Luna: a Flexible Java Protection System

Chris Hawblitzel, Dartmouth College

Thorsten von Eicken, Expertcity, Inc.


Extensible Java systems face a difficult trade-off between sharing and protection. On one hand, Java's ability to run different protection domains in a single virtual machine enables domains to share data easily and communicate without address space switches. On the other hand, unrestricted sharing blurs the boundaries between protection domains, making it difficult to terminate domains and enforce restrictions on resource usage. Existing solutions to these problems restrict sharing in an ad-hoc fashion, ruling out many desirable programming styles.

This paper presents an extension to Java's type system that systematically addresses the issues of data sharing, revocation, thread control, and resource control. Multiple tasks running in a single virtual machines share data using special remote pointers, which have different types from local pointers. The distinction between local and remote pointers allows the Java run-time system to mediate the communication between tasks without slowing down operations on ordinary pointers. The extensions to Java are implemented by a system called Luna, based on the Guavac and Marmot compilers, extended with special optimizations to support both fast inter-task communication and dynamic access control. The paper describes two applications written in Luna: a simple extensible web server, and an extension of the Squid web cache to support dynamic content generation.

  1. Introduction

Traditional operating systems such as Unix, Windows NT, and more recent microkernels use virtual memory to protect different programs from one another and to divide a computer's finite resources among the programs. In these systems, the process or task is the central unit of protection and resource control, and cleanly encapsulates each program's memory usage and processor usage. In recent years, language-based protection has become a viable alternative to virtual memory based protection for many types of extensible applications. For instance, the Java programming language is now used to extend browsers with applets, servers with servlets [Java], active network routers with new protocols [WGT98], databases with customizations [GMS+98], and agent systems with mobile agents [Gen]. In these systems, the language subsumes the role of a traditional operating system, and is responsible for protecting programs from one another.

Language-based protection relies on the safety of a programming language's type system to restrict the operations that a program is allowed to perform. The language provides a set of types (integers, functions, and records, for instance), and operations that can manipulate instances of different types. Some operations make sense for some types but not others. For instance, a Java program can invoke a method of an object, but it cannot perform a method invocation on an integer.

Type-safe languages implement capability based access control very naturally: a pointer (also called a reference in Java jargon) cannot be forged, and can therefore serve as a capability. Languages typically provide additional access control mechanisms, such as Java's private, default, protected, and public, and final access qualifiers that specify which code has access to which fields and methods of an object. Wallach et al [WBD+97REF] discusses Java access control mechanisms in detail.

Safe languages hold many potential advantages over virtual memory based systems: fine-grained sharing, no expensive address space switches, natural capability-based access control, abstract datatype enforcement, and code portability. Unfortunately, this list of language protection advantages is matched by an equally long list of drawbacks. The performance of safe languages tends to lag behind lower level languages like C, typical language-based systems support only one language, and relying on a language for protection requires trusting that the language's compiler (the just-in-time compiler in Java's case) and run-time system are written correctly. Other research has made great strides in attacking these problems ([BSP+95], [HLP98], [MWC+98], [NL98], [Sha97]). This paper focuses on a different set of problems, arising from the lack of OS features and structures in current language-based systems.

For example, early Java systems suffered from unexpected interactions between data, threads, and code. Consider Java's Thread.stop and ThreadGroup.stop methods, which asynchronously terminate a thread or group of threads. These would seem ideal for stopping a runaway applet that is consuming all of a browser's CPU time and memory. However, closer inspection reveals several difficult problems:

Sun deprecated the Thread.stop and ThreadGroup.stop methods, leaving no officially sanctioned way to stop an applet [Javb]. The Java Community Process is currently considering a new API for isolating protection domains, which would prevent the direct sharing of objects and threads between domains. In this case, it is much easier to terminate an applet and reclaim its resources. While a better isolation mechanism is a clear step forward for many applications, these applications still require communication between programs. The design of the communication mechanism is the key to obtaining the promised advantages of language-based protection, such as fine-grained sharing.

The rest of this paper discusses existing research on combining isolation and communication, and then attempts to synthesize desirable features of different approaches into a single model, where tasks implement isolation and remote pointers enable communication between tasks. The paper describes a type system for remote pointers, and then describes how tasks and remote pointers are implemented and optimized in an extension to Java that we call Luna. Luna is implemented as a source-level extension to the Guavac source-to-bytecode compiler, which produces special Luna bytecode that runs on an extension to the Marmot, a sophisticated optimizing Java virtual machine [FKR+99]. The extensions to the source language are small, while the extensions to Marmot's run-time system are more involved. Note that tasks and remote pointers would apply to languages besides Java; an earlier paper [HvE99] describes the application of these ideas both in a C-like procedural language with modules and as a formal extension of the typed lambda calculus for which we proved soundness.

