物件導向軟體之架構(Object-Oriented Software Construction)探討

Despite decades of research in extensible operating system technology, extensions such as device drivers remain a significant cause of system failures. In Windows XP, for example, drivers account for 85% of recently reported failures.This article describes Nooks, a reliability subsystem that seeks to greatly enhance operating system (OS) reliability by isolating the OS from driver failures. The Nooks approach is practical: rather than guaranteeing complete fault tolerance through a new (and incompatible) OS or driver architecture, our goal is to prevent the vast majority of driver-caused crashes with little or no change to the existing driver and system code. Nooks isolates drivers within lightweight protection domains inside the kernel address space, where hardware and software prevent them from corrupting the kernel. Nooks also tracks a driver's use of kernel resources to facilitate automatic cleanup during recovery.To prove the viability of our approach, we implemented Nooks in the Linux operating system and used it to fault-isolate several device drivers. Our results show that Nooks offers a substantial increase in the reliability of operating systems, catching and quickly recovering from many faults that would otherwise crash the system. Under a wide range and number of fault conditions, we show that Nooks recovers automatically from 99% of the faults that otherwise cause Linux to crash.While Nooks was designed for drivers, our techniques generalize to other kernel extensions. We demonstrate this by isolating a kernel-mode file system and an in-kernel Internet service. Overall, because Nooks supports existing C-language extensions, runs on a commodity operating system and hardware, and enables automated recovery, it represents a substantial step beyond the specialized architectures and type-safe languages required by previous efforts directed at safe extensibility.

/pdf/improving-the-reliability-of-commodity-operating-systems-2lw6yldm1m.pdf

Improving the reliability of commodity operating systems

We present data which, to the best of our knowledge, includes all known nontrivial values and bounds for specific graph, hypergraph and multicolor Ramsey numbers, where the avoided graphs are complete or complete without one edge. Many results pertaining to other more studied cases are also presented. We give references to all cited bounds and values, as well as to previous similar compilations. We do not attempt complete coverage of asymptotic behavior of Ramsey numbers, but concentrate on their specific values.

Small Ramsey Numbers

LETTERS to the EDITORPhilosophy of science

In mathematics and computer science, random numbers have the role of a resource for assisting proofs, making cryptography secure, and enabling computational protocols. This role motivates efforts to produce random numbers as a physical process. Potential physical sources abound, but arguably the most fundamental are those based on elementary quantum mechanical processes. This review discusses the current status of devices that generate quantum random numbers.

Quantum random number generators

We describe new ways to simulate 2-party communication protocols to get protocols with potentially smaller communication. We show that every communication protocol that communicates C bits and reveals I bits of information about the inputs to the participating parties can be simulated by a new protocol involving at most ~O(√CI) bits of communication. If the protocol reveals I bits of information about the inputs to an observer that watches the communication in the protocol, we show how to carry out the simulation with ~O(I) bits of communication.These results lead to a direct sum theorem for randomized communication complexity. Ignoring polylogarithmic factors, we show that for worst case computation, computing n copies of a function requires √n times the communication required for computing one copy of the function. For average case complexity, given any distribution μ on inputs, computing n copies of the function on n inputs sampled independently according to μ requires √n times the communication for computing one copy. If μ is a product distribution, computing n copies on n independent inputs sampled according to μ requires n times the communication required for computing the function. We also study the complexity of computing the sum (or parity) of nevaluations of f, and obtain results analogous to those above.

/pdf/how-to-compress-interactive-communication-2i7rk6h8c2.pdf

How to compress interactive communication

We show how to efficiently simulate the sending of a message M to a receiver who has partial information about the message, so that the expected number of bits communicated in the simulation is close to the amount of additional information that the message reveals to the receiver. This is a generalization and strengthening of the Slepian-Wolf theorem, which shows how to carry out such a simulation with low amortized communication in the case that M is a deterministic function of X. A caveat is that our simulation is interactive. 
As a consequence, we prove that the internal information cost (namely the information revealed to the parties) involved in computing any relation or function using a two party interactive protocol is exactly equal to the amortized communication complexity of computing independent copies of the same relation or function. We also show that the only way to prove a strong direct sum theorem for randomized communication complexity is by solving a particular variant of the pointer jumping problem that we define. Our work implies that a strong direct sum theorem for communication complexity holds if and only if efficient compression of communication protocols is possible.

