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INTRODUCTION

Microsoft's next-generation secure computing base aims to provide robust access control while retaining the openness of personal computers. Unlike closed systems, an NGSCB platform can run any software, but it provides mechanisms that allow operating systems and applications to protect themselves against other software running on the same machine. For example, it can make home finance data inaccessible to programs that the user has not specifically authorized.

To enable this mode of operation, NGSCB platforms implement
" Isolation among operating systems and among processes. OS isolation is related to virtual machine monitors. However, some key NGSCB innovations make it more robust than traditional VMMs by enabling a small machine monitor to isolate itself and other high-assurance components from the basic input/output system (BIOS), device drivers, and bus master devices.
" Hardware and software security primitives that allow software modules to keep secrets and authenticate themselves to local and remote entities. These primitives maintain the trustworthiness of OS access protections without preventing the platform from booting other operating systems.

We refer to a security regimen that allows any software to run but requires it to be identified in access-control decisions as authenticated operation, and we call a hardware-software platform that supports authenticated operation a trusted open system.

A variety of commercial requirements and security goals guided the NGSCB system design. The main commercial requirement was for an open architecture that allows arbitrary hardware peripherals to be added to the platform and arbitrary software to execute without involving a central authority. Furthermore, the system had to operate in the legacy environment of personal computers. While we introduced changes to core platform components, most of the PC architecture remained unmodified. The system had to be compatible with the majority of existing peripherals. Finally, the hardware changes had to be such that they would not have a significant impact on PC production costs.

Our main security goal was assurance. Assurance is not any particular security function. It refers to the degree of confidence the owner of a system can have in its correct behavior especially in the presence of attacks. A further goal was to enable authenticated operation.

The hardware platforms are not required to provide protection against hardware tampering. Protection against tampering costs money, and it is clear that most security attacks facing users are launched by malicious software, or are remotely launched and exploit bugs in otherwise benign software. However, we anticipate platforms will be deployed that are also robust against hardware attacks, especially in high-security corporate and government settings.

AUTHENTICATED OPERATION

Traditional access-control systems protect data against unauthorized access through an authentication mechanism such as a password, biometric data, or smart card. Each access request triggers a system component, the guard, that is part of the trusted computing base. The guard grants or denies access, and can audit access requests according to the user, the request, and the system's access-control policy.

Authenticated operation bases access-control decisions in part on the identity of the program making a request. For example, a user can restrict access to files containing financial data to only certain authorized programs.
It is straightforward to extend most existing user-based access-control systems to code-based access models. For example, a resource can have an access-control list that grants access only to a list of programs rather than to users who run these programs. We expect that most systems built to support authenticated operation will base access-control decisions on both program and user resource requests

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INTRODUCTION

What is a kernel ?

A set of code which directly interacts with hardware and allocate and manages resources such as CPU time, memory and I/O access .Kernel also contain system calls which provide specific functions.

HISTORY

The Linux kernel project was started in 1991 by Linus Torvalds as a Minix-like Operating System for his 386. (Linus had originally wanted to name the project Freax, but the now-familiar name is the one that stuck.) The first official release of Linux 1.0 was in March 1994, but it supported only single-processor i386 machines. Just a year later, Linux 1.2 was released (March 1995) and was the first version with support for different hardware platforms (specifically: Alpha, Sparc, and Mips), but still only single-processor models. Linux 2.0 arrived in June of 1996 and also included support for a number of new architectures, but more importantly brought Linux into the world of multi-processor machines (SMP). After 2.0, subsequent major releases have been somewhat slower in coming (Linux 2.2 in January 1999 and 2.4 in January 2001), each revision expanding Linux's support for new hardware and system types as well as boosting scalability. (Linux 2.4 was also notable in being the release that really broke Linux into the desktop space with kernel support for ISA Plug-and-Play, USB, PC Card support, and other additions.) Linux 2.6, released 12/17/03, stands not only to build on these features, but also to be another "major leap" with improved support for both significantly larger systems and significantly smaller ones (PDAs and other devices.)