  1. Balancing sharing and isolation: existing approaches

    The two goals of sharing and isolation conflict with each other, and building a system to support both requires compromises. For example, DrScheme/MrEd [FFK+99] restricts communication to a hierarchical parent-to-child pattern, providing no support for peer-to-peer sharing (e.g. applet-to-applet or agent-to-agent sharing), in order to allow a parent to cleanly terminate a child. DrScheme's approach also leaves much of the responsibility for clean termination to the programmer. For example, the parent is expected to explicitly relinquish pointers to objects in a child domain in order to avoid damaged objects and ensure memory reclamation; MrEd's "custodians" do not do this automatically.

    At the opposite extreme, the J-Kernel [HCC+98] allows communication between arbitrary protection domains (even mutually suspicious domains), but only allows the domains to share special "capability" objects through which they can perform remote method invocations. The system ensures that these capability objects are revocable, so that when a protection domain is shut down, the capabilities that it exported are revoked and the domain's memory is reclaimed. Bryce et al [BR00] describe a similar "object space" model that makes it easier to share indirect references to objects, but still does not allow direct sharing of arrays or object fields. The Java Community Process is also considering a mechanism to let isolated programs pass data by copy. Unfortunately, these heavyweight approaches abandon many of the advantages of lightweight protected sharing, and rule out programming styles based on shared memory.

    In contrast with the J-Kernel and object space approaches, KaffeOS [BHL00] is oriented towards a shared memory style of programming rather than a remote method invocation style. Two domains can hold pointers to the same object in a "shared heap", so that they can share object fields and array elements directly. However, an untrusted domain is not allowed to place an arbitrary object in a shared heap: such an object's methods might contain arbitrary, untrusted code, and invoking the method would let one domain's thread call another domain's code (causing the problems with domain termination described in the previous section).

  2. Remote pointers and the task model

    All the systems in the previous section strive to draw clear boundaries between different domains. This common goal motivates DrScheme's parent-to-child communication model, the J-Kernel's restriction on object sharing, and KaffeOS's restriction on shared object types.

    We propose expressing the domain boundaries explicitly in Luna's type system, in order to build a system that supports object-oriented remote method invocations, shared memory, and arbitrary peer-to-peer communication in a natural way. In our approach, a single virtual machine will contain many tasks, each with its own objects, threads, and code. Inter-task communication is organized around the concept of a revocable remote pointer, which is built into Luna's type system. Pointers from one task to an object in another task have a special type indicated by a tilde (e.g. "String~", "Hashtable~"). These remote pointers are dynamically revocable, so that when a task is shut down, pointers into the task are revoked, and the task's objects, code and threads are safely deallocated. This allows fine-grained sharing between arbitrary tasks to coexist with clean termination, effective thread and resource control, and powerful optimization, as discussed in the following subsections.

    Resource control

    When objects are shared at a fine granularity, which programs should be charged for the objects and how can memory usage be tracked? One solution is to charge for all the objects reachable from a program's roots. While this may work in traditional operating systems with fixed sized shared memory buffers of "raw data", it is dangerous for abstract data types and pointer based data structures. Program A can give program B a single object with some private field pointing to all of A's private data, and program B gets charged for all of A's data. Fine-grained sharing looks less pleasant when any shared object can act as a resource Trojan horse. Luna offers a simple alternative: a task pays only for those objects that it explicitly allocates with the Java new operator; it is not charged for objects allocated by other tasks.


    In ordinary Java, pointers can serve as capabilities, but once a program is given a pointer to an object, that pointer cannot be revoked. Why would anyone want to revoke a capability? Revocation assists termination: Luna revokes all remote pointers into a task when the task is terminated. This neatly solves the damaged object problem: a dead task may contain damaged objects, but revocation ensures that these objects are not accessible from other tasks. Revocation also helps to implement the principle of least privilege, since it is better to give someone access to something for only the necessary duration and then revoke the access than to give them access forever. Revocation accommodates changing preferences over time. An agent once considered trustworthy may abuse the trust and necessitate revoking its privileges. At a lower level, revocation is a good way to give someone temporary, fast access to a resource, such as idle network buffer space or a rectangle in video memory. In addition to these device-specific examples, revocation is also used in general purpose operating system mechanisms: for instance, FBufs [DP93] dynamically revoke write access to buffers when data is transferred between protection domains.

