Click here to close now.


Java IoT Authors: Deep Bhattacharjee, Liz McMillan, Anders Wallgren, Greg O'Connor, Tim Hinds

Related Topics: Java IoT

Java IoT: Article

Turbo-Charging Java for Real-Time Applications

Accelerating code execution

The Java platform is usually perceived as inadequate for real-time applications because of its lack of determinism, that is, its unpredictable execution time.

For example, garbage collection (GC), which removes no-longer-needed Java objects and reduces memory overhead, may automatically and transparently freeze the system from time to time. Such behavior is obviously unacceptable in the real-time world. (A commonly recognized goal of real-time computing is to meet an application's time constraints.)

To address this issue, new Java Virtual Machines (JVM) are being developed (e.g., JVM with concurrent GC). In addition, a new Real-Time Specification for Java (RTSJ, JSR-001) has been finalized.

Unfortunately, these solutions achieve predictability to the detriment of performance. For example, concurrent GC is less efficient than "stop the world GC" (which requires total CPU usage), and the memory model advocated by the RTSJ requires runtime checks that impact performance.

This article examines a new solution, one that provides determinism for real-time threads and also has the positive side effect of significantly "accelerating" code execution.

The High Cost of Object Creation
Creating new objects in Java has a significant memory/CPU impact. The impact is somewhat proportional to the object size, but creating even small objects is quite expensive. The memory has to be allocated and initialized and, eventually, when the object is no longer needed, garbage collection is used to free up the memory.

Avoiding memory allocation can significantly increase the performance of your application. (The J.A.D.E. library provides an XML parser significantly faster [2x-3x] than any conventional XML parser only because it does not perform dynamic allocation.)

To minimize object creation and its associated overhead, Java programmers can:

  • Use primitive types: For example, using primitive type "double" is 10 times faster and requires one-third less memory than creating instances of class java.lang.Double.
  • Use the "return value" parameter technique: The basic idea is to avoid object creation by passing a local static object to a function. The function returns this extra parameter after modifying its state to correspond to the desired value. Numerous examples of this technique can be found in the Java standard library (for example, Component.getLocation(Point rv)).
Both of these approaches are error-prone, however. Java primitive types cannot be strongly typed, and the "return value" parameter has to be mutable (modifiable at runtime), which is inherently unsafe (see "Item 13: Favor Immutability," in Effective Java Programming Language Guide by Joshua Bloch for a detailed explanation). Also, the "return value" approach unnecessarily increases the number of Java methods because conventional functions "without" the additional parameter are still provided (for example, Component.getLocation()). To Sun's credit, it should be mentioned that the expense is mitigated quite a bit in their latest versions of Java compared to, say, 1.3.x, especially for short-lived, small objects - thanks to the HotSpot Generational Collector. It may be worthwhile to point this out, even if the improvements don't scale to the same speed as object reuse.

A Real-Time Solution for All Virtual Machines
Garbage collection occurs when memory is being allocated. Therefore, if "new" ready-to-use objects exist (and need not be allocated/initialized because they have been recycled), the memory/CPU is not stressed. As a result, code execution:

  1. Is faster
  2. Is not interrupted by the garbage collector (thereby providing more predictable scheduling)
  3. Has no assignment constraint, as all objects originate from the heap (see RTSJ assignment rules where heap objects cannot refer to scoped objects [JSR-001, pg. 8])
All Java Virtual Machines work in a "heap context" where objects are allocated on demand ("new") and recycled through garbage collection. To support object "recycling" in a transparent manner, we could either use some reference-counting mechanism or work with thread stacks. Due to possible circularities in the general case, the first approach is difficult to implement. The second approach is easier and faster, but the application has to ensure that stack objects are not referenced anymore after the stack is "popped." Fortunately, this risk can be greatly mitigated in practice (using the export method, as we'll see later), which makes this approach far more attractive as a general purpose solution.

Context Programming to the Rescue
Often the same piece of code might have to behave differently based on some thread-locale information. It's not always practical to pass this information as extra parameters to the methods' calls. For example, arithmetic operations might depend on a common modulo number or concurrent threads might log information in separate files. For such situations, the open source J.A.D.E. library defines specific zones called Context, where threads may execute independently from each other (see Java Addition to Default Environment, The scope of a context is defined by a try-finally block statement, which starts with a static enter call and ends with a static exit call, the class name identifying the type of context; for example:

LocalContext.enter(); // Context used for local setting.
} finally {

Context can be nested; it inherits the setting/behaviors of its outer contexts (unless these setting/behaviors are mutually exclusive). This characteristic also applies to concurrent threads executed while in the context's scope (see Listing 1).

