An Intelligent Job Scheduling System in Cloud Computing

2013 ◽  
Vol 303-306 ◽  
pp. 1391-1394
Author(s):  
Jing Liu ◽  
Xing Guo Luo ◽  
Bai Nan Li
Keyword(s):  
Cloud Computing ◽  
Job Scheduling ◽  
Cost Benefit ◽  
Research Directions ◽  
Scheduling System ◽  
Computing Paradigm ◽  
Computational Architecture ◽  
Main Components ◽  
The Cost ◽  
Computing Infrastructure

Cloud computing is a new computing and business paradigm with flexible and powerful computational architecture to offer universal services to users via Internet. The performance of the scheduling system influences the cost benefit of this computing paradigm. Thus, jobs should be scheduled efficiently to reduce the execution cost and time. In this paper, we present an intelligent scheduling system, which considers both the requirements of different service requests and the circumstances of the computing infrastructure which consists of various resource, then, the main components of the system are introduced in detail, at last, the conclusions are drawn and the further research directions of the scheduling systems are pointed out.

scholarly journals Fog Computing Framework for Smart City Design

2020 ◽  
Vol 14 (01) ◽  
pp. 109
Author(s):  
Mais Haj Qasem ◽  
Alaa Abu-Srhan ◽  
Hutaf Natoureah ◽  
Esra Alzaghoul
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Real Time ◽  
Smart City ◽  
Network Architecture ◽  
Data Exchange ◽  
Fog Computing ◽  
Computing Paradigm ◽  
Network Usage ◽  
The Cost

Fog-computing is a new network architecture and computing paradigm that uses user or near-users devices (network edge) to carry out some processing tasks. Accordingly, it extends the cloud computing with more flexibility the one found in the ubiquitous networks. A smart city based on the concept of fog-computing with flexible hierarchy is proposed in this paper. The aim of the proposed design is to overcome the limitations of the previous approaches, which depends on using various network architectures, such as cloud-computing, autonomic network architecture and ubiquitous network architecture. Accordingly, the proposed approach achieves a reduction of the latency of data processing and transmission with enabled real-time applications, distribute the processing tasks over edge devices in order to reduce the cost of data processing and allow collaborative data exchange among the applications of the smart city. The design is made up of five major layers, which can be increased or merged according to the amount of data processing and transmission in each application. The involved layers are connection layer, real-time processing layer, neighborhood linking layer, main-processing layer, data server layer. A case study of a novel smart public car parking, traveling and direction advisor is implemented using IFogSim and the results showed that reduce the delay of real-time application significantly, reduce the cost and network usage compared to the cloud-computing paradigm. Moreover, the proposed approach, although, it increases the scalability and reliability of the users’ access, it does not sacrifice much time, nor cost and network usage compared to fixed fog-computing design.

Job Scheduling Algorithm for Cloud Computing Based on Particle Swarm Optimization

2013 ◽  
Vol 662 ◽  
pp. 957-960 ◽  
Author(s):  
Jing Liu ◽  
Xing Guo Luo ◽  
Xing Ming Zhang ◽  
Fan Zhang
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
High Performance ◽  
Large Scale ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Autonomous Systems ◽  
Particle Swarm ◽  
Cost Benefit ◽  
Swarm Optimization

Cloud computing is an emerging high performance computing environment with a large scale, heterogeneous collection of autonomous systems and flexible computational architecture. The performance of the scheduling system influences the cost benefit of this computing paradigm. To reduce the energy consumption and improve the profit, a job scheduling model based on the particle swarm optimization(PSO) algorithm is established for cloud computing. Based on open source cloud computing simulation platform CloudSim, compared to GA and random scheduling algorithms, the results show that the proposed algorithm can obtain a better solution concerning the energy cost and profit.

scholarly journals Task Balanced Workflow Scheduling Technique considering Task Processing Rate in Spot Market

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Daeyong Jung ◽  
JongBeom Lim ◽  
JoonMin Gil ◽  
Eunyoung Lee ◽  
Heonchang Yu
Keyword(s):  
Cloud Computing ◽  
High Performance ◽  
Task Completion ◽  
Workflow Scheduling ◽  
Performance Improvements ◽  
Processing Rate ◽  
Computing Paradigm ◽  
Low Performance ◽  
The Cost ◽  
Advanced Computing

Recently, the cloud computing is a computing paradigm that constitutes an advanced computing environment that evolved from the distributed computing. And the cloud computing provides acquired computing resources in a pay-as-you-go manner. For example, Amazon EC2 offers the Infrastructure-as-a-Service (IaaS) instances in three different ways with different price, reliability, and various performances of instances. Our study is based on the environment using spot instances. Spot instances can significantly decrease costs compared to reserved and on-demand instances. However, spot instances give a more unreliable environment than other instances. In this paper, we propose the workflow scheduling scheme that reduces the out-of-bid situation. Consequently, the total task completion time is decreased. The simulation results reveal that, compared to various instance types, our scheme achieves performance improvements in terms of an average combined metric of 12.76% over workflow scheme without considering the processing rate. However, the cost in our scheme is higher than an instance with low performance and is lower than an instance with high performance.

