Linear Scalability of Distributed Applications

The explosion of social applications such as Facebook, LinkedIn and Twitter, of electronic commerce with companies like Amazon.com and Ebay.com, and of Internet search has created the need for new technologies and appropriate systems to manage effectively a considerable amount of data and users. These applications must run continuously every day of the year and must be capable of surviving sudden and abrupt load increases as well as all kinds of software, hardware, human and organizational failures. Increasing (or decreasing) the allocated resources of a distributed application in an elastic and scalable manner, while satisfying requirements on availability and performance in a cost-effective way, is essential for the commercial viability but it poses great challenges in today's infrastructures. Indeed, Cloud Computing can provide resources on demand: it now becomes easy to start dozens of servers in parallel (computational resources) or to store a huge amount of data (storage resources), even for a very limited period, paying only for the resources consumed. However, these complex infrastructures consisting of heterogeneous and low-cost resources are failure-prone. Also, although cloud resources are deemed to be virtually unlimited, only adequate resource management and demand multiplexing can meet customer requirements and avoid performance deteriorations. In this thesis, we deal with adaptive management of cloud resources under specific application requirements. First, in the intra-cloud environment, we address the problem of cloud storage resource management with availability guarantees and find the optimal resource allocation in a decentralized way by means of a virtual economy. Data replicas migrate, replicate or delete themselves according to their economic fitness. Our approach responds effectively to sudden load increases or failures and makes best use of the geographical distance between nodes to improve application-specific data availability. We then propose a decentralized approach for adaptive management of computational resources for applications requiring high availability and performance guarantees under load spikes, sudden failures or cloud resource updates. Our approach involves a virtual economy among service components (similar to the one among data replicas) and an innovative cascading scheme for setting up the performance goals of individual components so as to meet the overall application requirements. Our approach manages to meet application requirements with the minimum resources, by allocating new ones or releasing redundant ones. Finally, as cloud storage vendors offer online services at different rates, which can vary widely due to second-degree price discrimination, we present an inter-cloud storage resource allocation method to aggregate resources from different storage vendors and provide to the user a system which guarantees the best rate to host and serve its data, while satisfying the user requirements on availability, durability, latency, etc. Our system continuously optimizes the placement of data according to its type and usage pattern, and minimizes migration costs from one provider to another, thereby avoiding vendor lock-in

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Augmented Reality

Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning

Innovative Asia: Advancing the Knowledge-Based Economy - The Next Policy Agenda

[Excerpt] This study by the Asian Development Bank (ADB) seeks to analyze the ways in which Asia’s middle- and low-income countries can tap knowledge-based economic development to maintain and strengthen the growth momentum and to move up global value chains. The ADB study uses the Knowledge Economy Index (KEI) rubric to benchmark the performance of developing economies in Asia against advanced economies of the world. It is clear that on all the four pillars of the knowledge economy—innovation, education and skills, ICT, and the economic incentive and institutional regime—developing economies in Asia significantly lag behind advanced nations. Policy makers in developing Asia need ensure appropriate investments and conducive policies across all the four pillars. The report traces the journey of the Republic of Korea, Singapore, and Finland as KBEs and the lessons developing economies can derive. However, going beyond this, the report also highlights a number of special advantages that Asia can effectively tap that will help them leapfrog to the knowledge frontier. The relative lack of legacy infrastructure in developing economies, particularly in information communication technology, could enable developing economies to leapfrog over certain technology cycles and access the latest technologies, such as moving to cloud computing solutions. Asia needs to effectively combine established wisdom from the experience of developed economies with contemporary knowledge and options that new technologies bring to strengthen KBE processes. An important dimension for developing economies in Asia to consider, given the rising inequality in the region, is making KBE processes inclusive. This report explores a number of opportunities in this direction