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Data management techniques

Data management techniques

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Today, it is projected that data storage and management is becoming one of the key challenges in order to achieve ultrascale computing for several reasons. First, data is expected to grow exponentially in the coming years and this progression will imply that disruptive technologies will be needed to store large amounts of data and more importantly to access it in a timely manner. Second, the improvement of computing elements and their scalability are shifting application execution from CPU bound to I/O bound. This creates additional challenges for significantly improving the access to data to keep with computation time and thus avoid high-performance computing (HPC) from being underutilized due to large periods of I/O activity. Third, the two initially separate worlds of HPC that mainly consisted on one hand of simulations that are CPU bound and on the other hand of analytics that mainly perform huge data scans to discover information and are I/O bound are blurring. Now, simulations and analytics need to work cooperatively and share the same I/O infrastructure.

Chapter Contents:

  • 4.1 Intra-node scaling of an efficient KV store on modern multicore servers
  • 4.1.1 Introduction
  • 4.1.2 Background
  • 4.1.3 Tucana Design
  • 4.1.4 Experimental evaluation
  • Throughput analysis
  • CPU utilization analysis
  • I/O analysis
  • Network and I/O traffic analysis
  • 4.1.5 Summary
  • 4.2 Data-centric workflow runtime for data-intensive applications on Cloud systems
  • 4.2.1 DMCF overview
  • 4.2.2 Hercules overview
  • 4.2.3 Integration between DMCF and Hercules
  • 4.2.4 Data-aware scheduling strategy
  • 4.2.5 Experimental evaluation
  • 4.2.6 Conclusions
  • Acknowledgment
  • 4.3 Advanced conflict-free replicated datatypes
  • 4.3.1 Scalability and availability tradeoffs
  • 4.3.2 Conflict-free replicated datatypes
  • 4.3.3 A case study: dataClay distributed platform
  • 4.3.4 Conclusions and future directions
  • 4.4 Summary

Inspec keywords: input-output programs; Big Data; parallel processing; microprocessor chips

Other keywords: high-performance computing; HPC; ultrascale computing; I/O; large amounts of data; CPU

Subjects: Database management systems (DBMS); Data handling techniques; Operating systems; Multiprocessing systems

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