scholarly journals Enhancing the Distribution of Idle Cost for Scheduling Tasks without Setup Cost in Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Redwan A. Al-dilami ◽  
Ammar T. Zahary ◽  
Adnan Z. Al-Saqqaf
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Setup Time ◽  
Setup Cost ◽  
Idle State ◽  
The Cost ◽  
Computing Infrastructure ◽  
Better Than

Issues of task scheduling in the centre of cloud computing are becoming more important, and the cost is one of the most important parameters used for scheduling tasks. This study aims to investigate the problem of online task scheduling of the identified job of MapReduce on cloud computing infrastructure. It was proposed that the virtualized cloud computing setup comprised machines that host multiple identical virtual machines (VMs) that need to be activated earlier and run continuously, and booting a VM requires a constant setup time. A VM that remains running even though it is no longer used is considered an idle VM. Furthermore, this study aims to distribute the idle cost of the VMs rather than the cost of setting up them among tasks in a fair manner. This study also is an extension of previous studies which solved the problems that occurred when distributing the idle cost and setting up the cost of VMs among tasks. It classifies the tasks into three groups (long, mid, and short) and distributes the idle cost among the groups then among the tasks of the groups. The main contribution of this paper is the developing of a clairvoyant algorithm that addressed important factors such as the delay and the cost that occurred by waiting to setup VM (active VM). Also, when the VMs are run continually and some VMs become in idle state, the idle cost will be distributed among the current tasks in a fair manner. The results of this study, in comparison with previous studies, showed that the idle cost and the setup cost that was distributed among tasks were better than the idle cost and the setup cost distributed in those studies.

scholarly journals Multi-objective Prediction based Task Scheduling Method in Cloud Computing

2019 ◽  
Vol 8 (4) ◽  
pp. 9388-9394 ◽  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Job Scheduling ◽  
Sigmoid Function ◽  
Service Model ◽  
Multi Objective ◽  
Computing Paradigm ◽  
Optimal Node ◽  
Scheduling Method

Cloud Computing is Internet based computing where one can store and access their personal resources from any computer through Internet. Cloud Computing is a simple pay-per-utilize consumer-provider service model. Cloud is nothing but large pool of easily accessible and usable virtual resources. Task (Job) scheduling is always a noteworthy issue in any computing paradigm. Due to the availability of finite resources and time variant nature of incoming tasks it is very challenging to schedule a new task accurately and assign requested resources to cloud user. Traditional task scheduling techniques are improper for cloud computing as cloud computing is based on virtualization technology with disseminated nature. Cloud computing brings in new challenges for task scheduling due to heterogeneity in hardware capabilities, on-demand service model, pay-per-utilize model and guarantee to meet Quality of Service (QoS). This has motivated us to generate multi-objective methods for task scheduling. In this research paper we have presented multi-objective prediction based task scheduling method in cloud computing to improve load balancing in order to satisfy cloud consumers dynamically changing needs and also to benefit cloud providers for effective resource management. Basically our method gives low probability value for not capable and overloaded nodes. To achieve the same we have used sigmoid function and Euclidean distance. Our major goal is to predict optimal node for task scheduling which satisfies objectives like resource utilization and load balancing with accuracy.

Custom-Made Cloud Enterprise Architecture for Small Medium and Micro Enterprises

2013 ◽  
pp. 205-217
Author(s):  
Promise Mvelase ◽  
Nomusa Dlodlo ◽  
Quentin Williams ◽  
Matthew O. Adigun
Keyword(s):  
Cloud Computing ◽  
Enterprise Architecture ◽  
Virtual Enterprise ◽  
Cost Benefit ◽  
Process Models ◽  
Virtual Enterprises ◽  
Market Opportunities ◽  
Ict Infrastructure ◽  
The Cost ◽  
Micro Enterprises

Small, Medium, and Micro enterprises (SMMEs) usually do not have adequate funds to acquire ICT infrastructure and often use cloud computing. In this paper, the authors discuss the implementation of virtual enterprises (VE) to enable SMMEs to respond quickly to customers’ demands and market opportunities. The virtual enterprise model is based on the ability to create temporary co-operations and realize the value of a short term business opportunity that the partners cannot fully capture on their own. The model of virtual enterprise is made possible through virtualisation technology, which is a building block of cloud computing. To achieve a common goal, enterprises integrate resources, organisational models, and process models. Through the virtual business operating environment offered by cloud computing, the SMMEs are able to increase productivity and gain competitive advantage due to the cost benefit incurred. In this paper, the authors propose a virtual enterprise enabled cloud enterprise architecture based on the concept of virtual enterprise at both business and technology levels. The business level comprises of organisational models, process models, skills, and competences whereas the technology level comprises of IT resources.

