scholarly journals Green Metascheduler Architecture to Provide QoS in Cloud Computing

2015 ◽  
Author(s):  
Osvaldo Adilson De Carvalho Junior ◽  
Sarita Mazzini Bruschi ◽  
Regina Helena Carlucci Santana ◽  
Marcos José Santana
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Energy Consumption ◽  
Response Time ◽  
Large Scale ◽  
Quality Of Services ◽  
Average Response ◽  
Average Response Time ◽  
Private Cloud

The aim of this paper is to propose and evaluate GreenMACC (Green Metascheduler Architecture to Provide QoS in Cloud Computing), an extension of the MACC architecture (Metascheduler Architecture to provide QoS in Cloud Computing) which uses greenIT techniques to provide Quality of Service. The paper provides an evaluation of the performance of the policies in the four stages of scheduling focused on energy consumption and average response time. The results presented confirm the consistency of the proposal as it controls energy consumption and the quality of services requested by different users of a large-scale private cloud.

scholarly journals An Efficient Adaptive Load Balancing Algorithm for Cloud Computing Under Bursty Workloads

2015 ◽  
Vol 5 (3) ◽  
pp. 795-800 ◽  
Author(s):  
S. F. Issawi ◽  
A. Al Halees ◽  
M. Radi
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Quality Of Services ◽  
Average Response ◽  
Average Response Time ◽  
It Industry ◽  
Request Rate ◽  
Wide Range ◽  
Load Balancing Algorithm

Cloud computing is a recent, emerging technology in the IT industry. It is an evolution of previous models such as grid computing. It enables a wide range of users to access a large sharing pool of resources over the internet. In such complex system, there is a tremendous need for an efficient load balancing scheme in order to satisfy peak user demands and provide high quality of services. One of the challenging problems that degrade the performance of a load balancing process is bursty workloads. Although there are a lot of researches proposing different load balancing algorithms, most of them neglect the problem of bursty workloads. Motivated by this problem, this paper proposes a new burstness-aware load balancing algorithm which can adapt to the variation in the request rate by adopting two load balancing algorithms: RR in burst and Random in non-burst state. Fuzzy logic is used in order to assign the received request to a balanced VM. The algorithm has been evaluated and compared with other algorithms using Cloud Analyst simulator.  Results show that the proposed algorithm improves the average response time and average processing time in comparison with other algorithms.

scholarly journals A Decentralized Virtual Machine Migration Approach of Data Centers for Cloud Computing

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoying Wang ◽  
Xiaojing Liu ◽  
Lihua Fan ◽  
Xuhan Jia
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Large Scale ◽  
Data Centers ◽  
Virtual Machines ◽  
Quality Of Services ◽  
Virtual Machine Migration ◽  
Power Models ◽  
Key Steps

As cloud computing offers services to lots of users worldwide, pervasive applications from customers are hosted by large-scale data centers. Upon such platforms, virtualization technology is employed to multiplex the underlying physical resources. Since the incoming loads of different application vary significantly, it is important and critical to manage the placement and resource allocation schemes of the virtual machines (VMs) in order to guarantee the quality of services. In this paper, we propose a decentralized virtual machine migration approach inside the data centers for cloud computing environments. The system models and power models are defined and described first. Then, we present the key steps of the decentralized mechanism, including the establishment of load vectors, load information collection, VM selection, and destination determination. A two-threshold decentralized migration algorithm is implemented to further save the energy consumption as well as keeping the quality of services. By examining the effect of our approach by performance evaluation experiments, the thresholds and other factors are analyzed and discussed. The results illustrate that the proposed approach can efficiently balance the loads across different physical nodes and also can lead to less power consumption of the entire system holistically.

scholarly journals Randomized routing of virtual machines in IaaS data centers

2019 ◽  
Vol 5 ◽  
pp. e211
Author(s):  
Hadi Khani ◽  
Hamed Khanmirza
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Power Consumption ◽  
Analytical Model ◽  
Data Center ◽  
Large Scale ◽  
Data Centers ◽  
Virtual Machines ◽  
General Distribution

Cloud computing technology has been a game changer in recent years. Cloud computing providers promise cost-effective and on-demand resource computing for their users. Cloud computing providers are running the workloads of users as virtual machines (VMs) in a large-scale data center consisting a few thousands physical servers. Cloud data centers face highly dynamic workloads varying over time and many short tasks that demand quick resource management decisions. These data centers are large scale and the behavior of workload is unpredictable. The incoming VM must be assigned onto the proper physical machine (PM) in order to keep a balance between power consumption and quality of service. The scale and agility of cloud computing data centers are unprecedented so the previous approaches are fruitless. We suggest an analytical model for cloud computing data centers when the number of PMs in the data center is large. In particular, we focus on the assignment of VM onto PMs regardless of their current load. For exponential VM arrival with general distribution sojourn time, the mean power consumption is calculated. Then, we show the minimum power consumption under quality of service constraint will be achieved with randomize assignment of incoming VMs onto PMs. Extensive simulation supports the validity of our analytical model.

