scholarly journals Performance Evaluation of Cloud Data Centers with Batch Task Arrivals

2013 ◽  
pp. 199-223 ◽  
Author(s):  
Hamzeh Khazaei ◽  
Jelena Mišić ◽  
Vojislav B. Mišić
Keyword(s):  
Performance Evaluation ◽  
Queue Length ◽  
Blocking Probability ◽  
General Distribution ◽  
Cloud Data ◽  
Wide Range ◽  
Accurate Performance ◽  
Cloud Data Centers ◽  
Poisson Arrivals

Accurate performance evaluation of cloud computing resources is a necessary prerequisite for ensuring that Quality of Service (QoS) parameters remain within agreed limits. In this chapter, the authors consider cloud centers with Poisson arrivals of batch task requests under total rejection policy; task service times are assumed to follow a general distribution. They describe a new approximate analytical model for performance evaluation of such systems and show that important performance indicators such as mean request response time, waiting time in the queue, queue length, blocking probability, probability of immediate service, and probability distribution of the number of tasks in the system can be obtained in a wide range of input parameters.

Energy-Efficient Virtualized Scheduling and Load Balancing Algorithm in Cloud Data Centers

2021 ◽  
Vol 11 (3) ◽  
pp. 34-48
Author(s):  
J. K. Jeevitha ◽  
Athisha G.
Keyword(s):  
Quality Of Service ◽  
Load Balancing ◽  
Energy Efficient ◽  
Data Centers ◽  
Scheduling Algorithm ◽  
Cloud Data ◽  
The Third ◽  
Cloud Data Centers ◽  
Load Balancing Algorithm

To scale back the energy consumption, this paper proposed three algorithms: The first one is identifying the load balancing factors and redistribute the load. The second one is finding out the most suitable server to assigning the task to the server, achieved by most efficient first fit algorithm (MEFFA), and the third algorithm is processing the task in the server in an efficient way by energy efficient virtual round robin (EEVRR) scheduling algorithm with FAT tree topology architecture. This EEVRR algorithm improves the quality of service via sending the task scheduling performance and cutting the delay in cloud data centers. It increases the energy efficiency by achieving the quality of service (QOS).

scholarly journals Service Quality Assurance in Cloud Data Centers Using Migration Scaling

2020 ◽  
Vol 02 (01) ◽  
pp. 53-63
Author(s):  
Dr. Akey Sungheetha ◽  
Dr. Rajesh Sharma R
Keyword(s):  
Quality Of Service ◽  
Service Quality ◽  
User Satisfaction ◽  
Data Centers ◽  
Vital Role ◽  
Cloud Data ◽  
Similar Work ◽  
Cloud Data Centers ◽  
Multiple Metrics

The continuous and swift progress in the number of the cloud data centers have led to establishment of multitudes of the computational nodes and the huge paradigm. But the assuring the quality of services through these paradigms is still questionable. So tit has become a prominent areas of research. As the quality of service of the data centers plays a vital role in the user satisfaction. The present work carried out in the paper survey the service quality rendered in the previous similar work, identifies the drawbacks and proposes a strategy of migration taking into consideration the multiple metrics. The proposed structure is validated through the cloud simulator to evince its capability in efficiently handling the resources and guaranteeing the quality of service.

scholarly journals Performance Evaluation of Multiple Cloud Data Centers Allocations for HPC

2017 ◽  
pp. 18-32
Author(s):  
Eduardo Roloff ◽  
Emmanuell Diaz Carreño ◽  
Jimmy K. M. Valverde-Sánchez ◽  
Matthias Diener ◽  
Matheus da Silva Serpa ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Data Centers ◽  
Cloud Data ◽  
Cloud Data Centers

scholarly journals A Spectrum Defragmentation Algorithm Using Jellyfish Optimization Technique in Elastic Optical Network (EON)

2021 ◽  
Author(s):  
Selvakumar S ◽  
S S Manivannan
Keyword(s):  
Quality Of Service ◽  
Blocking Probability ◽  
Optical Network ◽  
Optimization Technique ◽  
Spectrum Utilization ◽  
Network Efficiency ◽  
Internet Service ◽  
Cloud Data ◽  
Elastic Optical Network

Abstract The rapid growth of the technologies, high bandwidth applications and cloud data centers consume heavy internet service. So, the consumer of the internet expects a high capacity medium for communication. The Elastic Optical Network (EON) provides a flexible and reliable transmission service for the consumers. The spectrum fragmentation is a key challenge in EON. In simple terms, unaligned Frequency Slots (FSs) in the network are referred to as fragmented spectrum, while in defragmentation, the available FSs need to be rearranged to create room for the new connection requests. The problem in defragmentation occurs due to the lack of a continuous spectrum and it leads to depreciation in spectrum usage and simultaneously increasing the Blocking Probability (BP) which disrupts the majority of the existing connections in the network. Several techniques and approaches were suggested to reduce the possibility of disruption and reconfiguration in the network while defragmenting the frequency slots. This paper proposes a new algorithm to overcome the drawbacks and improvement in the quality of service of the network. The proposed algorithm holds the approach of proactive and reactive along with the meta-heuristic nature-inspired optimization technique called Jellyfish Search Optimization (JSO). The proposed combination, PR-DF-JFSO outperforms well in terms of spectrum utilization, network efficiency, and quality of service offered when compared to the state-of-the-art spectrum defragmentation algorithms according to the results of experiments done using standard quality metrics.

scholarly journals Lifetime-Aware Cloud Data Centers: Models and Performance Evaluation

Energies ◽  
2016 ◽  
Vol 9 (6) ◽  
pp. 470 ◽  
Author(s):  
Luca Chiaraviglio ◽  
Antonio Cianfrani ◽  
Marco Listanti ◽  
William Liu ◽  
Marco Polverini
Keyword(s):  
Performance Evaluation ◽  
Data Centers ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
And Performance

scholarly journals Research on Auto-Scaling of Web Applications in Cloud: Survey, Trends and Future Directions

2019 ◽  
Vol 20 (2) ◽  
pp. 399-432 ◽  
Author(s):  
Parminder Singh ◽  
Pooja Gupta ◽  
Kiran Jyoti ◽  
Anand Nayyar
Keyword(s):  
Cloud Computing ◽  
Web Application ◽  
Web Applications ◽  
Service Providers ◽  
Cloud Service ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
The Cost ◽  
Auto Scaling

Cloud computing emerging environment attracts many applications providers to deploy web applications on cloud data centers. The primary area of attraction is elasticity, which allows to auto-scale the resources on-demand. However, web applications usually have dynamic workload and hard to predict. Cloud service providers and researchers are working to reduce the cost while maintaining the Quality of Service (QoS). One of the key challenges for web application in cloud computing is auto-scaling. The auto-scaling in cloud computing is still in infancy and required detail investigation of taxonomy, approach and types of resources mapped to the current research. In this article, we presented the literature survey for auto-scaling techniques of web applications in cloud computing. This survey supports the research community to find the requirements in auto-scaling techniques. We present a taxonomy of reviewed articles with parameters such as auto-scaling techniques, approach, resources, monitoring tool, experiment, workload, and metric, etc. Based on the analysis, we proposed the new areas of research in this direction.

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