Resource Allocation in Cloud Using Enhanced Max-Min Algorithm

With the advancement of information technology need to perform computing tasks everywhere and all the time there, in cloud computing environments and heterogeneous users have access to different sources with different characteristics that these resources are geographically in different areas. Due to this, the allocation of resources in cloud computing comes to the main issue is considered a major challenge to achieve high performance. Due to the nature of cloud computing is a distributed system to account, comes to business, economic methods such as auctions are used to allocate resources for decentralization. As an important economic bilateral hybrid auction model is the perfect solution for the allocation of resources in cloud computing, on the other hand, providers of cloud resources similarly, their sources of supply combined addressing. One of the problems auction two-way combination with maximum benefit for the parties to the transaction is the efficient allocation of resources to the problem of determining an auction winner is known. Given that the winning auction is NP-Hard. It results in a problem, several methods have been proposed to solve it. In this dissertation, taking into account the strength simulated annealing algorithm, a modified version of it is proposed for solving the winner determination in combinatorial double auction problem in cloud computing. The proposed approach is simulated along with genetic and simulated annealing algorithms and the results show that the proposed approach finds better solutions than the two mentioned algorithms.

The most complicated process in multi‐cloud computing is resource allocation, as it needs to cope with a number of configurations and constraints of cloud providers and customers. At the time of resource allocation, the centralized cloud broker monitors the virtual machines (VM) status, scheduling process, and fitness. However, VM scheduling is found tedious and has received huge attention in business, academia, and research. This enhances the demand for both task scheduling and resource allocation in a multi‐cloud environment. To bridge the gap between the consumer requirement and server infrastructure, a joint optimization‐based resource allocation and task scheduling concept is analyzed in the proposed framework. The first phase introduces the task scheduling mechanism, which uses Hybrid Electro Search and Beetle Swarm Optimization to determine the optimal task for specific VMs. The optimal selection procedure is done by analyzing a multi‐cloud environment's makespan, energy, cost, and throughput parameters. In the second step, an Adaptive Game Theory‐based Seagull optimization approach performs several rounds of reassignment iteratively to minimize the variation in the expected completion time, consequently decreasing high energy consumption and load balancing. The experimental analysis for the proposed model is implemented using Python. The proposed methodology is shown to achieve cheaper costs, shorter waiting times, improved resource allocation, and efficient load balancing. Finally, a comparative analysis is performed with some hybrid optimization models, which illustrate the efficiency of the proposed hybrid optimization model.

Resource allocation in the cloud environment for 5G applications can be explained by referring to the strategic distribution and necessary assignment of computing resources such as virtual machines, storage, and network bandwidth that meet the dynamic demands of applications and services. The framework proposed is on resource allocation in the cloud environment by BRoML for 5G applications. In the proposed BRoML model, the Backtracking Regularized model is incorporated for the effective calculation of the resources in the cloud environment. The optimization is performed for the effective computation of resources in the cloud environment through the computed resources. Using the estimated optimized values, a machine learning model can be trained and tested to classify resource allocation. In this regard, the simulation analysis is compared to BRoML with traditional schemes like SVM and RF. The result shows that BRoML has a higher resource utilization while exhibiting lower latency, higher increased throughput, and a better efficiency score overall. Machine learning techniques and optimization mechanisms give flexibility and intelligence to BRoMl in solving resource allocation issues within cloud computing. These results reinforce the view that BRoML can create a strong impact on the development process of cloud computing with its dynamic, intelligent solution in resource allocation optimization under various scenarios.

Finding the method to most efficiently allocate resources in cloud computing environments has long been a challenge facing the cloud computing field, necessitating unique strategies to optimize performance while minimizing costs. The dynamic nature of cloud computing environments paired with users’ desire for cost-effective solutions requires intelligent and adaptive resource allocation methods. In this paper, we investigate the possibility of utilizing reinforcement learning (RL) algorithms to effectively handle this resource allocation problem. The findings provide insight into the possibility of using RL algorithms for cloud resource management and provide a base for further research and exploration in the area.

