Resource management (RM) is a challenging task in a cloud computing environment where a large number of virtualized, heterogeneous, and distributed resources are hosted in the datacentres. The uncertainty, heterogeneity, and the dynamic nature of such resources affect the efficiency of provisioning, allocation, scheduling, and monitoring tasks of RM. The most existing RM techniques and strategies have insufficiency in handling such cloud resources dynamic behaviour. To resolve these limitations, there is a need for the design and development of intelligent and efficient autonomic RM techniques to ensure the Quality-of-Service (QoS) of cloud-based applications, satisfy the cloud user requirements, and avoid a Service-Level Agreement (SLA) violations. This paper presents a comprehensive review along with a taxonomy of the most recent existing autonomic and elastic RM techniques in a cloud environment. The taxonomy classifies the existing autonomic and elastic RM techniques into different categories based on their design, objective, function, and applications. Moreover, a comparison and qualitative analysis is provided to illustrate their strengths and weaknesses. Finally, the open issues and challenges are highlighted to help researchers in finding significant future research options.

Efficient autonomic and elastic resource management techniques in cloud environment: taxonomy and analysis

It is difficult to analyze the workload in complex cloud computing environments with a single prediction algorithm as each algorithm has its own shortcomings A self-adaptive prediction algorithm combining the advantages of linear regression LR and a BP neural network to predict workloads in clouds is proposed in this paper The main idea of the self-adaptive prediction algorithm is to choose the better prediction method of the future workload Some experiments of prediction algorithms are conducted with workloads on the public cloud servers The experimental results show that the proposed algorithm has a relatively high accuracy on the workload predictions compared with the BP neural network and LR Furthermore, in order to use the proposed algorithm in a cloud data center, a dynamic scheduling architecture of cloud resources is designed to improve resource utilization and reduce energy consumption

A Self-Adaptive Prediction Algorithm for Cloud Workloads

In the problem of VMs consolidation for cloud energy saving, different workloads will ask for different resources. Thus, considering workload characteristic, the VM placement solution will be more reasonable. In the real world, different workload works in a varied CPU utilization during its work time according to its task characteristics. That means energy consumption related to both the CPU utilization and CPU frequency. Therefore, only using the model of CPU frequency to evaluate energy consumption is insufficient. This paper theoretically verified that there will be a CPU frequency best suit for a certain CPU utilization in order to obtain the minimum energy consumption. According to this deduction, we put forward a heuristic CPU frequency scaling algorithm VP-FS (virtual machine placement with frequency scaling). In order to carry the experiments, we realized three typical greedy algorithms for VMs placement and simulate three groups of VM tasks. Our efforts show that different workloads will affect VMs allocation results. Each group of workload has its most suitable algorithm when considering the minimum used physical machines. And because of the CPU frequency scaling, VP-FS has the best results on the total energy consumption compared with the other three algorithms under any of the three groups of workloads.

/pdf/workload-aware-and-cpu-frequency-scaling-for-optimal-energy-4d7tr52yr3.pdf

Workload-Aware and CPU Frequency Scaling for Optimal Energy Consumption in VM Allocation

In order to distribute cloud resources and improve the efficiency of tasks, this paper proposes a resource scheduling strategy based on the improved ant colony algorithm. Based on cluster service and user quality of service preference, we construct an ant optimization model to design the parameterization definition and select the preference constraints; the fitness function of multi-dimensional quality of service is given and then the local or global update is performed accordingly. The search for Pareto optimal set of multi-objective problems is implemented. Finally, the optimum node distribution structure with the highest fitness value is obtained. It's shown that the approach gives sufficient consideration of multidimensional user quality of service requirements. The results from the test show a significant improvement in throughput rate, service time and request times compared with other similar algorithms.

The Cloud Parameters Specification and Scheduling Optimization on Multidimensional Qos Constraints

In order to realize the transient and accident analysis of reactor system and verification of system control scheme, the latest computer technology is used to develop advanced graphical modeling tool for TRANTH code, the human-machine interaction interface, and integration research between software and digital reactor design and verification platform, so as to the design and simulation system of digital reactor fluid instruction control system is built up. The nuclear power plant (ACP1000) is chosen as a research object some typical operating conditions are selected and executed. Results show that the design and simulation system can implement the modeling, calculation, interactive manipulation and display of reactor fluid instruction control system, which brings a multi-professional joint simulation of reactor fluid instruction control system to fruition, and provides technology support for rapid generation, evaluation, verification and optimization of design scheme about reactor fluid instruction control system.

Development and verification of Design and Simulation System for Digital Reactor Fluid Instruction Control System

Cloud computing resource scheduling was studied.The current static grid resource scheduling algorithms consider only the minimization of the makespan,so they can not meet the demands of cloud computing resource scheduling.In order to adapt to the dynamic and real-time nature and solve the issue of cloud computing resource scheduling and decrease the power consumption of datacenter,we proposed a cloud computing resource scheduling algorithm based on prediction and ACO algorithm.When the utility of datacenter is low,the improved ACO algorithm is executed to assign VMs to hosts.Dynamic tendency prediction strategy was used to predict the load of datacenter and turn on/off hosts.The results of simulation show that in the case of running VMs normally,cloud computing resource scheduling strategy based on prediction and ACO algorithm can reduce the power consumption of datacenter effectively.

Cloud Computing Resource Scheduling Strategy Based on Prediction and ACO Algorithm

In WLAN-based location estimation problem,the grid spacing,the number of access points,and the interference of environment are key factors for average error of location estimation,however,there isn't a general strategy on how to determine these parametersThey are mostly determined by experienceA mathematical model of average error of localization was proposed,which formally described the key factors for errors in WLAN-based location estimationBased on the model,the relationship between the average error and these factors was analyzedSimulation results show that it satisfies to localize users with a grid spacing of one meter and using more than three access points can remarkably reduce the average error of location estimation and alleviate the environmental interference

Analysis and Simulation of Key Factors for Errors in WLAN-based Location Estimation

The Multi-Agent-Oriented modeling and simulation technology is one of the most dynamic and influential methodsIn order to research the Multi-Agent-Oriented Simulation,and to aim at the problem that some other simulation platforms have not taken the versatility of simulation and the convenience of modeling into account,this "Multi-Agent-Oriented Distributed Simulation Platform" designed a simulation platform's framework,which described a distributed simulation platform that used Multi-Agent technology and combined "modeling,deployment and simulation" together,then proposed a general solution which allowed users to create Multi-Agent models,and the platform will support for distributed simulationExperiments show that users can use this distributed simulation platform to configure simulation scenarios conveniently and then generat specific casesBy comparing the performances of distributed environment and single-machine environment,the correctness and efficiency of the simulation platform are proved

Multi-Agent-Oriented Distributed Simulation Platform

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Cao Jian

Author Tools

Chat about Author

Papers

Cloud Computing Resource Scheduling Strategy Based on Prediction and ACO Algorithm

Analysis and Simulation of Key Factors for Errors in WLAN-based Location Estimation

Multi-Agent-Oriented Distributed Simulation Platform