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.

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

Edge computing (EC) has recently emerged as a promising paradigm that supports resource-hungry Internet of Things (IoT) applications with low latency services at the network edge. However, the limited capacity of computing resources at the edge server poses great challenges for scheduling application tasks. In this paper, a task scheduling problem is studied in the EC scenario, and multiple tasks are scheduled to virtual machines (VMs) configured at the edge server by maximizing the long-term task satisfaction degree (LTSD). The problem is formulated as a Markov decision process (MDP) for which the state, action, state transition, and reward are designed. We leverage deep reinforcement learning (DRL) to solve both time scheduling (i.e., the task execution order) and resource allocation (i.e., which VM the task is assigned to), considering the diversity of the tasks and the heterogeneity of available resources. A policy-based REINFORCE algorithm is proposed for the task scheduling problem, and a fully-connected neural network (FCN) is utilized to extract the features. Simulation results show that the proposed DRL-based task scheduling algorithm outperforms the existing methods in the literature in terms of the average task satisfaction degree and success ratio.

https://www.mdpi.com/1424-8220/21/5/1666/pdf

Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing

Fog computing has become the primary infrastructure on the Internet for improving the quality of service. We consider a fog queueing system with limited infrastructure resources to accommodate real-time tasks with heterogeneities in task types and execution deadlines. Owing to the uncertain execution duration, such a fog system should jointly consider fog resource allocation and a task offloading to satisfy the deadline requirements. To consider the task heterogeneity, a parallel virtual queue model is applied to buffer each type of task in a separate queue. Subsequently, we use a framework, including three parallel algorithms, namely, offloading, buffering, and resource allocation, to improve resource allocation balance, throughput, and task completion ratio. The task offloading is decided according to the task urgencies in terms of the laxity times, which accounts for the deadline, estimated execution time, and transmission delay to the cloud. The buffering process rearranges the arriving tasks based on their laxity time and the estimated task execution time at the fog tier. The resource allocation uses an adaptive queue weight based on the Lyapunov drift to avoid task starvation that may lead to a long queueing delay for tasks with long execution time. The simulation results indicate that our proposed policies can avoid task starvation and yields a tradeoff between high throughput and a high task completion ratio.

Resource Allocation and Task Offloading for Heterogeneous Real-Time Tasks With Uncertain Duration Time in a Fog Queueing System

With the rapid development of processing and storage technologies and the success of the Internet, computing resources have become cheaper, more powerful and more ubiquitously available than ever before. This technological trend has enabled the realization of a new computing model, called cloud computing. In cloud, scheduling is an important application. In cloud environments, load balancing task scheduling is an important problem that directly affects resource utilization. Undoubtedly, load balancing scheduling is a serious aspect that should be considered because of its significant impact on both the back end and the front end of the cloud research industry. Good resource utilization is achieved whenever an effective load balance is achieved in the cloud. But, load balancing in cloud computing is an NP-hard optimization problem. In order to accomplish this problem, a novel load balancing task scheduling algorithm in cloud using Adaptive Dragonfly algorithm (ADA) is proposed. The ADA is a combination of dragonfly algorithm and firefly algorithm. Moreover, to attain the better performance, multi-objective function is developed based on three parameters namely, completion time, processing costs and load. Finally, the performance of proposed methodology is evaluated in terms of different metrics namely, execution cost and execution time. The experimental results demonstrate that a proposed approach accomplishes better load balancing result compared to other approaches.

An efficient load balancing system using adaptive dragonfly algorithm in cloud computing

As a novel computing technology closer to business ends, edge computing has become an effective solution for delay sensitive business of power Internet of Things (IoT) and promotes the application and development of the IoT technology in smart grids. However, the inherent characteristics of a single edge node with limited resources may fail to meet the delay requirements for access ubiquitous IoT businesses of massive access. Multiple edge nodes are needed to cooperate with each other to optimize workload allocation to provide lower delay services. To this end, this paper proposes a workload allocation mechanism, orienting edge computing-based power IoT, which minimizes service delay. The workload optimization allocation model is established, and the optimal workload allocation oriented on delay among multiple edge nodes is further realized on the basis of computing resource optimization within the single edge node. The balanced initialization, resource allocation, and task allocation (BRT) algorithm are proposed. Based on the balanced initialization of workload within edge nodes, the particle swarm algorithm modified by the pheromone strategy is used to solve the problem of the computing resources’ allocation inside edge nodes. Finally, the task allocation among multiple edge servers is converted into a semi-definite programming problem. The simulation results show that the proposed BRT algorithm reduces the service delay by 9.1%, 16.9%, and 26.4%, and the service delay growth rate by 24.6%, 34.5%, and 38.7%, respectively, compared with the simulated annealing algorithm (SAA), LoAd Balancing (LAB), and Latency-awarE workloAd offloaDing (LEAD) algorithms.

/pdf/workload-allocation-mechanism-for-minimum-service-delay-in-3v5uxl7zup.pdf

Workload Allocation Mechanism for Minimum Service Delay in Edge Computing-Based Power Internet of Things

This paper studies the delay-optimal virtual machine (VM) scheduling problem in cloud computing systems, which have a constant amount of infrastructure resources, such as CPU, memory, and storage in the resource pool. The cloud computing system provides VMs as services to users. Cloud users request various types of VMs randomly over time, and the requested VM-hosting durations vary vastly. We first adopt a queuing model for the heterogeneous and dynamic workloads. Then, we formulate the VM scheduling in such a queuing cloud computing system as a decision-making process, where the decision variable is the vector of VM configurations and the optimization objective is the delay performance in terms of average job completion time. A low-complexity online scheme that combines the shortest-job-first (SJF) buffering and min–min best fit (MMBF) scheduling algorithms, i.e., SJF-MMBF, is proposed to determine the solutions. Another scheme that combines the SJF buffering and reinforcement learning (RL)-based scheduling algorithms, i.e., SJF-RL, is further proposed to avoid the potential of job starvation in SJF-MMBF. The simulation results show that SJF-RL achieves its goal of delay-optimal scheduling of VMs by provisioning a low delay at various job arrival rates for various shapes of job length distributions. The simulation results also illustrate that although SJF-MMBF is sub-delay-optimal in a heavy-loaded and highly dynamic environment, it is efficient in throughput performance in terms of the average job hosting rate provisioning.

Optimal Scheduling of VMs in Queueing Cloud Computing Systems With a Heterogeneous Workload

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.

Wende Ke

Author Tools

Chat about Author

Papers

Optimal Scheduling of VMs in Queueing Cloud Computing Systems With a Heterogeneous Workload