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computer and simply have it work at any time, all the time. Users expect the same level of service as from, say, the phone system: When you pick up the phone you get a dial tone. True autonomic computing will seamlessly handle varied file formats, deliver extremely high levels of availability by diagnosing and repairing problems before they affect users, and operate through a natural language interface rather than through keyboards and complex sets of commands. Their data will be automatically protected so that users will not need to be concerned with system backups or viruses. Performance agents will examine the activity on the system at any given moment, and optimize resource allocation to deliver the best possible performance to the user. Newly connected devices such as printers will automatically configure themselves and announce their presence to the network, without requiring any user activity or system reboots. Achieving this vision of pure policy-based computing requires that the basic discovery, connectivity, and monitoring tools become much more networkfocused than system-, or single-box , focused. Identifying a disk or memory failure on a single system is relatively straightforward because the system is well connected and predictable in its configuration; when you add the dynamic element of networking to the inherent unreliability and lack of predictable performance on a network, you have a much more difficult problem to solve. Whether it s autonomic, policy-based, or zero routine administration, these systems will change the shape of availability planning with new tools and new degrees of control for administrators.



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Recently, there has been much interest in the behavior of queues with heavy-tailed service time distributions. This interest has been triggered by a large number of traf c measurements on modern communication network traf c (e.g., see Willinger et al. [38] for Ethernet LAN traf c, Paxson and Floyd [30] for WAN traf c, and Beran et al. [3] for VBR video traf c; see also various chapters in this volume). These measurements and their statistical analysis (e.g., see Leland et al. [26]) suggest that modern communication traf c often possesses the properties of selfsimilarity and long-range dependence. A natural possibility to introduce long-range dependence in an input traf c process is to take a uid queue and to assume that at least one of the input quantities I (on or off periods in the uid queue fed by on=off sources) has the following ``heavy-tail'' behavior: P I > t $ hn t n ;





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Amazon.com shows us that the Internet isn t a disintermediating technology; it s one that creates new forms of intermediation. Amazon.com is the new channel between publishers and the public, just as ebay.com creates a new intermediation channel for small-scale collectors. Intermediation provides opportunities for creating more reliable and trusted computing models than are possible in the real, two-party world. For example, using an online payment service to transfer funds for an item you bought on eBay eliminates the need to send credit card information to an unknown entity if you trust the payment provider and believe you re going to get the goods (due to eBay feedback ratings), then the three-party system creates more trust than is possible in a two-party system.

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48. Languages spoken by participating consumers 49. Time spent with distributed content 50. Time spent on site through social media referrals 51. Method of content discovery (search, pass-along, discovery engines, etc.) 52. Clicks 53. Percentage of traf c generated from earned media 54. View-throughs 55. Number of interactions 56. Interaction/engagement rate 57. Frequency of social interactions per consumer 58. Percentage of videos viewed 59. Polls taken/votes received 60. Brand association 61. Purchase consideration 62. Number of user-generated submissions received 63. Exposures of virtual gifts 64. Number of virtual gifts given 65. Relative popularity of content 66. Tags added 67. Attributes of tags (e.g., how well they match the brand s perception of itself)

Self-Similar Network Traf c and Performance Evaluation, Edited by Kihong Park and Walter Willinger ISBN 0-471-31974-0 Copyright # 2000 by John Wiley & Sons, Inc.

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with hn a positive constant and 1 < n < 2 (here and later, f t $ g t stands for f t =g t 3 1 with t 3 I; and many-valued functions like t n are de ned by their principal value, so t n is real for t positive). In this context, regularly varying and subexponential distributions [5] have received special attention. We refer to Boxma and Dumas [12] for a survey on uid queues with heavy-tailed on-period distributions. In this chapter, we concentrate on the ordinary single server queue with regularly varying service and=or interarrival time distribution. The uid queue is closely related to this ordinary queue (cf. Remarks 6.2.6 and 6.6.3), so that several of the results of the present chapter will also be relevant for uid queues. When G x is the probability distribution of a nonnegative random variable G, then 1 G x P G > x is said to be regularly varying at in nity of index n when 1 G x x n L x ; x ! 0; 6:2

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