    Revocation has been a historical problem for capability systems [WLH81], but it is particularly difficult at the language level. While adding a level of indirection to every capability maybe a way to implement revocation in an operating system with coarse-grained capabilities [Red74], adding a level of indirection to every Java object is undesirable. Luna's static distinction between local and remote pointers ensures that only pointers shared between tasks incur the overhead of supporting revocation (as described below, the high cost of locks on modern processors makes this overhead a serious concern).

    Controlling threads

    If a call from one program to another is just a function call, as it is in standard Java, the caller and callee programs share the same thread. Who is allowed to terminate or suspend the shared thread? In current Java browsers, an applet can call the browser and then kill the thread while the browser is in the middle of a sensitive operation. Luna, on the other hand, switches threads during a method invocation on a remote pointer, so that the caller and callee each have their own threads, which is similar to IPC and RPC mechanisms in traditional operating systems. Luna's remote pointer types statically signal which method invocations are on local objects (needing no thread switch) and which are cross-task method invocations on remote objects.

    Reasoning about the system structure

    Analyzing the security of a system requires analyzing the communication patterns between programs. Limited, well-defined communication channels make this easy; unconstrained fine-grained sharing makes a mess. Luna's remote pointer types statically mark the boundaries between tasks, and thus form a static blueprint of the system's cross-task communication patterns.


    While dynamic loading is a useful tool for system extensibility, many optimization techniques rely on global information (such as "this method is never overridden and may therefore be inlined") that may be invalidated as code is dynamically loaded. One way to reconcile these whole-program optimizations with dynamic loading is to undo optimizations as necessary when new code is loaded. Suppose a browser uses the Java class Vector, but never overrides the methods of this class. In this situation, the browser may inline method invocations on objects of type Vector. If a newly loaded applet introduces a class (say, SortedVector) that overrides these methods, however, the browser's code must be dynamically recompiled to remove the inlining, because the applet might pass a SortedVector object to the browser, and the browser might invoke one of the object's overridden methods. Dynamic recompilation is complicated to implement, requiring close cooperation between the compiler and run-time system. Moreover, in a language-based protection system, where multiple programs are loaded into a single environment, it penalizes one program for the actions of another program. Why should a browser have to undo its internal optimizations because of code contained in a dynamically loaded applet? It is difficult to predict the performance a program when its optimization depends on other programs' code.

    Luna solves this problem by making the task the fundamental unit of loading, instead of loading classes individually. This enables "whole-task optimizations" of operations on local pointers, so that Luna is able to exploit Marmot's inlining and static method binding. In the example above, the browser would continue to make inlined calls to its own Vector objects, because a local pointer of type Vector cannot point to the applet task's SortedVector objects. The only Marmot whole-program optimization that Luna does not implement is object stack allocation, which adds extra method table entries to some classes (Luna must ensure that method tables and field layouts are consistent across tasks, to support operations on remote pointers).

  3. Remote pointers

    In order to preserve the advantages of safe language protection (fine-grained sharing, low cross-task call overheads, simple capability-based access control, and enforcement of abstract data types), remote pointers support the same operations as local pointers: field/array element access, method invocation, synchronization, equality testing, casting, and instanceof testing, although most of these operations have different semantics and performance for remote pointers. As Figure 1 indicates, there is one remote pointer type for each class/interface type and array type. For convenience, we will refer to the objects pointed to by local and remote pointers as "local objects" and "remote objects" respectively.

    Type = PrimitiveType | ReferenceType | ReferenceType~

    PrimitiveType = boolean | byte

    | short | int | long | char | float | double

    ReferenceType = ClassType | InterfaceType | Type[]

    Figure 1: Luna's type system

    The key difference between remote pointers and local pointers is revocation. Luna's task model requires that remote pointers into a task be revoked when the task is killed, but the previous section argues that revocation is also useful at a finer granularity. To realize these uses, Luna gives the programmer a special handle with which to control access to remote pointers. This handle is a Java object called a permit. A permit is allocated in an unrevoked state, and can later be revoked:

    public class Permit {
      public Permit();
      public void revoke();

    A remote pointer is implemented as a two-word value that consists of a local pointer paired with a permit. The @ operator converts a local pointer into a remote pointer:

    Permit p = new Permit();
    String s = "hello";
    String~ sr = s @ p;

    Once a task has used the @ operator to create a remote pointer, it can pass the remote pointer to other tasks, which can use them until the remote pointer's permit is revoked. Operations on remote pointers perform a run-time access check: the expression "sr.length()" will evaluate sr's length if p is unrevoked, and raise an exception if p is revoked. Permits can selectively revoke access to data: if permit p1 is revoked while p2 is not, then s is accessible through the remote pointer (s @ p2) but inaccessible through the remote pointer (s @ p1). Note there is no way to decompose a remote pointer into its two parts: the local pointers to s and p can't be extracted from sr; this prevents other tasks from gaining direct access to them. In other words, a task's access to another task's data is always mediated by a permit ("complete mediation" is one cornerstone of a secure system [SS75][WBD+97]).

