A Simulation-based Approach to Optimize the Execution Time and Minimization of Average Waiting Time Using Queuing Model in Cloud Computing Environment

2016 ◽  
Vol 6 (2) ◽  
pp. 743
Author(s):  
Souvik Pal ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Cloud Computing ◽  
Waiting Time ◽  
Execution Time ◽  
Queue Length ◽  
Service Providers ◽  
Business Systems ◽  
It Industry ◽  
Queuing Model ◽  
Simulation Based ◽  
Time Minimization

Cloud computing is the emerging domain in academia and IT Industry. It is a business framework for delivering the services and computing power on-demand basis. Cloud users have to pay the service providers based on their usage. For enterprises, cloud computing is the worthy of consideration and they try to build business systems with lower costs, higher profits and quality-of-service. Considering cost optmization, service provider may initially try to use less number of CPU cores and data centers. For that reason, this paper deals with CloudSim simulation tool which has been utilized for evaluating the number of CPU cores and execution time. Minimization of waiting time is also a considerable issue. When a large number of jobs are requested, they have to wait for getting allocated to the servers which in turn may increase the queue length and also waiting time. This paper also deals with queuing model with multi-server and finite capacity to reduce the waiting time and queue length.

scholarly journals A Simulation-based Approach to Optimize the Execution Time and Minimization of Average Waiting Time Using Queuing Model in Cloud Computing Environment

2016 ◽  
Vol 6 (2) ◽  
pp. 743
Author(s):  
Souvik Pal ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Cloud Computing ◽  
Waiting Time ◽  
Execution Time ◽  
Queue Length ◽  
Service Providers ◽  
Business Systems ◽  
It Industry ◽  
Queuing Model ◽  
Simulation Based ◽  
Time Minimization

Cloud computing is the emerging domain in academia and IT Industry. It is a business framework for delivering the services and computing power on-demand basis. Cloud users have to pay the service providers based on their usage. For enterprises, cloud computing is the worthy of consideration and they try to build business systems with lower costs, higher profits and quality-of-service. Considering cost optmization, service provider may initially try to use less number of CPU cores and data centers. For that reason, this paper deals with CloudSim simulation tool which has been utilized for evaluating the number of CPU cores and execution time. Minimization of waiting time is also a considerable issue. When a large number of jobs are requested, they have to wait for getting allocated to the servers which in turn may increase the queue length and also waiting time. This paper also deals with queuing model with multi-server and finite capacity to reduce the waiting time and queue length.

A Hybrid Multiple Parallel Queuing Model to Enhance QoS in Cloud Computing

2020 ◽  
Vol 12 (1) ◽  
pp. 18-34 ◽  
Author(s):  
Shahbaz Afzal ◽  
G. Kavitha
Keyword(s):  
Cloud Computing ◽  
Response Time ◽  
Waiting Time ◽  
Queue Length ◽  
Utilization Rate ◽  
Queuing Model ◽  
Total Response Time ◽  
Proposed Model ◽  
Mathematical Equations ◽  
And Performance

Among the different QoS metrics and parameters considered in cloud computing are the waiting time of cloud tasks, execution time of tasks in VM's, and the utilization rate of servers. The proposed model was developed to overcome some of the pitfalls in the existing systems among which are sub-optimal markdown in the queue length, waiting time, response time, and server utilization rate. The proposed model contemplates on the enhancement of these metrics using a Hybrid Multiple Parallel Queuing approach with a joint implementation of M/M/1: ∞ and M/M/s: N/FCFS to achieve the desired objectives. A neoteric set of mathematical equations have been formulated to validate the efficiency and performance of the hybrid queuing model. The results have been validated with reference to the workload traces of Bit Brains infrastructure provider. The results obtained indicate the significant reduction in the queue length by 60.93 percent, waiting time in the queue by 73.85 percent, and total response time by 97.51%.