A Salp Swarm Optimization for Dynamic Resource Management to Improve Quality-of-Service in Cloud Computing and IoT Environment

2021 ◽  
Vol 11 ◽  
Author(s):  
Narander Kumar ◽  
Surendra Kumar
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Data Centers ◽  
Service Providers ◽  
Cloud Service ◽  
Research Problem ◽  
Resource Provisioning ◽  
Quality Of Services ◽  
Computing Services

Background: Cloud Computing can utilize processing and efficient resources on a metered premise. This feature is a significant research problem, like giving great Quality-of-Services (QoS) to the cloud clients. Objective: Quality of Services confirmation with minimum utilization of resource and their time/costs, cloud service providers ought to receive self-versatile of the resource provisioning at each level. Currently, various guidelines, as well as model-based methodologies, have been intended to the management of resources aspects in the cloud computing services. Method: In this Research article, manage resource allocations dependent optimization Salp Swarm Algorithm (SSA) areused to merge various numbers of VMs on lessening Data Centers to SLA as well as required Quality-of-Service (QoS) with most extreme data centers use. Result: We compared with the various approaches like the First fit (FF), greedy crow search (GCS), and hybrid crow search with the response time and resource utilization. Conclusion: The proposed mechanism is simulated on Cloudsim Simulator, the simulation results show less migration time that improves the QoS as well minimize the energy consumssion in a cloud computing and IoT environment.

scholarly journals Efficient Task Scheduling for Quality of Service in Cloud Computing Network

2020 ◽  
Vol 8 (5) ◽  
pp. 3193-3196
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Response Time ◽  
Task Scheduling ◽  
Waiting Time ◽  
Threshold Values ◽  
Specific Time ◽  
Required Quality ◽  
Cloud Resources

Task scheduling in cloud is the process of allocating a resource to a task at specific time. The allocation of limited cloud resources to large number of tasks to satisfy the required quality of service is the key challenge in cloud. Allocation of a resource with less capability to a task increases the response time, makespan of the task and waiting time of the entire tasks in the waiting queue. This problem will result to an unsatisfied Quality of Service. In this paper we proposed an efficient task scheduling that uses three threshold values to specify the resource to be allocated to a task at a given time. This method ensures that a capable resource is allocated to task such that the response time and makespan of the all task are minimized. The proposed method was simulated using CloudSim and the result shows a better response time and makespan than the well known Min-Min and Max-Min Method.

scholarly journals Analisis Evaluasi Aplikasi Augmented Reality Untuk Informasi Kebudayaan Bali Berdasarkan Standar ISO 25010

2018 ◽  
Vol 1 (1) ◽  
pp. 34-40
Author(s):  
I Gede Andika ◽  
Christina Purnama Yanti
Keyword(s):  
Augmented Reality ◽  
Response Time ◽  
Software Quality ◽  
Quality Standards ◽  
Average Response ◽  
Average Response Time ◽  
Smartphone Technology ◽  
Time Test ◽  
To Come

Currently there are still many people of Bali who just know the museum is a place to store historical items but are reluctant to come. Therefore built an application that combines smartphone technology with Augmented Reality so that later can attract people to come visit the Museum of Bali. In order to obtain application results that meet the standards, evaluation is required to assess the quality of an application based on ISO 25010 standards. The software quality standards that are tested are aspects of functional suitability, usability, performance efficiency, and compatibility. Based on the results of all research that has been done, it can be concluded that the quality of Augmented Reality Museum Bali application has met the software quality standards that refer to the ISO 25010 standard. The result of testing the aspect of functional suitability is 100%. Usability aspect got the conclusion that AR Museum Bali application have aspect of learnability, efficiency, memorability, and satisfaction with good category. Performance performance aspect is 3 test and get average response time test 1 is 27.29 ms; test 2 of 38.52 ms; and test 3 of 20.31 ms. The compatibility aspect is 100%.

scholarly journals Improving Quality of Service in Cloud Computing

2019 ◽  
Author(s):  
Sobia Sajid ◽  
Sadia Ahmed ◽  
Arooj Waheed ◽  
Mehreen Sirshar
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Data Storage ◽  
Emerging Technology ◽  
Quality Of Services ◽  
Quality Performance ◽  
Data Accessibility ◽  
Service Oriented ◽  
Research Gap

Quality of Service (QoS) has a significant role in the provision of resources within service oriented distributed systems. In Quality of Service, cloud computing creates new challenges for improvements using the concept of virtualization. Currently, Cloud Computing is very emerging technology in every field of data storage and resource distribution over the network. Considering this new emerging technology, for the ease of data accessibility, price, resource use, restoration, response time and number of constraints the quality performance measures need to be upgraded. The paper highlights the research gap in providing a solution to achieve a Quality of Services in Cloud Computing. We also review the issues and challenges arising in cloud computing to guarantee quality.

Energy Consumption and Quality of Service Optimization in Containerized Cloud Computing

2018 ◽  
Author(s):  
Rewer Canosa ◽  
Andrei Tchernykh ◽  
Jorge M. Cortes-Mendoza ◽  
Raul Rivera-Rodriguez ◽  
Jose Lozano Rizk ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Energy Consumption ◽  
Service Optimization
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