Kerry Jiang Biography

Software Manager in System Optimization Technology Center (SOTC), Intel Corporation. Kerry has been working with Intel for eight years, four years in open source mobile OS software stack on IA, which includes Android optimization and MeeGo SDK. Before joining Intel, Kerry worked in Motorola on mobile platform and 2G wireless base-station software developments. Kerry holds a master degree in electronics engineering.


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Xiao-Feng Li Biography

Architect of System Software Optimization Center, Intel Corporation. Xiao-Feng has been working with Intel for 12 years, with extensive technical experience in parallel system, compiler design and runtime technologies, where he has authored about 20 academic papers and 10 U.S. patents. Two years ago, Xiao-Feng initiated the evaluation and optimization efforts for best Android user experience on Intel platforms. Before he joined Intel, Xiao-Feng was a technical manager in Nokia Research Center. Xiao-Feng holds a PhD degree in computer science, and is a member of Apache Software Foundation. His personal homepage can be found at


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Bingwei Liu Biography

Engineering manager of Open Source Technology Center, Intel Corporation. Bingwei has been working in Intel for 11 years, with abundant experience in Linux OS, open source software and system engineering. His working scope spans from enterprise to client platforms and now is focused on Mobile OS. Bingwei holds a master degree of computer science.


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Yong Wang Biography

Senior software engineer from Intel’s Open Source Technology Center. He has been with Intel for 7 years working on a variety of projects, including virtualization, manageability, OSV enabling, etc. Most recently Yong has been working on power management for a wide range of mobile operating systems such as Moblin, Meego, Tizen and Android. Yong graduated from Beijing University of Aeronautics and Astronautics and holds a master degree of computer science. He enjoys sports and reading in his spare time.


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Weihua Jackie Wu Biography

Engineering Manager in Intel's Open Source Technology Center leading a team on Mobile OS and HTML5 tools development. Before that, as a research engineer, Jackie was focused on wireless network and energy efficient communications. Prior to joining Intel in 2004, Jackie was in Chinese Academy of Sciences developing embedded operating system and smartphone products. Jackie received his M.S. and B.S. in Engineering Mechanics from Beijing University of Aeronautics and Astronautics in 2002 and 1999 respectively. He has two US patent applications.


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Mobile OS Architecture Trends - Part I Published: August 28, 2013 • Service Technology Magazine Issue LXXV PDF

The world is flat, because it becomes increasingly mobile, fast, connected, and secure. People expect to move around easily with their mobile devices, keeping close communications with their partners and family, enjoying the versatile usage models and infinite contents, and without worrying about the device and data management. These all put requirements on the mobile devices, of which the mobile OS is the soul. Based on our years of experience in mobile OS design and an extensive survey of current industry situation, we believe there are several commonalities in future mobile OS architecture, such as user experience, power management, security design, cloud support, and openness design. We develop an analysis model to guide our investigation. In this article, we describe our investigations in the trends of mobile OS architecture over the next decade by focusing on the major commonalities. Based on the findings, we also review the characteristics of today's mobile operating systems from the perspective of architecture trends.


Mobile OS design has experienced a three-phase evolution: from the PC- based OS to an embedded OS to the current smartphone-oriented OS in the past decade. Throughout the process, mobile OS architecture has gone from complex to simple to something in-between. The evolution process is naturally driven by the technology advancements in hardware, software, and the Internet:

  • Hardware. The industry has been reducing the factor size of microprocessors and peripherals to design actual mobile devices. Before the form factor size was reduced enough, the mobile device could not achieve both small size and processing capability at the same time. We had either a PC-sized laptop computer or a much weaker personal data assistant (PDA) in phone size. Mobile operating systems for PDAs usually did not have full multitasking or 3D graphics support. Features like sensors, such as accelerometers, and capacitor-based touchscreens were not available in the past mobile operating systems.
  • Software. With a laptop computer, the software is mainly focused on the user's productivity, where support for keyboard and mouse that have precise inputs are essential. The software for a personal data assistant, as its name implies, helps the user to manage personal data such as contacts information, e-mail, and so on. The mobile operating systems were not designed for good responsiveness or smoothness with a rich user interface (UI) including both touchscreen and other sensors.
  • Internet. Along with Internet development, especially after Web 2.0, there is abundant information in the network waiting to be searched, organized, mined, and brought to users. People are increasingly living with the Internet instead of just browsing the Web. More and more people are involved in the development, including information contribution, application development, and social interactions. The mobile operating systems cannot be self-contained, but have to be open systems.

