Resource Bundling for Distributed Computing

Wouldn\u27t it be nice if a Java compiler could make sure that our resources were present, before run-time? In this way, we trade off a run-time error for a compile-time error. Some techniques are presented that allow resources to be integrated directly into the source code

Economic algorithms for the management of resources in computer systems

Cloud computing and distributed Grid computations in the e-science and commercial spheres are beginning to make accessible huge amounts of computing power with “just in time” availability. However, the economic models surrounding these systems are static and uniform, with charging models that, for web-based cloud systems work on a price per unit per hour basis, whilst for educational type resources, fixed contractual arrangements and multi-year projects are more prevalent. The common place practice of using just-in-time capacity planning and variable pricing algorithms, such as those pioneered by airlines like EasyJet, tells us that the cost of delivering these services and the price that should be paid for them is a much more complex beast. Future Grid and Cloud Computing computations will be enabled by participants trading resources in order to construct bundles of goods or services in both new commercial arenas and the more well established “e-science” experiments in science, engineering and, now emerging, social sciences. A combinatorial auction (CA) is a natural choice for determining the optimal allocation for a bundle of required goods and services, but the space and time dimensions that characterise a Grid compute cloud would appear to indicate they are incompatible. This thesis proposes that an analogue of a physical commodities market is more appropriate for distributed resource allocation and that there is a class of bundling problems whose complexity properties appear to make the utilisation of a CA impractical. We therefore compare the two techniques for resource bundling and investigate the crossover point, to enrich our understanding of how combinatorial auctions and distributed markets may be used together to improve distributed resource allocation practices.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Privacy Management and Optimal Pricing in People-Centric Sensing

With the emerging sensing technologies such as mobile crowdsensing and Internet of Things (IoT), people-centric data can be efficiently collected and used for analytics and optimization purposes. This data is typically required to develop and render people-centric services. In this paper, we address the privacy implication, optimal pricing, and bundling of people-centric services. We first define the inverse correlation between the service quality and privacy level from data analytics perspectives. We then present the profit maximization models of selling standalone, complementary, and substitute services. Specifically, the closed-form solutions of the optimal privacy level and subscription fee are derived to maximize the gross profit of service providers. For interrelated people-centric services, we show that cooperation by service bundling of complementary services is profitable compared to the separate sales but detrimental for substitutes. We also show that the market value of a service bundle is correlated with the degree of contingency between the interrelated services. Finally, we incorporate the profit sharing models from game theory for dividing the bundling profit among the cooperative service providers.Comment: 16 page