Establishing Energy Consumption Plans for Green Star-Queries in Data Warehousing Systems

During the last few years many initiatives were taken away in response to high levels of energy consumption verified in data centers. We all know that this is a critical issue nowadays. Many studies carried out raised a lot of concerns about the energy demands of data centers, discussing solutions to reduce it effectively without affecting their day-by-day operation. In this work we made a small contribution to help that. We studied in a data warehousing system how could be possible to establish an energy consumption plan for a star-query. With these plans, we can establish in each phase of a star-query execution the energy consumed by all the elementary tasks that were executed to satisfy it. With this purpose in mind, and motivated by the usual methods and heuristics used on query execution optimization, we designed and developed a method to estimate the energy consumption of each element (operator) integrated in the execution plan of a query at compile time. With it, we also build up the corresponding energy consumption used on executing the star-query, taking into consideration the characteristics of the computational platforms used for.

[1]  João Saraiva,et al.  Defining Energy Consumption Plans for Data Querying Processes , 2014, 2014 IEEE Fourth International Conference on Big Data and Cloud Computing.

[2]  Laura Schweitzer,et al.  Database Systems A Practical Approach To Design Implementation And Management , 2016 .

[3]  Parthasarathy Ranganathan,et al.  Energy Efficiency: The New Holy Grail of Data Management Systems Research , 2009, CIDR.

[4]  Kirk W. Cameron,et al.  Memory MISER: Improving Main Memory Energy Efficiency in Servers , 2009, IEEE Transactions on Computers.

[5]  Raghunath Othayoth Nambiar,et al.  Energy cost, the key challenge of today's data centers: a power consumption analysis of TPC-C results , 2008, Proc. VLDB Endow..

[6]  Mahadev Satyanarayanan,et al.  PowerScope: a tool for profiling the energy usage of mobile applications , 1999, Proceedings WMCSA'99. Second IEEE Workshop on Mobile Computing Systems and Applications.

[7]  Xiaorui Wang,et al.  Exploring power-performance tradeoffs in database systems , 2010, 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010).

[8]  Jignesh M. Patel,et al.  Rethinking Query Processing for Energy Efficiency: Slowing Down to Win the Race , 2011, IEEE Data Eng. Bull..

[9]  Matteo Golfarelli DFM as a Conceptual Model for Data Warehouse , 2009, Encyclopedia of Data Warehousing and Mining.

[10]  Surajit Chaudhuri,et al.  Rethinking Query Processing for Energy Efficiency: Slowing Down to Win the Race. , 2011 .

[11]  Kenneth J. Christensen,et al.  Reducing the Energy Consumption of Ethernet with Adaptive Link Rate (ALR) , 2008, IEEE Transactions on Computers.

[12]  Xianfeng Li,et al.  Estimating the Worst-Case Energy Consumption of Embedded Software , 2006, 12th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'06).

[13]  Jignesh M. Patel,et al.  Towards Eco-friendly Database Management Systems , 2009, CIDR.

[14]  Surajit Chaudhuri,et al.  An overview of query optimization in relational systems , 1998, PODS.

[15]  Xiaorui Wang,et al.  Dynamic Energy Estimation of Query Plans in Database Systems , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems.

[16]  Beng Chin Ooi,et al.  The Claremont report on database research , 2008, SGMD.