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Redundant Data Update (SRDU) algorithm for updating redundant data in erasure-coded distributed storage. This algorithm exploits the structure of Reed-Solomon erasure correction codes to enable the reuse of old redundant data in generating the new redundant data. This enables the algorithm to cut down the amount of data transfer by as much as 70% for the SRDU algorithm. It is worth noting that the algorithms presented in this chapter are not limited to parallel server systems, but are also applicable to any striped data storage, such as disk arrays, RAID [1], or even the emerging peer-to-peer systems. For this reason we will use the term node to refer to a device in the striped storage, e.g., a server in a parallel streaming server, a disk in a RAID, or a user host in a peer-to-peer streaming system.



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11.5.3.4. The Applications Layer The BT digital library applications utilise the components of the semantic layer. In general, applications such as the search and browse, and, search agent applications, query the data held in the BT digital library ontology through the inference engine via the SIP. The architecture also allows for applications to interface directly to semantic layer components where necessary. The alerting component, which is common to all applications that push information to users, enables information alerts to be delivered at a time and in a format that is suitable to the user. A pro le construction component, which is integrated with a web browser, enables pro les of users interests to be constructed. 11.5.3.5. The Presentation Layer Client devices interact with the presentation layer of the architecture. A device independent presentation component presents the user interface for each end-user application according to the capabilities of the device being used and to the preferences set by the user.





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The problem of data reorganization has been studied previously in the context of disk arrays [2 3] The study by Ghandeharizadeh and Kim [2] is the earliest study on data reorganization known to the author They investigated the data reorganization problem in the context of adding disks to a continuous media server They studied the round-robin data striping commonly found in disk arrays and proposed techniques to perform data reorganization online, ie, without disrupting on-going video streams Due to the round-robin placement requirement, a large portion of the data blocks will need to be redistributed to maintain the data placement order when a new disk is added, thus incurring signi cant data reorganization overhead The advantage is that this approach can maintain perfect streaming load balance when data reorganization is completed In a more recent study by Goel et al.

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[3], an algorithm called SCADDAR for data placement and data reorganization is proposed for use in disk arrays In this algorithm, each data block is initially randomly distributed to the disks with equal probabilities When a new disk is added to the disk array, each block will obtain a new sequence number according to their randomized SCADDAR algorithm If the remainder of this number is equal to the disk number of the newly added disk, the corresponding block will be moved to this new disk Otherwise, the block will stay on the original disk As SCADDAR no longer needs to maintain a strict round-robin placement order, it can reduce the reorganization overhead to levels approaching the theoretical lower bound However, the SCADDAR algorithm did not consider streaming load balance.

The BT digital library architecture has been implemented on two Sun Microsystems servers. All components in the semantic, application and presentation layers have been deployed on a Sun Blade 1500 server running SunOS 5.9. The back end databases for Inspec and ABI/ INFORM are provided on the existing BT digital library Sun Fire V240 server, running SunOS 5.8.

That means you are implementing a system And if you have a system that you are following, it doesn t really matter if the trade is a winner or a loser, as long as you understand that over time the system is going to return profits The goal is to make money, right.

If we apply SCADDAR to a streaming server, then the pseudo-random placement policy can result in signi cant streaming load imbalance, especially after a large number of disks have been added to the system This load imbalance complicates data transmission scheduling and may also reduce the usable streaming capacity and/or increase the response time of the system Interestingly, the previous two studies can be considered as two extremes of the trade-off between data reorganization overhead and streaming load balance In particular, Ghandeharizadeh and Kim s algorithm achieves perfect load balance at the expense of substantial data reorganization overhead, while the SCADDAR algorithm achieves near-minimal data reorganization overhead at the expense of load imbalance The Row-Permutated Data Reorganization described in Section 153, by comparison, can achieve perfect streaming load balance and yet incurs signi cantly lower reorganization overhead than the round-robin algorithm..

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