Big Data Processing in the Cloud: a Hydra/Sufia Experience

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Competitive advantage through proprietary erp implementation: analysis of the project Väinämö

In the market where companies similar in size and resources are competing, it is challenging to have any advantage over others. In order to stay afloat company needs to have capability to perform with fewer resources and yet provide better service. Hence development of efficient processes which can cut costs and improve performance is crucial. As business expands, processes become complicated and large amount of data needs to be managed and available on request. Different tools are used in companies to store and manage data, which facilitates better production and transactions. In the modern business world the most utilized tool for that purpose is ERP - Enterprise Resource Planning system. The focus of this research is to study how competitive advantage can be achieved by implementing proprietary ERP system in the company; ERP system that is in-house created, tailor made to match and align business needs and processes. Market is full of ERP software, but choosing the right one is a big challenge. Identifying the key features that need improvement in processes and data management, choosing the right ERP, implementing it and the follow-up is a long and expensive journey companies undergo. Some companies prefer to invest in a ready-made package bought from vendor and adjust it according to own business needs, while others focus on creating own system with in-house IT capabilities. In this research a case company is used and author tries to identify and analyze why organization in question decided to pursue the development of proprietary ERP system, how it has been implemented and whether it has been successful. Main conclusion and recommendation of this research is for companies to know core capabilities and constraints before choosing and implementing ERP system. Knowledge of factors that affect system change outcome is important, to make the right decisions on strategic level and implement on operational level. Duration of the project in the case company has lasted longer than anticipated. It has been reported that in cases of buying ready product from vendor, projects are delayed and completed over budget as well. In general, in case company implementation of proprietary ERP has been successful both from business performance figures and usability of system by employees. In terms of future research, conducting a study to calculate statistically ROI of both approaches; of buying ready product and creating own ERP will be beneficial.

Implementing Green IT approach for transferring Big Data over Parallel Data Link

The whole research of the current Master Thesis project is related to Big Data transfer over Parallel Data Link and my main objective is to assist the Saint-Petersburg National Research University ITMO research team to accomplish this project and apply Green IT methods for the data transfer system. The goal of the team is to transfer Big Data by using parallel data links with SDN Openflow approach. My task as a team member was to compare existing data transfer applications in case to verify which results the highest data transfer speed in which occasions and explain the reasons. In the context of this thesis work a comparison between 5 different utilities was done, which including Fast Data Transfer (FDT), BBCP, BBFTP, GridFTP, and FTS3. A number of scripts where developed which consist of creating random binary data to be incompressible to have fair comparison between utilities, execute the Utilities with specified parameters, create log files, results, system parameters, and plot graphs to compare the results. Transferring such an enormous variety of data can take a long time, and hence, the necessity appears to reduce the energy consumption to make them greener. In the context of Green IT approach, our team used Cloud Computing infrastructure called OpenStack. It’s more efficient to allocated specific amount of hardware resources to test different scenarios rather than using the whole resources from our testbed. Testing our implementation with OpenStack infrastructure results that the virtual channel does not consist of any traffic and we can achieve the highest possible throughput. After receiving the final results we are in place to identify which utilities produce faster data transfer in different scenarios with specific TCP parameters and we can use them in real network data links.