Just like the internet, Internet of things (IoT) with right standards and technologies, will impact multiple aspects of human lives including Smart Home, Smart Car and Healthcare. Healthcare of things (HoT) would enhance healthcare using the insight provided by sensor data. Healthcare users will subscribe to HoT services that use sensors to get activity data (such as steps walked, hours slept and so on) along with other health vitals (such as heart rate, temperature and weight) to improve user's health. Similarly Smart home users subscribe to a Smart home cloud service that will use data from home sensors. Most of these IoT scenarios have multiple producer, multiple consumer (MPMC) environments, with multiple sensors as producers of data, and multiple IoT services as consumers of data. Current literature and standards provide many protocols for data paths (data transfers) in these MPMC environments that can be used for IoT. However, data paths in MPMC environment must be easily configurable and manageable by end users in order for IoT to be more appealing to a broader user community. Multiple sensors with multiple applications from each manufacturer to control them would not be preferred by users. Also, IoT users need to be able easily choose between multiple options for service providers. In this demonstration, we highlight how user experience perspectives impact the data framework design in MPMC environments, using our drops data framework infrastructure. We hope to have these concepts included IoT standardization efforts. This demonstration was also made at CES at the IEEE booth.

Smartphones to enterprise servers, most computing systems are built with commodity components, such as flash (NAND) storage, memory, etc. Although such generalization of commodity components reduces the cost of electronic products, generic components result in severe inefficiency in terms of energy and performance. We introduce Application Defined Computing (ADC) to specialize generic hardware based on runtime application characteristics. Our experimental results show that brokering just a couple of parameters in DRAM (memory) systems could improve Energy Delay Product by 23% and performance by 17% for memory-intensive workloads in smartphones. Our approach could be used in most consumer electronics products with diverse application workloads.

Emerging storage technologies such as flash memories, phase-change memories, and spin-transfer torque memories are poised to close the enormous performance gap between disk-based storage and main memory. We evaluate several approaches to integrating these memories into computer systems by measuring their impact on IO-intensive, database, and memory-intensive applications. We explore several options for connecting solid-state storage to the host system and find that the memories deliver large gains in sequential and random access performance, but that different system organizations lead to different performance trade-offs. The memories provide substantial application-level gains as well, but overheads in the OS, file system, and application can limit performance. As a result, fully exploiting these memories' potential will require substantial changes to application and system software. Finally, paging to fast non-volatile memories is a viable option for some applications, providing an alternative to expensive, powerhungry DRAM for supporting scientific applications with large memory footprints.

Data intensive computing can be defined as computation involving large datasets and complicated I/O patterns. Data intensive computing is challenging because there is a five-orders-of-magnitude latency gap between main memory DRAM and spinning hard disks; the result is that an inordinate amount of time in data intensive computing is spent accessing data on disk. To address this problem we designed and built a prototype data intensive supercomputer named DASH that exploits flash-based Solid State Drive (SSD) technology and also virtually aggregated DRAM to fill the latency gap . DASH uses commodity parts including Intel® X25-E flash drives and distributed shared memory (DSM) software from ScaleMP®. The system is highly competitive with several commercial offerings by several metrics including achieved IOPS (input output operations per second), IOPS per dollar of system acquisition cost, IOPS per watt during operation, and IOPS per gigabyte (GB) of available storage. We present here an overview of the design of DASH, an analysis of its cost efficiency, then a detailed recipe for how we designed and tuned it for high data-performance, lastly show that running data-intensive scientific applications from graph theory, biology, and astronomy, we achieved as much as two orders-of- magnitude speedup compared to the same applications run on traditional architectures.

International data grids are now being built that support joint management of shared collections. An emerging strategy is to build multiple independent data grids, each managed by the local institution. The data grids are then federated to enable controlled sharing of files. We examine the management issues associated with maintaining federations of production data grids, including management of access controls, coordinated sharing of name spaces, replication of data between data grids, and expansion of the data grid federation.

The 'Grid' is an emerging infrastructure providing coordinated and consistent services to access distributed, heterogeneous computational and information storage resources amongst autonomous organizations. Data grids are being built across the world as the next generation inter-organizational data management systems for coordinated sharing of data as well as storage resources. A data grid is a location independent logical namespace consisting persistent global identifiers for digital entities, storage resources and users in an autonomous inter-organizational collaboration. Data Grid Management Systems (DGMS) provide services on the data grid’s logical name space for management and organization of digital entities and resources. These data-intensive environments involve long run processes, which could be aggregated as Grid Workflows (Gridflows). Gridflows are executed as peer-to-peer workflows in the data grid infrastructure by the Gridflow Management Systems. The tutorial will introduce the grid, provide use-cases in large projects, examine requirements, introduce some possible solutions for managing data in the grid and using a services-oriented infrastructure. It will take real-world examples from current systems, in particular the Storage Resource Broker and the SDSC Matrix project.

Data Grids are being built across the world as the next generation data handling systems to manage peta-bytes of inter organizational data and storage space. A data grid (datagrid) is a logical name space consisting of storage resources and digital entities that is created by the cooperation of autonomous organizations and its users based on the coordination of local and global policies. Data Grid Management Systems (DGMSs) provide services for the confluence of organizations and management of inter-organizational data and resources in the datagrid.The objective of the tutorial is to provide an introduction to the opportunities and challenges of this emerging technology. Novices and experts would benefit from this tutorial. The tutorial would cover introduction, use cases, design philosophies, architecture, research issues, existing technologies and demonstrations. Hands on sessions for the participants to use and feel the existing technologies could be provided based on the availability of internet connections.

Data grids enable next generation scientific explorations that require intensive computation and analysis of petabyte-scale shared data collections. Apart from the challenge in creation and management of the data, another major challenge is the discovery of derived data products that have already been created. This work addresses the later challenge in minimizing response time and conserving the computing cycles in the grids by using "virtual services" to access derived data products. We analyze the role of virtual services for long running services in a service-oriented architecture and contribute a technique called "semantic mirror". A prototype was developed to discover derived data products of grid services using SRB-MCAT technology and GriPhyN Virtual Data Language (VDL).

The rapid evolution of the Internet and its business tools is enabling the transformation and deployment of business processes as highly modular e-services that can be flexibly and dynamically composed to form ad-hoc workflow. This research prepares for the proliferation of automated, Internet-based workflow, by contributing a suite of protocols for self-organizing brokering communities that enables the discovery of relevant e-services. We present protocols and architecture for e-service brokering communities, and discuss their use in workbase, an Internet-based, automated workflow system over e-services. The brokering protocols are based on a three-tier architecture of agents, brokers and superbrokers. We also present an infrastructure for dynamically composing new services from exiting e-services on the Internet. An implementation using JKQML of brokering communities is provided along with the architecture and design of our e-services and workflow concepts.