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AD RMS is far more powerful than 20 pages can cover Read through the help on RMS in Windows Server 2008 help center that is part of the product However, let s brie y look at other capabilities of the RMS What happens if someone leaves the company and protected all his documents You have the option to create a Super Users group whose members have full control over all rights-protected material This is obviously not desirable under normal circumstances Rights-protected material should be visible only to its owner and those who have been given access to it By default, the Super Users group is disabled and has no group assigned If you nd the need to view protected materials, you can enable the Super Users group and assign a universal group whose members have the full privileges to access protected content If you do, enable the Super Users group via Security Policies, Super Users, and turn off the Super Users group when you nish retrieving the required information By default, anyone can use the RMS environment However, you can add exclusions to block certain users, certain versions of Windows, certain applications, and certain versions of the RMS client via the Exclusion Policies tab of the RMS MMC snap-in For example, a vulnerability in a version of RMS might have been found, so you can choose to block clients not running an updated version The nal item for discussion is a licensing-only RMS cluster You can add it as a subordinate to a root cluster, which can only give out licenses The reason for using a separate licensing-only cluster is primarily to isolate different departments within an organization A department could be allocated to a separate licensing-only RMS cluster, which would then have its own rights templates, separate Super Users groups, separate logging, and separate exclusion policies As you have seen, AD RMS is an attractive technology for applications that have RMS support Today, that is primarily the Microsoft of ce suite, but this ill expand in the future As you increase the usage of RMS in your organization, ensure that you scale your RMS infrastructure accordingly with additional RMS servers in the RMS cluster.

$5,000 this year for the system on his behalf His employer has two options: (1) pay the computer supplier the $5,000 for the system, or (2) pay Floyd the $5,000 so that he can pay for the system himself In the first case, a $5,000 taxable benefit is added to Floyd s T4 slip, and this will cost him $2,500 in tax at a marginal tax rate of 50 percent This $2,500 tax bill will not be owing until April 30 next year when he files his tax return for this year In the second case, Floyd s employer will pay him the $5,000 directly, and $2,500 in taxes deducted at-source will be withheld immediately Floyd will have to come up with another $2,500 to bring his available cash to $5,000 in order to buy the computer system In both cases, the out-of-pocket cost to Floyd is $2,500 The difference is in the timing of that outlay In the first case, the $2,500 is paid as additional taxes when Floyd files his tax return next April In the second case, Floyd pays the $2,500 to the computer store today in order to buy the computer Better to pay that $2,500 next year than this year If you re going to be taxed on a bene t received from your employer, exactly how much will be added to your income Simple You ll be taxed on your employer s cost of the bene t In Floyd s example, the computer system cost his mployer $5,000, so Floyd s taxable bene t is $5,000 The good news is that your employer may be able to negotiate lower prices on certain things because of your employer s size and number of employees If this is the case, the amount of your taxable bene t may be reduced and your true cost will shrink even further.

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for an unlimited number of processors Nevertheless, it is often proposed as an initial step in scheduling or a limited number of processors To distinguish between the limited number of (physical) processors and the unlimited number of (virtual) processors assumed in the clustering step, the latter are called clusters, hence the term clustering In clustering, the nodes of the task graph are mapped and scheduled into these clusters De nition 53 (Clustering) Let G = (V, E, w, c) be a task graph A clustering C is a schedule of G on an implicit parallel system C (De nition 43) with an unlimited number of processors; that is, |C| = |V| The processors C C are called clusters Clustering based scheduling algorithms for a limited number of processors consist of several steps, similar to the two-phase scheduling outlined by Algorithm 13 in Section 52 Algorithm 13 comprises two phases, where the rst phase is the mapping of the nodes and the second is their scheduling In clustering based algorithms, three steps are necessary: (1) clustering, (2) mapping of the clusters to the (physical) processors, and (3) scheduling of the nodes Algorithm 14 outlines a generic three-step clustering based scheduling heuristic for a limited number of processors Algorithm 14 Generic Three-Step Clustering Based Scheduling Algorithm (G = (V, E, w, c), P) Clustering nodes (1) Find a clustering C of G Mapping clusters to processors (2) Assign clusters of C to (physical) processors of P Scheduling/ordering nodes (3) Attribute start time ts (n) to each node n V, adhering to Condition 41 (processor constraint) and Condition 42 (precedence constraint) In contrast to the two-phase Algorithm 13, where the rst step is the pure processor allocation of the nodes, clustering also includes the scheduling of the nodes in the clusters As will be seen later, this is done for an accurate estimation of the execution time of the task graph It also makes clustering a complete scheduling algorithm for an unlimited number of processors While the third step is theoretically identical to the second phase of Algorithm 13, its actual implementation may differ, since the partial node orders as established by the clustering C might be considered in determining the nal node order In terms of parallel programming terminology, clustering correlates to the step of parallelization designated by orchestration (Culler and Singh [48]) or agglomeration (Foster [69]) as described in Section 23 The often cited motivation for clustering was given by Sarkar [167]: if tasks are best executed in the same processor (cluster) of an ideal system, that is, a system that possesses more processors (clusters) than tasks, they should also be executed on the same processor in any real system Due to the NP-hardness of scheduling, it cannot be.

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