Context programming is somewhat complementary to aspect-oriented programming. Whereas context programming is dynamic by nature (thread based), AOP is typically code based (AspectJ tool/compiler). Both can be used in conjunction to insert custom context code automatically.

The Pool Context
This context implements the "stack" approach mentioned earlier. It ensures that most of the CPU is used to perform the actual task and not maintenance tasks such as memory allocation and garbage collection. In other words the CPU is used at its maximum efficiency.

Pool contexts allow objects to be recycled so that after the pool/stack of recycled objects gets large enough, no memory allocation need ever be performed.

As far as the application is concerned, pool objects need not be mutable; in fact, it's better (safer) if they are immutable. Remember that within a pool context, creating immutable objects is as efficient as reusing mutable objects.

All objects that have been allocated while in a pool context are recycled at the same time when the thread exits the pool context. Recycling is extremely fast and independent from the number of objects allocated (a lot faster than GC). (Recycling is almost instantaneous; it basically consists of resetting the pool/stack's pointers.)

Listing 2 illustrates how pool contexts can be used to accelerate calculations on multiple inputs.

As you can see in Listing 2, it may be necessary to export important results from the current pool context to the outer context to keep these results from being overwritten after the pool objects are recycled. In most cases, the only object that needs to be exported is the result of the operation; all intermediate/temporary objects can be ignored (they are automatically recycled).

No Garbage Collection Ever
For some, a real-time application being interrupted by the garbage collector and consequently missing a deadline is simply not acceptable (considered a critical error in hard real time). Fortunately, by using pool contexts it's relatively easy to avoid running the garbage collector.

There will be no garbage collection ever as long as all your threads run in a pool context, only static constants are exported to the heap, and your system state can be updated without allocating new objects (e.g., StringBuffer instead of string or FastMap instead of HashMap) (see Figure 1). (FastMap class, unlike HashMap, does not allocate a new entry each time a new object is added to the collection.)

For concurrent access/modification of the system state, the use of a reentrant lock is recommended, such as com.dautelle.util.ReentrantLock or the new (JDK1.5) java.util.concurrent.locks.ReentrantLock. Provided that factory methods are used instead of the new keyword for object creations, most of the application code is oblivious of the garbage collection issue. (The new keyword always allocates on the heap. The J.A.D.E. library cannot/does not change the virtual machine behavior with regards to class instantiation.) Particular care should be taken with some JDK library methods that may allocate temporary objects onto the heap at each call (setup/initialization heap allocations are okay), and therefore should be avoided or replaced by cleaner classes (e.g., TypeFormat [J.A.D.E. class: com.dautelle.util.TypeFormat] for parsing/formatting of primitive types). Listing 3 provides an example of a real-time handler processing UDP messages

A Nice Side-Effect: Increase of Execution Speed
The cost of allocating an object on the heap is somewhat proportional to that object's size. The cost of reusing an object, however, is independent of its size. In other words, the larger the object, the more performance gain you can expect from using a pool context. For example, adding 1024-bits immutable integers is up to five times faster (LargeInteger versus BigInteger, J.A.D.E. benchmark results). The high performance associated with pool contexts is due not only to object reuse but also to a more efficient use of the CPU internal cache (cache hits are a lot more frequent when objects are being reused).

Recycling objects is more powerful than just recycling memory (a.k.a. GC). It's particularly true for objects requiring some CPU-intensive setup at initialization (e.g., preallocated linked lists or tables). Unlike hardware recycled objects, software recycled objects are as good as new.

The strength of Java resides mostly in its comprehensive library. Unfortunately, the Java API may allocate temporary objects on the heap, which may annihilate the performance gained from using pool contexts (if you save 100 allocations, that's good…but if the API does 1,000 allocations in the process of running your code, saving 100 allocations isn't as big a gain as might be imagined). One solution is for the JVM to support pool contexts, making the new keyword context-sensitive. This change would be backward compatible, as the default context is the heap context. Then the whole Java API would be more deterministic and execute faster.

Concurrent Context: Harnessing Hyper-Threading and Multiprocessors Potential
With the JDK1.5 Tiger release, a significant effort has been accomplished with regard to concurrent programming. Still, the JDK1.5 concurrency packages (java.util.concurrent, java.util.concurrent.atomic, and java.util.concurrent.locks) rely on the dynamic creation of new threads in order to take advantage of concurrent algorithms, which is usually a no-no in the real-time world. Furthermore, it's inefficient for low-level libraries (too much overhead) and synchronization can be tricky.