Custom-Made Cloud Enterprise Architecture for Small Medium and Micro Enterprises

2012 ◽  
pp. 589-601 ◽  
Author(s):  
Promise Mvelase ◽  
Nomusa Dlodlo ◽  
Quentin Williams ◽  
Matthew O. Adigun
Keyword(s):  
Cloud Computing ◽  
Enterprise Architecture ◽  
Virtual Enterprise ◽  
Cost Benefit ◽  
Process Models ◽  
Virtual Enterprises ◽  
Market Opportunities ◽  
Ict Infrastructure ◽  
The Cost ◽  
Micro Enterprises

Small, Medium, and Micro enterprises (SMMEs) usually do not have adequate funds to acquire ICT infrastructure and often use cloud computing. In this paper, the authors discuss the implementation of virtual enterprises (VE) to enable SMMEs to respond quickly to customers’ demands and market opportunities. The virtual enterprise model is based on the ability to create temporary co-operations and realize the value of a short term business opportunity that the partners cannot fully capture on their own. The model of virtual enterprise is made possible through virtualisation technology, which is a building block of cloud computing. To achieve a common goal, enterprises integrate resources, organisational models, and process models. Through the virtual business operating environment offered by cloud computing, the SMMEs are able to increase productivity and gain competitive advantage due to the cost benefit incurred. In this paper, the authors propose a virtual enterprise enabled cloud enterprise architecture based on the concept of virtual enterprise at both business and technology levels. The business level comprises of organisational models, process models, skills, and competences whereas the technology level comprises of IT resources.

scholarly journals Analysis of Load Balancing Techniques in Cloud Computing

2005 ◽  
Vol 4 (2) ◽  
pp. 737-741 ◽  
Author(s):  
Amandeep Sidhu ◽  
Supriya Kinger
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Data Storage ◽  
Cloud Storage ◽  
Job Scheduling ◽  
Single Node ◽  
Computing Paradigm ◽  
System A ◽  
Share Data ◽  
Scalable Network

Cloud Computing is an emerging computing paradigm. It aims to share data, calculations, and service transparently over a scalable network of nodes. Since Cloud computing stores the data and disseminated resources in the open environment. So, the amount of data storage increases quickly. In the cloud storage, load balancing is a key issue. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. A few existing scheduling algorithms can maintain load balancing and provide better strategies through efficient job scheduling and resource allocation techniques as well. In order to gain maximum profits with optimized load balancing algorithms, it is necessary to utilize resources efficiently. This paper discusses some of the existing load balancing algorithms in cloud computing and also their challenges.

Rationale for Use of Cloud Computing

2015 ◽  
pp. 2166-2197
Author(s):  
Amir Zeid ◽  
Ahmed Shawish ◽  
Maria Salama
Keyword(s):  
Cloud Computing ◽  
Cost Benefit Analysis ◽  
Service Selection ◽  
Cost Benefit ◽  
Quality Parameters ◽  
Cloud Services ◽  
Computing Paradigm ◽  
Model Combining ◽  
Quality Aspects ◽  
And Performance

Cloud Computing is the most promising computing paradigm that provides flexible resource allocation on demand with the promise of realizing elastic, Internet-accessible, computing on a pay-as-you-go basis. With the growth and expansion of the Cloud services and participation of various services providers, the description of quality parameters and measurement units start to diversify and sometime contradict. Such ambiguity does not only result in the rise of various Quality of Service (QoS) interoperability problems but also in the distraction of the services consumers who find themselves unable to match quality requirements with the providers' offerings. Yet, employing the available QoS models that cover certain quality aspects while neglecting others drive consumers to perform their service selection based only on cost-benefit analysis and performance evaluation, without being able to perform subjective selection based on a comprehensive set of well-defined quality aspects. This chapter presents a novel QoS ontology that combines and defines all of the existing quality aspects in a unified way to efficiently overcome all existing diversities. Using such an ontology, a comprehensive broad QoS model combining all quality-related parameters of both service providers and consumers for different Cloud platforms is presented. The chapter also provides a mathematical model that formulates the Cloud Computing service provider selection optimization problem based on QoS guarantees. The validation of the provided model is addressed in the chapter through extensive simulation studies conducted on benchmark data of Content Delivery Network providers. The studies report the efficient matching of the model with the market-oriented different platform characteristics.

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