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We know intuitively that high-risk ventures should yield a risk premium and thus higher returns. In 1 we even quanti ed risk in terms of a standard deviation s of a probability distribution of returns. What we want to do in this chapter is to go one step further to answer the question How much more should I get for taking additional risk We will answer this question according to traditional methods, and point to places that these methods can be improved or, in some cases, where these methods have already been improved but the improvement has not been widely implemented. Standard deviation is a valuable concept for modeling volatility of stock returns, but the estimated standard deviations often seem foreign to an investor and not easily transferred to the bottom line: the price. Since business managers and bankers think of risk in terms of dollars of loss, standard deviation does not t very well into the language of business. It is not necessarily true that the higher the risk as measured by s, the higher are the losses. The senior management needs a number that gives the worst-case scenario of dollar loss, in a prescribed holding period. Enter value at risk (VaR). VaR puts a dollar amount on the highest expected loss on an investment. VaR has become popular since the US nancial giant J.P. Morgan published RiskMetricsTM methods in 1995. We will provide an overview of VaR in this chapter and many extensions that have been developed. For further details see surveys of VaR by Duffe and Pan (1997) and Jorion (1997). Some issues involving the time horizon for VaR need

[ yi - q]

From this we calculate that the probability that 900 sources should produce more than 950 cells in a slot is about 00000155 Thus for a CLP of 1:55 10 5 , we can take xstat D 900 and there is a statistical multiplexing gain of 900=190 D 4:74 This gain increases as the capacity of the link increases For example, if C is multiplied tenfold, to 9500, then 9317 sources can be multiplexed with the same CLP of 00000155 The SMG is now 9317=1900 D 4:90 As C tends to in nity the SMG tends to h=m D 5 As we will see in Section 412, some multiplexing gain is possible even if we require CLP D 0.

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Challenges include the lack of a global Network Context; the need to minimize overhead required for coordination across nodes; limited decision times; possible lossy communications; and so on To address these challenges, an anytime coordination approach can be used that builds upon three recent advances: (i) factored MDPs, a unique approach for solving large-scale MDPs that exploits problem structure; (ii) coordination graphs, a distributed coordination algorithm that allows multiple nodes to ef ciently nd the optimal action setting through neighborhood communication; and (iii) distributed inference for coordinated decision-making The impact of the communication required for coordination on the available resources is also incorporated This effect can be evaluated by combining the models with the predictive approach developed for the Network Context Manager Using estimates, the level of coordination can be traded against the bandwidth required to achieve this coordination.

This gives us a simplified formula for I sc :

Some of these ideas have been put to test in a sensor network environment Robustness to failures is a key issue in sensor networks To address this issue, Paskin et al [2005] describes a robust distributed inference architecture that addresses a wide class of important inference tasks The distributed nature of the approach provides formal guarantees in terms of robustness to unreliable communication and sensor failures Planning and control techniques using factored MDPs have been used with great success in sensor networks that control actuators and have local parameters that can be optimized 66333 Objective Function Broadly speaking, the goal of the objective function is to enable the COF to estimate the goodness of proposed control actions, in order to select the best offered action.

of granularity, from a high-level architectural design to a low-level object view. The design diagrams can be navigated by zooming in to a lower-level diagram to see a more speci c view of the system, or zooming out to a higher-level diagram to see a more general view of the system. This zooming technique is useful in gaining perspective on the data and activity ow of the different levels of granularity. Diagrams in Figures 6.3 and 6.4 show a progressively detailed view of the design.

For example, if sources are policed by leaky buckets and links are buffered, then it is possible to carry more connections than would be allowed under the peak rate constraint of (42) 43 Accepting calls Consider a network comprising only a single link Suppose that contracts specify exact traf c types and that there are xi contracts of type i, with i D 1; : : : ; k Suppose that the only contract obligation is the QoS constraint CLP p, for say p D 10 8 The technology set A, which we also call the acceptance region, is that set of x D x1 ; : : : ; x k / corresponding to quantities of traf c types that it is possible to carry simultaneously without violating this QoS constraint (see Figure 42).

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