To address this particular issue, a concurrent context has been created. It allows real-time applications to take advantage of parallel algorithms on multiprocessor cards or even single processors with hyper-threading technology without creating new threads. (HyperThreading doubles the number of executing threads per processor.) This objective is achieved by maintaining a limited number of threads on stand-by. These threads can then be utilized on demand to perform concurrent executions. If all concurrent threads are busy, the current thread executes the concurrent operation itself. Concurrent context is easy to use, provides automatic load-balancing between processors with almost no overhead, and does not require any synchronization code as the parent thread is not allowed (blocks on the exit() call) to exit its concurrent context until all concurrent executions are complete. As soon as a concurrent thread completes its execution, it becomes available again for more, resulting in concurrent threads/processors being busy most of the time. Last but not least, concurrent contexts guarantee the same behavior whether or not the execution is performed by the current thread or a concurrent thread, granted that the concurrent execution's order has no impact on the behavior. In particular, any exception raised by a concurrent thread is propagated to the parent thread and concurrent threads execute in the same context as their parent.

try {
} finally {
   ConcurrentContext.exit(); // Waits for all concurrent threads
}    // to complete.

Direct Memory Access: Struct and Union
It's not rare for real-time/embedded projects to use Java and C/C++ together. By mixing them, projects get the best of both worlds: the high-performance of C/C++ with the rapid development cycle typically associated with Java.

Until recently data exchange was problematic as the storage layout of Java objects is not determined by the compiler. The layout of objects in memory is deferred to runtime and determined by the interpreter (or just-in-time compiler). This approach allows for dynamic loading and binding, but also makes interfacing with C/C++ code difficult.

This particular issue has been addressed in the form of two public domain classes: Struct and Union. These two classes mimic the C struct and union types. They follow the same alignment rules, support the same features (e.g., bit fields, packing), and make it extremely easy to convert C header files to Java classes (one-to-one mapping).

Using these classes, embedded systems can map Java objects to a physical address to control hardware devices or communicate through shared memory with external apps.

Garbage collection is not the only issue preventing Java from being used for a real-time system. Other issues include thread scheduling, accurate timer, synchronization overhead, lock queuing order, class initialization, and maximum interrupt response latency. Until now it has definitively been a "stopper." Because of it, most real-time systems today are developed in C/C++ despite the existence of Java compilers.

The good news is that whereas before you had to use C/C++ and some real-time OS, now you can use GCJ/J.A.D.E. and the same real-time OS (with JNI/Struct for the interface).

Pool contexts are a substitute for the complicated memory model of the RTSJ. The concept of scoped memory and immortal memory and how to transfer data between these areas leads to a cumbersome programming style. And the runtime checks for this model are a real performance killer. However, to see the full advantage of this approach for real time, you need a real-time kernel. Since the RTSJ (implemented as Reference Implementation or jRate) is the only available Java real time, it would be interesting to see some results on top of it.


  • J.A.D.E. Real-Time FAQ:
  • RTJ API:
  • Ajile RTJ chips:
  • JStamp:
  • Restriction of Java for Embedded Real-Time Systems:
  • The Real-Time for Java Expert Group:
  • Brosgol, B., et al. (2000). The Real-Time Specification for Java. Addison-Wesley.
  • RTSC (JSR-001):
  • Comments (3) View Comments

    Share your thoughts on this story.

    Add your comment
    You must be signed in to add a comment. Sign-in | Register

    In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.

    Most Recent Comments
    Pat 08/04/04 10:18:39 AM EDT

    If a real-time Java VM is what you need and you absolutely must have both determinism AND performance... Take a look at PERC from Aonix/NewMonics ( These guys have been doing this from the beginning and have the best set of tools for building real-world RTJ apps.

    larry 08/03/04 10:40:01 PM EDT

    Interesting article but the author is out of date w.r.t. current state of the art with RTSJ.

    I implemented a RTSJ for J2SE on solaris based on a 1.4.1 codebase. While there was some performance degradation on runtime checks. It was less than 15% for a 1.4.1 VM. It''s real time determinacy characteristics were comparable to and in some cases exceeded many realtime OS''s.

    The algorithm we used for managing the checks between heap, immortal, and scoped memory was very efficient and can be found in the literature.

    With a well constructed commercial grade RTSJ VM performance is very good. One should not rely on the reference implementation to base viability estimations of the technology. The reference implementation is designed for correctness and was not intended for performance measurements.

    Anthony Berglas 07/13/04 06:05:09 AM EDT

    Does 1.4 optimize out alloctions for inlined value parameters? Eg. Does the following actually create any garbage?

