Modeling and Analyzing the Power Consumption in Query Processing For Distributed Database

Green computing has been generally practiced in almost all kind of fields especially in the recent years as environmental sustainability is getting more important. High power consumption increases the carbon emission which is adverse to the environment. This project focuses on applying green computing in query processing specifically for distributed database in healthcare industry. The information about a patient is stored in the database of the hospital the patient visited. However, currently this information is not being shared among hospitals which are crucial for diagnosis purpose. Hence, the objective of this project is to model the process of data retrieval from database distributed at different hospitals by using different query processing strategies and analyzes the energy consumption to access data from these distributed databases. Two strategies are used to retrieve the distributed data during simulation which are complete replication and horizontal fragmentation. Based on the analyzed result from the simulation, the identified energy-efficient strategy is complete replication which consumed lower power consumption by enabling local access to data stored in distributed database

Dynamic Physiological Partitioning on a Shared-nothing Database Cluster

Traditional DBMS servers are usually over-provisioned for most of their daily workloads and, because they do not show good-enough energy proportionality, waste a lot of energy while underutilized. A cluster of small (wimpy) servers, where its size can be dynamically adjusted to the current workload, offers better energy characteristics for these workloads. Yet, data migration, necessary to balance utilization among the nodes, is a non-trivial and time-consuming task that may consume the energy saved. For this reason, a sophisticated and easy to adjust partitioning scheme fostering dynamic reorganization is needed. In this paper, we adapt a technique originally created for SMP systems, called physiological partitioning, to distribute data among nodes, that allows to easily repartition data without interrupting transactions. We dynamically partition DB tables based on the nodes' utilization and given energy constraints and compare our approach with physical partitioning and logical partitioning methods. To quantify possible energy saving and its conceivable drawback on query runtimes, we evaluate our implementation on an experimental cluster and compare the results w.r.t. performance and energy consumption. Depending on the workload, we can substantially save energy without sacrificing too much performance

Energy-Efficient VoD content delivery and replication in integrated metro/access networks

Today's growth in the demand for access bandwidth is driven by the success of the Video-on-Demand (VoD) bandwidth-consuming service. At the current pace at which network operators increase the end users' access bandwidth, and with the current network infrastructure, a large amount of video traffic is expected to flood the core/metro segments of the network in the near future, with the consequent risk of congestion and network disruption. There is a growing body of research studying the migration of content towards the users. Further, the current trend towards the integration of metro and access segments of the network makes it possible to deploy Metro Servers (MSes) that may serve video content directly from the novel integrated metro/access segment to keep the VoD traffic as local as possible. This paper investigates a potential risk of this solution, which is the increase in the overall network energy consumption. First, we identify a detailed power model for network equipment and MSes, accounting for fixed and load-proportional contributions. Then, we define a novel strategy for controlling whether to switch MSes and network interfaces on and off so as to strike a balance between the energy consumption for content transport through the network and the energy consumption for processing and storage in the MSes. By means of simulations and taking into account real values for the equipment power consumption, we show that our strategy is effective in providing the least energy consumption for any given traffic load

Modeling and Analyzing the Power Consumption in Query Processing For Distributed Database

Green computing has been generally practiced in almost all kind of fields especially in the recent years as environmental sustainability is getting more important. High power consumption increases the carbon emission which is adverse to the environment. This project focuses on applying green computing in query processing specifically for distributed database in healthcare industry. The information about a patient is stored in the database of the hospital the patient visited. However, currently this information is not being shared among hospitals which are crucial for diagnosis purpose. Hence, the objective of this project is to model the process of data retrieval from database distributed at different hospitals by using different query processing strategies and analyzes the energy consumption to access data from these distributed databases. Two strategies are used to retrieve the distributed data during simulation which are complete replication and horizontal fragmentation. Based on the analyzed result from the simulation, the identified energy-efficient strategy is complete replication which consumed lower power consumption by enabling local access to data stored in distributed database