Author: Eden Kim, Keith Orsak and Chuck Paradon

Power Efficiency Measurement – Our Experts Make It Clear – Part 3

Eden Kim, Keith Orsak and Chuck Paradon Leave a comment

Measuring power efficiency in datacenter storage is a complex endeavor. A number of factors play a role in assessing individual storage devices or system-level logical storage for power efficiency. Luckily, our SNIA experts make the measuring easier!

In this SNIA Experts on Data blog series, our experts in the SNIA Solid State Storage Technical Work Group and the SNIA Green Storage Initiative explore factors to consider in power efficiency measurement, including the nature of application workloads, IO streams, and access patterns; the choice of storage products (SSDs, HDDs, cloud storage, and more); the impact of hardware and software components (host bus adapters, drivers, OS layers); and access to read and write caches, CPU and GPU usage, and DRAM utilization.

Join us on our journey to better power efficiency as we continue with Part 3: Traditional Differences in Power Consumption: Hard Disk Drives vs Solid State Drives. And if you missed our earlier segments, click on the titles to read them:  Part 1: Key Issues in Power Efficiency Measurement, and Part 2: Impact of Workloads on Power Efficiency Measurement..  Bookmark this blog  and check back in April for the final installment of our four-part series. And explore the topic further in the SNIA Green Storage Knowledge Center.

Traditional Differences in Power Consumption: Hard Disk Drives vs Solid State Drives

There are significant differences in power efficiency between Hard Disk Drives (HDDs) and Solid State Drives (SSDs). While some commentators have examined differences in power efficiency measurement for HDDs v SSDs, much of the analysis has not accounted for the key power efficiency contributing factors outlined in this blog.

As a simple generalization at the individual storage device level, HDDs show higher power consumption than SSDs.  In addition, SSDs have higher performance (IOPS and MB/s) often by an order of magnitude or more.  Hence, cursory consideration of device power efficiency measurement, expressed as IOPS/W or MB/s/W, will typically favor the faster SSD with lower device power consumption.

On the other hand, depending on the workload and IO transfer size, HDD devices and systems may exhibit better IOPS/W and MB/s/W if measured to large block sequential RW workloads where head actuators can reside on the disk OD (outer diameter) with limited seek accesses.

The above traditional HDD and SSD power efficiency considerations can be described at the device level as involving the following key points:

HDDs (Hard Disk Drives):

  1. Mechanical Components: HDDs consist of spinning disks and mechanical read/write heads. These moving parts consume a substantial amount of power, especially during startup and when seeking data.
  2. Idle Power Consumption: Even when not actively reading or writing data, HDDs still consume a notable amount of power to keep the disks spinning and ready to access data
  3. Access Time Impact: The mechanical nature of HDDs leads to longer access times compared to SSDs. This means the drive remains active for longer periods during data access, contributing to higher power consumption.

SSDs (Solid State Drives):

  1. No Moving Parts: SSDs are entirely electronic and have no moving parts. As a result, they consume less power during both idle and active states compared to HDDs
  2. Faster Access Times: SSDs have much faster access times since there are no mechanical delays. This results in quicker data retrieval and reduced active time, contributing to lower power consumption
  3. Energy Efficiency: SSDs are generally more energy-efficient, as they consume less power during read and write operations. This is especially noticeable in laptops and portable devices, where battery life is critical
  4. Less Heat Generation: Due to their lack of moving parts, SSDs generate less heat during operation, which can lead to better thermal efficiency in systems.

In summary, SSDs tend to be more power-efficient than HDDs due to their lack of mechanical components, faster access times, and lower energy consumption during both active and idle states. This power efficiency advantage is one of the reasons why SSDs have become increasingly popular in various computing devices, from laptops to data centers.

Power Efficiency Measurement – Our Experts Make It Clear – Part 2

Eden Kim, Keith Orsak and Chuck Paradon Leave a comment

Measuring power efficiency in datacenter storage is a complex endeavor. A number of factors play a role in assessing individual storage devices or system-level logical storage for power efficiency. Luckily, our SNIA experts make the measuring easier!

In this SNIA Experts on Data blog series, our experts in the SNIA Solid State Storage Technical Work Group and the SNIA Green Storage Initiative explore factors to consider in power efficiency measurement, including the nature of application workloads, IO streams, and access patterns; the choice of storage products (SSDs, HDDs, cloud storage, and more); the impact of hardware and software components (host bus adapters, drivers, OS layers); and access to read and write caches, CPU and GPU usage, and DRAM utilization.

Join us on our journey to better power efficiency as we continue with Part 2: Impact of Workloads on Power Efficiency Measurement.  And if you missed Part 1: Key Issues in Power Efficiency Measurement, you can find it here.  Bookmark this blog  and check back in March and April for the continuation of our four-part series. And explore the topic further in the SNIA Green Storage Knowledge Center.

