PROBLEM TO BE SOLVED: To solve the problem, wherein it is impossible for an electronic content information provider to provide commercially secure and effective method, for a configurable general-purpose electronic commercial transaction/distribution control system. SOLUTION: In this system, having at least one protected processing environment for safely controlling at least one portion of decoding of digital information, a secure content distribution method comprises a process for encapsulating digital information in one or more digital containers; a process for encrypting at least a portion of digital information; a process for associating at least partially secure control information for managing interactions with encrypted digital information and/or digital container; a process for delivering one or more digital containers to a digital information user; and a process for using a protected processing environment, for safely controlling at least a portion of the decoding of the digital information. COPYRIGHT: (C)2006,JPO&NCIPI

https://apps.dtic.mil/dtic/tr/fulltext/u2/a423264.pdf

Systems and Methods for Secure Transaction Management and Electronic Rights Protection

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

Parallel discrete event simulation (PDES), sometimes called distributed simulation, refers to the execution of a single discrete event simulation program on a parallel computer. PDES has attracted a considerable amount of interest in recent years. From a pragmatic standpoint, this interest arises from the fact that large simulations in engineering, computer science, economics, and military applications, to mention a few, consume enormous amounts of time on sequential machines. From an academic point of view, parallel simulation is interesting because it represents a problem domain that often contains substantial amounts of parallelism (e.g., see [59]), yet paradoxically, is surprisingly difficult to parallelize in practice. A sufficiently general solution to the PDES problem may lead to new insights in parallel computation as a whole. Historically, the irregular, data-dependent nature of PDES programs has identified it as an application where vectorization techniques using supercomputer hardware provide little benefit [14].A discrete event simulation model assumes the system being simulated only changes state at discrete points in simulated time. The simulation model jumps from one state to another upon the occurrence of an event. For example, a simulator of a store-and-forward communication network might include state variables to indicate the length of message queues, the status of communication links (busy or idle), etc. Typical events might include arrival of a message at some node in the network, forwarding a message to another network node, component failures, etc.We are especially concerned with the simulation of asynchronous systems where events are not synchronized by a global clock, but rather, occur at irregular time intervals. For these systems, few simulator events occur at any single point in simulated time; therefore parallelization techniques based on lock-step execution using a global simulation clock perform poorly or require assumptions in the timing model that may compromise the fidelity of the simulation. Concurrent execution of events at different points in simulated time is required, but as we shall soon see, this introduces interesting synchronization problems that are at the heart of the PDES problem.This article deals with the execution of a simulation program on a parallel computer by decomposing the simulation application into a set of concurrently executing processes. For completeness, we conclude this section by mentioning other approaches to exploiting parallelism in simulation problems.Comfort and Shepard et al. have proposed using dedicated functional units to implement specific sequential simulation functions, (e.g., event list manipulation and random number generation [20, 23, 47]). This method can provide only a limited amount of speedup, however. Zhang, Zeigler, and Concepcion use the hierarchical decomposition of the simulation model to allow an event consisting of several subevents to be processed concurrently [21, 98]. A third alternative is to execute independent, sequential simulation programs on different processors [11, 39]. This replicated trials approach is useful if the simulation is largely stochastic and one is performing long simulation runs to reduce variance, or if one is attempting to simulate a specific simulation problem across a large number of different parameter settings. However, one drawback with this approach is that each processor must contain sufficient memory to hold the entire simulation. Furthermore, this approach is less suitable in a design environment where results of one experiment are used to determine the experiment that should be performed next because one must wait for a sequential execution to be completed before results are obtained.

/pdf/parallel-discrete-event-simulation-m759whzicd.pdf

Parallel discrete event simulation

Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

There is currently considerable enthusiasm around the MapReduce (MR) paradigm for large-scale data analysis [17]. Although the basic control flow of this framework has existed in parallel SQL database management systems (DBMS) for over 20 years, some have called MR a dramatically new computing model [8, 17]. In this paper, we describe and compare both paradigms. Furthermore, we evaluate both kinds of systems in terms of performance and development complexity. To this end, we define a benchmark consisting of a collection of tasks that we have run on an open source version of MR as well as on two parallel DBMSs. For each task, we measure each system's performance for various degrees of parallelism on a cluster of 100 nodes. Our results reveal some interesting trade-offs. Although the process to load data into and tune the execution of parallel DBMSs took much longer than the MR system, the observed performance of these DBMSs was strikingly better. We speculate about the causes of the dramatic performance difference and consider implementation concepts that future systems should take from both kinds of architectures.

