arXiv subjects

Baochun Li

Publications and source records attributed to Baochun Li.

39 records · Page 3Linked to original sources

arXiv2013

Spot Transit: Cheaper Internet Transit for Elastic Traffic

We advocate to create a \emph{spot} Internet transit market, where transit is sold using the under-utilized backbone capacity at a lower price. The providers can improve profit by capitalizing the perishable capacity, and customers can buy transit on-demand without a minimum commitment level for elastic traffic, and as a result improve its surplus (i.e. utility gains). We conduct a systematic study of the economical benefits of spot transit both theoretically and empirically. We propose a simple analytical framework with a general demand function, and solve the pricing problem of maximizing the expected profit, taking into account the revenue loss of regular transit when spot transit traffic hikes. We rigorously prove the price advantage of spot transit, as well as profit and surplus improvements for tier-1 ISPs and customers, respectively. Using real-world price data and traffic statistics of 6 IXPs with more than 1000 ISPs, we quantitatively evaluate spot transit and show that significant financial benefits can be achieved in both absolute and relative terms, robust to parameter values.

cs.NI↗

arXiv2013

Dominant Resource Fairness in Cloud Computing Systems with Heterogeneous Servers

We study the multi-resource allocation problem in cloud computing systems where the resource pool is constructed from a large number of heterogeneous servers, representing different points in the configuration space of resources such as processing, memory, and storage. We design a multi-resource allocation mechanism, called DRFH, that generalizes the notion of Dominant Resource Fairness (DRF) from a single server to multiple heterogeneous servers. DRFH provides a number of highly desirable properties. With DRFH, no user prefers the allocation of another user; no one can improve its allocation without decreasing that of the others; and more importantly, no user has an incentive to lie about its resource demand. As a direct application, we design a simple heuristic that implements DRFH in real-world systems. Large-scale simulations driven by Google cluster traces show that DRFH significantly outperforms the traditional slot-based scheduler, leading to much higher resource utilization with substantially shorter job completion times.

cs.DC↗

arXiv2013

To Reserve or Not to Reserve: Optimal Online Multi-Instance Acquisition in IaaS Clouds

Infrastructure-as-a-Service (IaaS) clouds offer diverse instance purchasing options. A user can either run instances on demand and pay only for what it uses, or it can prepay to reserve instances for a long period, during which a usage discount is entitled. An important problem facing a user is how these two instance options can be dynamically combined to serve time-varying demands at minimum cost. Existing strategies in the literature, however, require either exact knowledge or the distribution of demands in the long-term future, which significantly limits their use in practice. Unlike existing works, we propose two practical online algorithms, one deterministic and another randomized, that dynamically combine the two instance options online without any knowledge of the future. We show that the proposed deterministic (resp., randomized) algorithm incurs no more than 2-alpha (resp., e/(e-1+alpha)) times the minimum cost obtained by an optimal offline algorithm that knows the exact future a priori, where alpha is the entitled discount after reservation. Our online algorithms achieve the best possible competitive ratios in both the deterministic and randomized cases, and can be easily extended to cases when short-term predictions are reliable. Simulations driven by a large volume of real-world traces show that significant cost savings can be achieved with prevalent IaaS prices.

cs.DC↗