Quality of service aware power management for virtualized data centers

2013 ◽  
Vol 59 (4-5) ◽  
pp. 245-259 ◽  
Author(s):  
Yongqiang Gao ◽  
Haibing Guan ◽  
Zhengwei Qi ◽  
Bin Wang ◽  
Liang Liu
Keyword(s):  
Quality Of Service ◽  
Power Management ◽  
Data Centers ◽  
Virtualized Data Centers

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 Power Management of Server Farms

2014 ◽  
Vol 492 ◽  
pp. 453-459 ◽  
Author(s):  
Giuseppe Iazeolla ◽  
Alessandra Pieroni
Keyword(s):  
Quality Of Service ◽  
Power Consumption ◽  
Power Management ◽  
Management Strategies ◽  
Power Saving ◽  
Performance Per Watt ◽  
The World ◽  
Small Set ◽  
The Universe

The power management of server farms (Sf) is becoming a relevant problem in economical terms. Server farms totalize millions of servers all over the world that need to be electrically powered. Research is thus expected to investigate into methods to reduce Sf power consumption. However, saving power may turn into waste of performance (high response_times), in other words, into waste of Sf Quality of Service (QoS). By use of a Sfmodel, this paper investigates Sf power management strategies that look at compromises between power-saving and QoS. Various optimizing Sf power management policies are studied combined with the effects of job queueing disciplines. The (policy, discipline) pairs, or strategies, that optimize the Sf power consumption (minimum absorbed Watts), the Sf performance (minimum response_time), and the Sf performance-per-Watt (minimum response_time-per-Watt) are identified.By use of the model, the work the server-manager has to do to direct hisSf is greatly simplified, since the universe of all possible (strategies he needs to choose from is drastically reduced to a very small set of most significant strategies.

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.

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