    When a task is terminated, all the permits created by the task are revoked. This mass revocation makes all of a task's objects unreachable and therefore garbage collectable. The permits stored in the remote pointers supply sufficient information for this garbage collection; Luna does not need to keep a table of all remote references. This means that remote pointer creation is extremely fast: it consists only of pairing two words together, which requires no heap allocation, lock acquisitions, or bookkeeping.

    In order to preserve the invariant that only remote pointers cross task boundaries, local pointers cannot be written to remote object fields or passed as arguments to remote method invocations, while primitive types and remote pointers can. Reads from remote objects are more flexible: a local pointer can be read from a remote object or returned from a remote method invocation, but the local pointer is automatically promoted to a remote pointer. This means that in the function below, the expression "" has type List~, not type List, because from the perspective of the second function, a local field of a remote pointer is still remote. At run-time, "" evaluates to a remote pointer containing the same permit as the remote pointer list contained. This allows a single permit to control access to the entire list, not just the first element (as would be the case with indirection-based implementations of revocation [HCC+98, Red74]). This aggregation of access control is crucial for mediating access to complex shared data structures; it would be unwieldy for a programmer to have to revoke every object in a large data structure individually.

    class List {
      int i;
      List next;
    List~ second(List~ list) {

    Remote pointers cannot be used interchangeably with local pointers. For instance, a hash table object expecting keys of type "Object" cannot be passed a remote pointer (which has type "Object~"). This forces the programmer to either design a new type of hash table that can accommodate remote keys (and can deal with their revocation robustly), or, more commonly, to make a local copy of a remote object's data and use that as the hash table key. Similarly, remote pointers, like Java's primitive types, must be boxed by the programmer to be placed in Java's standard container classes, such as Vector, that store Objects (this also serves a pragmatic implementation purpose, because remote pointers are a different size than local pointers, and are treated differently by the garbage collector).

    These restrictions on remote pointers raise questions about Luna's expressiveness–do programmers have to write two versions of every function, one for local pointers and one for remote pointers? In fact, Luna is not usually programmed this way. Instead, Luna tasks are typically very similar to programs based on remote method invocation. In the example below, the call to x.f is like a remote method invocation, except that the code explicitly copies the argument and return value, rather than using an RMI-generated stub (note: the remote keyword is an access control keyword, like publicor private; it gives other tasks the right to call the method). If desired, the remote pointer manipulation below can easily be hidden behind RMI-style interfaces.

    class X {
      remote String f(Vector~ v) {
        Vector vlocal = new Vector(v);
        return "ok";
    X~ x = ...;
    Vector~ v1 = ...;
    String s = new String(x.f(v1));

    Constructors that copy data from a remote object are common in Luna; snippets of the String constructor are shown below. Although Java has a clone method that makes a copy of any Cloneable object, Luna does not have an analogous "remote clone" operation for making local copies of remote objects. This is deliberate: suppose that someone passed a malicious subclass of Vector in as the v argument to the f method above. If we made a complete local copy of this object, we would have to import the code defined by the malicious subclass into our own task, in order to handle the local method invocations on the copied object. Luna never implicitly imports code from another task like this–this would violate the invariant that a task's threads only run the task's own code. In the example above, the expression "new Vector(v)" copies the data from v, but not the code.

    public String(String~ value) {
      char[]~ vchars = value.chars;
      for(int i = 0; i < len; i++)
        lchars[i] = vchars[i + off];

    Although Luna's style of "remote method invocation" is less automated than Java RMI's automatically generated stubs, it is more flexible. Programmers can delay copying data until it is needed, or use shared objects directly rather than copying.

    The ability to share data between tasks raises synchronization and consistency issues: what happens if a task is terminated in the middle of an operation on shared data? Although Luna doesn't have a transaction mechanism, it allows a class to acquire a remote lock on one of its own objects for reading shared data. For writing, it's often safest to make a remote call to the task owning the object, and let that task modify its own object (if a task dies while writing to its own object, the object becomes unreachable along with the rest of the task's objects).

    While remote pointers provide a revocation mechanism, they don't set a particular policy for handling revocation. It's still the application's duty to decide what to do in case of one of its communication partners becomes unreachable. Typically, well-behaved applications coordinate their actions with each other, and rely on Luna's revocation as an enforcement mechanism of last resort.

