Defining Energy Consumption Plans for Data Querying Processes

During the last few years, we have been witnessing a significant increase in research about the development and production of hardware and software components with low levels of energy consumption. Today, energy consumption is one of the most critical issues in the area of information technologies and communication. One of the fractions in which this concern is most evident is in the management of database systems, with particular emphasis on those commonly designated as data centers. On these systems daily run a large amount of data querying processes, monitored and controlled by high sophisticated database management systems, which are responsible to establish efficient processing plans to support them. Using the information provided by a querying execution plan, especially the one related to the operators they used to perform database operations, we designed and developed an alternative method to define energy consumption plans for database queries. In this paper we present how such method works on the estimation of the energy consumption of each database operator integrated in the execution plan of a query at compile time. With it, we build up its corresponding energy consumption plan for executing the query, taking into consideration as well the characteristics of the computational platforms used for that.

[1]  Alfred V. Aho,et al.  Equivalences Among Relational Expressions , 1979, SIAM J. Comput..

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

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

[6]  Jennifer Widom,et al.  Database Systems: The Complete Book , 2001 .

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

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

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

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

[11]  Hyeonsang Eom,et al.  Scatter-Gather-Merge: An efficient star-join query processing algorithm for data-parallel frameworks , 2011, Cluster Computing.

[12]  Luiz André Barroso,et al.  The Case for Energy-Proportional Computing , 2007, Computer.

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

[14]  Guy M. Lohman,et al.  Index scans using a finite LRU buffer: a validated I/O model , 1989, ACM Trans. Database Syst..

[15]  Donald D. Chamberlin,et al.  Access Path Selection in a Relational Database Management System , 1989 .

[16]  Arie Shoshani,et al.  Strategies for processing ad hoc queries on large data warehouses , 2002, DOLAP '02.

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