Performance Analysis of Cloud Computing Centers for Bulk Services

2012 ◽  
Vol 2 (4) ◽  
pp. 53-65 ◽  
Author(s):  
Veena Goswami ◽  
Sudhansu Shekhar Patra ◽  
G. B. Mund
Keyword(s):  
Cloud Computing ◽  
Performance Measures ◽  
Service Providers ◽  
Blocking Probability ◽  
Cloud Service ◽  
Model Parameters ◽  
Queuing Model ◽  
Service Oriented ◽  
Computing Centers ◽  
Access To Data

Cloud is a service oriented platform where all kinds of virtual resources are treated as services to users. Several cloud service providers have offered different capabilities for a variety of market segments over the past few years. The most important aspects of cloud computing are resource scheduling, performance measures, and user requests. Sluggish access to data, applications, and web pages spoils employees and customers alike, as well as cause application crashes and data losses. In this paper, the authors propose an analytical queuing model for performance evaluation of cloud server farms for processing bulk data. Some important performance measures such as mean number of tasks in the queue, blocking probability, and probability of immediate service, and waiting-time distribution in the system have also been discussed. Finally, a variety of numerical results showing the effect of model parameters on key performance measures are presented.

Adoption of Cloud Computing in UAE

2021 ◽  
pp. 2496-2518
Author(s):  
Juno Srivastava ◽  
Krishnadas Nanath
Keyword(s):  
Cloud Computing ◽  
Data Security ◽  
Service Providers ◽  
New Technology ◽  
It Industry ◽  
Cloud Adoption ◽  
Hype Cycle ◽  
Cloud Computing Adoption ◽  
Legal Implications ◽  
Significant Factors

With the advent of new technology, the IT industry continuously strives to innovate in terms of deploying products or providing services and Cloud Computing is rapidly moving in the hype cycle. With practically all the service providers offering products and services with cloud features and functionality and investing in creating a cloud computing ecosystem, it has become important to understand what these ecosystem means to the organizations based out of UAE who have to decide whether to adopt cloud computing or shun it. There are several factors impacting an organization's decision on its choice of cloud computing adoption (like data security, Legal implications and derived benefits especially in UAE) (Al Tamimi & Company, 2005). There is a need for an assessment of cloud ecosystem in UAE which would be one of the significant factors considered cloud adoption in this region. This study analyzes the current cloud ecosystem providers in UAE and their product and services on cloud computing. It also tries to relate the relation between the cloud ecosystem and the factors impacting organization's decision to adopt cloud computing.

Study of the Security Risks and Measures in Cloud Computing

2015 ◽  
Vol 9 (1and2) ◽  
Author(s):  
Akshay Mehta ◽  
Dr. Sanjay Kumar Dubey
Keyword(s):  
Cloud Computing ◽  
Service Providers ◽  
Security And Privacy ◽  
It Industry ◽  
Security Risks ◽  
Resource Pooling ◽  
Computing Services ◽  
Huge Data ◽  
Cloud Computing Services ◽  
The Cost

Cloud Computing has emerged very fast in the IT industry. It is based on virtualization technology and provides internet based computing which provides resource pooling, services sharing and on demand access. Its evolution has reduced must of the cost of enterprises as well as of the other industries working with a huge data. With cloud computing emerging at a much faster rate, the situation may soon be changed. But, despite the fact that it provides a number of facilities to the service providers, it has quite a number of issues related to it. The most important issue related to cloud is its security. From the consumer’s perspective, cloud computing security concerns, especially data security and privacy protection issues, remain the primary inhibitor of cloud computing services. Security is the reason that hinders many enterprises to enter into cloud. So this paper gives a detail of the security risks related to cloud and the possible measures which the enterprises need to ensure before entering Cloud Computing.

