Tornado: a capability-aware peer-to-peer storage overlay

Hsiao, Hung‐Chang; King, Chung-Ta

doi:10.1016/j.jpdc.2003.10.008

Cited by 24 publications

(18 citation statements)

References 16 publications

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“…The table also includes information about the receiving SSUs who have interacted with the (originating) SSU. The local tables give SSUs and clients autonomy [20]. The localization table also keeps latency values to indicate the transmission delay that each block undergoes (and used for scheduling the retrieval of these blocks).…”

Section: Segmentation and Localization Of Datasetsmentioning

confidence: 99%

Scheduling memory access on a distributed cloud storage network

Rojas‐Cessa

Cai

Kijkanjanarat

2012

2012 21st Annual Wireless and Optical Communications Conference (WOCC)

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Abstract-Memory-access speed continues falling behind the growing speeds of network transmission links. High-speed network links provide a means to connect memory placed in hosts, located in different corners of the network. These hosts are called storage system units (SSUs), where data can be stored. Cloud storage provided with a single server can facilitate large amounts of storage to a user, however, at low access speeds. A distributed approach to cloud storage is an attractive solution. In a distributed cloud, small high-speed memories at SSUs can potentially increase the memory access speed for data processing and transmission. However, the latencies of each SSUs may be different. Therefore, the selection of SSUs impacts the overall memory access speed. This paper proposes a latency-aware scheduling scheme to access data from SSUs. This scheme determines the minimum latency requirement for a given dataset and selects available SSUs with the required latencies. Furthermore, because the latencies of some selected SSUs may be large, the proposed scheme notifies SSUs in advance of the expected time to perform data access. The simulation results show that the proposed scheme achieves faster access speeds than a scheme that randomly selects SSUs and another hat greedily selects SSUs with small latencies.

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Section: Segmentation and Localization Of Datasetsmentioning

confidence: 99%

Scheduling memory access on a distributed cloud storage network

Rojas‐Cessa

Cai

Kijkanjanarat

2012

2012 21st Annual Wireless and Optical Communications Conference (WOCC)

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“…Structured P2P systems [8,11,12,14,16] build a DHT by partitioning the hash table among the peers. Information of the object to be looked up is published to the DHT using the hash key of the object.…”

Section: Dht-based Systemsmentioning

confidence: 99%

“…The interconnection structure and the mapping mechanism allow the lookup operation to be performed with a bounded complexity. Examples of DHT-based P2P systems include CAN [11], Chord [14], Pastry [12], Tapestry [16], and Tornado [8].…”

Section: Introductionmentioning

confidence: 99%

Attribute-based Overlay Network for Non-DHT Structured Peer-to-peer Lookup

Sun

King

Sun

et al. 2007

2007 International Conference on Parallel Processing (ICPP 2007)

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Most DHT-based P2P search systems assume implicitly that the participating peers contain multiple objects. The distributed hash table (DHT) is used as a directory to store the keys of these objects for efficient lookup. However, maintaining the DHT and keeping its contents fresh require substantial message and storage overhead. In this paper, we study whether it is possible to do without a directory. We consider a special case of the search problem in which each peer is characterized by a single set of attributes and the peers satisfying a given range or k-nearest-neighbor (KNN) query are looked up. The attribute values may change constantly. For this special search problem, we study the feasibility of interconnecting the peers directly using their attribute values instead of using hash keys. The resultant attribute-based overlay network (AON) thus preserves the attribute semantics in the interconnection. Such a distributed structure can easily support range and KNN queries and accommodate dynamically changing attribute values.

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“…Structured P2P systems, such as CAN, Chord, Pastry, Tapestry and Tornado, manage the peer nodes with a logical structure. For example, Chord adopts a hypercube-like structure with a ring threading through all the nodes [4,5,7].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Hashing Approach to Supporting Load Balance in P2P Systems

Chang

Chen

et al. 2008

2008 the 28th International Conference on Distributed Computing Systems Workshops

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In a structured P2P system, peers maintain information about what resources neighbor peers offer. Chord is one of well-known structured P2P systems to efficiently support resource finding based on the hashing approach. However, in Chord, most of data keys may be assigned to the same peer, since it uses the static hashing scheme, resulting in the case that the load of Chord is unbalanced. Therefore, we propose a strategy which uses the dynamic hashing scheme to locate the data key based on the Chord architecture, and to maintain the load balance. From our simulation results, we show that the load of the P2P system based on our strategy is much more balanced than that based on the original strategy used in Chord.

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Tornado: a capability-aware peer-to-peer storage overlay

Cited by 24 publications

References 16 publications

Scheduling memory access on a distributed cloud storage network

Scheduling memory access on a distributed cloud storage network

Attribute-based Overlay Network for Non-DHT Structured Peer-to-peer Lookup

A Dynamic Hashing Approach to Supporting Load Balance in P2P Systems

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