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In the past decade, corporations are increasingly engaging in efforts whose aim is the analysis and wide-ranging use of big data. The majority of academic big data articles have been focused on methods, approaches, opportunities, and organizational impact of big data analytics. In this article, the focus is on the ability of big data (while acting as a direct source for impactful analysis) to also augment and enrich the analytical power of data warehouses.

This paper proposes a resource allocation and pricing mechanism for a service system that serves multiple classes of jobs within an organization. Each class of service request is subject to a class-dependent quality of service (QoS) guarantee on the expected delay bound, which may be imposed by business rules in an organization or other application-specific technical constraints. We develop an extension of a resource allocation and pricing mechanism for an M/M/1 system. In contrast to the system without the QoS guarantee, where a fixed priority scheduling policy--known as the c\mu rule--is optimal, we show that the system may need to adopt a more general randomized priority scheduling policy to maximize the overall system profit. We also develop a transfer pricing scheme that is both optimal and incentive compatible, allowing users to act in their self-interests while collectively achieving the system optimum. We show that the pricing scheme with the QoS guarantee depends on the scheduling policy implemented and has different characteristics from that without the QoS guarantee.capacity planning and investment, technology management and process design, service operations

T he Internet is making a significant transition from primarily a network of desktop computers to a network variety of connected information devices such as personal digital assistants and global positioning systembased devices. On the other hand, new paradigms such as overlay networks are defining service-based logical architecture for the network services that make locating content and routing more efficient. Along with Internet2's proposed service-based routing, overlay networks will create a new set of challenges in the provision and management of content over the network. However, a lack of proper infrastructure investment incentive may lead to an environment where network growth may not keep pace with the service requirements. In this paper, we present an analysis of investment incentives for network infrastructure owners under two different pricing strategies: congestion-based negative externality pricing and the prevalent flat-rate pricing. We develop a theoretically motivated gradient-based heuristic to compute maximum capacity that a network provider will be willing to invest in under different pricing schemes. The heuristic appropriates different capacities to different network components based on demand for these components. We then use a simulation model to compare the impact of dynamic congestion-based pricing with flat-rate pricing on the choice of capacity level by the infrastructure provider. The simulation model implements the heuristic and ensures that near-optimal level of capacity is allocated to each network component by checking theoretical optimality conditions. We investigate the impact of a variety of factors, including the per unit cost of capacity of a network resource, average value of the users' requests, average level of users' tolerance for delay, and the level of exogenous demand for services on the network. Our results indicate that relationships between these factors are crucial in determining which of the two pricing schemes results in a higher level of socially optimal network capacity. The simulation results provide a possible explanation for the evolution of the Internet pricing from time-based to flat-rate pricing. The results also indicate that regardless of how these factors are related, the average stream of the net benefits realized under congestion-based pricing tends to be higher than the average net benefits realized under flat-rate pricing. These central results point to the fallacy of the arguments presented by the supporters of net neutrality that do not consider the incentives for private investment in network capacity.

No abstract