Adaptive Resource Management for Distributed Data Analytics

Increasingly large datasets make scalable and distributed data analytics necessary. Frameworks such as Spark and Flink help users in efficiently utilizing cluster resources for their data analytics jobs. It is, however, usually difficult to anticipate the runtime behavior and resource demands of these distributed data analytics jobs. Yet, many resource management decisions would benefit from such information. Addressing this general problem, this chapter presents our vision of adaptive resource management and reviews recent work in this area. The key idea is that workloads should be monitored for trends, patterns, and recurring jobs. These monitoring statistics should be analyzed and used for a cluster resource management calibrated to the actual workload. In this chapter, we motivate and present the idea of adaptive resource management. We also introduce a general system architecture and we review specific adaptive techniques for data placement, resource allocation, and job scheduling in the context of our architecture

Managing Distributed Cloud Applications and Infrastructure

The emergence of the Internet of Things (IoT), combined with greater heterogeneity not only online in cloud computing architectures but across the cloud-to-edge continuum, is introducing new challenges for managing applications and infrastructure across this continuum. The scale and complexity is simply so complex that it is no longer realistic for IT teams to manually foresee the potential issues and manage the dynamism and dependencies across an increasing inter-dependent chain of service provision. This Open Access Pivot explores these challenges and offers a solution for the intelligent and reliable management of physical infrastructure and the optimal placement of applications for the provision of services on distributed clouds. This book provides a conceptual reference model for reliable capacity provisioning for distributed clouds and discusses how data analytics and machine learning, application and infrastructure optimization, and simulation can deliver quality of service requirements cost-efficiently in this complex feature space. These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities

The Strategic Migration of Telephony into an Internet Protocol World : a South African Perspective

Internet Protocol Telephony (IPT), also known as Voice over Internet Protocol (VoIP), has evolved from a niche technology to one that is adopted fairly well in developed countries. The aim of this research report was to determine whether IPT will also be a success in the Republic of South Africa, which is one of many developing countries. The technology was analysed and it was found that cost reduction, increased productivity and enhanced applications were the most valuable benefits the technology could offer. Particular interesting impediments of the technology were discovered and it was found that there existed both hard issues like security and quality problems, as well as softer issues like internal politics, that could hinder the global success of the technology. The adoption rate of South Africa was compared to that of industrial countries and it was found that South African organisations overall posed a wait-and-see attitude towards IPT. Various implementation models were discussed and it was found that a hybrid approach would be the most viable option for local organisations. The South African environment were analysed and it was discovered that the biggest obstacle for success in South Africa was the regulatory environment. But it was also found that the environment would soon change and that competitors, including Black Economic Empowerment companies, should use the opportunities available. Social factors like HIV/AIDS and theft as well as economic factors like the exchange rate could hamper the competitiveness of local companies using IPT. IPT technology can only be a success in South Africa if it is intensely supported by Government, implemented in the correct manner and adopted aggressively by the local market.Dissertation (MBA)--University of Pretoria, 2006.Graduate School of Managementunrestricte

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field