  4. Implementation

    This section describes the implementation of remote pointer operations in more detail. Operations on remote pointers require revocation checks, and the compiler and run-time system cooperate to implement these checks. For field and array accesses to remote pointers, the compiler emits code that enters a critical section, checks a flag in the remote pointer's permit, performs the access (or raises an exception, if the permit is revoked), and exits the critical section.

    Method invocations are more intricate. A method invocation on a remote pointer calls another task's code, and therefore must execute in one of the other task's threads. This thread switch allows the caller thread to be killed without abruptly terminating the callee thread. However, Luna is implemented over Win32 kernel threads, and switching kernel threads is an expensive operation. Therefore, cross-task calls only switch kernel threads lazily, when a call must be interrupted. In the normal case, a cross-task call only switches stacks, which can be done without involving Win32. To see how this works, consider a method in task A that calls a method in task B, which in turn calls a method in task C. This sequence executes in a single kernel thread, but involves 3 separate stacks. If task B is now terminated, B's stack is deallocated, C's method continues to run in the original kernel thread, and a new kernel thread is allocated which resumes A's method (and raises an exception in A's method to indicate that the call to B aborted abnormally). This model is similar to RPC models described for the Mach[FL94] and Spring[HK93] microkernels.

    In more detail, then, a cross-task method invocation enters a critical section, checks the remote pointer's permit, grabs a stack from the target task's free stack pool, exits the critical section, switches the stack pointer, pushes the arguments, and makes the call. A return from the invocation enters a critical section, returns the stack to the free pool, and exits the critical section.

    At this point, the reader would be correct to worry about the cost of entering and exiting a critical section. On an 800MHz Pentium III processor, a lock followed by an unlock, both written in hand-optimized assembly using an atomic compare and exchange instruction, takes 85 cycles when measured in a tight loop. The bottleneck is access to the bus: on a multiprocessor, an atomic operation must "lock" the bus while it executes. On a uniprocessor, the bus lock may be safely omitted, which cuts the cost of a lock/unlock sequence to 21 cycles. Because the choice of uniprocessor vs. multiprocessor has such a large impact on the cost of remote pointer accesses, we report both numbers in our benchmarks. User-level thread scheduling allows further reductions of lock costs [SCM99], but we have not implemented a user-level threads package for Luna. One last optimization is possible on a uniprocessor: remote pointer field reads (but not writes) can omit the critical section by checking the permit after reading the field. If the check finds that the permit is revoked, then the read is discarded, and if the check finds that the permit is valid, then the permit must also have been valid when the read occurred. Unfortunately, relaxed cache consistency prohibits this optimization on multiprocessors.

    Figure 2 shows the performance of local and remote field accesses, and local and remote method invocations to a function with an empty body. Both the local and remote method invocations perform a simple method table dispatch. All measurements indicate the number of cycles taken on an 800MHz Pentium III processor with 256MB of RAM and a 256K L2 cache, measured in a tight loop (note: an empty loop takes 2 cycles per iteration; this was not subtracted from the numbers below).



    remote (uniprocessor)

    remote (multiprocessor)

    field read




    field write




    method invocation




    allocate new remote pointer




    allocate, revoke, and garbage collect permit




    Figure 2: Remote pointer performance, without caching optimizations

    Allocating a new remote pointer on the stack simply pairs two words (a local pointer and a permit) together on the stack; if these words are already on the stack, then the pairing operation is essentially free. Allocating a new permit, however, requires heap allocation (which accounts for over half the cost shown in the table) and some data manipulation and synchronization to maintain the permits in a tree structure for each task.

    The cross-task invocations, while slower than local invocations, are nevertheless faster than round-trip IPC on the fastest x86 uniprocessor microkernels [LES+97], and are orders of magnitude faster than Win32 LRPC calls. Unfortunately, the field accesses that require locks are slow to the point of being useless: if shared data were always so expensive to access, it would make more sense to copy data than to share it. Luckily, standard caching and invalidation techniques apply to remote pointer accesses, because accesses to the data are more frequent than revocation of the data.