/pdf/a-comparison-of-approaches-to-large-scale-data-analysis-2ueig83wcw.pdf

A comparison of approaches to large-scale data analysis

Many software applications today need to handle data from different data models; typically objects from the host programming language along with the relational and XML data models. The ROX impedance mismatch makes programs awkward to write and hard to maintain.The .NET Language-Integrated Query (LINQ) framework, proposed for the next release of the .NET framework, approaches this problem by defining a pattern of general-purpose standard query operators for traversal, filter, and projection. Based on this pattern, any .NET language can define special query comprehension syntax that is subsequently compiled into these standard operators (our code examples are in VB).Besides the general query operators, the LINQ framework also defines two domain specific APIs that work over XML (XLinq) and relational data (DLinq) respectively. The operators over XML use a lightweight and easy to use in-memory XML representation to provide XQuery-style expressiveness in the host programming language. The operators over relational data provide a simple OR mapping by leveraging remotable queries that are executed directly in the back-end relational store.

LINQ: reconciling object, relations and XML in the .NET framework

This paper describes the Time Warp Operating System, under development for three years at the Jet Propulsion Laboratory for the Caltech Mark III Hypercube multi-processor. Its primary goal is concurrent execution of large, irregular discrete event simulations at maximum speed. It also supports any other distributed applications that are synchronized by virtual time.The Time Warp Operating System includes a complete implementation of the Time Warp mechanism, and is a substantial departure from conventional operating systems in that it performs synchronization by a general distributed process rollback mechanism. The use of general rollback forces a rethinking of many aspects of operating system design, including programming interface, scheduling, message routing and queueing, storage management, flow control, and commitment.In this paper we review the mechanics of Time Warp, describe the TWOS operating system, show how to construct simulations in object-oriented form to run under TWOS, and offer a qualitative comparison of Time Warp to the Chandy-Misra method of distributed simulation. We also include details of two benchmark simulations and preliminary measurements of time-to-completion, speedup, rollback rate, and antimessage rate, all as functions of the number of processors used.

Time warp operating system

Intelligent browsing through a collection of reusable software components is facilitated with a computer having a video monitor and a user input interface such as a keyboard or a mouse for transmitting user selections, by presenting a picture of encyclopedia volumes with respective visible labels referring to types of software, in accordance with a metaphor in which each volume includes a page having a list of general topics under the software type of the volume and pages having lists of software components for each one of the generic topics, altering the picture to open one of the volumes in response to an initial user selection specifying the one volume to display on the monitor a picture of the page thereof having the list of general topics and altering the picture to display the page thereof having a list of software components under one of the general topics in response to a next user selection specifying the one general topic, and then presenting a picture of a set of different informative plates depicting different types of information about one of the software components in response to a further user selection specifying the one component.

/pdf/encyclopedia-of-software-components-313isbfnb4.pdf

Encyclopedia of software components

This paper describes the Time Warp Operating System, under development for three years at the Jet Propulsion Laboratory for the Caltech Mark III Hypercube multiprocessor. Its primary goal is concurrent execution of large, irregular discrete event simulations at maximum speed. It also supports any other distributed applications that are synchronized by virtual time. In this paper, the authors review the mechanics of Time Warp, describe the TWOS operating system, show how to construct simulations in object-oriented form to run under TWOS, and offer a qualitative comparison of Time Warp to the Chandy-Misra method of distributed simulation. They also include details of two benchmark simulations and preliminary measurements of time-to-completion, speedup, rollback rate, and antimessage rate, all as functions of the number of processors used.

/pdf/distributed-simulation-and-the-time-wrap-operating-system-2i697fzq4t.pdf

Distributed Simulation and the Time Wrap Operating System.

The invention includes a method and system for distributing and executing executable code. Before sending executable code to a client, a distributing authority associates a privilege request code with the executable code. The privilege request code indicates a requested set of privileges that the executable code will potentially exercise during execution. The distributing authority digitally signs the executable code and associated privilege request code and then distributes it for eventual execution by clients. Before executing the executable code, a client verifies the digital signature to confirm the authenticity and integrity of the executable code and associated privilege request code. This verification utilizes a hierarchy of certifying authorities. While the code executes, the client monitors it and prevents it from exercising privileges that are not in the requested set of privileges.

Brian C. Beckman

Papers

LINQ: reconciling object, relations and XML in the .NET framework

Time warp operating system

Encyclopedia of software components

Distributed Simulation and the Time Wrap Operating System.

System and method for safety distributing executable objects