Part 2: Impact of Workloads on Power Efficiency Measurement

Workloads are a significant driving force behind power consumption in computing systems. Different tasks and applications place diverse demands on hardware, leading to fluctuations in the amount of power used. Here’s a breakdown of how workloads can influence power consumption:

  • CPU Utilization. The CPU’s power consumption increases as it processes tasks, with more demanding workloads that involve complex calculations or multitasking leading to higher CPU utilization and, consequently, elevated power usage.
  • Memory Access is another key factor. Accessing memory modules consumes power, and workloads that heavily rely on frequent memory read and write operations can significantly contribute to increased power consumption.
  • Disk Activity, particularly read and write operations on storage devices (whether HDDs or SSDs), consumes power. Workloads that involve frequent data access or large file transfers can lead to an uptick in power consumption. GPU Usage plays a crucial role, especially in tasks like gaming, video editing, and machine learning. High GPU utilization for rendering complex graphics or training deep neural networks can result in substantial power consumption.
  • Network Communication tasks, such as data transfers, streaming, or online gaming, require power from both the CPU and the network interface. The extent of communication and data throughput can significantly affect overall power usage.
  • In devices equipped with displays, Screen Brightness directly impacts power consumption. Brighter screens consume more power, which means workloads involving continuous display usage contribute to higher power consumption.
  • I/O Operations encompass interactions with peripherals like storage devices or printers. These operations can lead to short bursts of power consumption, especially if multiple devices are connected.
  • Understanding the contrast between Idle and Active States is essential. Different workloads can transition devices between these states, with idle periods generally exhibiting lower power consumption. However, certain workloads may keep components active even during seemingly idle times.
  • Dynamic Voltage and Frequency Scaling are prevalent in many systems, allowing them to adjust the voltage and frequency of components based on workload demands. Increased demand leads to higher clock speeds and voltage, ultimately resulting in more significant power consumption.
  • Background Processes also come into play. Background applications, updates, and system maintenance tasks can impact power consumption, even when the user isn’t actively engaging with the device.

In practical terms, comprehending how various workloads affect power consumption is vital for optimizing energy efficiency. For instance, laptops can extend their battery life by reducing screen brightness, closing unnecessary applications, and selecting power-saving modes.

Moreover, SSDs are designed with optimizations for background processes in mind. Garbage collection and NAND Flash cell management often occur during idle periods or periods of low-impact workloads.

Likewise, data centers and cloud providers strategically manage workloads to minimize energy consumption and operational costs while upholding performance standards.

Power Efficiency Measurement – Our Experts Make It Clear – Part 1

Eden Kim, Keith Orsak and Chuck Paradon Leave a comment

Measuring power efficiency in datacenter storage is a complex endeavor. A number of factors play a role in assessing individual storage devices or system-level logical storage for power efficiency. Luckily, our SNIA experts make the measuring easier!

In this SNIA Experts on Data blog series, our experts in the SNIA Solid State Storage Technical Work Group and the SNIA Green Storage Initiative explore factors to consider in power efficiency measurement, including the nature of application workloads, IO streams, and access patterns; the choice of storage products (SSDs, HDDs, cloud storage, and more); the impact of hardware and software components (host bus adapters, drivers, OS layers); and access to read and write caches, CPU and GPU usage, and DRAM utilization.

Join us on our journey to better power efficiency as we begin with Part 1: Key Issues in Power Efficiency Measurement. Bookmark this blog and check back in February, March, and April for the continuation of our four-part series. And explore the topic further in the SNIA Green Storage Knowledge Center.

Part 1: Key Issues in Power Efficiency Measurement

Ensuring accurate and precise power consumption measurements is challenging, especially at the individual device level, where even minor variations can have a significant impact. Achieving reliable data necessitates addressing factors like calibration, sensor quality, and noise reduction.

Furthermore, varying workloads in systems require careful consideration to accurately capture transient power spikes and average power consumption. Modern systems are composed of interconnected components that affect each other’s power consumption, making it difficult to isolate individual component power usage.

The act of measuring power itself consumes energy, creating a trade-off between measurement accuracy and the disturbance caused by measurement equipment. To address this, it’s important to minimize measurement overheads while still obtaining meaningful data.

Environmental factors such as temperature, humidity, and airflow, can unpredictably influence power consumption, emphasizing the need for standardized test environments. Rapid workload changes can lead to transient power behavior that may require specialized equipment for accurate measurement.

Software running on a system significantly influences power consumption, emphasizing the importance of selecting representative workloads and ensuring consistent software setups across measurements. Dynamic voltage and frequency scaling are used in many systems to optimize power consumption, and understanding their effects under different conditions is crucial.

Correctly interpreting raw power consumption data is essential to draw meaningful conclusions about efficiency. This requires statistical analysis and context-specific considerations. Real-world variability, stemming from manufacturing differences, component aging, and user behavior, must also be taken into account in realistic assessments.

Addressing these challenges necessitates a combination of precise measurement equipment, thoughtful experimental design, and a deep understanding of the system and device being investigated.

In our next blog, Part 2, we will examine the impact of workloads on power efficiency measurement.