Cloud resource optimization based on poisson linear deep gradient learning for mobile cloud computing

2021 ◽  
Vol 40 (1) ◽  
pp. 787-797
Author(s):  
G. Saravanan ◽  
N. Yuvaraj
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Mobile Applications ◽  
Mobile Cloud Computing ◽  
Service Providers ◽  
Mobile User ◽  
Mobile Users ◽  
Mobile Cloud ◽  
Queuing Model ◽  
Gradient Learning

Mobile Cloud Computing (MCC) addresses the drawbacks of Mobile Users (MU) where the in-depth evaluation of mobile applications is transferred to a centralized cloud via a wireless medium to reduce load, therefore optimizing resources. In this paper, we consider the resource (i.e., bandwidth and memory) allocation problem to support mobile applications in a MCC environment. In such an environment, Mobile Cloud Service Providers (MCSPs) form a coalition to create a resource pool to share their resources with the Mobile Cloud Users. To enhance the welfare of the MCSPs, a method for optimal resource allocation to the mobile users called, Poisson Linear Deep Resource Allocation (PL-DRA) is designed. For resource allocation between mobile users, we formulate and solve optimization models to acquire an optimal number of application instances while meeting the requirements of mobile users. For optimal application instances, the Poisson Distributed Queuing model is designed. The distributed resource management is designed as a multithreaded model where parallel computation is provided. Next, a Linear Gradient Deep Resource Allocation (LG-DRA) model is designed based on the constraints, bandwidth, and memory to allocate mobile user instances. This model combines the advantage of both decision making (i.e. Linear Programming) and perception ability (i.e. Deep Resource Allocation). Besides, a Stochastic Gradient Learning is utilized to address mobile user scalability. The simulation results show that the Poisson queuing strategy based on the improved Deep Learning algorithm has better performance in response time, response overhead, and energy consumption than other algorithms.

scholarly journals Allocation of Cloudlets using Solution of Job Assignment Problem

2019 ◽  
Vol 8 (9S4) ◽  
pp. 20-25
Keyword(s):  
Cloud Computing ◽  
Execution Time ◽  
Assignment Problem ◽  
Virtual Machines ◽  
Service Providers ◽  
Scheduling Algorithm ◽  
Cloud Service ◽  
Job Assignment ◽  
Total Execution Time ◽  
First Come First Serve

Resource allocation policies play a key role in determining the performance of cloud. Service providers in cloud computing have to provide services to many users simultaneously. So the job of allocating cloudlets to appropriate virtual machines is becoming one of the challenging issues of cloud computing. Many algorithms have been proposed to allocate cloudlets to the virtual machines. Here in our paper, we have represented cloudlet allocation problem as job assignment problem and we have proposed Hungarian algorithm based solution for allocating cloudlets to virtual machines. The main objective is to minimize total execution time of cloudlets. Proposed algorithm is implemented in Cloudsim-3.03 simulator. We have done comparative analysis of the simulation results of proposed algorithm with the existing First Come First Serve (FCFS) scheduling policy and Min-Min scheduling algorithm. Proposed algorithm performs better than the above mentioned algorithms in terms of total execution time and makespan time (finishing time of last cloudlet)

scholarly journals Estimated Interval-Based Checkpointing (EIC) on Spot Instances in Cloud Computing

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Daeyong Jung ◽  
JongBeom Lim ◽  
Heonchang Yu ◽  
Taeweon Suh
Keyword(s):  
Cloud Computing ◽  
Execution Time ◽  
Service Providers ◽  
Moving Average ◽  
Task Execution ◽  
Computing Services ◽  
Task Execution Time ◽  
Cloud Computing Services ◽  
Time Fall ◽  
The Cost

In cloud computing, users can rent computing resources from service providers according to their demand. Spot instances are unreliable resources provided by cloud computing services at low monetary cost. When users perform tasks on spot instances, there is an inevitable risk of failures that causes the delay of task execution time, resulting in a serious deterioration of quality of service (QoS). To deal with the problem on spot instances, we propose an estimated interval-based checkpointing (EIC) using weighted moving average. Our scheme sets the thresholds of price and execution time based on history. Whenever the actual price and the execution time cross over the thresholds, the system saves the state of spot instances. The Bollinger Bands is adopted to inform the ranges of estimated cost and execution time for user's discretion. The simulation results reveal that, compared to the HBC and REC, the EIC reduces the number of checkpoints and the rollback time. Consequently, the task execution time is decreased with EIC by HBC and REC. The EIC also provides the benefit of the cost reduction by HBC and REC, on average. We also found that the actual cost and execution time fall within the estimated ranges suggested by the Bollinger Bands.

