Striking a Balance Between Traffic Engineering and Energy Efficiency in Virtual Machine Placement

Belabed, Dallal; Secci, Stefano; Pujolle, Guy; Medhi, Deep

doi:10.1109/tnsm.2015.2413755

Cited by 26 publications

(7 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, a new hot research area in DCs is virtual machines (VMs) placement. rough appropriately placing VMs w.r.t the network and applications resource requirements, the network manager is able to save the superfluous resources for the other services and make the DCN much energy-efficient [15]. In this paper, we are not considering the whole network and placing the VMs.…”

Section: Architecturementioning

confidence: 99%

Designing of a Simulation Tool for the Performance Analysis of Hybrid Data Center Networks (DCNs)

et al. 2021

View full text Add to dashboard Cite

Data center (DC) technology changes the mode of computing. Traditional DCs consist of a single layer and only have Ethernet connections among switches. Those old-fashioned DCs cannot fulfill the high resource demand compared with today’s DCs. The architectural design of the DCs is getting substantial importance and acting as the backbone of the network because of its essential feature of supporting and maintaining the rapidly increasing Internet-based applications which include search engines (e.g., Google and Yandex) and social networking applications (e.g., YouTube, Twitter, and Facebook). Every application has its parameters, like latency and blocking in the DC network. Every data center network (DCN) has its specialized architecture. It has a specific arrangement of layers and switches, which increase or decrease the DC network’s efficiency. We develop a simulation tool that comprises two different DC architectures: basic tree architecture and c-Through architecture. Using this simulation, we analyze the traffic behavior and the performance of the simulated DCN. Our main purpose is to focus on mean waiting time, load, and blocking with respect to the traffic within the DCN.

show abstract

Section: Architecturementioning

confidence: 99%

Designing of a Simulation Tool for the Performance Analysis of Hybrid Data Center Networks (DCNs)

et al. 2021

View full text Add to dashboard Cite

show abstract

“…It must also improve resource utilization while trying to satisfy service level agreements (SLAs). Genetic algorithm, Ant Colony algorithm, linear programming, adaptive heuristics, and utility-based approaches are proposed for resource scheduling and VM migration [24][25][26][27][28][29][30][31][32][33][34]. For typical air-cooled data centers, researchers propose thermal-aware workload allocation strategy with respect to the chip temperature constraint [35] and use computational fluid dynamics (CFD) to model and validate Airflow in a Data Center [36].…”

Section: Related Workmentioning

confidence: 99%

Energy Aware Virtual Machine Scheduling in Data Centers

et al. 2019

View full text Add to dashboard Cite

Power consumption is a primary concern in modern servers and data centers. Due to varying in workload types and intensities, different servers may have a different energy efficiency (EE) and energy proportionality (EP) even while having the same hardware configuration (i.e., central processing unit (CPU) generation and memory installation). For example, CPU frequency scaling and memory modules voltage scaling can significantly affect the server’s energy efficiency. In conventional virtualized data centers, the virtual machine (VM) scheduler packs VMs to servers until they saturate, without considering their energy efficiency and EP differences. In this paper we propose EASE, the Energy efficiency and proportionality Aware VM SchEduling framework containing data collection and scheduling algorithms. In the EASE framework, each server’s energy efficiency and EP characteristics are first identified by executing customized computing intensive, memory intensive, and hybrid benchmarks. Servers will be labelled and categorized with their affinity for different incoming requests according to their EP and EE characteristics. Then for each VM, EASE will undergo workload characterization procedure by tracing and monitoring their resource usage including CPU, memory, disk, and network and determine whether it is computing intensive, memory intensive, or a hybrid workload. Finally, EASE schedules VMs to servers by matching the VM’s workload type and the server’s EP and EE preference. The rationale of EASE is to schedule VMs to servers to keep them working around their peak energy efficiency point, i.e., the near optimal working range. When workload fluctuates, EASE re-schedules or migrates VMs to other servers to make sure that all the servers are running as near their optimal working range as they possibly can. The experimental results on real clusters show that EASE can save servers’ power consumption as much as 37.07%–49.98% in both homogeneous and heterogeneous clusters, while the average completion time of the computing intensive VMs increases only 0.31%–8.49%. In the heterogeneous nodes, the power consumption of the computing intensive VMs can be reduced by 44.22%. The job completion time can be saved by 53.80%.

show abstract

“…The most similar study to our work is [21], where three-layer, fat-tree, BCube and DCell topologies have been considered for performance evaluation of a virtual machine placement policy that aims at addressing energy-efficiency and traffic engineering. However, this analysis lacks consideration of energy proportionality of the devices.…”

Section: Background and Motivationmentioning

confidence: 99%

Power comparison of cloud data center architectures

Ruiu

Bianco

Fiandrino

et al. 2016

2016 IEEE International Conference on Communications (ICC)

View full text Add to dashboard Cite

Abstract-Power consumption is a primary concern for cloud computing data centers. Being the network one of the nonnegligible contributors to energy consumption in data centers, several architectures have been designed with the goal of improving network performance and energy-efficiency. In this paper, we provide a comparison study of data center architectures, covering both classical two-and three-tier design and state-of-art ones as Jupiter, recently disclosed by Google. Specifically, we analyze the combined effect on the overall system performance of different power consumption profiles for the IT equipment and of different resource allocation policies. Our experiments, performed in small and large scale scenarios, unveil the ability of network-aware allocation policies in loading the the data center in a energyproportional manner and the robustness of classical two-and three-tier design under network-oblivious allocation strategies.

show abstract

Striking a Balance Between Traffic Engineering and Energy Efficiency in Virtual Machine Placement

Cited by 26 publications

References 25 publications

Designing of a Simulation Tool for the Performance Analysis of Hybrid Data Center Networks (DCNs)

Designing of a Simulation Tool for the Performance Analysis of Hybrid Data Center Networks (DCNs)

Energy Aware Virtual Machine Scheduling in Data Centers

Power comparison of cloud data center architectures

Contact Info

Product

Resources

About