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A second option is to take advantage of existing multi-purpose distributed testing sites11 These multi-purpose distributed testing sites can be set up in computer laboratories on college and university campuses, by using clusters of private networks at businesses, or in almost any setting that has locally networked computers hat meet technical speci cations and that have access to the Internet In principle, an enormous number of secure testing seats can be activated on a single day of testing similar to how large-scale paper-andpencil testing is currently done The transmission and test delivery software subsystems need to be extremely robust to allow these multi-purpose distributed testing sites to have plug-and-play capabilities That is, it should be possible to rapidly convert to a secure test center when needed The remainder of the time, the site may serve other purposes (eg serving as a computer laboratory for college students) One of the primary advantages of using multi-purpose distributed testing sites is that a testing program can offer a very high-volume testing event at multiple, convenient locations on a limited number of days, without carrying the overhead of dedicated centers In terms of disadvantages, there are certainly nontrivial logistical and engineering challenges associated with establishing a robust, wide area network of multi-purpose distributed testing sites However, the engineering technology currently exists to make this happen In fact, a number of testing organizations have successfully carried out large-scale eld studies using multi-purpose distributed testing sites In any case, this appears to be the only way to legitimately add the needed capacity to accommodate large-scale testing events on particular dates.

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node on every processor That node of all free nodes that allows he earliest start time is selected together with the processor on which this time is achieved The choice of the node and the processor is made simultaneously, based on the state of the partial schedule Two examples for dynamic list scheduling algorithms employing this technique are ETF (earliest time rst) by Hwang et al [94] and DLS (dynamic level scheduling) by Sih and Lee [169] In comparison to the simple list scheduling, the complexity increases by a factor of |V| Evaluating the start time of every free node on every processor is in the worst case O(P(V + E)), which is already the complexity of the simple list scheduling As this step is done |V| times, the total complexity is O(P(V2 + VE)) 513 Node Priorities Discussion of the list scheduling technique distinguished between static and dynamic node priorities How these priority schemes are realized is discussed in the next paragraphs Static Static priorities are based on the characteristics of the task graph In the simplest case, the priority metric itself establishes a precedence order among the nodes It is then suf cient to order the nodes according to their priorities with any sort algorithm, for example, with Mergesort (Cormen et al [42]), which has a complexity of O(V log V) Prominent examples for such priority metrics are the top and the bottom level of a node (Section 442) Lemma 51 (Level Orders Are Precedence Orders) Let G = (V, E, w, c) be a task graph The nonincreasing bottom level (bl) order and the nondecreasing top level (tl) order of the nodes n V are precedence orders Proof The lemma follows immediately from the recursive de nition of the bottom level and top level in Eqs (443) and (444), respectively A node always has a smaller bottom level than any of its predecessors and a higher top level than any of its successors If the priority metric is not compatible with the precedence constraints of the task graph, a correct order can be established by employing the free node concept Algorithm 12 shows how to create a priority ordered node list that complies with the precedence constraints As mentioned before, the general structure is similar to Algorithm 11, but a priority queue (Q) is used instead of a set (S), because the priorities assigned to the nodes are static The simple list scheduling outlined in Algorithm 9 could be rewritten by using the below algorithm and substituting line 5 with lines 5 and 6 from Algorithm 9 However, then the conceptually nice separation of the rst and the second part of simple list scheduling is lost Suppose the priority queue in Algorithm 12 is a heap (Cormen et al [42]); the algorithm s complexity is O(V log V + E): every node is inserted once in the heap,.

Connectivity and Data Transmission The connectivity and data transmission subsystem impacts a CBT enterprise system in two ways First, it directly affects the extent of interactivity that is possible within various channels Second, the type of connectivity employed can directly or indirectly affect the ef ciency, accuracy, and security of the test data within particular channels Two fundamental characteristics of any transmission medium are channel capacity (bandwidth) and ransmission speed These two characteristics are essential since large-scale CBT inevitably requires remote testing, further implying that testing organizations must deal with potentially different comunication channels between various system layers For example, local area networks (LANs) that connect the individual testing workstations to a local le server may be capable of relatively fast transmissions (100Base-T or better lines and network interface cards) These types of LAN can usually support dedicated channel, synchronous communications for transmitting blocks of Visual Studio .

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