The aim of this paper is to allocate resources to tasks and scheduling tasks on existing virtual machines (VMs) in cloud environments, so that the time to finish the last work and average of all tasks execution time are minimized, and loads are distributed balanced on virtual machines. Since task scheduling in the cloud environment is a continuous process, so scheduling improvements, although slight, play an important role in cloud efficiency. On the other hand the resource allocation problem in cloud computing and user tasks scheduling on existing virtual machines is a NP-hard problem, and traditional algorithms requires exponential time to examine search space of this problem in sequence and finding the best answer, therefore we used Gravitational Search Algorithm (GSA) that has a high efficiency in solving nonlinear problems, for solving this problem. To do this, we create masses by combining sequences of tasks assigned to all machines. Each mass position is a solution of the problem. Then we find the best possible assignment using the gravitational search algorithm. We used fuzzy logic to determine the number of masses that affect one another during the implementation of the GSA. To calculate the cost, we use a combination of Make_ span (Time to finish the last task) and Mean_ Flow_Time (Average of all tasks execution time) and Load_ imbalance. The results show that the proposed method achieves more optimal response than genetic algorithm and GSA without fuzzy for resource allocation. It means that proposed algorithm allocated resources to tasks with less make span and mean_ flow time and more load balancing than other two algorithms.

Cloud computing and big data: Technologies and applications

This paper presents a hybrid approach based discrete Particle Swarm Optimization (PSO) and chaotic strategies for solving multi-objective task scheduling problem in cloud computing. The main purpose is to allocate the summited tasks to the available resources in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The main challenges faced by Particle Swarm Optimization (PSO) when used to solve scheduling problems are premature convergence and trapping into local optimum. This paper presents an enhanced Particle Swarm Optimization algorithm hybridized with Chaotic Map strategies. The proposed approach is called Enhanced Particle Swarm Optimization based Chaotic Strategies (EPSOCHO) algorithm. Our proposed approach suggests two Chaotic Map strategies: sinusoidal iterator and Lorenz attractor to enhanced PSO algorithm in order to get good convergence and diversity for optimizing the task scheduling in cloud computing. The proposed approach is simulated and implemented in Cloudsim simulator. The performance of the proposed approach is compared with the standard PSO algorithm, the improved PSO algorithm with Longest job to fastest processor (LJFP-PSO), and the improved PSO algorithm with minimum completion time (MCT-PSO) using different sizes of tasks and various benchmark datasets. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization.

Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

Motivated by most of the current cloud resource allocation policy which are cloud service provider/owner centric in which criteria are maximum utilization, maximum revenue collection, and performance, of cloud resources. In this paper we present a framework which act as cloud storage broker and provide the cloud storage service to user the using user centric criteria are which based on minimum cost and maximum security. Like other computing model resources allocation is also a challenging task in cloud computing. Cloud storage services are widely used to eliminate the need for local storage to lowering operational and maintenance costs. However, budget, security and privacy are major concerns when data are out-sourced. In this paper, we also describe and implement proposed cloud resource allocation framework that integrates cloud storage resources that are provided by different cloud service providers. Finally evaluate the proposed framework using case study in which different user request and cloud resource are generated randomly. The simulation results suggest that approach reach near to optimal solution for resource allocation. The result shows that efficiency of the framework and algorithm depends on the configuration of available resources as well as on configuration of user request.