Information Equals Amortized Communication

During maintenance, systems are updated to correct faults, improve functionality, and adapt the software to changes in its execution environment. The typical software update process consists of stopping the system to be updated, performing the update of the code, and restarting the system. For systems such as banking and telecommunication software, however the cost of downtime can be prohibitive. The situation is even worse for systems such as air-traffic controllers and life-support software, for which a shut-down is in general not an option. In those cases, the use of some form of on-the-fly program modification is required. In this paper, we present a new technique for dynamic updating of Java software. Our technique is based oil the use of proxy classes and requires no support from the runtime system. The technique allows for updating a running Java program by substituting, adding, and deleting classes. We also present DUSC (dynamic updating through swapping of classes), a tool that we developed and that implements our technique. Finally, we describe an empirical study that we performed to validate the technique of a real Java subject. The results of the study show that our technique can be effectively applied to Java software with only little overhead in both execution time and program size.

/pdf/a-technique-for-dynamic-updating-of-java-software-3vdsglrcnu.pdf

A technique for dynamic updating of Java software

We describe new ways to simulate two-party communication protocols to get protocols with potentially less communication. We show that every communication protocol that communicates $C$ bits and reveals $I$ bits of information about the inputs to the participating parties can be simulated by a new protocol involving at most $\tilde{O}(\sqrt{CI})$ bits of communication. If the protocol reveals $I$ bits of information about the inputs to an observer that watches the communication in the protocol, we show how to carry out the simulation with $\tilde{O}(I)$ bits of communication. These results lead to a direct sum theorem for randomized communication complexity. Ignoring polylogarithmic factors, we show that for worst-case computation, computing $n$ copies of a function requires $\sqrt{n}$ times the communication required for computing one copy of the function. For average case complexity, given any distribution $\mu$ on inputs, computing $n$ copies of the function on $n$ inputs sampled independently according...

How to Compress Interactive Communication

The main result of this paper is an explicit disperser for two independent sources on n bits, each of entropy k=no(1) Put differently, setting N=2n and K=2k, we construct explicit N x N Boolean matrices for which no K x K submatrix is monochromatic Viewed as adjacency matrices of bipartite graphs, this gives an explicit construction of K-Ramsey bipartite graphs of size NThis greatly improves the previous bound of k=o(n) of Barak, Kindler, Shaltiel, Sudakov and Wigderson [4] It also significantly improves the 25-year record of k = O (√n) on the special case of Ramsey graphs, due to Frankl and Wilson [9]The construction uses (besides "classical" extractor ideas) almost all of the machinery developed in the last couple of years for extraction from independent sources, including:Bourgain's extractor for 2 independent sources of some entropy rate 1/2 [18]Rao's extractor for 2 independent block-sources of entropy nΩ (1) [17]The "Challenge-Response" mechanism for detecting "entropy concentration" of [4]The main novelty comes in a bootstrap procedure which allows the Challenge-Response mechanism of [4] to be used with sources of less and less entropy, using recursive calls to itself Subtleties arise since the success of this mechanism depends on restricting the given sources, and so recursion constantly changes the original sources These are resolved via a new construct, in between a disperser and an extractor, which behaves like an extractor on sufficiently large subsources of the given onesThis version is only an extended abstract, please see the full version, available on the authors' homepages, for more details

/pdf/2-source-dispersers-for-sub-polynomial-entropy-and-ramsey-13902i5vga.pdf

Anup Rao

Papers

How to compress interactive communication

Information Equals Amortized Communication

A technique for dynamic updating of Java software

How to Compress Interactive Communication

2-source dispersers for sub-polynomial entropy and Ramsey graphs beating the Frankl-Wilson construction