Amazon's Elastic Compute Cloud (EC2) uses auction-based spot pricing to sell spare capacity, allowing users bid for cloud resources at a highly reduced rate. Amazon sets the price dynamically and accepts user bids above this price. Jobs with lower (including those already running) are interrupted must wait before resuming. Spot thus raises two basic questions: how might provider set price, what prices should bid? Computing users' bidding strategies is particularly challenging: higher reduce...

A key challenge in wireless cognitive radio networks is to maximize the total throughput also known as sum rates of all users while avoiding interference unlicensed band secondary from overwhelming licensed primary users. We study weighted rate maximization problem with both power budget and temperature constraints a network. This nonconvex generally hard solve. propose reformulation-relaxation technique that leverages nonnegative matrix theory first obtain relaxed spectral radius...

Amazon's Elastic Compute Cloud (EC2) uses auction-based spot pricing to sell spare capacity, allowing users bid for cloud resources at a highly reduced rate. Amazon sets the price dynamically and accepts user bids above this price. Jobs with lower (including those already running) are interrupted must wait before resuming. Spot thus raises two basic questions: how might provider set price, what prices should bid? Computing users' bidding strategies is particularly challenging: higher reduce...

We study a utility maximization framework for spectrum sharing among cognitive secondary users and licensed primary in radio networks. All the maximize network by adapting their signal-to-interference-plus-noise ratio (SINR) assignment transmit power subject to budget constraints additional interference temperature constraint users. The problem is challenging solve optimally distributed manner due nonconvexity tight coupling between sets. first special case where egalitarian SINR fairness...

We study the feasibility of total power minimization problem subject to budget and Signal-to-Interference-plus-Noise Ratio (SINR) constraints in cognitive radio networks. As both primary secondary users are allowed transmit simultaneously on a shared spectrum, uncontrolled access degrades performance can even lead system infeasibility. To find largest feasible set (i.e., capacity) that be supported network, we formulate vector-cardinality optimization problem. This nonconvex is however hard...

The growing volume of mobile data traffic has led many Internet service providers (ISPs) to cap their users' monthly usage, with overage fees for exceeding caps. In this work, we examine a secondary market in which users can buy and sell leftover caps from each other. China Mobile Hong Kong recently introduced such market. While similar an auction that submit bids data, it differs traditional double auctions the ISP serves as middleman between buyers sellers. We derive optimal prices amount...

Cloud service providers (CSPs) often face highly dynamic user demands for their resources, which can make it difficult them to maintain consistent quality-of-service. Some CSPs try stabilize by offering sustained-use discounts jobs that consume more instance-hours per month. These present an opportunity users pool usage together into a single ``job.'' In this paper, we examine the viability of middleman, cloud virtual provider (CVSP), rents resources from CSP and then resells users. We show...

Rate adaptation and power control are two key resource allocation mechanisms in multiuser wireless networks. In the presence of interference, how do we jointly optimize end-to-end source rates link powers to achieve weighted max-min rate fairness for all sources network? This optimization problem is hard solve as physical layer functions nonlinear, nonconvex, coupled transmit powers. We show that can, fact, be decoupled into separate problems flow control. For a large class functions,...

This paper presents a systematic approach for solving wireless max-min utility fairness optimization problems in multiuser networks with general monotonic constraints. These are often challenging to solve due their nonconvexity. By establishing connection between this class of and the conditional eigenvalue that can be addressed by generalized nonlinear Perron-Frobenius theory, we show how these solved optimally using an iterative algorithm converges geometrically fast. The mathematical...

As infrastructure-as-a-service clouds become more popular, cloud providers face the complicated problem of maximizing their resource utilization by handling dynamics user demand. Auction-based pricing, such as Amazon EC2 spot provides an option for users to use idle resources at highly reduced yet dynamic prices; under a pricing scheme, place bids resources, and provider chooses threshold "spot" price above which are admitted. In this paper, we propose nonlinear dynamical system model...

Urban environments are a particularly important application scenario for the Internet of Things (IoT). These usually dense and dynamic; in contrast, IoT devices resource-constrained, thus making reliable data collection scalable coordination challenge. This work leverages fog networking paradigm to devise multi-tier offloading protocol suitable diverse data-centric applications urban scenarios. Specifically, it takes advantage heterogeneity network so that sensors can collaboratively offload...

Growing mobile data usage has led to end users paying substantial costs, while Internet service providers (ISPs) struggle upgrade their networks keep up with demand and maintain high quality-of-service (QoS). This problem is particularly severe for smaller ISPs less capital. Instead of simply upgrading network infrastructure, can pool provide a good QoS attract more users. Such vISP (virtual ISP), example, Google's Project Fi, allows access any its partner ISPs' networks. We the first...

