CloudJet4BigData: Streamlining Big Data via an Accelerated Socket Interface

Big data needs to feed users with fresh processing results and cloud platforms can be used to speed up big data applications. This paper describes a new data communication protocol (CloudJet) for long distance and large volume big data accessing operations to alleviate the large latencies encountered in sharing big data resources in the clouds. It encapsulates a dynamic multi-stream/multi-path engine at the socket level, which conforms to Portable Operating System Interface (POSIX) and thereby can accelerate any POSIX-compatible applications across IP based networks. It was demonstrated that CloudJet accelerates typical big data applications such as very large database (VLDB), data mining, media streaming and office applications by up to tenfold in real-world tests

Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams

Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. The proposed X-CEP query model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems, October 27, 201

Reporting an Experience on Design and Implementation of e-Health Systems on Azure Cloud

Electronic Health (e-Health) technology has brought the world with significant transformation from traditional paper-based medical practice to Information and Communication Technologies (ICT)-based systems for automatic management (storage, processing, and archiving) of information. Traditionally e-Health systems have been designed to operate within stovepipes on dedicated networks, physical computers, and locally managed software platforms that make it susceptible to many serious limitations including: 1) lack of on-demand scalability during critical situations; 2) high administrative overheads and costs; and 3) in-efficient resource utilization and energy consumption due to lack of automation. In this paper, we present an approach to migrate the ICT systems in the e-Health sector from traditional in-house Client/Server (C/S) architecture to the virtualised cloud computing environment. To this end, we developed two cloud-based e-Health applications (Medical Practice Management System and Telemedicine Practice System) for demonstrating how cloud services can be leveraged for developing and deploying such applications. The Windows Azure cloud computing platform is selected as an example public cloud platform for our study. We conducted several performance evaluation experiments to understand the Quality Service (QoS) tradeoffs of our applications under variable workload on Azure.Comment: Submitted to third IEEE International Conference on Cloud and Green Computing (CGC 2013

Data processing model for the CDF experiment

The data processing model for the CDF experiment is described. Data processing reconstructs events from parallel data streams taken with different combinations of physics event triggers and further splits the events into datasets of specialized physics datasets. The design of the processing control system faces strict requirements on bookkeeping records, which trace the status of data files and event contents during processing and storage. The computing architecture was updated to meet the mass data flow of the Run II data collection, recently upgraded to a maximum rate of 40 MByte/sec. The data processing facility consists of a large cluster of Linux computers with data movement managed by the CDF data handling system to a multi-petaByte Enstore tape library. The latest processing cycle has achieved a stable speed of 35 MByte/sec (3 TByte/day). It can be readily scaled by increasing CPU and data-handling capacity as required.Comment: 12 pages, 10 figures, submitted to IEEE-TN

Data production models for the CDF experiment

The data production for the CDF experiment is conducted on a large Linux PC farm designed to meet the needs of data collection at a maximum rate of 40 MByte/sec. We present two data production models that exploits advances in computing and communication technology. The first production farm is a centralized system that has achieved a stable data processing rate of approximately 2 TByte per day. The recently upgraded farm is migrated to the SAM (Sequential Access to data via Metadata) data handling system. The software and hardware of the CDF production farms has been successful in providing large computing and data throughput capacity to the experiment.Comment: 8 pages, 9 figures; presented at HPC Asia2005, Beijing, China, Nov 30 - Dec 3, 200