KERNEL 2.6 FEATURES

Features in kernel 2.6

" Scalability
" Preemptible kernel
" New scheduling algorithm
" Improved threading model
" Hyperthreading
" Module subsystem and device model
" System hardware support
" Block device support
" INPUT/OUTPUT support
" Audio and multimedia

HARDWARE SUPPORT

As Linux has moved forward over the years and into the mainstream, each new iteration of the kernel appeared to be leaps and bounds better than the previous in terms of what types of devices it could support-- both in terms of emerging technologies (USB in 2.4) and older "legacy" technologies (MCA in 2.2). As we arrive at the 2.6 however, the number of major devices that Linux does not support is relatively small. There are few, if any, major branches of the PC hardware universe yet to conquer. It is for that reason that most (but certainly not all) of improvements in i386 hardware support have been to add robustness rather than new features

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Rainfinity's technology originated in a research project at the California Institute of Technology (Caltech), in collaboration with NASA's Jet Propulsion Laboratory and the Defense Advanced Research Projects Agency (DARPA). The name of the original research project was RAIN, which stands for Reliable Array of Independent Nodes. The goal of the RAIN project was to identify key software building blocks for creating reliable distributed applications using off-the-shelf hardware. The focus of the research was on high-performance, fault-tolerant and portable clustering technology for space-borne computing. Two important assumptions were made, and these two assumptions reflect the differentiations between RAIN and a number of existing solutions both in the industry and in academia:

1. The most general share-nothing model is assumed. There is no shared storage accessible from all computing nodes. The only way for the computing nodes to share state is to communicate via a network. This differentiates RAIN technology from existing back-end server clustering solutions such as SUNcluster, HP MC Serviceguard or Microsoft Cluster Server.
2. The distributed application is not an isolated system. The distributed protocols interact closely with existing networking protocols so that a RAIN cluster is able to interact with the environment. Specifically, technological modules were created to handle high-volume network-based transactions. This differentiates it from traditional distributed computing projects such as Beowulf.

In short, the RAIN project intended to marry distributed computing with networking protocols. It became obvious that RAIN technology was well-suited for Internet applications. During the RAIN project, key components were built to fulfill this vision. A patent was filed and granted for the RAIN technology. Rainfinity was spun off from Caltech in 1998, and the company has exclusive intellectual property rights to the RAIN technology. After the formation of the company, the RAIN technology has been further augmented, and additional patents have been filed.

The guiding concepts that shaped the architecture are as follows:

1. Network Applications

The architecture goals for clustering data network applications are different from clustering data storage applications. Similar goals apply in the telecom environment that provides the Internet backbone infrastructure, due to the nature of applications and services being clustered.

2. Shared-Nothing

The shared-storage cluster is the most widely used for database and application servers that store persistent data on disks. This type of cluster typically focuses on the availability of the database or application service, rather than performance. Recovery from failover is generally slow, because restoring application access to disk-based data takes minutes or longer, not seconds. Telecom servers deployed at the edge of the network are often diskless, keeping data in memory for performance reasons, and tolerate low failover time. Therefore, a new type of share-nothing cluster with rapid failure detection and recovery is required. The only way for the shared-nothing cluster to share is to communicate via the network.

3. Scalability

While the high-availability cluster focuses on recovery from unplanned and planned downtimes, this new type of cluster must also be able to maximize I/O performance by load balancing across multiple computing nodes. Linear scalability with network throughput is important. In order to maximize the total throughput, load load-balancing decisions must be made dynamically by measuring the current capacity of each computing node in real-time. Static hashing does not guarantee
an even distribution of traffic.

4. Peer-to-Peer

A dispatcher-based, master-slave cluster architecture suffers from scalability by introducing a potential bottleneck. A peer-to-peer cluster architecture is more suitable for latency-sensitive data network applications processing shortlived sessions. A hybrid architecture should be considered to offset the need for more control over resource management. For example, a cluster can assign multiple authoritative computing nodes that process traffic in the round-robin order for each network interface that is clustered to reduce the overhead of traffic forwarding

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Biological computers have emerged as an interdisciplinary field that draws together molecular biology, chemistry, computer science and mathematics. The highly predictable hybridization chemistry of DNA, the ability to completely control the length and content of oligonucleotides, and the wealth of enzymes available for modification of the DNA, make the use of nucleic acids an attractive candidate for all of these nanoscale applications

A 'DNA computer' has been used for the first time to find the only correct answer from over a million possible solutions to a computational problem. Leonard Adleman of the University of Southern California in the US and colleagues used different strands of DNA to represent the 20 variables in their problem, which could be the most complex task ever solved without a conventional computer. The researchers believe that the complexity of the structure of biological molecules could allow DNA computers to outperform their electronic counterparts in future.

Scientists have previously used DNA computers to crack computational problems with up to nine variables, which involves selecting the correct answer from 512 possible solutions. But now Adleman's team has shown that a similar technique can solve a problem with 20 variables, which has 220 - or 1 048 576 - possible solutions.