    Foo foo() {return new Foo(123)}
    while (true) { // tight loop
    Foo f = foo()
    // no references to f or things in f here.

    (But either out/byref parameters or being able to return multiple values at once should have been added to Java long ago!)

    @ThingsExpo Stories
    Cloud computing delivers on-demand resources that provide businesses with flexibility and cost-savings. The challenge in moving workloads to the cloud has been the cost and complexity of ensuring the initial and ongoing security and regulatory (PCI, HIPAA, FFIEC) compliance across private and public clouds. Manual security compliance is slow, prone to human error, and represents over 50% of the cost of managing cloud applications. Determining how to automate cloud security compliance is critical to maintaining positive ROI. Raxak Protect is an automated security compliance SaaS platform and ma...
    Today air travel is a minefield of delays, hassles and customer disappointment. Airlines struggle to revitalize the experience. GE and M2Mi will demonstrate practical examples of how IoT solutions are helping airlines bring back personalization, reduce trip time and improve reliability. In their session at @ThingsExpo, Shyam Varan Nath, Principal Architect with GE, and Dr. Sarah Cooper, M2Mi’s VP Business Development and Engineering, explored the IoT cloud-based platform technologies driving this change including privacy controls, data transparency and integration of real time context with p...
    The Internet of Things (IoT) is growing rapidly by extending current technologies, products and networks. By 2020, Cisco estimates there will be 50 billion connected devices. Gartner has forecast revenues of over $300 billion, just to IoT suppliers. Now is the time to figure out how you’ll make money – not just create innovative products. With hundreds of new products and companies jumping into the IoT fray every month, there’s no shortage of innovation. Despite this, McKinsey/VisionMobile data shows "less than 10 percent of IoT developers are making enough to support a reasonably sized team....
    Just over a week ago I received a long and loud sustained applause for a presentation I delivered at this year’s Cloud Expo in Santa Clara. I was extremely pleased with the turnout and had some very good conversations with many of the attendees. Over the next few days I had many more meaningful conversations and was not only happy with the results but also learned a few new things. Here is everything I learned in those three days distilled into three short points.
    DevOps is about increasing efficiency, but nothing is more inefficient than building the same application twice. However, this is a routine occurrence with enterprise applications that need both a rich desktop web interface and strong mobile support. With recent technological advances from Isomorphic Software and others, rich desktop and tuned mobile experiences can now be created with a single codebase – without compromising functionality, performance or usability. In his session at DevOps Summit, Charles Kendrick, CTO and Chief Architect at Isomorphic Software, demonstrated examples of com...
    As organizations realize the scope of the Internet of Things, gaining key insights from Big Data, through the use of advanced analytics, becomes crucial. However, IoT also creates the need for petabyte scale storage of data from millions of devices. A new type of Storage is required which seamlessly integrates robust data analytics with massive scale. These storage systems will act as “smart systems” provide in-place analytics that speed discovery and enable businesses to quickly derive meaningful and actionable insights. In his session at @ThingsExpo, Paul Turner, Chief Marketing Officer at...
    In his keynote at @ThingsExpo, Chris Matthieu, Director of IoT Engineering at Citrix and co-founder and CTO of Octoblu, focused on building an IoT platform and company. He provided a behind-the-scenes look at Octoblu’s platform, business, and pivots along the way (including the Citrix acquisition of Octoblu).
    In his General Session at 17th Cloud Expo, Bruce Swann, Senior Product Marketing Manager for Adobe Campaign, explored the key ingredients of cross-channel marketing in a digital world. Learn how the Adobe Marketing Cloud can help marketers embrace opportunities for personalized, relevant and real-time customer engagement across offline (direct mail, point of sale, call center) and digital (email, website, SMS, mobile apps, social networks, connected objects).
    The Internet of Everything is re-shaping technology trends–moving away from “request/response” architecture to an “always-on” Streaming Web where data is in constant motion and secure, reliable communication is an absolute necessity. As more and more THINGS go online, the challenges that developers will need to address will only increase exponentially. In his session at @ThingsExpo, Todd Greene, Founder & CEO of PubNub, exploreed the current state of IoT connectivity and review key trends and technology requirements that will drive the Internet of Things from hype to reality.
    Two weeks ago (November 3-5), I attended the Cloud Expo Silicon Valley as a speaker, where I presented on the security and privacy due diligence requirements for cloud solutions. Cloud security is a topical issue for every CIO, CISO, and technology buyer. Decision-makers are always looking for insights on how to mitigate the security risks of implementing and using cloud solutions. Based on the presentation topics covered at the conference, as well as the general discussions heard between sessions, I wanted to share some of my observations on emerging trends. As cyber security serves as a fou...
