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If the application does nothing about its state, after a failure the user will have to log in again, or otherwise provide enough information to get back to a previous point Repeated user logins that are an expected part of the site experience (perhaps you timeout logins after 5 minutes, so failures can masquerade as shorter-than-average timeouts) require no other redundancy strategy Saving state in a user cookie has several limitations: the amount of data you can put into the cookie, privacy concerns that may restrict setting cookies on the user s browser, and the need to constantly fetch information from the cookie Cookies are useful for remembering where a user last was on a site, or for providing cross-session user identification If you ve noticed that sometimes a site like amazon.



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com welcomes you back, it s because your user information was provided in a cookie requested by the site when you hit their home page Doing nothing or using cookies are overly simplified where availability and the user impacts of failures are minor design concerns Sites that need to be continuously available and never interfere with user operations need to keep session state in memory, or in a form that can be reconstituted on the fly if needed The easiest approach is to use the in-memory state mechanisms offered by most application servers, and to turn on state replication or state sharing across instances of the application servers on your diagonally scaled servers.





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^ ^ To compute the LT P sj0 , we only need the LSTs f^jk s and gj s associated with the basic holding-time cdf 's Fjk and Gj . Abate and Whitt [3 5] give special attention to heavy-tail probability densities whose Laplace transforms can be computed and, ^ ^ thus, inverted. However, to compute P sjx , we also need to compute D sjx and ^ h sjx , which require computing the LSTs of the conditional cdf's Hjk tjx and Gj tjx in Eq. (17.9). In general, even if we know the LST of a cdf, we do not necesssarily know the LST of an associated conditional cdf. However, in special cases, the LSTs of conditional cdf 's are easy to obtain because the cdf's inherit their structure upon conditioning. For example, this is true for phase-type, hyperexponential and Pareto distributions; Duf eld and Whitt [15, Section 4]. Moreover, other cdf's can be approximated by hyperexponential or phase-type cdf 's; see Asmussen et al. [6] and Feldman and Whitt [17]. If the number of levels is not too large, then it will not be dif cult to compute the required matrix inverse I q s 1 for all required s. Note that, because of the probability structure, the inverse is well de ned for all complex s with Re s > 0. To illustrate with an important simple example, we next give the explicit formula for an on=off source. Suppose that there are two states with transition probabilities

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As you add to the state maintained in a session, it gets replicated to other servers; if one application server fails, the web servers routing requests will choose a different instance, and that new instance already has in-memory state for incoming requests The state replication engines keep each application server running as a warm spare environment for the working set of user sessions An alternative to using a pure application server solution is to write state information into a database, and then have a new session retrieve any necessary state from the database after a failure There s no need to replicate the session state between application server instances, because each new session fetches the last known state from the database, with the application server acting as a cache for the truth maintained by the database.

P12 P21 1 and holding time cdf 's G1 and G2 . From Eq. (17.9) or by direct calculation, ! ^ ^ P11 sj0 P12 sj0 ^ ^ ^ P sj0 I q s 1 D s ^ ^ P21 sj0 P22 sj0 ^ ^ ^ g1 s 1 g2 s 1 g1 s 1 : ^ ^ s 1 g1 s g2 s g2 s 1 g1 s ^ ^ ^ 1 g2 s

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