    Caching permit information

    This section describes how Luna imitates a hardware TLB to reduce the cost of repeated access checks of the same permit. However, Luna's approach differs from a hardware TLB in that a TLB operates entirely on dynamic information, while Luna takes advantage of static information to detect permit reuse. As a simple example, consider the following loop, which zeroes the elements of a remote list (using the list class defined in the previous section).

    void zero(List~ list) {
      while(list != null) {
        list.i = 0;
        list =;

    As Luna compiles this function from a typed high level representation to a typed low level representation, it adds type information to indicate repeated uses of the same permit. This information is computed using a straightforward intraprocedural dataflow analysis. In the zero loop, Luna's analysis sees that has the same permit as list, and therefore every field access to list uses the same permit as the loop runs. The typed representation generated by the analysis is similar to the type system formalized in [HvE99]: the function is quantified over a permit variable r , and the two word remote pointer values are split into separate one word pointer and permit values, of type List{r } and permit{r }, respectively:

    " r .void zero(List{r } list0, permit{r } p0) {
      List{r } list1 = list0;
      while(list1 != null) {
        list1.i = 0;
        list1 =;

    This representation allows the one word pointer and permit values to be stored in unrelated stack slots and registers, and for multiple values of type List{r } to be controlled by a single permit value. Furthermore, except for the revocation check, the list traversal can treat the list traversal like a traversal of a local list, so the "list1 =" statement need not create a new two word remote pointer each time it executes.

    A simple dataflow analysis determines that the same r is checked repeatedly in the inner loop. Based on this, Luna inserts code outside the loop to cache this permit's revocation flag in a register or stack slot, so that an access check is done with a simple test of the cached value, with no locking. At run-time, this caching code adds the current stack frame to a doubly linked list held by the permit, and removes the stack frame from the permit's list after the loop is finished. As a further optimization, each thread keeps recently cached permits in a small thread-local direct-mapped cache rather than releasing them immediately. The combined overhead of the pre-loop and post-loop caching code is about 30 cycles on a uniprocessor and 63 cycles on a multiprocessor if the permit is found in the thread's direct-mapped cache, and 125 cycles (uniprocessor) / 205 cycles (multiprocessor) if it is not. Thus, the optimizations pay off after about 1 to 3 accesses to the shared data. If the permit is revoked, it invalidates the cached information in each stack frame in its list by suspending each affected frame's thread, advancing the thread to a safe point (which Luna places at every call site and backwards branch), and then using the GC information at the safe point to determine which registers and stack slots must be modified to invalidate the cached access control information.

    Luna optimizes further under the following conditions: (i) the loop contains no call instructions, exception handlers, or other loops, (ii) the loop uses only one permit, (iii) all paths inside the loop from the loop header block back to itself check the permit for revocation. If these are satisfied (the zero loop, for example, satisfies them), Luna places safe points before the loop's revocation checks rather than at the loop's backwards branches, and then omits the code for the revocation checks entirely. If the permit is revoked, and the thread is advanced to a safe point inside the loop, then the thread is left at a point where a revocation check would have appeared, and a revocation exception is raised asynchronously in the thread at this point, so that it appears to the user that there was actually a check in the code there. Luna advances Java code to a safe point by setting breakpoints rather than by polling, so this optimization reduces the per-iteration cost of the revocation checks to zero.

    These optimizations preserve the original semantics of the Luna program: they only throw exceptions at points where the original Luna program could have thrown an exception, and precisely reflect the state of the program when the revocation exception is thrown. A call to Permit.revoke raises all the necessary exceptions before returning, so that afterwards, no threads can possibly access data using the permit (i.e.revocation is still immediate, not delayed). Stated more strongly, any possible program execution trace under these optimizations is also a legal execution trace in an unoptimized Luna implementation.

    Figure 3 shows the assembly language code generated for the body of the zero loop. While the loop body is essentially the same as a the code generated to zero a local list, the remote traversal must manipulate the permit's linked list before and after the inner loop, and the cost of this is significant: figure 4 shows the cost of repeatedly zeroing the elements of the same list, for local and remote lists of size 10 and 100. Luna's static and dynamic optimizations make the speed of these loops reasonable, though not at good as the local case.

    remote list traversal code

    loop: /* list.i = 0 (safe point) */

    mov dword ptr [eax+8],0

    /* list = (safe point) */

    mov eax,dword ptr [eax+12]

    /* if(list == 0) goto done */

    cmp eax,0

    je done

    jmp loop



    Figure 3: inner loop code for list traversal

    list size (elements)


    remote (uniprocessor)

    remote (multiprocessor)









    Figure 4: speed of local and remote list traversals

    Garbage collection

    GC information plays a part in caching/invalidation, and the garbage collector (an extension of Marmot's copying collector) treats remote pointers specially in other ways as well. When the collector traverses a remote pointer, it checks to see whether its permit was revoked. If the permit is unrevoked, then the remote pointer is treated as a strong pointer, while if the permit is null or revoked, the remote pointer is treated as a weak pointer, since the revocation makes the object semantically unreachable through the remote pointer. This allows a task's objects to be garbage collected even if there are outstanding revoked remote pointers to the objects. In particular, when a task is terminated, all of the task's permits are automatically revoked, causing all the task's objects to become collectable, so that a task's resources are reclaimed when the task is terminated.

