Ijcot v8p303

International Journal of Computer & Organization Trends – Volume 8 Number 1 – May 2014

Efficient Query Processing for Distributed Hash Tree with Less Maintenance C.Indumathi, M.Tech IT, Prist University, India

Ç Abstract- Hash Tree -An Efficient Query with Low

network dynamics and node failures that are

Maintenance Indexing scheme in DISTRIBUTED HASH

common in large-scale P2P networks.

TREEs. Two

novel

techniques

contribute

to

the

superior performance: a clever naming mechanism and a

tree

summarization

enhancements :

strategy.

We

present

two

an extensible technique for indexing

scheme unbounded data domains and a double-naming strategy for improving system load balance. Compared with state- of-the art indexing scheme scheme, it saves maintenance cost and substantially improves query

Load

balancing: Load balance in DISTRIBUTED HASH TREEs can be efficiently achieved thanks to uniform hashing.

While DISTRIBUTED HASH TREEs are popular in developing various P2P applications, such as large-scale data storage content distribution

performance in terms of both response time and

and scalable multicast/any cast services they are

bandwidth consumption.

extremely poor in supporting critical queries

Keywords—Hashing, maintenance

query

efficient,

less

This is primarily because data locality, which is crucial to processing such critical queries, is destroyed by uniform hashing employed in DISTRIBUTED HASH TREEs. Two issues are

1.INTRODUCTION: Distributed Hash Table is a widely used

critical

to

the

performance

of

an

over-

building block for scalable Peer-to-Peer (P2P)

DISTRIBUTED HASH TREE indexing scheme

systems. It provides a simple lookup service: given

scheme: query efficiency and index maintenance

a key, one can efficiently locate the peer node

cost. In conventional applications where queries

storing the key. By employing consistent hashing

are more frequent than data updates, achieving

and

query efficiency is considered the first priority.

carefully

designed

overlays,

these

DISTRIBUTED HASH TREEs exhibit several

However, in P2P systems, peer joins and departures usually result in data insertions and

advantages that fit in a P2P context: typical

deletions to/from the system; and the peer

DISTRIBUTED HASH TREE of N peers, the

join/departure rate can be as high as the query rate

lookup latency is O(n log n) hops with each peer

Such data updates incur constant index updates.

Scalability

maintaining

and

only

efficiency:

In

“neighbours.”

a

Robustness:

DISTRIBUTED HASH TREEs are resilient to

ISSN: 2249-2593

Thus, the cost of index maintenance becomes a non-negligible factor in evaluating system

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International Journal of Computer & Organization Trends – Volume 8 Number 1 – May 2014 performance. This perspective, however, is not realized in existing over-DISTRIBUTED HASH TREE indexing scheme schemes.

HASH

TREE

indexing

scheme

scheme:

Query

efficiency and Index maintenance cost.

2.2 PROPOSED SYSTEM

More specifically, in a distributed context, each peer maintains a local view of the global index; in order to achieve a better query performance, the common idea is to enlarge the local view and let each peer know more about the global index. In

In the proposed scheme, we introduce Hash Tree - a low-maintenance yet query-efficient scheme for data indexing scheme over DISTRIBUTED HASH TREEs. Two novel techniques contribute to the superior performance of : a naming mechanism that gracefully

DST the index structure remains static and is

distributes the index structure over the underlying

made known globally. However, this static design

DISTRIBUTED HASH TREE and a tree summarization

inherently goes against the dynamic nature of P2P

mechanism that offers each peer a scalable local view

systems and easily leads to load imbalance.

without incurring extra maintenance cost. can support

2.SYSTEM ANALYSIS

various critical queries with near optimal performance.

2.1 EXISTING SYSTEM

2.2.1 ADVANTAGES IN PROPOSED SYSTEM:

DISTRIBUTED HASH TREE provides a

We propose to address both query efficiency

simple lookup service: given a key, one can efficiently

and maintenance efficiency for data indexing scheme

locate the peer node storing the key. DISTRIBUTED

over DISTRIBUTED HASH TREEs.

HASH TREEs are popular in developing various P2P

modification of the underlying DISTRIBUTED HASH

applications such as large scale data storage, content

TREE and hence possesses the virtues of simplicity and

distribution and scalable multicast/ any cast services.

adaptability.