The usage model of past mobile devices is limited. A user mostly runs the device applications for data management and local gaming, only occasionally browses Internet static Web pages or accesses specific services like e-mail. In other words, the possible usages of the device are predefined with the preinstalled applications when the user purchases it. This is largely changed in new mobile devices, where the device is virtually a portal to various usage models. All the involved parties such as service providers, application developers, and other device users continuously contribute and interact through the device with its owner. Figure 1 shows the high-level usage model difference between the past and new mobile devices.


Figure 1 - high-level usage models of mobile devices (Source: Intel Corporation, 2012)

The representatives of current mobile operating systems include Apple's iOS* 5.0, Google Android* 4.0, Microsoft Windows* Phone 7.0, and a few others. In terms of their usage models, they share more similarities than differences:

  • All of them have a documented software development kit (SDK) with well- defined APIs that enable the common developers to develop applications for these systems.
  • All of them have online application stores for the developers to publish and for the users to download applications, such as Apple App Store, Google Play, and Windows Phone Marketplace.
  • All of them have a certain level of multitasking and 3D graphics support. Touchscreens and sensors are just no-brainers. Much effort have been spent to make the user interactions smooth and responsive.
  • Browsing experience is far beyond static Web pages. HTML5 is becoming the default so as to run Web-based applications.
  • All of the operating systems support device-based payment. Together with enterprise applications and private information, system security is always a key concern to the device users.
  • As mobile operating systems, one of key design differences from non- mobile operating systems is the focus on battery life. The systems try best to reduce the active power consumption of the device components and put them into idle whenever possible.

The similarities of the current mobile operating systems reflect the advancement trend in hardware, software, and the Internet. Anticipating the trend of mobile operating systems, we believe those areas are the major focuses of the next generation of mobile OS design, including user experience, battery life, cloud readiness, security, and openness. They are actually conflicting targets to a large extent:

  • User experience and battery life. To achieve best responsiveness and smoothness, the system expects all the hardware resource available to exploit their best capacity. At the same time, to sustain the battery life as a mobile device, the hardware components should be idle whenever possible.
  • Security and openness. One does not want to expose all of one's system functionalities to external entities, because that puts the system under security threat. On the other hand, without exposing enough system APIs, it is impossible for the developers to create innovative usages.
  • Cloud readiness. As more and more services and applications are offered from the cloud, it is natural to consider a thin client device model that trusts the cloud and that offloads computations to the cloud. But as of today, the thin client model still has technical challenges in user experience and security.

In this article, we try to investigate the various aspects of mobile OS design and present our opinions about the future of mobile OS architecture.

The article is arranged as follows. Based on the framework laid out in this section, we use separate sections to discuss the respective topics in text below. They are arranged in user experience, power management, security, openness, and cloud readiness. Finally we have discussions and a summary.

User Experience (UX)

Traditional performance is inadequate to characterize modern client devices. Performance is more about the steady execution state of the software stack and is usually reported with a final score of the total throughput in the processor or other subsystems. User experience is more about the dynamic state transitions of the system triggered by user inputs. The quality of the user experience is determined by such things as the user perceivable responsiveness, smoothness, coherence, and accuracy. Traditional performance could measure every link of the chain of the user interaction, while it does not evaluate the full chain of the user interaction as a whole. Thus the traditional performance optimization methodology cannot simply apply to the user experience optimization. It is time to invest in device user interaction optimizations so as to bring the end user a pleasant experience.

User Interactions with Mobile Devices

In a recent performance measurement with a few market Android devices, we found there was a device X behaving uniformly worse than another device Y with common benchmarks in graphics, media, and browsing. But the user perceivable experience with the device X was better than device Y. The root reason we identified was that traditional benchmarks or benchmarks designed in traditional ways did not really characterize user interactions, but measured the computing capability (such as executed instructions) or the throughput (such as processed disk reads) of the system and the subsystems.

Take video evaluation as an example. Traditional benchmarks only measure video playback performance with some metrics like FPS (frames-per-second), or frame drop rate. This methodology has at least two problems in evaluating user experience. The first problem is that video playback is only part of the user interactions in playing video. A typical life cycle of user interaction usually includes at least the following links: "launch player" > "start playing" > "seek progress" > "video playback" > "back to home screen." Yet good performance in video playback cannot characterize the real user experience in playing video. User interaction evaluation is a superset of traditional performance evaluation.