  5. Application 1: extensible web server

    We ported a small (~4000 line) web server, originally developed for the J-Kernel, to Luna. The port changed fewer than 100 lines of existing code and added about 600 lines of new code.

    The server implements Sun's servlet API [Java]. To preserve this API, the server includes wrapper classes which lazily copy remote object data into local objects when the servlet requests the data, so that a servlet need not concern itself with remote pointers directly:

    class RemoteHttpServlet {
      Servlet servlet;
      remote HttpResponseImpl service(HttpRequestImpl~ req) {
        HttpResponseImpl rep = new HttpResponseImpl();
        servlet.service(new RemoteRequest(req), rep);
        return rep;

    The server makes a remote method invocation on the servlet's RemoteHttpServlet wrapper object, which wraps the request in a RemoteRequest object before calling the servlet's local Servlet object. The RemoteRequest constructor copies only the remote pointer to the request data, while other methods (e.g. getContentType) copy data from the remote request object as needed.

    class RemoteRequest implements ServletRequest {
      public RemoteRequest(ServletRequestImpl~ req) {
        this.req = req;
      public String getContentType() {
        if(ContentType == null && req.ContentType != null)
          ContentType = new String(req.ContentType);
        return ContentType;

    Figure 5 shows the time taken to dispatch a request to a servlet (which lives in a different task from the server) and process the response, for a servlet which returns a fixed sized message, not counting the time to transfer the data to or from the underlying sockets, and not counting the cost of spawning a thread to handle the request. The figure shows Luna's performance with and without the caching optimizations, and compares this to the performance of a modified server where both the server and servlet reside in the same task, so that no remote pointers are used. The measurements show that Luna's caching optimizations pay off for large messages, especially for a multiprocessor.

    response size


    Single task (local pointers only)

    Multiple tasks,no caching (uni/multi-processor)

    Multiple tasks, caching (uni/multi-processor)


    233m s

    266m s / 270m s

    266m s / 266m s


    300m s

    400m s / 467m s

    333m s / 333m s


    1000m s

    1500m s / 2403m s

    1070m s / 1137m s

    Figure 5: servlet response speed

  6. Application 2: active cache

    The web server in the previous section used very high-level RMI-style communication between the server and its servlets. This section presents an extension to the popular Squid web cache [Squ] to support active content analysis and generation, built using a lower-level shared memory style of communication between the Squid cache and the Luna active extensions. We choose a low-level style partly for performance, and partly because this interfaces naturally with Squid (which is written in C, not Java). A low-level interface is not always convenient to use, but it would not be hard to build a higher-level interface, such as the Active Cache Protocol [CZB98] over the lower level. The low-level interface also meant that there were only about 400 lines of Luna code, compared to 60,000 lines of Squid C code.

    The active cache consists of Squid, one "root task" written in Luna, and an arbitrary number of extension tasks written in Luna. If an HTTP request hits in Squid's cache, and the cache entry is marked as belonging to an extension, then Squid selects a thread from the root task's pool of waiting threads, and passes the request to the thread. The root task then makes a cross-task call into the proper extension, which in turn calls the root task one or more times to read data from disk or send data out a socket.

    Squid must pass the original request data into the root task and then on into the extension task. Similarly, the extension task forms a response, which it passes back to the root task and ultimately into C code that writes the response to the socket. To avoid copying the data, the C code allocates a large pool of fixed-sized buffers. Each buffer contains a proper Java header to make it look like a Java object of type byte[]. However, the buffers do not live in the Java heap and are not garbage collected. This raises a delicate issue: if the C code wants to reuse or deallocate a buffer, how can it be sure that there are no Java pointers to it?

    For example, suppose that a buffer received an HTTP request for extension A, and this buffer is later overwritten with an HTTP request for extension B. A should not be able to use its pointer to the buffer to read B's data. To prevent this, the root task protects the buffers with permits, and revokes access to a buffer before giving the buffer to another extension task. Similarly, B should not be able to read A's old data in the buffer. Therefore, the C code sets the "length" field of the buffer's Java header to exactly the size of B's received data, so that none of A's old data is visible from Java (because of Java's bounds checking, which Marmot implements using a combination of static analysis and run-time checks).

