BitTorrent and fountain codes: friends or foes?

Spoto, Salvatore; Gaeta, Rossano; Grangetto, Marco; Sereno, Matteo

doi:10.1109/ipdpsw.2010.5470926

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…Discussion tributed across the network [44]. Shibo et al [46] propose adding redundant HMCs (Hybrid Memory Cubes) storing erasure codes to compute the data stored in HMCs currently placed in inaccessible power-down modes.…”

mentioning

confidence: 99%

Nonblocking Memory Refresh

Nguyen

Lyu

Meng

et al. 2018

2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA)

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Since its inception half a century ago, DRAM has required dynamic/active refresh operations that block read requests and decrease performance. We propose refreshing DRAM in the background without stalling read accesses to refreshing memory blocks, similar to the static/background refresh in SRAM. Our proposed Nonblocking Refresh works by refreshing a portion of the data in a memory block at a time and uses redundant data, such as Reed-Solomon codes, in the block to compute the block's refreshing/unreadable data to satisfy read requests. For proof of concept, we apply Nonblocking Refresh to server memory systems, where every memory block already contains redundant data to provide hardware failure protection. In this context, Nonblocking Refresh can utilize server memory system's existing per-block redundant data in the common-case when there are no hardware faults to correct, without requiring any dedicated redundant data of its own. Our evaluations show that on average across five server memory systems with different redundancy and failure protection strengths, Nonblocking Refresh improves performance by 16.2% and 30.3% for 16gb and 32gb DRAM chips, respectively. Nonblocking Memory RefreshKate Vy H Nguyen (GENERAL AUDIENCE ABSTRACT)Main memory is an essential component of computers, which stores data being actively used. The dominant type of computer main memory is Dynamic Random Access Memory (DRAM). DRAM is divided into thousands of memory cells. Each cell stores a single bit of data as a charge on a capacitor. Charges may leak over time, causing the data stored to be lost. To maintain the data stored in memory, DRAM must periodically restore charges held by memory cells through an operation known as memory refresh. Refresh operations decrease system performance because they stall read requests to refreshing memory blocks.A memory block refers to the unit of data transferred per memory request. Conventional memory systems refresh all the data within the block at a time, therefore the entire memory block is inaccessible while it is being refreshed. Our proposed Nonblocking Refresh reduces the amount of data in a memory block which is inaccessible due to refresh by refreshing only a portion the memory block at a time. To satisfy read requests, the block's refreshing/inaccessible data is computed using redundant data. Nonblocking Refresh improves DRAM performance by refreshing DRAM in the background without stalling read accesses to refreshing memory blocks. For proof of concept, we apply Nonblocking Refresh to server memory systems, where every memory block already contains redundant data to provide hardware failure protection. In this context, Nonblocking Refresh can utilize server memory system's existing redundant data to improve performance, without adding additional redundancy overhead. Our evaluations show that on average across five server memory systems with different redundancy and failure protection strengths, Nonblocking Refresh improves performance by 16%-30%.

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mentioning

confidence: 99%

Nonblocking Memory Refresh

Nguyen

Lyu

Meng

et al. 2018

2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA)

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“…Erasure coding is also a scheme used for data dissemination. Spoto et al presented a BitTorrent system based on LT fountain codes [52]. In [53] ToroVerde -a push based P2P content distribution system employing fountain codes is presented.…”

Section: Erasure Codingmentioning

confidence: 99%

Content Distribution Strategies in Opportunistic Networks

Masood¹,

Raza²,

Coates³

2013

Preprint

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The use of mobile data services is in high demand ever since the advent of smartphones and is expected to increase further with the evolution of various services and applications for future mobile devices. This excessive use of data has caused severe bottlenecks within the mobile networks due to overloading. The route towards network upgrades is an expensive one especially due to the high licensing fees attached to spectrum acquisition. Traffic offloading through opportunistic communication is a new paradigm exploiting the meeting opportunities within emerging Mobile Social Networks for the dissemination of delay tolerant contents without any additional investment. The idea is to save the network bandwidth by pushing the content optimally to as few users as possible from base station and then exploit the social interactions of social group members for content distribution among themselves by using point to point communications through WiFi or Bluetooth. Several studies suggests that there exists strong community architecture within contact graph of Mobile Social Networks i.e. the edges are not randomly distributed over nodes but in the form of clusters. We study the problem of dissemination of a large file in community based mobile social network where base station seeds the contents of the file initially to some users within each community. Furthermore, we determine whether it is more efficient in terms of latency and number of transmissions to transfer encoded or uncoded contents during opportunistic meetings by comparing different coded (Network Coding, Erasure Coding) and uncoded (Flooding, Epidemic Routing) data dissemination strategies in mobile social network.

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“…producing a greater number of independent encoded symbols) [7,8]. Reed Solomon, Tornado, Luby Transform, and Raptor codes all represent, respectively, increasingly more successful approximations of the DF paradigm and have been tested on P2P streaming platforms [9,10].…”

Section: Coding Techniques In P2p Streaming Networkmentioning

confidence: 99%