143 research outputs found
Paid Peering, Settlement-Free Peering, or Both?
With the rapid growth of congestion-sensitive and data-intensive
applications, traditional settlement-free peering agreements with best-effort
delivery often do not meet the QoS requirements of content providers (CPs).
Meanwhile, Internet access providers (IAPs) feel that revenues from end-users
are not sufficient to recoup the upgrade costs of network infrastructures.
Consequently, some IAPs have begun to offer CPs a new type of peering
agreement, called paid peering, under which they provide CPs with better data
delivery quality for a fee. In this paper, we model a network platform where an
IAP makes decisions on the peering types offered to CPs and the prices charged
to CPs and end-users. We study the optimal peering schemes for the IAP, i.e.,
to offer CPs both the paid and settlement-free peering to choose from or only
one of them, as the objective is profit or welfare maximization. Our results
show that 1) the IAP should always offer the paid and settlement-free peering
under the profit-optimal and welfare-optimal schemes, respectively, 2) whether
to simultaneously offer the other peering type is largely driven by the type of
data traffic, e.g., text or video, and 3) regulators might want to encourage
the IAP to allocate more network capacity to the settlement-free peering for
increasing user welfare
On Optimal Service Differentiation in Congested Network Markets
As Internet applications have become more diverse in recent years, users
having heavy demand for online video services are more willing to pay higher
prices for better services than light users that mainly use e-mails and instant
messages. This encourages the Internet Service Providers (ISPs) to explore
service differentiations so as to optimize their profits and allocation of
network resources. Much prior work has focused on the viability of network
service differentiation by comparing with the case of a single-class service.
However, the optimal service differentiation for an ISP subject to resource
constraints has remained unsolved. In this work, we establish an optimal
control framework to derive the analytical solution to an ISP's optimal service
differentiation, i.e. the optimal service qualities and associated prices. By
analyzing the structures of the solution, we reveal how an ISP should adjust
the service qualities and prices in order to meet varying capacity constraints
and users' characteristics. We also obtain the conditions under which ISPs have
strong incentives to implement service differentiation and whether regulators
should encourage such practices
Sampling Online Social Networks via Heterogeneous Statistics
Most sampling techniques for online social networks (OSNs) are based on a
particular sampling method on a single graph, which is referred to as a
statistics. However, various realizing methods on different graphs could
possibly be used in the same OSN, and they may lead to different sampling
efficiencies, i.e., asymptotic variances. To utilize multiple statistics for
accurate measurements, we formulate a mixture sampling problem, through which
we construct a mixture unbiased estimator which minimizes asymptotic variance.
Given fixed sampling budgets for different statistics, we derive the optimal
weights to combine the individual estimators; given fixed total budget, we show
that a greedy allocation towards the most efficient statistics is optimal. In
practice, the sampling efficiencies of statistics can be quite different for
various targets and are unknown before sampling. To solve this problem, we
design a two-stage framework which adaptively spends a partial budget to test
different statistics and allocates the remaining budget to the inferred best
statistics. We show that our two-stage framework is a generalization of 1)
randomly choosing a statistics and 2) evenly allocating the total budget among
all available statistics, and our adaptive algorithm achieves higher efficiency
than these benchmark strategies in theory and experiment
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