The other problem is, using FPS as the key metric to evaluate the smoothness of the user interactions cannot always reflect good user experience. For example, when we flung a picture in the Gallery3D application, the device Y had obvious stuttering during the picture scrolling, but the FPS value of device Y was higher than that of device X. In order to quantify the difference of the two devices, we collected the data of every frame during a picture fling operation in the Gallery3D application on both device X and device Y, as shown in Figure 2 and Figure 3 respectively. Every frame's data is given in a vertical bar, where the x-axis is the time when the frame is drawn, and the height of the bar is the time it takes the system to draw the frame. From the figures, we can see that device X obviously has a lower FPS value than device Y, but with smaller maximal frame time, less frames longer than 30 ms, and smaller frame time variance. This means that, to characterize the user experience of the picture fling operation, those metrics like maximal frame time and frame time variance should also be considered.


Figure 2 - Frame times of a fling operation in Gallery3D application on device X (Source: Intel Corporation, 2011)


Figure 3 - Frame times of a fling operation in Gallery3D application on device Y (Source: Intel Corporation, 2011)

As a comparison, Figure 4 shows the frame data of a fling operation after we optimized the device Y. Apparently all the metrics have been improved and the frame time distribution became much more uniform.

User experience is more about dynamic state transitions of the system triggered by user inputs. A good user experience is achieved with things such as user perceivable responsiveness, smoothness, coherence, and accuracy. Traditional performance could measure every link of the chain of the user interaction without evaluating the full chain of the user interaction as a whole.


Figure 4 - Frame times of a fling operation in Gallery3D application on device Y after optimization (Source: Intel Corporation, 2011)

Another important note is that user experience is a subjective process; just consider the experience when watching a movie or appreciating music. Current academic research uses various methodologies such as eyeball tracking, heartbeat monitoring, or just polling to understand user experience. For our software engineering purpose, in order to analyze and optimize the user interactions systematically, we categorize the interaction scenarios into four kinds:

  • Inputs to the device from the user, sensor, network, and so on. This category evaluates if the inputs can trigger the device to action accurately or fuzzily as expected. For touchscreen inputs, it measures the touch speed, pressure, range, and so forth.
  • Device response to the inputs. This category evaluates how responsive the device is to the inputs.
  • System state transition. This category especially evaluates how smooth graphics transition on the screen. It can be a follow-up of the device response to some input.
  • Continuous control of the device. People operating the device not only give a single input, but sometimes also control the graphic objects in the screen, such as to control a game jet-plane, or to drag an application icon. The category is to evaluate the controllability of the device.

Among them, "inputs to the device" and "control of the device" are related to the user experience aspect of how a user controls a device. "Device response to the inputs" and "system state transition" are related to the aspect of how the device reacts to the user. We can map a user interaction life cycle into scenarios that fall into the categories above; then for each scenario, we can identify the key metrics in the software stack to measure and optimize.

User Interaction Optimizations

As we have described in last subsection, there is no clear-cut and objective way to measure the user experience. We set up following criteria in our measurement of the user interactions:

  • Perceivable. The metric has to be perceivable by a human being. Otherwise, it is irrelevant to the user experience.
  • Measureable. The metric should be measurable by different teams. It should not depend on certain special infrastructure that can only be measured by certain teams.
  • Repeatable. The measured result should be repeatable in different measurements. Large deviations in the measurement mean that it is a bad metric.
  • Comparable. The measured data should be comparable across different systems. Software engineers can use the metric to compare the different systems.
  • Reasonable. The metric should help reason the causality of software stack behavior. In other words, the metric should be mapped to the software behavior and it should be possible to be computed based on software stack execution.
  • Verifiable. The metric can be used to verify an optimization. The measured result before and after the optimization should reflect the change of the user experience.
  • Automatable. For software engineering purposes, we expect the metric can be measured largely unattended. This is especially useful in regression tests or pre-commit tests. This criterion is not strictly required though, because it is not directly related to user experience analysis and optimization.

Guided by the measurement criteria, we focus on the following complementary aspects of the user experience, how a user controls a device and how a device reacts to a user. How a user controls a device has mainly two measurement areas:

  • Accuracy/fuzziness. It evaluates what accuracy, fuzziness, resolution, and range are supported by the system for inputs from the touch screen, sensors, and other sources. For example, how many pressure levels are supported by the system, how the sampled touch events' coordinates are close to the fingertip move track on the screen, how many fingers can be sampled at the same time, and so on.
  • Coherence. It evaluates the drag lag distance between the fingertip and the dragged graphic object in the screen. It also evaluates the coherence between the user operations and the sensor-controlled objects, such as the angle degree difference between the tilting controlled water flow and the device oblique angle.

How a device reacts to a user also has two measurement areas:

  • Responsiveness. It evaluates the time between an input being delivered to the device and device showing visible response. It also includes the time spent to finish an action.
  • Smoothness. This area evaluates graphic transition smoothness with the maximal frame time, frame time variance, FPS, frame drop rate, and so forth. As we have discussed, FPS alone cannot accurately reflect the user experience regarding smoothness.

