On efficient bandwidth allocation for traffic variability in datacenters

Guo, Jian; Liu, Fangming; Huang, Xiaomeng; Lui, John C. S.; Mi, Hu; Qiao, Ge‐Xia; Jin, Hai

doi:10.1109/infocom.2014.6848093

Cited by 57 publications

(27 citation statements)

References 14 publications

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“…Network links are global resources that are shared by tens to hundreds of PMs, each running tens to hundreds of virtual machines. The network traffic generated by each of these VMs is often variable and bursty [Guo et al 2014]. Furthermore, data center networks have a variety of topologies [Hammadi and Mhamdi 2014].…”

Section: Obstacles To Rapid Vm Deploymentmentioning

confidence: 99%

Handling Boot Storms in Virtualized Data Centers—A Survey

Samant

Bellur

2016

ACM Comput. Surv.

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Large-scale virtual machine (VM) deployment in virtualized data centers is a very slow process. This is primarily due to the resource bottlenecks that are created at the storage, network, and host physical machines when a large number of VMs are requested simultaneously. For companies that provide virtual desktops to their employees, it is common to encounter such requests each day, when their employees turn up for work. In addition, a large number of VMs are often required to be deployed instantly, in order to absorb a spike in the workload, at online e-commerce websites. In such scenarios, long deployment times are unacceptable, and reducing them is of paramount importance. In this article, we first abstract out the key techniques suggested in the literature to speed up this deployment process. We follow this with a classification of these techniques into a taxonomy and propose a framework that can be used to compare them. Finally, we identify problem areas that warrant further research and bring out the shortcomings of the current state-of-the-art solutions.

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Section: Obstacles To Rapid Vm Deploymentmentioning

confidence: 99%

Handling Boot Storms in Virtualized Data Centers—A Survey

Samant

Bellur

2016

ACM Comput. Surv.

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“…This strategy ensures that all customers reveal their real valuation of the required bandwidth. In [37], the authors studied the problem of network utilization decline due to significant traffic variability in datacenters. They then designed a virtual machine bandwidth allocation algorithm to handle highly dynamic traffic in datacenters.…”

Section: Related Workmentioning

confidence: 99%

Internet data budget allocation policies for diverse smartphone applications

Rahman

Prima

Razzaque

et al. 2016

J Wireless Com Network

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In recent times, there has been a significant growth in the number of smartphone users and number and types of mobile applications (apps). Such a trend has resulted in increased Internet data consumption, particularly for users of "data hungry" apps. Thus, smartphone apps should be allocated to their required budget to minimize resource wastage without compromising on user's quality of experience. In this paper, we develop a prioritized and dynamic budget allocation policy framework for ensuring an optimal budget allocation to each app as well as improving system performance. We formulate the optimal Internet data budget management (O-IDM) problem as a mixed-integer nonlinear programming (MINLP) problem, which maximizes the resource utilization and minimizes user penalties. We also employ runtime monitoring technique to estimate future bandwidth utilization so as to ensure budget reservation as close as to the required amount. A heuristic Internet data budget management algorithm (H-IDM) is also presented, which is designed to reduce time complexity and computational overhead of the O-IDM system. The experimental results from test-bed implementation demonstrate the effectiveness of the proposed IDM systems, in comparison to state-of-the-art approaches.

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“…Seawall [32] is a hypervisor-based framework that allows data center administrators to divide network and fairly share bandwidth among VM-pairs. Guo et al considered the effects of larger numbers of short flows and massive bursty traffic in the datacenter, and designed a novel distributed bandwidth allocation algorithm based on the Logistic model [33].…”

Section: Bandwidth Reservation For Cloud Computingmentioning

confidence: 99%

STAR: Strategy-Proof Double Auctions for Multi-Cloud, Multi-Tenant Bandwidth Reservation

Zheng

Gui

et al. 2015

IEEE Trans. Comput.

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Bandwidth reservation has been recognized as a value-added service to the cloud provider in recent years. We consider an open market of cloud bandwidth reservation, in which cloud providers offer bandwidth reservation services to cloud tenants, especially online streaming service providers, who have strict requirements on the amount of bandwidth to guarantee their quality of services. In this paper, we model the open market as a double-sided auction, and propose the first family of STrategy-proof double Auctions for multi-cloud, multi-tenant bandwidth Reservation (STAR). STAR contains two auction mechanisms. The first one, STAR-Grouping, divides the tenants into groups by a bid-independent way, and carefully matches the cloud providers with the tenant groups to form good trades. The second one, STAR-Padding, greedily matches the cloud providers with the tenants, and fills the partially reserved cloud provider(s) with a novel virtual padding tenant who can be a component of the auctioneer. Our analysis shows that both of the two auction mechanisms achieve strategy-proofness and ex-post budget balance. Our evaluation results show that they achieve good performance in terms of social welfare, cloud bandwidth utilization, and tenant satisfaction ratio.

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On efficient bandwidth allocation for traffic variability in datacenters

Cited by 57 publications

References 14 publications

Handling Boot Storms in Virtualized Data Centers—A Survey

Handling Boot Storms in Virtualized Data Centers—A Survey

Internet data budget allocation policies for diverse smartphone applications

STAR: Strategy-Proof Double Auctions for Multi-Cloud, Multi-Tenant Bandwidth Reservation

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