13-08-2014, 12:12 PM
Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud
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ABSTRACT:
In recent years ad-hoc parallel data processing has emerged to be one of the killer applications for Infrastructure-as-a-Service (IaaS) clouds. Major Cloud computing companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. However, the processing frameworks which are currently used have been designed for static, homogeneous cluster setups and disregard the particular nature of a cloud. Consequently, the allocated compute resources may be inadequate for big parts of the submitted job and unnecessarily increase processing time and cost. In this paper we discuss the opportunities and challenges for efficient parallel data processing in clouds and present our research project. It is the first data processing framework to explicitly exploit the dynamic resource allocation offered by today’s IaaS clouds for both, task scheduling and execution. Particular tasks of a processing job can be assigned to different types of virtual machines which are automatically instantiated and terminated during the job execution.
EXISTING SYSTEM:
A growing number of companies have to process huge amounts of data in a cost-efficient manner. Classic representatives for these companies are operators of Internet search engines. The vast amount of data they have to deal with every day has made traditional database solutions prohibitively
Expensive .Instead, these companies have popularized an architectural paradigm based on a large number of commodity servers. Problems like processing crawled documents or regenerating a web index are split into several independent subtasks, distributed among the available nodes, and computed in parallel.
Job Scheduling and Execution:
After having received a valid Job Graph from the user, Nephele’s Job Manager transforms it into a so-called Execution Graph. An Execution Graph is Nephele’s primary data structure for scheduling and monitoring the execution of a Nephele job. Unlike the abstract Job Graph, the Execution Graph contains all the concrete information required to schedule and execute the received job on the cloud.
QUERY PROCESSING:
Processing is based on implementation of the theorem uses (network-based) search operations as off the shelf building blocks. Thus, the NAP query evaluation methodology is readily deployable on existing systems, and can be easily adapted to different network storage schemes. In this case, the queries are evaluated in a batch. we propose the network-based anonymization and processing (NAP) framework, the first system for K- anonymous query processing in road networks. NAP relies on a global user ordering and bucketization that satisfies reciprocity and guarantees K-anonymity. We identify the ordering characteristics that affect subsequent processing, and qualitatively compare alternatives. Then, we propose query evaluation techniques that exploit these characteristics. In addition to user privacy, NAP achieves low computational and communication costs, and quick responses overall. It is readily deployable, requiring only basic network operations.
[attachment=66603]
ABSTRACT:
In recent years ad-hoc parallel data processing has emerged to be one of the killer applications for Infrastructure-as-a-Service (IaaS) clouds. Major Cloud computing companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. However, the processing frameworks which are currently used have been designed for static, homogeneous cluster setups and disregard the particular nature of a cloud. Consequently, the allocated compute resources may be inadequate for big parts of the submitted job and unnecessarily increase processing time and cost. In this paper we discuss the opportunities and challenges for efficient parallel data processing in clouds and present our research project. It is the first data processing framework to explicitly exploit the dynamic resource allocation offered by today’s IaaS clouds for both, task scheduling and execution. Particular tasks of a processing job can be assigned to different types of virtual machines which are automatically instantiated and terminated during the job execution.
EXISTING SYSTEM:
A growing number of companies have to process huge amounts of data in a cost-efficient manner. Classic representatives for these companies are operators of Internet search engines. The vast amount of data they have to deal with every day has made traditional database solutions prohibitively
Expensive .Instead, these companies have popularized an architectural paradigm based on a large number of commodity servers. Problems like processing crawled documents or regenerating a web index are split into several independent subtasks, distributed among the available nodes, and computed in parallel.
Job Scheduling and Execution:
After having received a valid Job Graph from the user, Nephele’s Job Manager transforms it into a so-called Execution Graph. An Execution Graph is Nephele’s primary data structure for scheduling and monitoring the execution of a Nephele job. Unlike the abstract Job Graph, the Execution Graph contains all the concrete information required to schedule and execute the received job on the cloud.
QUERY PROCESSING:
Processing is based on implementation of the theorem uses (network-based) search operations as off the shelf building blocks. Thus, the NAP query evaluation methodology is readily deployable on existing systems, and can be easily adapted to different network storage schemes. In this case, the queries are evaluated in a batch. we propose the network-based anonymization and processing (NAP) framework, the first system for K- anonymous query processing in road networks. NAP relies on a global user ordering and bucketization that satisfies reciprocity and guarantees K-anonymity. We identify the ordering characteristics that affect subsequent processing, and qualitatively compare alternatives. Then, we propose query evaluation techniques that exploit these characteristics. In addition to user privacy, NAP achieves low computational and communication costs, and quick responses overall. It is readily deployable, requiring only basic network operations.