scholarly journals Integrated resource management pipeline for dynamic resource-effective cloud data center

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Hanan A. Hassan ◽  
Aya I. Maiyza ◽  
Walaa M. Sheta
Keyword(s):  
Energy Consumption ◽  
Resource Management ◽  
Network Traffic ◽  
Data Center ◽  
Power Saving ◽  
Service Level ◽  
Cloud Data Center ◽  
Integrated Resource Management ◽  
Cloud Data ◽  
Dynamic Resource

AbstractCloud computing is a popular emerging computing technology that has revolutionized information technology through flexible provisioning of computing resources. Therefore, efforts to develop an effective resource management approach have found that implementing efficient resource sharing among multiple customers that considers power saving, service-level agreements, and network traffic simultaneously is difficult. This paper proposes a practical integrated pipeline that can use various algorithms. The performance of each algorithm is evaluated independently to obtain the combination of algorithms that guarantees a resource-effective cloud data center framework. This integrated resource management pipeline approach would optimize performance based on several key performance indicators, such as power saving, network traffic, and service-level agreements, for either the whole system or the end-user. The performance of the proposed resource management framework was evaluated using a real testbed. The results demonstrated that the proactive double exponential smoothing algorithm prevents unnecessary migrations, the MMTMC2 VM selection algorithm improved the quality of service for end-users and reduced overall energy consumption and network traffic, and the medium-fit placement algorithm provided load balancing between active servers and decreased service level agreement violations. The performance comparison illustrated that the combination of these algorithms was considered to be the best solution toward a dynamic resource-effective cloud data center. Our results showed that energy consumption and the total number of migrations decreased by 16.64% and 49.44%, respectively.

Power Aware Meta Scheduler for Adaptive VM Provisioning in IaaS Cloud

2013 ◽  
pp. 190-204
Author(s):  
R. Jeyarani ◽  
N. Nagaveni ◽  
Satish Kumar Sadasivam ◽  
Vasanth Ram Rajarathinam
Keyword(s):  
Data Center ◽  
Power Saving ◽  
Resource Provisioning ◽  
Configurable Computing ◽  
Cloud Data Center ◽  
Workload Prediction ◽  
Cloud Data ◽  
Wise Decision ◽  
Microprocessor Technology ◽  
Lifecycle Management

Cloud Computing provides on-demand access to a shared pool of configurable computing resources. The major issue lies in managing extremely large agile data centers which are generally over provisioned to handle unexpected workload surges. This paper focuses on green computing by introducing Power-Aware Meta Scheduler, which provides right fit infrastructure for launching virtual machines onto host. The major challenge of the scheduler is to make a wise decision in transitioning state of the processor cores by exploiting various power saving states inherent in the recent microprocessor technology. This is done by dynamically predicting the utilization of the cloud data center. The authors have extended existing cloudsim toolkit to model power aware resource provisioning, which includes generation of dynamic workload patterns, workload prediction and adaptive provisioning, dynamic lifecycle management of random workload, and implementation of power aware allocation policies and chip aware VM scheduler. The experimental results show that the appropriate usage of different power saving states guarantees significant energy conservation in handling stochastic nature of workload without compromising the performance, both when the data center is in low as well as moderate utilization.

Minimization of VM Placement Change in Energy-Aware Resource Provisioning for Cloud Data Center

2013 ◽  
Vol 325-326 ◽  
pp. 1730-1733 ◽  
Author(s):  
Si Yuan Jing ◽  
Shahzad Ali ◽  
Kun She
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Network Flow ◽  
Resource Provisioning ◽  
Approximate Algorithm ◽  
Vm Placement ◽  
Energy Aware ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Placement Change

Numerous part of the energy-aware resource provision research for cloud data center just considers how to maximize the resource utilization, i.e. minimize the required servers, without considering the overhead of a virtual machine (abbreviated as a VM) placement change. In this work, we propose a new method to minimize the energy consumption and VM placement change at the same time, moreover we also design a network-flow-theory based approximate algorithm to solve it. The simulation results show that, compared to existing work, the proposed method can slightly decrease the energy consumption but greatly decrease the number of VM placement change

scholarly journals Efficient Resource Utilization Based on Energy Management in Cloud Data Center

2019 ◽  
Vol 8 (6) ◽  
pp. 340-344
Keyword(s):  
Power Consumption ◽  
Resource Utilization ◽  
Data Center ◽  
Vital Role ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Network Bandwidth ◽  
Efficient Resource ◽  
Novel Method ◽  
Dynamic Resource

Efficient resource utilization plays a vital role in cloud computing since the shared computational power of the resources is offered on demand. During dynamic resource allocation sometimes a server may be over utilized or underutilized thus leading to excess of energy consumption in the data centers. So the proposed system calculates the over utilization and underutilization of a CPU and RAM usage and also considers the network bandwidth usage to reduce power consumption in the cloud data center. Hence, a novel method is used for minimizing power consumption in the data center

Power Aware Meta Scheduler for Adaptive VM Provisioning in IaaS Cloud

2011 ◽  
Vol 1 (3) ◽  
pp. 36-51 ◽  
Author(s):  
R. Jeyarani ◽  
N. Nagaveni ◽  
Satish Kumar Sadasivam ◽  
Vasanth Ram Rajarathinam
Keyword(s):  
Data Center ◽  
Virtual Machines ◽  
Power Saving ◽  
Resource Provisioning ◽  
Configurable Computing ◽  
Cloud Data Center ◽  
Workload Prediction ◽  
Cloud Data ◽  
Wise Decision ◽  
Microprocessor Technology

Cloud Computing provides on-demand access to a shared pool of configurable computing resources. The major issue lies in managing extremely large agile data centers which are generally over provisioned to handle unexpected workload surges. This paper focuses on green computing by introducing Power-Aware Meta Scheduler, which provides right fit infrastructure for launching virtual machines onto host. The major challenge of the scheduler is to make a wise decision in transitioning state of the processor cores by exploiting various power saving states inherent in the recent microprocessor technology. This is done by dynamically predicting the utilization of the cloud data center. The authors have extended existing cloudsim toolkit to model power aware resource provisioning, which includes generation of dynamic workload patterns, workload prediction and adaptive provisioning, dynamic lifecycle management of random workload, and implementation of power aware allocation policies and chip aware VM scheduler. The experimental results show that the appropriate usage of different power saving states guarantees significant energy conservation in handling stochastic nature of workload without compromising the performance, both when the data center is in low as well as moderate utilization.

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.

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