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The IETF developed the DiffServ concept in order to create a framework for describing and implementing a basic, predicable QoS. DiffServ uses concepts of traffic classification associated with forwarding behavior that implements services classes. DiffServ also provides for on-going monitoring and adjustments, e.g., through packet drops, scheduling, and other measurable parameters. DiffServ provides a specification for individually marking packets, a function undertaken by edge routers, on a packet-by-packet basis, so that they can be identified and classified within specific traffic categories, such as high, low, or normal priority. Per Hop Behavior (PHB) designations are provided by the designation of values, within a field of the packet header [12]. The DiffServ field, an octet IP header, is used to designate flows within categories. The initial six bits (allowing for 64 classes) are designated as the codepoint, or Differentiated Services Codepoint (DSCP), that indicates the PHB. The other bits are reserved. Because the packets are marked, traffic behavior requirements can be related to individual packets, or classes of packets based on the DiffServ field values. These markings specify a particular type of PHB, which is provided by network routers as the traffic flows through the network. Forwarding behavior can be implemented within DiffServ routers based on those values. Using PHB, specific traffic treatments, such as traffic conditioning, can be linked to specific packets and flows within designated classifications across a network fabric. For example, common traffic can be segmented from other high-priority traffic that may require special treatment, such as faster forwarding. This technique addresses the need to identify different types of traffic, within an appropriate policy context. Routers can be configured to treat different packets or packet classes in accordance with preset policy, related to previously defined parameters, e.g., priorities. Policies implemented within DiffServ routers can ensure that high-priority traffic receives required resource allocations and that low-priority traffic does not receive more resource allocations than is required. Because of the association of resources with specific flows, these policy implementations are sometimes termed policing profiles. This technique can be used to limit the ingress flows of designated traffic in order to govern submission to specific services to prevent them from being oversubscribed. Part of this architectural approach includes the options for using queues that are each associated with a particular class of traffic quality. For example, high-priority traffic can be sent to special queues that forward their packets faster than alternative queues. Traffic behavior can be treated as micro-streams or as aggregated streams. The IETF has also created standards for marking traffic for particular types of PHBs, such as Expedited Forwarding (EF) (RFC 2598), which can be used to create a type of virtual leased line across autonomous networks [13,14]. In other words, it can be used to set up an end-to-end path across a DiffServ-compliant network for designated priority traffic that has high-quality attributes, such as guaranteed bandwidth levels
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X can be considered as an ordinary con guration space. Classical planning algorithms for a single robot with multiple bodies [191] may be applied without adaptation in the case of centralized planning (planning that takes into account all robots). The main concern, however, is that the dimension of X grows linearly with the number of robots. Complete algorithms require time that is exponential in dimension, which makes them unlikely candidates for such problems. Sampling-based algorithms are more likely to scale well in practice when there many robots, but the resulting dimension might still be too high. The motions of the robots may be decoupled in many interesting ways. This leads to several interesting methods which rst develop some kind of partial plan for the robots independently, and then consider the plan interactions to produce a solution. This idea is referred to as decoupled planning. In [191], two approaches are given: (i) prioritized planning considers one robot at a time according to a global priority, while (ii) the path coordination method essentially plans paths by scheduling the con guration space-time resource.
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Figure 4.5. Directed diffusion
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Atomicity: Transactions implement several operations, which must be completed in order for the transaction to be successful. If any one of the constituent operations fails, the entire transaction fails. We say that transactions are atomic in nature, meaning the operations of the transaction must be considered as an indivisible unit when deciding whether or not the transaction is successful. Consistency: Transactions must ensure that the system remains in a consistent state, regardless of whether or not the transactions successfully execute. The consistency of the system is defined by business rules, which the application must enforce and implement. Isolation: The results of transaction execution should be independent of other executing transactions. Stated differently, the effects of one transaction should not be visible to any other transaction until the transaction completes. Although several transactions may be executing in parallel, the net effect is as if the transactions were executing in serial. Durability: Changes made by a transaction must be permanent. Once the transaction successfully completes its work, the system ensures that subsequent failures (software, hardware, network, and so on) cannot undo the changes to the system resulting from a transaction.
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approximation along with the exact density for the Weibull(0.9, 1.5). Figure 3.17 presents the exact convolution density along with its saddlepoint approximation for purposes of comparison. On all of the plots, we can see that the saddlepoint density approximation does very well. The three gures also plot the CDFs corresponding to the displayed densities. Each CDF plot has three curves: the exact CDF, the Lugannani and Rice (1980) saddlepoint CDF of (3.26), and the approximate CDF obtained by numerically integrating the saddlepoint density. The line that breaks down on each plot is the Lugannani and Rice (1980) saddlepoint CDF approximation. This approximation is unstable near the mean of the distribution. In general,
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