Analyzing Power Consumption in Query Processing for Centralized Database System

Green computing is the environmentally responsible use of computers and related resources as well as reduced power resource consumption. In supporting to green computing, this project is being carried out with the main purpose to analyze the efficient performance of query processing activity in consuming power from a centralized database by implementing different query strategies in data grouping. In achieving the goal, each data query strategy retrieved from the database is measured based on its power consumption by using an energy saving power meter. A consolidated hotel management system is developed as to indicate the context of the project in testing on the query power usage from a centralized database system. This project emphasizes on the amount of power (in watts) that the query consumed based on the size of the query and the strategy used in assembling the data. It is more focusing on the simulation of query dataset in consuming power and not on developing a real-time system. Hence, the functionality and reliability of the system is not the main focus and will not be discussed in this paper work. By the end of this project, readers would be able to see and analyze that various query strategies applied in retrieving the same output under a specified condition gives dissimilar power reading which indicates the different amount of power consumption by each data query

Reducing energy consumption of queries in memory-resident database systems

The tremendous growth of system memories has increased the capacities and capabilities of memory-resident embedded databases, yet current embedded databases need to be tuned in order to take advantage of new memory technologies. In this paper, we study the implications of hosting memory resident databases, and propose hardware and software (query-driven) techniques to improve their performance and energy consumption. We exploit the structured organization of memories, which enables a selective mode of operation in which banks are accessed selectively. Unused banks are placed in a lower power mode based on access pattern information. We propose hardware techniques that dynamically control the memory by making the system adapt to the access patterns that arise from queries. We also propose a software (query-directed) scheme that directly modifies the queries to reduce the energy consumption by ensuring uniform bank accesses. Our results show that these optimizations could lead to at the least 40 % reduction in memory energy. We also show that query-directed schemes better utilize the low-power modes, achieving up to 68 % improvement