Profit Maximization Auction and Data Management in Big Data Markets

A big data service is any data-originated resource that is offered over the Internet. The performance of a big data service depends on the data bought from the data collectors. However, the problem of optimal pricing and data allocation in big data services is not well-studied. In this paper, we propose an auction-based big data market model. We first define the data cost and utility based on the impact of data size on the performance of big data analytics, e.g., machine learning algorithms. The big data services are considered as digital goods and uniquely characterized with "unlimited supply" compared to conventional goods which are limited. We therefore propose a Bayesian profit maximization auction which is truthful, rational, and computationally efficient. The optimal service price and data size are obtained by solving the profit maximization auction. Finally, experimental results on a real-world taxi trip dataset show that our big data market model and auction mechanism effectively solve the profit maximization problem of the service provider.Comment: 6 pages, 9 figures. This paper was accepted by IEEE WCNC conference in Dec. 201

A Case Study in Optimization of Resource Distribution to Cope with Unanticipated Changes in Requirements

It is a known fact that requirements change continuously, and as a consequence, it may be necessary to reschedule development activities so that the new requirements can be addressed in a costeffective manner. Unfortunately, changes in requirements cannot be specified precisely. Moreover, current software development methods do not provide explicit means to adapt development processes with respect to unanticipated changes in requirements. This article first proposes a method based on Markov Decision Theory, which determines the estimated optimal development schedule with respect to probabilistic product demands and resource constraints. Second, a tool is described that is built to support the method. Finally, some experimental results are presented on the applicability of the proposed method

Greening Multi-Tenant Data Center Demand Response

Data centers have emerged as promising resources for demand response, particularly for emergency demand response (EDR), which saves the power grid from incurring blackouts during emergency situations. However, currently, data centers typically participate in EDR by turning on backup (diesel) generators, which is both expensive and environmentally unfriendly. In this paper, we focus on "greening" demand response in multi-tenant data centers, i.e., colocation data centers, by designing a pricing mechanism through which the data center operator can efficiently extract load reductions from tenants during emergency periods to fulfill energy reduction requirement for EDR. In particular, we propose a pricing mechanism for both mandatory and voluntary EDR programs, ColoEDR, that is based on parameterized supply function bidding and provides provably near-optimal efficiency guarantees, both when tenants are price-taking and when they are price-anticipating. In addition to analytic results, we extend the literature on supply function mechanism design, and evaluate ColoEDR using trace-based simulation studies. These validate the efficiency analysis and conclude that the pricing mechanism is both beneficial to the environment and to the data center operator (by decreasing the need for backup diesel generation), while also aiding tenants (by providing payments for load reductions).Comment: 34 pages, 6 figure

Asymptotic Behavior of Ultra-Dense Cellular Networks and Its Economic Impact

This paper investigates the relationship between base station (BS) density and average spectral efficiency (SE) in the downlink of a cellular network. This relationship has been well known for sparse deployment, i.e. when the number of BSs is small compared to the number of users. In this case the SE is independent of BS density. As BS density grows, on the other hand, it has previously been shown that increasing the BS density increases the SE, but no tractable form for the SE-BS density relationship has yet been derived. In this paper we derive such a closed-form result that reveals the SE is asymptotically a logarithmic function of BS density as the density grows. Further, we study the impact of this result on the network operator's profit when user demand varies, and derive the profit maximizing BS density and the optimal amount of spectrum to be utilized in closed forms. In addition, we provide deployment planning guidelines that will aid the operator in his decision if he should invest in densifying his network or in acquiring more spectrum.Comment: This paper will appear in Proc. IEEE Global Commun. Conf. (GLOBECOM) 201