With the widespread application of cloud computing technology, the energy consumption problem of data centers is becoming increasingly severe. This research is dedicated to presenting a highly efficient and smart solution for optimizing energy usage in cloud computing systems, especially since effectively managing energy consumption through resource allocation and task scheduling has now become a critical challenge in the field. In view of this, the study first constructs a resource allocation model based on redundant resource recovery to dynamically identify and recover redundant resources. Second, a task scheduling optimization method based on dual deep reinforcement learning is established, which utilizes directed acyclic graphs to model task dependencies and optimizes task execution order and resource allocation strategies through reinforcement learning agents. In the testing of the resource allocation optimization model, when the number of tasks is 500, the redundant resource recovery rate is 87.0%, and the computational complexity is 16.7 s. The response time in the regional resource environment is 2.28 s, and the load adaptability is 95.8%. In the task scheduling method test, the proposed method has a memory occupancy of 85 MB and a scheduling delay of 0.13 s in a high-bandwidth environment of 300 Mbps. The results show that the proposed method outperforms traditional scheduling strategies in terms of resource utilization and energy consumption, and contributes to achieving the goal of green cloud computing. The research aims to provide an efficient and intelligent solution for energy optimization in cloud computing systems.

Priority in task scheduling and resource allocation for cloud computing has attracted significant attention from the research community. However, traditional scheduling algorithms often lack the ability to differentiate between tasks with varying levels of importance. This limitation presents a challenge when cloud servers must handle diverse tasks with distinct priority classes and strict quality of service requirements. To address these challenges in cloud computing environments, particularly within the infrastructure of service models, we propose a novel, self-adaptive, multiclass priority algorithm with VM clustering for resource allocation. This algorithm implements a four-tiered prioritization system to optimize key objectives, including makespan and energy consumption, while simultaneously optimizing resource utilization, degree of imbalance, and waiting time. Additionally, we propose a resource prioritization and load-balancing model based on the clustering technique. The proposed work was validated through multiple simulations using the CloudSim simulator, comparing its performance against well-known task scheduling algorithms. The simulation results and analysis demonstrate that the proposed algorithm effectively optimizes makespan and energy consumption. Specifically, our work achieved percentage improvements ranging from +0.97% to +26.80% in makespan and +3.68% to +49.49% in energy consumption while also improving other performance metrics, including throughput, resource utilization, and load balancing. This novel model demonstrably enhances task scheduling and resource allocation efficiency, particularly in complex scenarios with tight deadlines and multiclass priorities.

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The applications that are being installed and run on the mobile devices need a platform of services offered on-demand and be reliable. These services are promised by cloud computing, where resource allocation plays a major role in providing the services and delivering the quality of service (QoS) to the cloud users. The task of provisioning the resources to the cloud users becomes challenging considering the proliferation in the number of users using the cloud on every day basis. The users in this context of proliferation can be considered as mobile nodes that need faster access to communicate among different devices and services. In this paper, the resource allocation on cloud is considered as a game between the clients or the users of the cloud submitting their tasks onto the cloud. Further, a utility maximisation approach as a game theoretic model for an efficient resource allocation in cloud computing is explored in this paper.

Application of a Long Short Term Memory neural predictor with asymmetric loss function for the resource allocation in NFV network architectures

Objectives: In this paper various task scheduling algorithm along with load distribution techniques investigated to ensure efficient mapping of tasks to resources and for coherent resource utilization in heterogeneous environment. Statistical Analysis: A detailed comparative approach is conferred for different job scheduling and load balancing algorithm available in the literature. Considered parameters for comparisons are scheduling approaches, techniques, findings, benefits, pros and cons. This study facilitates to identify the efficient scheduling and load balancing approach which optimizes resource use, minimizes completion time as well as task waiting time and balanced load among resources. Findings: Make span time and flow time are two important parameters to compute and minimize job execution time. This paper explores an intense survey of minimization of task execution time with minimum make span time and flow time. Apart from this a joint review on job scheduling, load balancing and minimizing job completion time using Expected Time to Compute (ETC) matrix is presented. At the end of this paper a research model is proposed which is a novel and hybrid approach with a combination of the efficiency of scheduling and load balancing. Application: The Load Balancing (LB) is an important feature of the grid environment. The efficient LB algorithm assures cost effective resource usage by the provisioning of resources to users on demand schedule in pay-as-you-say-manner.