Cloud Infrastructure as a Service (IaaS) providers continually seek higher resource utilization to better amortize capital costs. Higher not only can enable profit for IaaS but also provides mechanism raise energy efficiency; therefore creating greener cloud services. Unfortunately, achieving high is difficult mainly due infrastructure needing maintain spare capacity service demand fluctuations.

Cloud service providers (CSPs) often face highly dynamic user demands for their resources, which can make it difficult them to maintain consistent quality-of-service. Some CSPs try stabilize by offering sustained-use discounts jobs that consume more instance-hours per month. These present an opportunity users pool usage together into a single ``job.'' In this paper, we examine the viability of middleman, cloud virtual provider (CVSP), rents resources from CSP and then resells users. We show...

We study the network utility maximization problems in wireless networks for service differentiation that optimize Signal-to-Interference-plus-Noise Radio (SINR) and reliability under Rayleigh fading. Though seemingly nonconvex, we show these can be decomposed into an optimization framework where each user calculates a payment given resource allocation, uses to performance of user. three important examples this maximization, namely weighted sum logarithmic SINR inverse minimization...

Internet service providers (ISPs) struggle to invest in upgrading their networks catch up with growing mobile data demand, while users have face significant overage fees. Pooling ISPs' network infrastructures can potentially enable better user experience and lower prices. For example, Google recently launched a cross-carrier MVNO (mobile virtual operator) plan called Project Fi, where users' devices automatically access either of two partner cellular or any available open WiFi network. We...

In this paper, we investigate the joint design of transmit beamforming and power control to maximize weighted sum rate in multiple-input single-output (MISO) cognitive radio network constrained by arbitrary budgets interference temperatures. The nonnegativity physical quantities, e.g., channel parameters, powers, rates, is exploited enable key tools nonnegative matrix theory, such as (linear nonlinear) Perron-Frobenius quasi-invertibility, Friedland-Karlin inequalities, tackle nonconvex...

Internet service providers (ISPs) have been facing heavy competition to attract more users in the mobile data market, along with growing operational costs. Most plans charge a fixed fee for monthly quota, and any unused at end of each month will be wasted. In beginning 2015, both AT&T T-Mobile reinstated rollover constrained eligibility. Users could then keep portion their quotas use future month(s). this work, we evaluate benefits ISPs as well identify types who would upgrade plans. To do...

The growing volume of mobile data traffic has led many Internet service providers (ISPs) to cap the monthly usage their users and charge overage fees, when caps are exceeded. Yet imperfectly capture reality heterogeneous over a month-even same user may have varied requirements from month month. In response, some ISPs providing alternative avenues for customize plans needs. this paper, we examine secondary market, as example created by China Mobile Hong Kong, in which can buy sell leftover...

As the U.S. mobile data market matures, Internet service providers (ISPs) generally charge their users with some variation on a quota-based plan overage charges. Common variants include unlimited, prepaid, and usage-based plans. However, despite recent flurry of research optimizing pricing, few works have considered how these plans affect users' consumption behavior. In particular, while such strong incentive to usage over month, they also face uncertainty in future needs that would make...

This paper presents a unifying and systematic framework to solve wireless max-min utility fairness optimization problems in multiuser networks with generalized monotonic constraints. These are often challenging due their nonlinearity non-convexity. Our leverages general result nonlinear Perron-Frobenius theory characterize the global optimal solution of these analytically, design scalable fast-convergent algorithms for computation solution. work advances state-of-the-art handling...

A social learning network (SLN) emerges when users exchange information on educational topics with structured interactions. The recent proliferation of massively scaled online (human) learning, such as massive open courses (MOOCs), has presented a plethora research challenges surrounding SLN. In this paper, we ask: how efficient are these networks? We propose method in which the SLN efficiency is determined by comparing user benefit observed to benchmark maximum utility achievable through...

A cloud computing cluster equipped with a deadline-aware job scheduler faces fairness and efficiency challenges when greedy users falsely advertise the urgency of their jobs. Penalizing such untruthfulness without demotivating from using service calls for advanced mechanism design techniques that work together scheduling. We propose Bayesian incentive compatible pricing based on matching by replica-surrogate valuation functions. User valuations can be discovered mechanism, even themselves do...

Fog computing, the distribution of computing resources closer to end devices along cloud-to-things continuum, is recently emerging as an architecture for scaling Internet Things (IoT) sensor networking applications. requires novel program decompositions heterogeneous hierarchical settings. To evaluate these new decompositions, we designed, developed, and instrumented a fog testbed that includes cloud gateway execution points collaborating finish complex data analytics operations. In this...