Browsing by Subject "Data management"

Now showing 1 - 3 of 3

Open Access
Fog-Based Data Distribution Service (F-DAD) for Internet of Things (IoT) applications
(Elsevier, 2019) Karataş, Fırat; Körpeoğlu, İbrahim
With advances in technology, devices, machines, and appliances get smarter, more capable and connected to each other. This defines a new era called Internet of Things (IoT), consisting of a huge number of connected devices producing and consuming large amounts of data that may be needed by multiple IoT applications. At the same time, cloud computing and its extension to the network edge, fog computing, become an important way of storing and processing large amounts of data. Then, an important issue is how to transport, place, store, and process this huge amount of IoT data in an efficient and effective manner. In this paper, we propose a geographically distributed hierarchical cloud and fog computing based IoT architecture, and propose techniques for placing IoT data into the components, i.e., cloud and fog data centers, of the proposed architecture. Data is considered in different types and each type of data may be needed by multiple applications. Considering this fact, we model the data placement problem as an optimization problem and propose algorithms for efficient and effective placement of data generated and consumed by geographically distributed IoT nodes. Data used by multiple applications is stored only once in a location that is efficiently accessed by applications needing that type of data. We perform extensive simulation experiments to evaluate our proposal and the results show that our architecture and placement techniques can place and store data efficiently while providing good performance for applications and network in terms of access latency and bandwidth consumed.
Open Access
Hybrid fog-cloud based data distribution for internet of things applications
(2019-09) Karataş, Fırat
Technological advancements keep making machines, devices, and appliances faster, more capable, and more connected to each other. The network of all interconnected smart devices is called Internet of Things (IoT). It is envisioned that there will be billions of interconnected IoT devices producing and consuming petabytes of data that may be needed by multiple IoT applications. This brings challenges to store and process such a large amount of data in an efficient and effective way. Cloud computing and its extension to the network edge, fog computing, emerge as new technology alternatives to tackle some of these challenges in transporting, storing, and processing petabytes of IoT data in an efficient and effective manner. In this thesis, we propose a geographically distributed hierarchical cloud and fog computing based IoT storage and processing architecture, and propose techniques for placing IoT data into its components, i.e., cloud and fog data centers. Data is considered in different types and each type of data may be needed by multiple applications. Considering this fact, we generate feasible and realistic network models for a large-scale distributed storage architecture, and propose algorithms for efficient and effective placement of data generated and consumed by large number of geographically distributed IoT nodes. Data used by multiple applications is stored only once in a location that is easily accessed by applications needing that type of data. We performed extensive simulation experiments to evaluate our proposal. The results show that our network architecture and placement techniques can be used to store IoT data efficiently while providing reduced latency for IoT applications without increasing network bandwidth consumed.
Open Access
Research issues in peer-to-peer data management
(IEEE, 2007-11) Ulusoy, Özgür
Data management in Peer-to-Peer (P2P) systems is a complicated and challenging issue due to the scale of the network and highly transient population of peers. In this paper, we identify important research problems in P2P data management, and describe briefly some methods that have appeared in the literature addressing those problems. We also discuss some open research issues and directions regarding data management in P2P systems. ©2007 IEEE.