Modeling Data Centers as Economic Markets for Dynamic Service Provisioning and Resource Management

2006 ◽  
Author(s):  
David Cohen ◽  
Stuart Schaefer
Keyword(s):  
Resource Management ◽  
Data Centers ◽  
Service Provisioning ◽  
Economic Markets ◽  
Modeling Data

Energy-Saving QoS Resource Management of Virtualized Networked Data Centers for Big Data Stream Computing

Big Data ◽  
2016 ◽  
pp. 848-886
Author(s):  
Nicola Cordeschi ◽  
Mohammad Shojafar ◽  
Danilo Amendola ◽  
Enzo Baccarelli
Keyword(s):  
Big Data ◽  
Resource Management ◽  
Real Time ◽  
Data Stream ◽  
Data Centers ◽  
Software As A Service ◽  
Stream Computing ◽  
Management Optimization ◽  
Hard Real Time ◽  
Optimal Scheduler

In this chapter, the authors develop the scheduler which optimizes the energy-vs.-performance trade-off in Software-as-a-Service (SaaS) Virtualized Networked Data Centers (VNetDCs) that support real-time Big Data Stream Computing (BDSC) services. The objective is to minimize the communication-plus-computing energy which is wasted by processing streams of Big Data under hard real-time constrains on the per-job computing-plus-communication delays. In order to deal with the inherently nonconvex nature of the resulting resource management optimization problem, the authors develop a solving approach that leads to the lossless decomposition of the afforded problem into the cascade of two simpler sub-problems. The resulting optimal scheduler is amenable of scalable and distributed adaptive implementation. The performance of a Xen-based prototype of the scheduler is tested under several Big Data workload traces and compared with the corresponding ones of some state-of-the-art static and sequential schedulers.

scholarly journals An Energy and SLA-Aware Resource Management Strategy in Cloud Data Centers

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Chi Zhang ◽  
Yuxin Wang ◽  
Yuanchen Lv ◽  
Hao Wu ◽  
He Guo
Keyword(s):  
Energy Consumption ◽  
Resource Management ◽  
Management Strategy ◽  
Data Centers ◽  
Minimum Energy ◽  
Service Level Agreement ◽  
Cloud Data ◽  
Cpu Utilization ◽  
Cloud Data Centers ◽  
Detecting Method

Reducing energy consumption of data centers is an important way for cloud providers to improve their investment yield, but they must also ensure that the services delivered meet the various requirements of consumers. In this paper, we propose a resource management strategy to reduce both energy consumption and Service Level Agreement (SLA) violations in cloud data centers. It contains three improved methods for subproblems in dynamic virtual machine (VM) consolidation. For making hosts detection more effective and improving the VM selection results, first, the overloaded hosts detecting method sets a dynamic independent saturation threshold for each host, respectively, which takes the CPU utilization trend into consideration; second, the underutilized hosts detecting method uses multiple factors besides CPU utilization and the Naive Bayesian classifier to calculate the combined weights of hosts in prioritization step; and third, the VM selection method considers both current CPU usage and future growth space of CPU demand of VMs. To evaluate the performance of the proposed strategy, it is simulated in CloudSim and compared with five existing energy–saving strategies using real-world workload traces. The experimental results show that our strategy outperforms others with minimum energy consumption and SLA violation.

Memory-aware resource management algorithm for low-energy cloud data centers

2020 ◽  
Vol 113 ◽  
pp. 329-342
Author(s):  
Bin Liang ◽  
Xiaoshe Dong ◽  
Yufei Wang ◽  
Xingjun Zhang
Keyword(s):  
Resource Management ◽  
Data Centers ◽  
Low Energy ◽  
Cloud Data ◽  
Management Algorithm ◽  
Cloud Data Centers

Efficient resource management for data centers

2018 ◽  
Author(s):  
Vasileios Karakostas ◽  
Georgios Goumas ◽  
Ewnetu Bayuh Lakew ◽  
Erik Elmroth ◽  
Stefanos Gerangelos ◽  
...  
Keyword(s):  
Resource Management ◽  
Data Centers ◽  
Efficient Resource

Online Learning of Run-Time Models for Performance and Resource Management in Data Centers

2017 ◽  
pp. 507-528 ◽  
Author(s):  
Jürgen Walter ◽  
Antinisca Di Marco ◽  
Simon Spinner ◽  
Paola Inverardi ◽  
Samuel Kounev
Keyword(s):  
Online Learning ◽  
Resource Management ◽  
Data Centers ◽  
Run Time
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