Deploying deep neural network (DNN) inference applications in data centers and clouds to empower various services is on the increase. The significant power consumption of these applications contributes to the carbon emissions of underlying infrastructures. Therefore, minimizing the carbon emissions of these applications leads to a reduced carbon footprint of data centers and clouds. This paper introduces CarbonDIS, a carbon-aware inference scheduler designed to maintain the latency of DNN inference applications while minimizing their carbon emissions by leveraging heterogeneous GPUs. CarbonDIS considers an inference architecture where high-end and low-end GPUs are used in tandem to serve inference jobs. By leveraging the varying computing capability and power consumption of heterogeneous GPUs, CarbonDIS can effectively balance the performance and carbon emissions. Evaluation using three types of GPUs, 10 DNN models, and real-world carbon intensity traces show that CarbonDIS can find a trade-off between carbon footprint and latency of the jobs and reduce the carbon emissions by 16% compared with a performance-centric approach.

GenAI is playing an increasingly important role in computing courses at all levels, offering new opportunities to support teaching and learning. However, using GenAI effectively raises important concerns regarding trust, academic integrity, and broader social and ethical dimensions. This Working Group was formed to report on the current state of the art in using GenAI in upper-level computing courses to aid educators. The working group will undertake a methodological review of published work and solicit input from the computing educational community as part of the report.

In this study, we propose an energy-efficient database system called GreenDB running on clusters. GreenDB applies a workload-skewness strategy by managing hot nodes coupled with a set of cold nodes in a database cluster. GreenDB fetches popular data tables to hot nodes, aiming to keep cold nodes in the low-power mode in increased time periods. GreenDB is conducive to reducing the number of power-state transitions, thereby lowering energy-saving overhead. A prefetching model and an energy saving model are seamlessly integrated into GreenDB to facilitate the power management in database clusters. We quantitatively evaluate GreenDB's energy efficiency in terms of managing, fetching, and storing data. We compare GreenDB's prefetching strategy with the one implemented in Postgresql. Experimental results indicate that GreenDB conserves the energy consumption of the existing solution by up to 98.4 percent. The findings show that the energy efficiency of GreenDB can be optimized by tuning system parameters, including table size, hit rates, number of nodes, number of disks, and inter-arrival delays.

The goal of this study is to optimize energy efficiency of database clusters through prefetching and caching strategies. We design a workload-skewness scheme to collectively manage a set of hot and cold nodes in a database cluster system. The prefetching mechanism fetches popular data tables to the hot nodes while keeping unpopular data in cold nodes. We leverage a power management module to aggressively turn cold nodes in the low-power mode to conserve energy consumption. We construct a prefetching model and an energy-saving model to govern the power management module in database lusters. The energy-efficient prefetching and caching mechanism is conducive to cutting back the number of power-state transitions, thereby offering high energy efficiency. We systematically evaluate energy conservation technique in the process of managing, fetching, and storing data on clusters supporting database applications. Our experimental results show that our prefetching/caching solution significantly improves energy efficiency of the existing PostgreSQL system.

Summary Cluster‐level thermal management has gained much attention over the past decade due to rising cooling costs associated with data centers. In this research, we propose and implement a static scheduler called SSched and a dynamic one named DSched. These 2 algorithms schedule jobs based on CPU and disk temperatures of a Hadoop cluster's nodes. Our schedulers rely on a monitoring mechanism to keep track of CPU and disk utilization, maintaining CPU and disk temperatures below a threshold through thermal‐aware scheduling decisions. To facilitate the design of SSched and DSched, we classify jobs into the CPU‐intensive and disk‐intensive categories. When a job arrives, SSched retrieves the utilization stats from a profiled log, estimates the thermal behavior, and places the job on NodeManager to minimize thermal impacts. Unlike SSched, DSched improves thermal efficiency of Hadoop clusters through dynamic load balancing. DSched keeps track of the coolest and hottest nodes in the cluster; tasks are migrated from hot nodes into cool ones if any hot spot is detected. To evaluate the effectiveness of our schedulers, we keep track of average CPU and disk temperatures in a node, managing an optimal outlet temperature across a cluster. We demonstrate that compared with the traditional Hadoop scheduler, SSched and DSched achieve approximately 15% savings in terms of cooling cost with little performance overhead.

In this paper, we propose an erasure-coded data archival system called aHDFS for Hadoop clusters, where RS(k + r; k) codes are employed to archive data replicas in the Hadoop distributed file system or HDFS. We develop two archival strategies (i.e., aHDFS-Grouping and aHDFS-Pipeline) in aHDFSto speed up the data archival process. aHDFS-Groupinga MapReduce-based data archiving scheme - keeps each mapper's intermediate output Key-Value pairs in a local key-value store. With the local store in place, aHDFS-Grouping merges all the intermediate key-value pairs with the same key into one single key-value pair, followed by shuffling the single Key-Value pair to reducers to generate final parity blocks. aHDFS-Pipeline forms a data archival pipeline using multiple data node in a Hadoop cluster. aHDFS-Pipeline delivers the merged single key-value pair to a subsequent node's local key-value store. Last node in the pipeline is responsible for outputting parity blocks. We implement aHDFS in a real-world Hadoop cluster. The experimental results show that aHDFS-Grouping and aHDFS-Pipeline speed up Baseline's shuffle and reduce phases by a factor of 10 and 5, respectively. When block size is larger than 32 MB, aHDFS improves the performance of HDFS-RAID and HDFS-EC by approximately 31.8 and 15.7 percent, respectively.

Energy saving is rapidly becoming one of the hottest topics in technology field within recent decades. With the development of technology, it brings a sheer increasing trend of data and the growth scale of clusters and data centers. Meanwhile, it also raises another essential issue into the path: energy cost. In this paper, we are diving into this key issue and evaluating energy- efficiency based on TPC-W benchmark: a notable web transaction e-commerce benchmark. We simulate the web transaction with different database sizes and collect the energy data by KILL-A-WATT. Also, we deploy this setup on four different cluster systems: PC nodes and wimpy nodes, and two different heterogeneous systems: using PC as front server and wimpy as Database server, and using wimpy as Web server and PC as Database server. Energy result demonstrates different characteristics among them, which can give lightening advice for future works in data center.