The transformative impact of business models

The macroeconomic impact of advances in information and communications technologies is significant but problematic to assess. Research on these developments has been isolated to specific disciplines, easily outpaced by new innovations and few studies describe the multiple changes and their macroeconomic consequences in a holistic way. The increasing ability to organize, price and transmit information to the market is ushering in an era where economic actors are highly responsive to the market. Technological advance alone does not capture the benefits of these developments. It is the innovative business model that lies at the heart of this revolution in responsiveness. We outline four major economic shifts in this study by reference to some paradigmatic business models. These shifts include pricing strategy innovations and their effect on the creation and expansion of market spaces, structural shifts in electronic markets and the effects on transaction costs, the deeper interaction between firms and consumers and the effects on more efficient matching of supply and demand, and finally the economic impact of elasticity and infinite scalability in computing resources when delivered as a utility by cloud computing providers. These advances do not only increase the commercial possibilities, they actively alter the competitive landscape and the role of the firm and consumer. This paper establishes some key areas where the increased responsiveness of economic actors is increasingly stimulating innovation, efficiency and productivity

The transformative impact of business models

The macroeconomic impact of advances in information and communications technologies is significant but problematic to assess. Research on these developments has been isolated to specific disciplines, easily outpaced by new innovations and few studies describe the multiple changes and their macroeconomic consequences in a holistic way. The increasing ability to organize, price and transmit information to the market is ushering in an era where economic actors are highly responsive to the market. Technological advance alone does not capture the benefits of these developments. It is the innovative business model that lies at the heart of this revolution in responsiveness. We outline four major economic shifts in this study by reference to some paradigmatic business models. These shifts include pricing strategy innovations and their effect on the creation and expansion of market spaces, structural shifts in electronic markets and the effects on transaction costs, the deeper interaction between firms and consumers and the effects on more efficient matching of supply and demand, and finally the economic impact of elasticity and infinite scalability in computing resources when delivered as a utility by cloud computing providers. These advances do not only increase the commercial possibilities, they actively alter the competitive landscape and the role of the firm and consumer. This paper establishes some key areas where the increased responsiveness of economic actors is increasingly stimulating innovation, efficiency and productivity

A proactive fault tolerance framework for high performance computing (HPC) systems in the cloud

High Performance Computing (HPC) systems have been widely used by scientists and researchers in both industry and university laboratories to solve advanced computation problems. Most advanced computation problems are either data-intensive or computation-intensive. They may take hours, days or even weeks to complete execution. For example, some of the traditional HPC systems computations run on 100,000 processors for weeks. Consequently traditional HPC systems often require huge capital investments. As a result, scientists and researchers sometimes have to wait in long queues to access shared, expensive HPC systems. Cloud computing, on the other hand, offers new computing paradigms, capacity, and flexible solutions for both business and HPC applications. Some of the computation-intensive applications that are usually executed in traditional HPC systems can now be executed in the cloud. Cloud computing price model eliminates huge capital investments. However, even for cloud-based HPC systems, fault tolerance is still an issue of growing concern. The large number of virtual machines and electronic components, as well as software complexity and overall system reliability, availability and serviceability (RAS), are factors with which HPC systems in the cloud must contend. The reactive fault tolerance approach of checkpoint/restart, which is commonly used in HPC systems, does not scale well in the cloud due to resource sharing and distributed systems networks. Hence, the need for reliable fault tolerant HPC systems is even greater in a cloud environment. In this thesis we present a proactive fault tolerance approach to HPC systems in the cloud to reduce the wall-clock execution time, as well as dollar cost, in the presence of hardware failure. We have developed a generic fault tolerance algorithm for HPC systems in the cloud. We have further developed a cost model for executing computation-intensive applications on HPC systems in the cloud. Our experimental results obtained from a real cloud execution environment show that the wall-clock execution time and cost of running computation-intensive applications in the cloud can be considerably reduced compared to checkpoint and redundancy techniques used in traditional HPC systems

Analysis and Modeling of Time-Correlated Failures in Large-Scale Distributed Systems

The analysis and modeling of the failures bound to occur in today’s large-scale production systems is invaluable in providing the understanding needed to make these systems fault-tolerant yet efficient. Many previous studies have modeled failures without taking into account the time-varying behavior of failures, under the assumption that failures are identically, but independently distributed. However, the presence of time correlations between failures (such as peak periods with increased failure rate) refutes this assumption and can have a significant impact on the effectiveness of fault-tolerance mechanisms. For example, the performance of a proactive fault-tolerance mechanism is more effective if the failures are periodic or predictable; similarly, the performance of checkpointing, redundancy, and scheduling solutions depends on the frequency of failures. In this study we analyze and model the time-varying behavior of failures in largescale distributed systems. Our study is based on nineteen failure traces obtained from (mostly) production large-scale distributed systems, including grids, P2P systems, DNS servers, web servers, and desktop grids. We first investigate the time correlation of failures, and find that many of the studied traces exhibit stron

Improving reliability of service oriented systems with consideration of cost and time constraints in clouds

Web service technology is more and more popular for the implementation of service oriented systems. Additionally, cloud computing platforms, as an efficient and available environment, can provide the computing, networking and storage resources in order to decrease the budget of companies to deploy and manage their systems. Therefore, more service oriented systems are migrated and deployed in clouds. However, these applications need to be improved in terms of reliability, for certain components have low reliability. Fault tolerance approaches can improve software reliability. However, more redundant units are required, which increases the cost and the execution time of the entire system. Therefore, a migration and deployment framework with fault tolerance approaches with the consideration of global constraints in terms of cost and execution time may be needed. This work proposes a migration and deployment framework to guide the designers of service oriented systems in order to improve the reliability under global constraints in clouds. A multilevel redundancy allocation model is adopted for the framework to assign redundant units to the structure of systems with fault tolerance approaches. An improved genetic algorithm is utilised for the generation of the migration plan that takes the execution time of systems and the cost constraints into consideration. Fault tolerant approaches (such as NVP, RB and Parallel) can be integrated into the framework so as to improve the reliability of the components at the bottom level. Additionally, a new encoding mechanism based on linked lists is proposed to improve the performance of the genetic algorithm in order to reduce the movement of redundant units in the model. The experiments compare the performance of encoding mechanisms and the model integrated with different fault tolerance approaches. The empirical studies show that the proposed framework, with a multilevel redundancy allocation model integrated with the fault tolerance approaches, can generate migration plans for service oriented systems in clouds with the consideration of cost and execution time