Cost Minimization for Big Data Processing in Geo-Distributed Data Centres
نویسنده
چکیده
Demand on big data is being increasing day by day and also increasing heavy burden on computation, storage and communication in data centres, which lead to considerable expenditure to data centre providers. So, cost minimization became an issue for the upcoming bid data.one of the main feature of bid data is coupling of data and computation as computation task. This can be done only when that corresponding is available for computation. Three tasks like data placement, task assignment and data movement influence the expense of data centres. In this paper we study how to minimize cost through joint optimization of these above three factors for big data service in geo distributes data centres. Here we propose 2-D Markov chain to describe time to complete a particular task with consideration of data transmission and computation to derive average task completion time in closed time. In addition, we here model the problem as mixed integer nonlinear programming and propose a solution to linearize it.
منابع مشابه
Cloud Computing Technology Algorithms Capabilities in Managing and Processing Big Data in Business Organizations: MapReduce, Hadoop, Parallel Programming
The objective of this study is to verify the importance of the capabilities of cloud computing services in managing and analyzing big data in business organizations because the rapid development in the use of information technology in general and network technology in particular, has led to the trend of many organizations to make their applications available for use via electronic platforms hos...
متن کاملHyper Elliptic Curve Encryption and Cost Minimization Approach in Moving Big Data to Cloud
Cloud computing is a latest computational system which can be used for big data processing. Huge amount of unstructured, structured and semi structured data can be called as big data. Map-Reduce and the Hadoop facilitate an affordable mechanism to handle and process data from multiple sources and store the big data in distributed cloud. This paper explains the secured and cost minimizing approa...
متن کاملEnergy-efficient Analytics for Geographically Distributed Big Data
Big data analytics on geographically distributed datasets (across data centers or clusters) has been attracting increasing interests in both academia and industry, posing significant complications for system and algorithm design. In this article, we systematically investigate the geo-distributed big-data analytics framework by analyzing the fine-grained paradigm and the key design principles. W...
متن کاملBSDM: Big Spatial Data Management
We are living in the era of Big Data. Spatial and Spatiotemporal Data are not an exception. Mobile apps, cars, GPS devices, UAVs, ships, airplanes, space telescopes, medical devices and IoT devices are generating explosive amounts of data with spatial characteristics. Web apps and social networking systems also store vast amounts of geo-located information, like geo-located tweets, or captured ...
متن کاملEntropy-based Consensus for Distributed Data Clustering
The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...
متن کامل