simulation, we see that the proposed AC-CIWAS can reduce the workload of the job scheduled with QoS assurance by around 20% decrease compared to existing baseline allocation methods. The report also covers the ways in which the proposed technology could be used in cloud computing and offers suggestions for future study.

An AHP based Task Scheduling and Optimal Resource Allocation in Cloud Computing

Cloud systems by virtue characterize ultimate resource utilization with ever evolving user requirements facilitating adaptivity. With a scope of enhancing the QoS needs of user applications, numerous factors are considered for tunning among which Task scheduling promises to grab focus. The Task Scheduling mechanism ascertains improvement by distributing the subtasks to specific set of resources pertaining to prevailing Quality models. The work emphasizes the need for effective task scheduling and optimizing resource allocation by modelling a modified AHP (Analytical Hierarchy Process) driven approach. The proposed method guarantees the functionality in two phases pertaining to Task ranking and pipelined with Optimized scheduling algorithms resulting in maximization of resource utilization. The former phase of task ranking is aided by improved AHP with substantial usage of fuzzy clustering followed by an enhanced CUCMCA (Chimp Updated and Cauchy Mutated Coot Algorithm) algorithm for optimal resource allocation of cloud applications. The contributed model promises leveraged performance of 32% for memory usage, 33.5% for execution time, 29% for makespan and 18% for communication cost over pre-existing conventional models considered.

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.

Implementing the ACO Algorithm and Fog Nodes for Efficient Resource and Energy Allocation in Cloud Computing

Implementing the ACO Algorithm and Fog Nodes for Efficient Resource and Energy Allocation in Cloud Computing

Multi-Objective Optimization for Resource Allocation in Vehicular Cloud Computing Networks

Modern transportation is associated with considerable challenges related to safety, mobility, the environment and space limitations. Vehicular networks are widely considered to be a promising approach for improving satisfaction and convenience in transportation. However, with the exploding popularity among vehicle users and the growing diverse demands of different services, ensuring the efficient use of resources and meeting the emerging needs remain challenging. In this paper, we focus on resource allocation in vehicular cloud computing (VCC) and fill the gaps in the previous research by optimizing resource allocation from both the provider’s and users’ perspectives. We model this problem as a multi-objective optimization with constraints that aims to maximize the acceptance rate and minimize the provider’s cloud cost. To solve such an NP-hard problem, we improve the nondominated sorting genetic algorithm II (NSGA-II) by modifying the initial population according to the matching factor, dynamic crossover probability and mutation probability to promote excellent individuals and increase population diversity. The simulation results show that our proposed method achieves enhanced performance compared to the previous methods.

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.

A novel data size‐aware offloading technique for resource provisioning in mobile cloud computing

SummaryMobile device users are involved in social networking, gaming, learning, and even some office work, so the end users expect mobile devices with high‐response computing capacities, storage, and high battery power consumption. The data‐intensive applications, such as text search, online gaming, and face recognition usage, have tremendously increased. With such high complex applications, there are many issues in mobile devices, namely, fast battery draining, limited power, low storage capacity, and increased energy consumption. The novelty of this work is to strike a balance between time and energy consumption of mobile devices while using data‐intensive applications by finding the optimal offloading decisions. This paper proposes a novel efficient Data Size‐Aware Offloading Model (DSAOM) for data‐intensive applications and to predict the appropriate resource provider for dynamic resource allocation in mobile cloud computing. Based on the data size, the tasks are separated and gradually allocated to the appropriate resource providers for execution. The task is placed into the appropriate resource provider by considering the availability services in the fog nodes or the cloud. The tasks are split into smaller portions for execution in the neighbor fog nodes. To execute the task in the remote side, the offloading decision is made by using the min‐cut algorithm by considering the monetary cost of the mobile device. This proposed system achieves low‐latency time 13.2% and low response time 14.1% and minimizes 24% of the energy consumption over the existing model. Finally, according to experimental findings, this framework efficiently lowers energy use and improves performance for data‐intensive demanding application activities, and the task offloading strategy is effective for intensive offloading requests.