Data Similarity-Aware Computation Infrastructure

Data Similarity-Aware Computation Infrastructure

For The Cloud

ABSTRACT:

The cloud is emerging for scalable and efficient cloud services. To meet the needs of handling massive data anddecreasing data migration, the computation infrastructure requires efficient data placement and proper management for cached data.In this paper, we propose an efficient and cost-effective multilevel caching scheme, called MERCURY, as computation infrastructure ofthe cloud. The idea behind MERCURY is to explore and exploit data similarity and support efficient data placement. To accurately andefficiently capture the data similarity, we leverage a low-complexity locality-sensitive hashing (LSH).

Our design, in addition to theproblem of space inefficiency, we identify that a conventional LSH scheme also suffers from the problem of homogeneous dataplacement. To address these two problems, we design a novel multicore-enabled locality-sensitive hashing (MC-LSH) that accuratelycaptures the differentiated similarity across data. The similarity-aware MERCURY, hence, partitions data into the L1 cache, L2 cache,and main memory based on their distinct localities, which help optimize cache utilization and minimize the pollution in the last-levelcache. Besides extensive evaluation through simulations, we also implemented MERCURY in a system. Experimental results basedon real-world applications and data sets demonstrate the efficiency and efficacy of our proposed schemes.

Existing System

The cloud is emerging for scalable and efficient cloud services. To meet the needs of handling massive data anddecreasing data migration, the computation infrastructure requires efficient data placement and proper management for cached data.

Proposed system

we propose an efficient and cost-effective multilevel caching scheme, called MERCURY, as computation infrastructure ofthe cloud. The idea behind MERCURY is to explore and exploit data similarity and support efficient data placement. To accurately andefficiently capture the data similarity, we leverage a low-complexity locality-sensitive hashing (LSH).

System Configuration:-

Hardware Configuration:-

Processor-Pentium –IV

Speed- 1.1 Ghz

RAM- 256 MB(min)

Hard Disk- 20 GB

Key Board- Standard Windows Keyboard

Mouse- Two or Three Button Mouse

Monitor- SVGA

Software Configuration:-

Operating System: Windows XP

Programming Language: JAVA

Java Version: JDK 1.6 & above.

Further Details Contact: A Vinay 9030333433, 08772261612

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