For these four measurement areas, once we identify a concrete metric to use, we need to understand how this metric is related to a "good" user experience. Since user experience is a subjective topic that highly depends on human being's physiological status and personal taste, there is not always scientific conclusion about what value of a metric constitutes a "good" user experience. For those cases, we just adopt the industry experience values. The Table 1 gives some examples of the industry experience values.

Best Good Acceptable
Response Delay ≤ 100 ms ≤ 200 ms ≤ 500 ms
Graphics Animation ≥ 120 fps ≥ 60 fps ≥ 30 fps
Video Playback ≥ 60 fps ≥ 30 fps ≥ 20 fps

Table 1 - example Industry experience Values for user experience (Source: Intel Corporation, 2011)

Due to human nature, there are two notes for software engineers to pay attention to the user experience optimizations.

The value of a metric usually has a range for "good" user experience. A "better" value than the range does not necessarily bring "better" user experience. Anything beyond the range limit could be invisible to the user.

The values here are only rough guidelines for common cases with typical people. For example, a seasoned game player may not be satisfied with the 120 fps animation. On the other hand, a well-designed cartoon may bring perfect smoothness with 20 fps animation.

Now we can set up our methodology for user experience optimization. It can be summarized into following steps.

Step 1: Receive the user experience sightings from the users or identify the interaction issues with manual operations. This can be assisted by high-speed camera or other available techniques.

Step 2: Define the software stack scenarios and metrics that transform a user experience issue into a software symptom.

Step 3: Develop a software workload to reproduce the issue in a measureable and repeatable way. The workload reports the metric values that reflect the user experience issue.

Step 4: Use the workload and related tools to analyze and optimize the software stack. The workload also verifies the optimization.

Step 5: Get feedback from the users and try more applications with the optimization to confirm the user experience improvement.

Based on this methodology, we have developed an Android Workload Suite (AWS)[REF-33] that includes almost all the typical use cases of an Android device. We have also developed a toolkit called UXtune[REF-34] that assists user interaction analysis in the software stack. Different from the traditional performance tuning tools, UXtune correlates the user-visible events and the system low- level events. As the next step, we are porting the work from Android to other systems.

Mobile OS Design for User Experience

Based on our experience with Android, we found the UX optimization is somehow similar to parallel application optimization, only with more complexities, for the following four reasons:

  • UX involves multiple hardware components, and multiple software processes, and their interactions;
  • UX on a client device has to consider the power consumption, because UX also includes the battery life and device temperature.
  • UX has precise timing requirements, such as smoothness where the user expects no frame time variance. Neither faster nor slower is acceptable; hitting the exact time point is required. This point is more like a real-time requirement.
  • UX has some subjective factors one has to consider in mobile OS design, such as whether some animation is just a hint or essential to UX, and whether the system can drop some frames to get better response.

One critical lesson learned from our experience is to understand the critical path of certain operations. Different from parallel application tuning, mobile OS design does not always have strict explicit synchronization between the involved hardware components and software threads. For example:

  • Every application uses an event loop to handle requests. When a thread A has a request to another thread B, it may not directly invoke the function, but instead posts a message to thread B. The message is then queued in the event loop of thread B waiting for handling. It is then out of the caller's control how fast the event could be handled if there are multiple events in the queue.
  • Another example is when a thread A executes a sequence of operations and then posts a message to another thread B for follow-up actions and response to the user. Not all the sequence of the operations by thread A must be done in order. For example, it could post the message to thread B as early as possible so that thread B can respond to the user earlier.

The major point regarding power is that, with user experience, faster is not necessarily better—contrary to traditional performance optimizations. When the system already reaches the user-perceivable best responsiveness, next step optimization is the slower the better within the best perceivable range. For example, if a game has 60 fps without problem, then the mobile OS should try to get both the CPU utilization and CPU frequency as low as possible. We have to always distinguish the two cases (faster-better and slower-better) clearly. The optimization techniques for the two cases can be quite different.

When multiple cores and GPUs are introduced, the two cases above become more obvious. More cores help "faster-better" scenarios usually, but hurt battery life for "slower-better" scenarios. A mobile OS has to turn on and off the cores with a smart policy, because the on/off latency could be longer than a touch operation (usually in a range of 100 ms to 500 ms).

For parallel application performance tuning, people found "execution replay" useful in debugging. It is usually one multithreaded application reply, that is, within one process. For UX, the interactions cross processes through IPC, and between CPU, GPU, and display controller (DC), are a whole-system-wide collaboration. The traditional replay technique cannot help.





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