DISTRIBUTED

We present two enhancements to

HASH

TREE

indexing

scheme

schemes are of great simplicity to design and

requires no

Hash Tree: Query

efficient and Low maintenance.

implement. However as hashing technique employed in

is the first over-DISTRIBUTED HASH TREE

DISTRIBUTED HASH TREEs destroys data locality, it

indexing scheme scheme with such flexibility. saves

is not a trivial task to support cmplex queries in

maintenance cost and substantially improves query

DISTRIBUTED HASH TREE-based P2P system.In

performance in terms of both response time and

DISTRIBUTED HASH TREEs the cost of index

bandwidth consumption.

maintenance becomes a non-negligible factor in

3. PROJECT DESCRIPTION

evaluating system performance.

2.1.1 DRAWBACKS IN EXISTING SYSTEM

3.1 PROBLEM DEFINITION We address a challenging problem of how to

DISTRIBUTED HASH TREES does not support critical queries in efficient way. Two issues are critical to the performance of an over-DISTRIBUTED

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efficiently support critical query processing with query efficient and low maintenance cost in

existing

DISTRIBUTED HASH TREE-based P2P systems. To

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International Journal of Computer & Organization Trends – Volume 8 Number 1 – May 2014 tackle this problem, we propose

Hash Tree ()- An

extremely poor in supporting critical queries such

Efficient Query with Low Maintenance Indexing

as range queries and k-nearest-neighbor (k-NN)

scheme Scheme in DISTRIBUTED HASH TREEs.

queries.

employs a novel naming mechanism and a tree summarization strategy for graceful distribution of

its

This is primarily because data locality,

index structure.

which is crucial to processing such critical queries, 3.2 OVERVIEW

is destroyed by uniform hashing employed in

In a Distributed Hash Table is a widely used building block for scalable Peer-to-Peer (P2P) systems. It provides a simple lookup service: given a key, one can efficiently locate the peer node storing the key. By employing consistent hashing and

carefully

designed

overlays,

these

DISTRIBUTED HASH TREEs exhibit several

DISTRIBUTED HASH TREEs.Two issues are critical

to

the

performance

of

an

over-

DISTRIBUTED HASH TREE indexing scheme scheme: query efficiency and index maintenance cost. In conventional applications where queries are more frequent than data updates, achieving query efficiency is considered the first priority.

advantages that fit in a P2P context: However, in P2P systems, peer joins and Scalability and efficiency: In a typical DISTRIBUTED HASH TREE of N peers, the lookup latency is OðlogNÞ hops with each peer

departures usually result in data insertions and deletions to/from the system; and the peer join/departure rate can be as high as the query rate Such data updates incur constant index updates.

maintaining only “neighbors.”

Thus, the cost of index maintenance becomes a Robustness:

DISTRIBUTED

HASH

TREEs are resilient to network dynamics and node failures that are common in large-scale P2P

factor

in

evaluating

system

performance. More specifically, in a distributed context, each peer maintains a local view of the global index; in order to achieve a better query

networks.

Load

non-negligible

balancing:

Load

balance

in

DISTRIBUTED HASH TREEs can be efficiently

performance, the common idea is to enlarge the local view and let each peer know more about the global index.In DST the index structure remains

achieved thanks to uniform hashing.

static and is made known globally.

While DISTRIBUTED HASH TREEs are popular in developing various P2P applications, such as large-scale data storage content distribution and scalable multicast/any cast services they are

However, this static design inherently goes against the dynamic nature of P2P systems and easily leads to load imbalance. As an alternative,

ISSN: 2249-2593

RST

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allows

for

dynamic

tree

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International Journal of Computer & Organization Trends – Volume 8 Number 1 – May 2014 growth/contraction

and

broadcasting mechanism

further

employs

a

to maintain its global

DISTRIBUTED HASH TREE indexing scheme schemes, the main challenge of

is to find a

view. However, the index maintenance cost is

mapping from data keys to DISTRIBUTED HASH

prohibitively high as a single node split causes a

TREE keys such that data locality is preserved

broadcast to all other nodes, which may render the

with minimal maintenance cost.The figure gives an

whole P2P system unscalable.

overview of the mapping operation in . First, employs a space partition tree to index data keys.

In the index, a data unit is called a record,

Then, after the partition tree is decomposed and

and each record is identified by a data key. We

summarized in a data structure called a leaf bucket,

assume that the data keys to be indexed fall into a

uses a novel naming function to map leaf buckets

bounded one dimensional space

to DISTRIBUTED HASH TREE keys. In the

Fig.1. System Architecture for indexing scheme scheme

following, we explain these two procedures in detail.