    Since the buffers live in the C heap, they aren't automatically "charged" to the task that is using them at any given moment. This is a limitation of Luna's simple task model, but it wouldn't be hard to work around: Squid would simply have to track the buffer usage itself, and bill the appropriate task. The extensions presumably trust Squid to generate an accurate bill. Rather than attempt to stretch the task model to cover all possible trust/sharing/billing models, it seems simpler to use tasks as a default resource accounting model, and have trusted tasks account for resources that fall outside the task model.

    Using the Pentium's cycle counter, we measured the total time for each stage of a request, where the request is about 370 bytes long, and the extension sends a preallocated 500 byte buffer back as a response. The dominant costs are in Squid and the C socket routines: it takes 6397m s for Squid to read and parse request and see that it is a cache hit, and it takes 5930m s to write the final response to a C socket. By contrast, the cost of the Luna code is small (in part because no data copying is required): 4m s to prepare the request data for the extension and signal a thread in the thread pool, 12m s to context switch to the new thread, and 57m s for the root task and extension code to generate and deliver the response to the C socket routines.

  7. Comparison to related work

    SPIN [BSP+95] used Modula-3 to safely extend an operating system kernel. It demonstrated that a high level language could eloquently express the interfaces between layers and enforce the layers' abstractions. Luna maintains both these properties: although the tilde throws in an additional constraint, Luna still expresses interfaces in the language itself, and uses the language features to enforce abstractions.

    Several projects [FFK+99] [HCC+98] [VB99] [BHL00] [BTS+00] [RCW01] have explored the idea of safe language tasks. Rudys et. al. [RCW01] propose a model where a task is defined by only code and threads, and object ownership is not tracked. Alta [BTS+00] restricts the types of objects available for inter-task sharing to some extent, but otherwise provides an all-or-nothing choice: either unrestricted sharing between two tasks or no sharing at all. Luna tries for the best of both worlds: allow sharing between arbitrary tasks, but use the type system to track the sharing.

    KaffeOS [BHL00] uses write barriers to restrict the propagation of inter-task pointers, so that sharing between tasks is confined to specially allocated buffers. KaffeOS doesn't have a static distinction between local and remote pointers, so the write barrier cost is incurred for all pointers, and errors are caught at run-time rather than statically. On the other hand, KaffeOS requires only changes to Java's implementation, not the Java language.

    Although this paper discusses implementation issues directly related to remote pointers, there are other important implementation issues. KaffeOS, for example, garbage collects different tasks separately to minimize the interference between tasks (with respect to memory accounting and predictable GC behavior), while Luna still only uses a single collector. MVM [CD01] and ShMVM [CDN02], in addition to dealing with separate garbage collection, attempt to share code transparently between tasks to a much greater extent than Luna does, and provide a mechanism for each task to load isolated native code. We believe that the techniques from these systems could be integrated into Luna without much difficulty, although there is one difficult time/space tradeoff: greater sharing of compiled code between tasks would save memory but limit Luna's whole-task optimizations.

    Both KaffeOS and Alta concentrate on shared memory communication; while Alta supports inter-task RPC, it is more than an order of magnitude slower than Luna's cross-task calls. The J-Kernel [HCC+98], on the other side, supports RPC (with deep copies of arguments and return values) but not shared memory. The J-Kernel's copy routine is trusted and not customizable, whereas Luna lets the user write their own copy routines by using field accesses to remote objects (such as the lazy copies implemented in our web server). A drawback to Luna's approach is that such customization demands serious optimization to prevent excessive revocation checking overhead.

    Luna's remote pointer types and the lower level annotated typed representation were inspired by the region annotations of Tofte et al [TT94][CWM99]. Originally, we imagined having one "region" per task, but then decided to let a task allocate multiple "permits", so that it can selectively revoke some permits but not others.

  8. Conclusions

When we first started working on language based protection, we had wild hopes of outperforming traditional operating systems and microkernels. The lack of separate address spaces would allow fine-grained sharing and make protection and communication dirt cheap. All that we needed to do was to solve the (apparently) minor problems of revocation, resource control, and terminating tasks so that the system would be robust enough to handle servers, agents systems, and active network systems. We found that these issues weren't nearly as easy to solve in a tightly coupled safe-language system as they were in a traditional system that clearly divides tasks into separate address spaces (and has a very fast TLB enforcing this division). Rather than admit defeat to traditional systems on these issues, we introduced a clear separation between tasks into Luna, but unlike the dynamic separation based on virtual memory address spaces, we made a static distinction in the type system. The contribution of this paper lies in:

The result is a system that combines the robustness, structure, and communication flexibility of a microkernel with the ease of expression, portability, and abstraction enforcement of a high-level programming language.


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