    We all know that data growth is exploding and storage budgets are shrinking. Instead of showing you charts on about how much data there is, in his General Session at 17th Cloud Expo, Scott Cleland, Senior Director of Product Marketing at HGST, showed how to capture all of your data in one place. After you have your data under control, you can then analyze it in one place, saving time and resources.
    With all the incredible momentum behind the Internet of Things (IoT) industry, it is easy to forget that not a single CEO wakes up and wonders if “my IoT is broken.” What they wonder is if they are making the right decisions to do all they can to increase revenue, decrease costs, and improve customer experience – effectively the same challenges they have always had in growing their business. The exciting thing about the IoT industry is now these decisions can be better, faster, and smarter. Now all corporate assets – people, objects, and spaces – can share information about themselves and thei...
    The cloud. Like a comic book superhero, there seems to be no problem it can’t fix or cost it can’t slash. Yet making the transition is not always easy and production environments are still largely on premise. Taking some practical and sensible steps to reduce risk can also help provide a basis for a successful cloud transition. A plethora of surveys from the likes of IDG and Gartner show that more than 70 percent of enterprises have deployed at least one or more cloud application or workload. Yet a closer inspection at the data reveals less than half of these cloud projects involve production...
    Continuous processes around the development and deployment of applications are both impacted by -- and a benefit to -- the Internet of Things trend. To help better understand the relationship between DevOps and a plethora of new end-devices and data please welcome Gary Gruver, consultant, author and a former IT executive who has led many large-scale IT transformation projects, and John Jeremiah, Technology Evangelist at Hewlett Packard Enterprise (HPE), on Twitter at @j_jeremiah. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.
    Discussions of cloud computing have evolved in recent years from a focus on specific types of cloud, to a world of hybrid cloud, and to a world dominated by the APIs that make today's multi-cloud environments and hybrid clouds possible. In this Power Panel at 17th Cloud Expo, moderated by Conference Chair Roger Strukhoff, panelists addressed the importance of customers being able to use the specific technologies they need, through environments and ecosystems that expose their APIs to make true change and transformation possible.
    Too often with compelling new technologies market participants become overly enamored with that attractiveness of the technology and neglect underlying business drivers. This tendency, what some call the “newest shiny object syndrome” is understandable given that virtually all of us are heavily engaged in technology. But it is also mistaken. Without concrete business cases driving its deployment, IoT, like many other technologies before it, will fade into obscurity.
    Microservices are a very exciting architectural approach that many organizations are looking to as a way to accelerate innovation. Microservices promise to allow teams to move away from monolithic "ball of mud" systems, but the reality is that, in the vast majority of organizations, different projects and technologies will continue to be developed at different speeds. How to handle the dependencies between these disparate systems with different iteration cycles? Consider the "canoncial problem" in this scenario: microservice A (releases daily) depends on a couple of additions to backend B (re...
    The Internet of Things is clearly many things: data collection and analytics, wearables, Smart Grids and Smart Cities, the Industrial Internet, and more. Cool platforms like Arduino, Raspberry Pi, Intel's Galileo and Edison, and a diverse world of sensors are making the IoT a great toy box for developers in all these areas. In this Power Panel at @ThingsExpo, moderated by Conference Chair Roger Strukhoff, panelists discussed what things are the most important, which will have the most profound effect on the world, and what should we expect to see over the next couple of years.
    Container technology is shaping the future of DevOps and it’s also changing the way organizations think about application development. With the rise of mobile applications in the enterprise, businesses are abandoning year-long development cycles and embracing technologies that enable rapid development and continuous deployment of apps. In his session at DevOps Summit, Kurt Collins, Developer Evangelist at, examined how Docker has evolved into a highly effective tool for application delivery by allowing increasingly popular Mobile Backend-as-a-Service (mBaaS) platforms to quickly crea...
    Growth hacking is common for startups to make unheard-of progress in building their business. Career Hacks can help Geek Girls and those who support them (yes, that's you too, Dad!) to excel in this typically male-dominated world. Get ready to learn the facts: Is there a bias against women in the tech / developer communities? Why are women 50% of the workforce, but hold only 24% of the STEM or IT positions? Some beginnings of what to do about it! In her Day 2 Keynote at 17th Cloud Expo, Sandy Carter, IBM General Manager Cloud Ecosystem and Developers, and a Social Business Evangelist, wil...