InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services

Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels. Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load. To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions. The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments. The proposed InterCloud environment supports scaling of applications across multiple vendor clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape

Radio Spectrum Allocation and Management in Central American Countries and their Impact on the Development of the Mobile Telecommunications Services Sector

Spanish version available in IDRC Digital Library: Asignación y Administración del Espectro Radioeléctrico en Países de Centroamérica y su Impacto en el Desarrollo del Sector de Servicios de Telecomunicación MóvilResearch findings from this study help to make inferences about the significance of spectrum allocation in the reduction of prices of mobile services provided in telecommunications in Central American countries. Spectrum allocation policy is an effective instrument in promoting greater competition in the telecommunications sector and consequently, the provision of lower-cost services in the region. Policy recommendations are made regarding the allocation and use of radio spectrum/broadband

Improving shared access to Cloud of Things resources.

Cloud of Things (CoT) is an emerging paradigm that integrates Cloud Computing and Internet of Things (IoT) to support a wide range of real-world applications. Resource allocation plays a vital role in CoT, especially when allocating IoT physical resources to Cloud-based applications to ensure seamless application execution. Due to the heterogeneity and the constrained capacities of IoT resources, resource allocation is a challenge. This complexity leads to missing/limiting shared access to the IoT physical resources and consequently lessen the reusability of the resources across multiple applications. This issue results in, 1) replicating IoT deployments making them expensive and not feasible for many prospective users, 2) existing IoT infrastructures are over-provisioned to meet the unpredictable application requirements in which resources may be significantly underutilised, and 3) the adoption of CoT is slowed. Improving shared access to CoT resources can provide efficient resource allocation, improve resource utilisation and likely to reduce the cost of IoT deployments. Existing solutions include small-scale, hardware and platform-dependent mechanisms to enable or improve shared access to IoT resources. The research presented in this thesis considers trading CoT resources in a marketplace as an approach to improve shared access to CoT resources. It proposes a solution to Cot resource allocation that re-imagines CoT resources as commodities that can be provided and consumed by the marketplace participants. The novel contributions of the research presented in this thesis are summarised as follows: 1) a model to describe and quantify the value of CoT resources, 2) a resource sharing and allocation strategy called Exclusive Shared Access (ESA) to CoT resources, 3) a QoS-aware optimisation model for trading CoT resources as a single and multipleobjective optimisation problem, and 4) a marketplace architecture and experimental evaluation to verify its performance and scalability

Dynamics of Information Diffusion and Social Sensing

Statistical inference using social sensors is an area that has witnessed remarkable progress and is relevant in applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This chapter presents a tutorial description of four important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models and risk averse social learning is considered with applications in finance and online reputation systems. Third, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. Finally, the interaction of social sensors with YouTube channel owners is studied using time series analysis methods. All four topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this chapter stem from recent developments in network science, economics and signal processing. At a deeper level, this chapter considers mean field dynamics of networks, risk averse Bayesian social learning filtering and quickest change detection, data incest in decision making over a directed acyclic graph of social sensors, inverse optimization problems for utility function estimation (revealed preferences) and statistical modeling of interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112

Systems-compatible Incentives

Originally, the Internet was a technological playground, a collaborative endeavor among researchers who shared the common goal of achieving communication. Self-interest used not to be a concern, but the motivations of the Internet's participants have broadened. Today, the Internet consists of millions of commercial entities and nearly 2 billion users, who often have conflicting goals. For example, while Facebook gives users the illusion of access control, users do not have the ability to control how the personal data they upload is shared or sold by Facebook. Even in BitTorrent, where all users seemingly have the same motivation of downloading a file as quickly as possible, users can subvert the protocol to download more quickly without giving their fair share. These examples demonstrate that protocols that are merely technologically proficient are not enough. Successful networked systems must account for potentially competing interests. In this dissertation, I demonstrate how to build systems that give users incentives to follow the systems' protocols. To achieve incentive-compatible systems, I apply mechanisms from game theory and auction theory to protocol design. This approach has been considered in prior literature, but unfortunately has resulted in few real, deployed systems with incentives to cooperate. I identify the primary challenge in applying mechanism design and game theory to large-scale systems: the goals and assumptions of economic mechanisms often do not match those of networked systems. For example, while auction theory may assume a centralized clearing house, there is no analog in a decentralized system seeking to avoid single points of failure or centralized policies. Similarly, game theory often assumes that each player is able to observe everyone else's actions, or at the very least know how many other players there are, but maintaining perfect system-wide information is impossible in most systems. In other words, not all incentive mechanisms are systems-compatible. The main contribution of this dissertation is the design, implementation, and evaluation of various systems-compatible incentive mechanisms and their application to a wide range of deployable systems. These systems include BitTorrent, which is used to distribute a large file to a large number of downloaders, PeerWise, which leverages user cooperation to achieve lower latencies in Internet routing, and Hoodnets, a new system I present that allows users to share their cellular data access to obtain greater bandwidth on their mobile devices. Each of these systems represents a different point in the design space of systems-compatible incentives. Taken together, along with their implementations and evaluations, these systems demonstrate that systems-compatibility is crucial in achieving practical incentives in real systems. I present design principles outlining how to achieve systems-compatible incentives, which may serve an even broader range of systems than considered herein. I conclude this dissertation with what I consider to be the most important open problems in aligning the competing interests of the Internet's participants

