A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures

Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, co-placement and scheduling of data with compute resources, and storing and transferring large volumes of data. We analyze the ecosystems of the two prominent paradigms for data-intensive applications, hereafter referred to as the high-performance computing and the Apache-Hadoop paradigm. We propose a basis, common terminology and functional factors upon which to analyze the two approaches of both paradigms. We discuss the concept of "Big Data Ogres" and their facets as means of understanding and characterizing the most common application workloads found across the two paradigms. We then discuss the salient features of the two paradigms, and compare and contrast the two approaches. Specifically, we examine common implementation/approaches of these paradigms, shed light upon the reasons for their current "architecture" and discuss some typical workloads that utilize them. In spite of the significant software distinctions, we believe there is architectural similarity. We discuss the potential integration of different implementations, across the different levels and components. Our comparison progresses from a fully qualitative examination of the two paradigms, to a semi-quantitative methodology. We use a simple and broadly used Ogre (K-means clustering), characterize its performance on a range of representative platforms, covering several implementations from both paradigms. Our experiments provide an insight into the relative strengths of the two paradigms. We propose that the set of Ogres will serve as a benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure

A framework for health care planning and control

Rising expenditures spur health care organizations to organize their processes more efficiently and effectively. Unfortunately, health care planning and control lags far behind manufacturing planning and control. Successful manufacturing planning and control concepts can not be directly copied, because of the unique nature of health care delivery. We analyze existing planning and control concepts or frameworks for health care operations management, and find that they do not properly address various important planning and control problems. We conclude that they only focus on hospitals, and are too narrow, focusing on a single managerial area, such as resource capacity planning, or ignoring hierarchical levels. We propose a modern framework for health care planning and control. Our framework integrates all managerial areas involved in health care delivery operations and all hierarchical levels of control, to ensure completeness and coherence of responsibilities for every managerial area. The framework can be used to structure the various planning and control functions, and their interaction. It is applicable broadly, to an individual department, an entire health care organization, and to a complete supply chain of cure and care providers. The framework can be used to identify and position various types of managerial problems, to demarcate the scope of organization interventions, and to facilitate a dialogue between clinical staff and managers. We illustrate the application of the framework with examples

A hierarchical approach to multi-project planning under uncertainty

We survey several viewpoints on the management of the planning complexity of multi-project organisations under uncertainty. A positioning framework is proposed to distinguish between different types of project-driven organisations, which is meant to aid project management in the choice between the various existing planning approaches. We discuss the current state of the art of hierarchical planning approaches both for traditional manufacturing and for project environments. We introduce a generic hierarchical project planning and control framework that serves to position planning methods for multi-project planning under uncertainty. We discuss multiple techniques for dealing with the uncertainty inherent to the different hierarchical stages in a multi-project organisation. In the last part of this paper we discuss two cases from practice and we relate these practical cases to the positioning framework that is put forward in the paper