Service Level and Performance Aware Dynamic Resource Allocation in Overbooked Data Centers

2016 ◽  
Author(s):  
Luis Tomas ◽  
Ewnetu Bayuh Lakew ◽  
Erik Elmroth
Keyword(s):  
Resource Allocation ◽  
Data Centers ◽  
Service Level ◽  
Dynamic Resource Allocation ◽  
Dynamic Resource ◽  
And Performance

scholarly journals Reproducible experiments on dynamic resource allocation in cloud data centers

2016 ◽  
Vol 59 ◽  
pp. 98-101 ◽  
Author(s):  
Andreas Wolke ◽  
Martin Bichler ◽  
Fernando Chirigati ◽  
Victoria Steeves
Keyword(s):  
Resource Allocation ◽  
Data Centers ◽  
Dynamic Resource Allocation ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Dynamic Resource

Dynamic Resource Allocation Method for Load Balance Scheduling Over Cloud Data Center Networks

2021 ◽  
Author(s):  
Sakshi Chhabra ◽  
Ashutosh Kumar Singh
Keyword(s):  
Resource Allocation ◽  
Load Balancing ◽  
Response Time ◽  
Resource Utilization ◽  
Service Level Agreement ◽  
Service Level ◽  
Dynamic Resource Allocation ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Dynamic Resource

The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource information is collected and updated where resources are sorted into four queues according to the loads of resources i.e. CPU intensive, Memory intensive, Energy intensive and Bandwidth intensive. We demonstarate that SLA-aware scheduling not only facilitates the cloud consumers by resources availability and improves throughput, response time etc. but also maximizes the cloud profits with less resource utilization and SLA (Service Level Agreement) violation penalties. This method is based on diversity of client’s applications and searching the optimal resources for the particular deployment. Experiments were carried out based on following parameters i.e. average response time; resource utilization, SLA violation rate and load balancing. The experimental results demonstrate that this method can reduce the wastage of resources and reduces the traffic upto 44.89% and 58.49% in the network.

scholarly journals A Review of Dynamic Resource Allocation Framework for Large Amount of Cloud Enterprises

2021 ◽  
Vol 12 (2) ◽  
pp. 1280-1284
Author(s):  
B Vijaya Laxmi, Et. al.
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Ad Hoc ◽  
Service Level ◽  
Dynamic Resource Allocation ◽  
Research Network ◽  
Data Handling ◽  
Parallel Data ◽  
Flexible Resource ◽  
Dynamic Resource

Cloud computing is an on-demand service because it offers dynamic flexible resource allocation for reliable and guaranteed services in pay as-you-use manner. Because of the consistently increasing demands of the clients for services or resources, it gets hard to allocate resources accurately to the client demands to satisfy their solicitations and also to take care of the Service Level Agreements (SLA) gave by the service suppliers. Dynamic resource allocation problem is one of the most challenging problems in the resource management problems. The dynamic resource allocation in cloud computing has attracted attention of the research network in the last couple of years. Many researchers around the world have thought of new ways of facing this challenge. Ad-hoc parallel data handling has arisen to be one of the executioner applications for Infrastructure-as-a-Service (IaaS) cloud. Number of Cloud supplier companies has started to incorporate frameworks for parallel data handling in their item which making it easy for clients to access these services and to convey their programs. The handling frameworks which are at present utilized have been intended for static and homogeneous bunch arrangements. So the allocated resources may be inadequate for large parts of the submitted tasks and unnecessarily increase preparing cost and time. Again because of opaque nature of cloud, static allocation of resources is conceivable, yet the other way around in dynamic situations. The proposed new generic data handling framework is expected to expressly misuse the dynamic resource allocation in cloud for task scheduling and execution.

More than bin packing: Dynamic resource allocation strategies in cloud data centers

2015 ◽  
Vol 52 ◽  
pp. 83-95 ◽  
Author(s):  
Andreas Wolke ◽  
Boldbaatar Tsend-Ayush ◽  
Carl Pfeiffer ◽  
Martin Bichler
Keyword(s):  
Resource Allocation ◽  
Data Centers ◽  
Bin Packing ◽  
Dynamic Resource Allocation ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Dynamic Resource ◽  
Allocation Strategies
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