Greenhead: Virtual Data Center Embedding across Distributed Infrastructures

Amokrane, Ahmed; Zhani, Mohamed Faten; Langar, Rami; Boutaba, Raouf; Pujolle, Guy

doi:10.1109/tcc.2013.5

Cited by 109 publications

(79 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Wide-area data transmission is the major contributor to the network costs [18]. Therefore, in order to model it accurately, our algorithm considers intra-DC local links with bandwidth (B L ) (to access the network-attached storage), and inter-DC connections, modeled as a full mesh backbone network topology with bandwidth (B bb ).…”

Section: Network and Latency Modelmentioning

confidence: 99%

See 1 more Smart Citation

Exploiting CPU-Load and Data Correlations in Multi-Objective VM Placement for Geo-Distributed Data Centers

Pahlevan

Valle

Atienza

2016

Proceedings of the 2016 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

View full text Add to dashboard Cite

Abstract-Cloud computing has been proposed as a new paradigm to deliver services over the internet. The proliferation of cloud services and increasing users' demands for computing resources have led to the appearance of geo-distributed data centers (DCs). These DCs host heterogeneous applications with changing characteristics, like the CPU-load correlation, that provides significant potential for energy savings when the utilization peaks of two virtual machines (VMs) do not occur at the same time, or the amount of data exchanged between VMs, that directly impacts performance, i.e. response time.This paper presents a two-phase multi-objective VM placement, clustering and allocation algorithm, along with a dynamic migration technique, for geo-distributed DCs coupled with renewable and battery energy sources. It exploits the holistic knowledge of VMs characteristics, CPU-load and data correlations, to tackle the challenges of operational cost optimization and energyperformance trade-off. Experimental results demonstrate that the proposed method provides up to 55% operational cost savings, 15% energy consumption, and 12% performance (response time) improvements when compared to state-of-the-art schemes.

show abstract

Section: Network and Latency Modelmentioning

confidence: 99%

“…We use only CPU-load correlation to allocate VMs to the minimum number of servers, since data correlation (and migrations) mainly contribute to inter-DC network bottlenecks [7], [18]. Hence, we base our implementation on the best algorithm [5] for VMs allocation.…”

Section: ) Local Phase -Vms Allocationmentioning

confidence: 99%

Exploiting CPU-Load and Data Correlations in Multi-Objective VM Placement for Geo-Distributed Data Centers

Pahlevan

Valle

Atienza

2016

Proceedings of the 2016 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

View full text Add to dashboard Cite

show abstract

“…Thus, many cloud services are deployed in distributed data centers to meet their high reliability requirements. Furthermore, some services may require to be within the proximity of end-users (e.g., Web servers) whereas others may not have such location constraints and can be embedded in any data center (e.g., MapReduce jobs) [3]. Therefore, reliable VDC embedding across distributed infrastructures is appealing for Service Providers (SP) as well as Infrastructure Providers (InPs).…”

Section: Introductionmentioning

confidence: 99%

“…There are also some studies on virtual data center or virtual network (VN) embedding across multiple domains. The work in [3] studied the problem of VDC embedding across distributed infrastructures. The authors in [7]presented a framework of cost efficient VN embedding across multiple domains, called MD-VNM.…”

Section: Introductionmentioning

confidence: 99%

Reliable Virtual Data Center Embedding Across Multiple Data Centers

Sun

Anand

et al. 2016

Proceedings of the International Conference on Internet of Things and Big Data

View full text Add to dashboard Cite

Abstract:Cloud computing has become a cost-effective paradigm for deploying online service applications in large data centers in recent years. Virtualization technology enables flexible and efficient management of physical resources in cloud data centers and improves the resource utilization. A request for resources to a data center can be abstracted as a virtual data center (VDC) request. Due to the use of a large number of resources and at various locations, reliability is an important issue that should be addressed in large and multiple data centers. However, most research focuses on the problem of reliable VDC embedding in a single data center. In this paper, we study the problem of reliable VDC embedding across multiple data centers, such that the total bandwidth consumption in the inter-data center backbone network is minimized, while satisfying the reliability requirement of each VDC request. We model the problem by using mixed integer linear programming (MILP) and propose a heuristic algorithm to address this NP-hard problem efficiently. Simulation results show that the proposed algorithm performs better in terms of lowering physical resource consumption and VDC request blocking ratio compared with existing solution.

show abstract

“…The increasing popularity of cloud computing has led to the deployment of multiple datacenters to satisfy service requirements and fault tolerance. Therefore, recent studies on cloud computing focus more on multiple datacenters [1,2]. To improve and/or maintain the service level agreements (SLAs), researchers proposed virtual machine (VM) migration technologies [3].…”

Section: Introductionmentioning

confidence: 99%