3.3 MODULE DESCRIPTION 3.3.1. MODULE 1: DATA STORING

is proposed to support critical queries over some existing DISTRIBUTED HASH TREEs, while exact-match queries can be directly and efficiently

answered

by

the

existing

DISTRIBUTED HASH TREE infrastructure.

OVERVIEW:

In the index, a data unit is called a record, and each record is identified by a data key .On the other hand, to assign the records in the underlying DISTRIBUTED HASH TREE, each data record is associated

with

another

key,

called

DISTRIBUTED HASH TREE key. In a native indexing scheme scheme, one may set the DISTRIBUTED HASH TREE key directly to be the data key. However, this would destroy data However, this would destroy data locality, as mentioned earlier, and lead to inefficient support to critical queries. Thus, similar to other over-

ISSN: 2249-2593

locality, as mentioned earlier, and lead to inefficient support to critical queries. Thus, similar to other

over-DISTRIBUTED HASH TREE

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International Journal of Computer & Organization Trends – Volume 8 Number 1 – May 2014 indexing scheme schemes, the main challenge of

Fig.2. An example of a space partition tree.

is to find a mapping from data keys to DISTRIBUTED HASH TREE keys such that data locality is preserved with minimal maintenance cost. First, employs a space partition tree to index data keys. Then, after the partition tree is decomposed and summarized in a data structure called a leaf bucket, uses a novel naming function to map leaf buckets to DISTRIBUTED HASH TREE keys.

SPACEPARTITION TREE: As the name implies, the space partition tree (or simply partition tree for short) recursively partitions the data space into two equal-sized

LOCAL TREE SUMMARIZATION:

subspaces until each subspace contains fewer than

Recall that data records are stored in leaf

split data keys. Only leaf nodes store data records

nodes; we need to map only leaf nodes to the underlying

(or just data entries with pointers pointing to actual

DISTRIBUTED HASH TREE. On the other hand, a

data records). Basically, the space partition tree is a

bare leaf node lacks the knowledge of the overall tree

binary tree with structural properties listed below:

structure, which, as we will see, is critical to critical query processing. Thus, we propose a distributed data

Double Root. Unlike a conventional

structure, termed leaf bucket, to store data records and

binary tree, the space partition tree has two roots.

summarize the partition tree’s structural information.

The additional root, termed virtual root, is a virtual

Each leaf bucket corresponds to a leaf node in the

node above the ordinary one.

tree.According to the completeness property of the partition tree, all branch nodes must exist in the tree.

Completeness. Every tree node, except the

Some branch nodes may contain a subtree, called the

virtual root and leaf nodes, has two children, that

neighboring subtree.The structures of these neighboring

is, every internal node has two children.

subtrees are unknown in the current local tree, but are maintained by some other leaves’ local trees. From a global viewpoint, the local trees of all leaves together guarantee the partition tree’s integrity. In other words, the leaf buckets collectively maintain the tree’s

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International Journal of Computer & Organization Trends – Volume 8 Number 1 – May 2014 structural information. Thus,the remaining issue is how

QUERYING

to map each leaf bucket as an atomic unit to the

Implementation of Range Queries Algorithm:

DISTRIBUTED HASH TREE key, which is achieved by a novel naming function..

A range query is a common database operation that retrieves all records where some value is between an upper and lower boundary.

NAMING FUNCTION:

Range queries are unusual because it is not After the partition tree is decomposed and

generally known in advance how many entries a

summarized in a data structure called a leaf bucket,

range query will return, or if it will return any at

uses a novel naming function to map leaf buckets

all. Many other queries, such as the top ten most

to DISTRIBUTED HASH TREE keys.

senior employees, or the newest employee, can be

3.3.2. MODULE 2 : INDEXING SCHEME AND

done more efficiently because there is an upper

QUERYING

bound to the number of results they will return. A

INDEXING SCHEME

query that returns exactly one result is sometimes

LSH Based Indexing scheme:

called a singleton.

Locality-sensitive hashing (LSH) functions are the functions that make closer objects collide with a higher probability than far apart objects. An LSH index is constructed with L separate hash tables with the functions, each of which is constructed with k hash functions independently

Implementation of Max/Min Query Algorithm: Min max is a decision rule used in decision theory, game theory, statistics and philosophy for minimizing the possible loss while maximizing the potential gain. Alternatively, it can be thought of as maximizing the minimum gain

and uniformly at random (with replacement) from a given LSH family H.