Adleman and colleagues chose an 'exponential time' problem, in which each extra variable doubles the amount of computation needed. This is known as an NP-complete problem, and is notoriously difficult to solve for a large number of variables. Other NP-complete problems include the 'travelling salesman' problem - in which a salesman has to find the shortest route between a number of cities - and the calculation of interactions between many atoms or molecules.
Adleman and co-workers expressed their problem as a string of 24 'clauses', each of which specified a certain combination of 'true' and 'false' for three of the 20 variables. The team then assigned two short strands of specially encoded DNA to all 20 variables, representing 'true' and 'false' for each one.

In the experiment, each of the 24 clauses is represented by a gel-filled glass cell. The strands of DNA corresponding to the variables - and their 'true' or 'false' state - in each clause were then placed in the cells.

Each of the possible 1,048,576 solutions were then represented by much longer strands of specially encoded DNA, which Adleman's team added to the first cell. If a long strand had a 'subsequence' that complemented all three short strands, it bound to them. But otherwise it passed through the cell.

To move on to the second clause of the formula, a fresh set of long strands was sent into the second cell, which trapped any long strand with a 'subsequence' complementary to all three of its short strands. This process was repeated until a complete set of long strands had been added to all 24 cells, corresponding to the 24 clauses. The long strands captured in the cells were collected at the end of the experiment, and these represented the solution to the problem.

THE WORLD'S SMALLEST COMPUTER

The world's smallest computer (around a trillion can fit in a drop of water) might one day go on record again as the tiniest medical kit. Made entirely of biological molecules, this computer was successfully programmed to identify - in a test tube - changes in the balance of molecules in the body that indicate the presence of certain cancers, to diagnose the type of cancer, and to react by producing a drug molecule to fight the cancer cells.

DOCTOR IN A CELL

In previous biological computers produced input, output and "software" are all composed of DNA, the material of genes, while DNA-manipulating enzymes are used as "hardware." The newest version's input apparatus is designed to assess concentrations of specific RNA molecules, which may be overproduced or under produced, depending on the type of cancer. Using pre-programmed medical knowledge, the computer then makes its diagnosis based on the detected RNA levels. In response to a cancer diagnosis, the output unit of the computer can initiate the controlled release of a single-stranded DNA molecule that is known to interfere with the cancer cell's activities, causing it to self-destruct.

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From computer-aided design (CAD) drawings of complex engineering parts to digital representations of proteins and complex molecules, an increasing amount of 3D information is making its way onto the Web and into corporate databases.

Because of this, users need ways to store, index, and search this information. Typical Web-searching approaches, such as Google's, can't do this. Even for 2D images, they generally search only the textual parts of a file, noted Greg Notess, editor of the online Search Engine Showdown newsletter.

However, researchers at universities such as Purdue and Princeton have begun developing search engines that can mine catalogs of 3D objects, such as airplane parts, by looking for physical, not textual, attributes. Users formulate a query by using a drawing application to sketch what they are looking for or by selecting a similar object from a catalog of images. The search engine then finds the items they want. The company must make it again, wasting valuable time and money
3D SEARCHING

Advances in computing power combined with interactive modeling software, which lets users create images as queries for searches, have made 3Dsearch technology possible.

Methodology used involves the following steps

" Query formulation
" Search process
" Search result

QUERY FORMULATION

True 3D search systems offer two principal ways to formulate a query: Users can select objects from a catalog of images based on product groupings, such as gears or sofas; or they can utilize a drawing program to create a picture of the object they are looking for. or example, Princeton's 3D search engine uses an application to let users draw a 2D or 3D representation of the object they want to find.

The above picture shows the query interface of a 3D search system.

SEARCH PROCESS

The 3D-search system uses algorithms to convert the selected or drawn image-based query into a mathematical model that describes the features of the object being sought. This converts drawings and objects into a form that computers can work with. The search system then compares the mathematical description of the drawn or selected object to those of 3D objects stored in a database, looking for similarities in the described features.

The key to the way computer programs look for 3D objects is the voxel (volume pixel). A voxel is a set of graphical data-such as position, color, and density-that defines the smallest cubeshaped building block of a 3D image. Computers can display 3D images only in two dimensions. To do this, 3D rendering software takes an object and slices it into 2D cross sections. The cross sections consist of pixels (picture elements), which are single points in a 2D image. To render the 3D image on a 2D screen, the computer determines how to display the 2D cross sections stacked on top of each other, using the applicable interpixel and interslice distances to position them properly. The computer interpolates data to fill in interslice gaps and create a solid image.

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-Manual Systems provides (technical) manuals for mobile
communications devices transmission equipment, voice-exchange device,
and subscriber products. Only members can access the enterprise system
manuals provided via the e-Manual Systems.
However, other contents and product manuals can be freely viewed and
browsed as no member registration is required.

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