Performance analysis of data intensive cloud systems based on data management and replication: a survey

Abstract

As we delve deeper into the 'Digital Age', we witness an explosive growth in the volume, velocity, and variety of the data available on the Internet. For example, in 2012 about 2.5 quintillion bytes of data was created on a daily basis that originated from myriad of sources and applications including mobiledevices, sensors, individual 123 Distrib Parallel Databases archives, social networks, Internet of Things, enterprises, cameras, software logs, etc. Such 'Data Explosions' has led to one of the most challenging research issues of the current Information and Communication Technology era: how to optimally manage (e.g., store, replicated, filter, and the like) such large amount of data and identify new ways to analyze large amounts of data for unlocking information. It is clear that such large data streams cannot be managed by setting up on-premises enterprise database systems as it leads to a large up-front cost in buying and administering the hardware and software systems. Therefore, next generation data management systems must be deployed on cloud. The cloud computing paradigm provides scalable and elastic resources, such as data and services accessible over the Internet Every Cloud Service 123 Distrib Parallel Databases (data replication and management) to provide different QoS attributes is deliberated. Furthermore, the performance advantages and disadvantages of data replication and management approaches in the cloud computing environments are analyzed. Open issues and future challenges related to data consistency, scalability, load balancing, processing and placement are also reported.

Cloud Computing (SaaS) Adoption as a Strategic Technology: Results of an Empirical Study

“…The following stand out among the benefits for businesses that are regularly attributed to cloud computing: the reduction in software and hardware resource costs and access to services from anywhere in the world [6,7]; the noncomplex use of cloud-based solutions [8], a scalable [9] marketoriented [10] architecture; the potential to transform business processes [11]; its potential to provide better knowledge management and a tighter link between information systems and management requirements [12] and even its ability to provide competitive advantages, given the reductions in capital outlay and IT-related operating and maintenance costs that enable resources to be redirected toward core business activities [13].…”

Big Data: Analytical Solutions, Research Challenges and Trends

“…Accordingly, we can identify the main challenges of Big Data that should be taken into consideration while handling a big data solution [28][29][30]: Understanding of Big Data, Data Storage, Data Quality, Data Integration, Data Processing, Data Privacy and Security, Data Visualization, and Data Scalability. Within these challenges, some promoted by the characteristics of big data, others are through the current analytical models and methods, and others are due to boundaries of existing data processing systems [31]. In this section, a brief discussion regarding all the challenges listed in the above presented as follows.…”

Optimization policy for file replica placement in fog domains

“…The problem of data placement and management has been previously widely studied for different types of distributed systems, such as data centers, data grids, or the cloud. [10][11][12][13][14][15][16][17] However, the characteristics of a fog architecture have important differences with regard to these other distributed storage systems, 6,18 ie, (a) the fog devices have limited resources, in contrast to data center nodes that have high computational and storage capacities; (b) the fog devices are geographically distributed over wide areas throughout large-scale networks, in contrast to data center nodes that are located in the same geographic region; (c) the latency between the nodes of a data center is negligible, differing from fog domains where latency is a challenge; (d) nodes in data centers are usually connected via redundant links, in contrast to fog networks, where regions of the network can be connected to the rest of the network with a single link, which, eg, could be a wireless one; and (e) fog devices are very heterogeneous devices, in contrast to data center devices, which usually have the same characteristics or very similar ones. These differences make it necessary to re-evaluate traditional solutions or define new ones.…”