Implementation of K-NN Query Algorithm: K-nearest neighbor search identifies the

Fig.3. Novel Naming function to map leaf buckets

top k nearest neighbors to the query. This technique is commonly used in predictive analytics to estimate or classify a point based on the consensus of its neighbors. K-nearest neighbor graphs are graphs in which every point is connected to its k nearest neighbors.

3.3.3. MODULE 3: LOOKUP

In this module , fundamental service in is the lookup operation: given a data key, a lookup returns the corresponding DISTRIBUTED HASH

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International Journal of Computer & Organization Trends – Volume 8 Number 1 – May 2014 TREE key. Essentially, this is to find the label of

systems. It provides a simple lookup service: given

the leaf bucket that covers , upon which we can

a key, one can efficiently locate the peer node

apply

storing the key. By employing consistent hashing

the

naming

function

to

obtain

the

DISTRIBUTED HASH TREE key.

and

carefully

designed

overlays,

these

DISTRIBUTED HASH TREEs exhibit several advantages that fit in a P2P context: Scalability

3.3.4. MODULE 4: MAINTENANCE Unlike overlay-dependent indexes that

and efficiency: In a typical DISTRIBUTED HASH

would update their structures with network

TREE of N peers, the lookup latency is O(n log n)

structure changes caused by system dynamics (i.e.,

hops

peer joins/departures/failures), the

“neighbours.”

index only

with

each

peer

maintaining

Robustness:

only

DISTRIBUTED

needs to handle data updates while leaving network

HASH TREEs are resilient to network dynamics

structure

and node failures that are common in large-scale

changes

to

the

underlying

DISTRIBUTED HASH TREE.

P2P networks. Load balancing: Load balance in

Data Insertion and Leaf Split:

DISTRIBUTED HASH TREEs can be efficiently

Inserting a data record into

involves a

achieved thanks to uniform hashing.

lookup and a possible leaf split process. In , one

While DISTRIBUTED HASH TREEs are

leaf bucket will stay on the current peer, denoted as

popular in developing various P2P applications,

the local leaf, while the other one, denoted as the

such as large-scale data storage content distribution

remote leaf, will be pushed out to some other peer.

and scalable multicast/any cast services they are

The local leaf is not pushed out and consumes no

extremely poor in supporting critical queries such

bandwidth overhead. This nice property, which we

as range queries and k-nearest-neighbour (k-NN)

call incremental leaf split

queries. This is primarily because data locality,

Data Deletion and Leaf Merge:

which is crucial to processing such critical queries,

To remove the data key , , similar to data insertion, first performs

Lookup to locate the leaf

is destroyed by uniform hashing employed in DISTRIBUTED HASH TREEs.

bucket. It then executes a local deletion operation

Two issues are critical to the performance of

to remove the corresponding record. Data deletion

an over-DISTRIBUTED HASH TREE indexing

may further lead to a merge of leaf buckets if the

scheme scheme: query efficiency and index

number of records (called load for brevity)

maintenance cost. In conventional applications where queries are more frequent than data updates,

4. SYSTEM IMPLEMENTATION

achieving query efficiency is considered the first priority.

Distributed Hash Table is a widely used

However, in P2P systems, peer joins and

building block for scalable Peer-to-Peer (P2P)

departures usually result in data insertions and

ISSN: 2249-2593

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International Journal of Computer & Organization Trends – Volume 8 Number 1 – May 2014 deletions to/from the system; and the peer

This paper proposed , a Hash Tree, for

join/departure rate can be as high as the query rate

efficient

Such data updates incur constant index updates.

DISTRIBUTED HASH TREEs. differs from PHT,

Thus, the cost of index maintenance becomes a

a

non-negligible

system

TREE indexing scheme scheme, in the following

performance. This perspective, however, is not

aspects: Both PHT and are based on the idea of

realized in existing over-DISTRIBUTED HASH

space partitioning. While PHT maps its index

TREE indexing scheme schemes.

structure into DISTRIBUTED HASH TREE in a

factor

in

evaluating

On the contrary, existing schemes improve

data

indexing

representative

scheme

over

over-DISTRIBUTED

HASH

straightforward manner, leverages a clever naming

query efficiency by sacrificing index maintenance

function,

which

cost as a trade-off. More specifically, in a

maintenance

distributed context, each peer maintains a local

DISTRIBUTED

view of the global index; in order to achieve a

performance.

significantly

cost

and

lowers

improves

HASH

the the

TREE-lookup

better query performance, the common idea is to

In this section, we further present two

enlarge the local view and let each peer know more

extensions to the index, including how to index

about the global index.

unbounded data domains and how to improve peer

In DST the index structure remains static and is made known globally. However, this static

load balance.