Exploring Sellers' Experiences in the C2C Online Auction Environment

Online auction websites are becoming increasingly important as an intermediary for both sellers and buyers. They offer consumers an alternative source of goods to those sold at retail stores and other second-hand traditional consumer-to-consumer (C2C) channels, such as garage sales or flea markets. They also represent a new market model which incorporates a new distribution channel and a new means of establishing prices. Some researchers predict that retailers are now facing a new competition and the potential for declining sales as a result of the cannibalisation effect of the C2C online auction market. Noticeably, although much research has been carried out in an attempt to understand online auctions in relation to buying behaviour, little effort has been made to investigate the dynamic nature of individual sellers, in particular C2C sellers, in the online auction environment. Therefore, this study is aimed at filling in the gaps by exploring the sellers' behaviour and experiences in the C2C online auction environment. Its objectives are: (1) to explore the learning process that individual sellers go through in the C2C online auction environment; and (2) to find out what skills and techniques are commonly used by sellers and how these skills have been applied when marketing their products in the online auction environment. This study used a qualitative method, and a market-oriented ethnography was adopted. Data was collected through semi-structured in-depth interviews with sellers on TradeMe and from a wide range of archival documents. Nineteen sellers were recruited to participate into this study. Consequently, a learning model has been built, based on the consumer socialisation model, to explain the learning process of sellers as they become experienced in the C2C online auction environment. The findings from this study highlighted that sellers went through a learning process to become more experienced in online auctions. Moreover, different learning methods occurred at different stages of the learning process, including social interaction, observing and imitating, rewards and punishments, and other sources of information. This study also demonstrated the fact that sellers both implicitly and explicitly perceived the importance of marketing strategies and tactics and had extensively applied them. Several implications and recommendations arise from this study, including the need for more in-depth research on sellers' behaviour and experiences, using a longitudinal approach. Additionally, it is recommended that TradeMe should continue to improve their auction site in order to attract more sellers which, in turn, will lead to a greater number of buyers

Economic-based Distributed Resource Management and Scheduling for Grid Computing

Computational Grids, emerging as an infrastructure for next generation computing, enable the sharing, selection, and aggregation of geographically distributed resources for solving large-scale problems in science, engineering, and commerce. As the resources in the Grid are heterogeneous and geographically distributed with varying availability and a variety of usage and cost policies for diverse users at different times and, priorities as well as goals that vary with time. The management of resources and application scheduling in such a large and distributed environment is a complex task. This thesis proposes a distributed computational economy as an effective metaphor for the management of resources and application scheduling. It proposes an architectural framework that supports resource trading and quality of services based scheduling. It enables the regulation of supply and demand for resources and provides an incentive for resource owners for participating in the Grid and motives the users to trade-off between the deadline, budget, and the required level of quality of service. The thesis demonstrates the capability of economic-based systems for peer-to-peer distributed computing by developing users' quality-of-service requirements driven scheduling strategies and algorithms. It demonstrates their effectiveness by performing scheduling experiments on the World-Wide Grid for solving parameter sweep applications

Synergies, cooperation and syndication in venture capital game, portfolio optimization with genetic algorithms and asset auctions: essays in finance

This thesis looks at all scientific phenomenon of financial decision-making from both the empirical and theoretical side, with empirical trying to strengthen theoretical assumptions or even to expand it. In chapter 2, we propose a two-stage financing model with three players that consider the output elasticities of all parties using the Cobb-Douglas utility function. Theoretical findings in chapter 2 suggest that a higher complementary coefficient between players on both stages can lead to a higher level of effort from all three players, taking game dynamics away from the moral hazard problem and causing higher exit stage payoffs. Previous track record of the angel and VC and output elasticity of the entrepreneur, combined with the company’s shares offered the angel and VC, impact the three-player game dynamic, causing some players to reduce their efforts after specific funding rounds. Our empirical results show that VC syndication increases the average amount of funding offered to entrepreneurs as well as that syndicated ventures have a higher number of funding rounds, resulting in a higher number of possible entry-points provided by those start-ups. Our results in chapter 4 suggested that a two-point GA that minimized the risk for a given level of expected return slightly outperformed the results of the SPEA2. Compared with the previous industry standard for risk measure—Value-at-Risk, we show that both frontiers differed, especially at the low return side. The converted Value-at-Risk solutions were not evenly distributed along the efficient frontier and even inadequate for some ES values. In chapter 5, we use the game theory approach to examine the first-price package auction design for illiquid asset auctions. Our theoretical work suggests that every case that can be presented as a two or three asset game, as well as longer games that can be presented as two and three asset subgames, has a strong equilibrium if the bidders’ budgets and utilities for every asset are common knowledge