A Dynamic Task Scheduling Algorithm for Cloud Computing Environment

2020 ◽  
Vol 13 (2) ◽  
pp. 296-307
Author(s):  
Hicham Ben Alla ◽  
Said Ben Alla ◽  
Abdellah Ezzati
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Waiting Time ◽  
Queue Length ◽  
Particle Swarm ◽  
Computing Environment ◽  
Swarm Optimization ◽  
Cloud Computing Environment ◽  
Tasks Scheduling ◽  
Annealing Algorithms

Background: Cloud computing environment is a novel paradigm in which the services are hosted, delivered and managed over the internet. Tasks scheduling problem in the cloud has become a very interesting research area. However, the problem is more complex and challenging due to the dynamic nature of cloud and users’ needs as well as cloud providers’ requirements including the quality of service, users’ priorities and computing capabilities. Objective: The main objective is to solve the problem of tasks scheduling through an algorithm which can not only improves the client satisfaction, but also allows cloud service provider to gain maximum profit and ensure that the cloud resources are utilized efficiently. Method: (a) Optimization of the waiting time and the queue length. Methods: (a) Optimization of the waiting time and the queue length. (b) Distribution of all requests into a novel queueing system in a dynamic manner based on a decision threshold. (c) Assignment of requests to VMs based on Particle Swarm Optimization and Simulated Annealing algorithms. (d) Incorporation of the priority constraint in the scheduling process by considering three priorities levels including the tasks, queues and VMs. Results: The results comparison of our algorithm with particle swarm optimization and First Come First Serve algorithms demonstrate the effectiveness of our algorithm in terms of waiting time, makespan, resources utilization and degree of imbalance. Conclusion: This study introduces an efficient strategy to schedule users’ tasks by using dynamic dispatch queues and particle swarm optimization with simulated annealing algorithms. Moreover, it incorporates the priority issue in the scheduling process.

scholarly journals Novel load balancing approach based on ant colony optimization technique in cloud computing

2021 ◽  
Vol 10 (4) ◽  
pp. 2320-2326
Author(s):  
Yasameen A. Ghani Alyouzbaki ◽  
Muaayed F. Al-Rawi
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Load Balancing ◽  
Ant Colony Optimization ◽  
Virtual Machine ◽  
Execution Time ◽  
Virtual Machines ◽  
Service Providers ◽  
Ant Colony ◽  
Time And Energy

The cloud is the framework in which communication is connected with virtual machines, data centers, hosts, and brokers. The broker searches for a highly reliable cloudlet virtual machine for execution. Vulnerability can occur in the network because of which framework gets overburden. A research strategy is introduced in this article to expand the fault tolerance of the framework. The proposed approach improvement depends on the algorithm of ant colony optimization (ACO) that can choose the better virtual machine on which is to migrate the cloudlet to reduce the execution time and energy consumption. The efficiency of the proposed approach simulated in terms of execution time, energy consumption and examined with CloudSim. The introduction is provided in this article with a detailed description of cloud computing and, in addition, green cloud computing with its models. This article also discussed the virtual machine (VM) in more depth in the introduction section, which allows cloud service providers to supervise cloud resources competently while dispensing with the need for human oversight. Then the article submitted and explained the related works with their discussion and then it explained the novel proposed load balancing based on ACO technique and concluded that the execution time and energy consumption of the proposed technique is better than the three-threshold energy saving algorithm (TESA) technique that is commonly used in cloud load balancing.

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