Extensible Indexing scheme

design inherently goes against the dynamic nature of P2P systems and easily leads to load imbalance.

We propose

The basic index deals with a bounded data domain (i.e., in the normalized [0, 1] space), which

Hash Tree ()-An Efficient

requires a priori knowledge of the indexed data.

Query with Low Maintenance Indexing scheme

However, in many applications, such knowledge

Scheme in DISTRIBUTED HASH TREEs. Two

cannot be obtained in advance; and even more the

novel techniques contribute to the superior

data domain may change over time.

performance of : a clever naming mechanism and a

Improvement of Peer Load Balance

tree summarization strategy. We present two

In general, DISTRIBUTED HASH TREEs offer

enhancements to : an extensible technique for

load balance quite efficiently, yet not that

indexing scheme unbounded data domains and a

effectively. Specifically, if the imbalance ratio

double-naming strategy for improving system load

denotes the ratio of the heaviest load to the average

balance. Compared with state-of-the art indexing

load

scheme scheme, namely PHT, and DST,

saves

DISTRIBUTED HASH TREEs only bound the

50%-75% of maintenance cost and substantially

imbalance ratio at O(logN) with high probability

improves query performance in terms of both

[5], [1].

for

the

peers

in

the

P2P

network,

response time and bandwidth consumption.

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International Journal of Computer & Organization Trends – Volume 8 Number 1 – May 2014 5. CONCLUSION AND FUTURE

peer load balance. As a comparison, PHT (and

ENHANCEMENT

other existing over-DISTRIBUTED HASH TREE

5.1. CONCLUSION:

schemes) only supports data indexing scheme of

This paper proposed , a Hash Tree, for efficient

data

indexing

scheme

over

bounded domains and achieves better load balance by

modifying

DISTRIBUTED

HASH

DISTRIBUTED HASH TREEs. differs from PHT,

TREEs.Experimental

a

HASH

comparison with the state-of-the-art indexing

TREE indexing scheme scheme, in the following

scheme techniques PHT and DST, saves 50-75

aspects: Both PHT and are based on the idea of

percent of index maintenance cost and supports

space partitioning. While PHT maps its index

more efficient lookup operations.

representative

over-DISTRIBUTED

structure into DISTRIBUTED HASH TREE in a

Moreover,

results

show

that

in

has a much better query

straightforward manner, leverages a clever naming

performance

function,

the

consumption and response time. As an over-

the

DISTRIBUTED HASH TREE scheme,

which

maintenance

significantly

cost

DISTRIBUTED

and HASH

lowers

improves

TREE-lookup

performance.

in

terms

of

both

bandwidth

is

adaptable to generic DISTRIBUTED HASH TREEs and can be easily implemented and

employs local tree summarization to

deployed in any DISTRIBUTED HASH TREE-

provide each bucket a local view. This local view

based P2P system.

is

5.2. FUTURE ENHANCEMENTS:

essentially

helpful

for

distributed

query

processing, but unlike PHT’s sequential leaf link,

In this section, we further present two

requires no extra maintenance cost. In PHT, all leaf

extensions to the index, including how to index

nodes and internal nodes are mapped to the

unbounded data domains and how to improve peer

DISTRIBUTED HASH TREE space, whereas only

load balance.

leaf nodes are mapped in . The processing of range queries in PHT has to go through all internal nodes

5.2.1 Extensible Indexing scheme

of the subtree in addition to the leaf nodes in the

The basic index deals with a bounded data

queried range, which at least doubles the search

domain (i.e., in the normalized [0, 1] space), which

cost.Thanks to the novel naming function, one can

requires a priori knowledge of the indexed data.

easily determine the leftmost/rightmost leaf node

However, in many applications, such knowledge

under a subtree in O(1) lookup.

cannot be obtained in advance; and even more the

As

such,

min/max

queries

can

be

efficiently supported in . can be extended to index unbounded

data

domains

For example, if we want to index the

naturally

publication dates of MP3 files in a P2P file-sharing

accommodate a double naming strategy to improve

application, the data domain for publication dates

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and

data domain may change over time.

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International Journal of Computer & Organization Trends – Volume 8 Number 1 – May 2014 Protocols for Computer Comm. (SIGCOMM), pp. 73-84,

is not fixed. In this section, we propose E-, an extensible that supports data indexing scheme of

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P2P

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