Trading Storage For Computation – Capstone Project Proposal And Plan

Project Blog

Trading Storage for Computation is generally needed for calculating results for maximizing efficiency. It is seen that traditionally computing is used for calculating the results and they store it so that the data can be used later however storing data commits large volumes of storage thus consumes both space as well as energy by making it one of the expensive strategy. However, with the development in innovation and technology, cloud computing is considered as one of the technology that is easily available as well as flexibly allocated for various types of computing resources that further helps in suggesting that cloud computing assists in re-computing results whenever it is required. I have analyzed that both the computation as well as storage are equivalent that further assists in finding the balance that exists for enhancing the efficiency that is quite difficult. I have analyzed that the fundamental challenge of this issue mainly occurs within the knowledge gap that separates both the users as well as the cloud administrators. As users have proper sematic understanding about their data and on the other hand, administration of proper understanding about the underlying structure of the cloud is totally different. In order to resolve the issues and for detailing both the user and system knowledge it is needed to construct the comprehensive cost model so that the users can focus on the development of Trading Storage for Computation. I have analyzed that the basic relationship that exists for governing the results of the computation can generally be expressed with the help of three entity model. The computation is considered to be properly rooted in one or more inputs which can be the result of the previous computation. This type of inputs is generally found to be acted upon and helps in transforming the entire procedure and the results of that procedure is considered as an output. Additionally, I have analyzed that within a traditional model of computing, the entire results are stored for future utilization. In contrast to this, it is found that cloud computing holistic view that is associated with computing as well as storage is mainly suited for intelligently selecting the results to store so that it can be recomputed when needed. The efficiency that is gained from this approach can be likened for file compression which helps in trading computation costs for the storage efficiency. I have identified that development of Trading Storage for Computation reflects that it generally assists in cutting the costs and time that is needed for calculating, storing as well as retrieving data and thus it assists in increasing the efficiency. Moreover, it is identified that the efficiency of calculation increases by developing the Trading storage for computation when it is desirable to recompute the results as opposed to the procedure of storing the results of calculation for future use.

It is found that standard programming paradigm is used so far for calculating, storing as well as retrieving data. However, development of cloud computing assists in providing flexible allocation of various computing resources and thus the rise of cloud computing technology assists in rendering the paradigm inefficient. This is mainly due to the storage of large volumes of data that are used rarely that further assists in making this paradigm very much expensive. It is found that while computation as well as storage are equivalent, it is very much difficult to find the balance for maximizing the efficiency.

Rationale

It is found that the utilization of standard programming paradigm for storing data creates lots of challenges and issues. In order to resolve this issue, trading storage are required to be developed for computation so that they can be able to flexibly allocate all the computing resources and will generally suggests proper alternative for storing the provenance of data.  The main advantage of utilizing the trading storage is to reflect the possibility of creating impact on the total costs as well as re-computation time that is mainly associated with re-computing.

  The development of the trading storage for computation is mainly suggested with the help of various multinational companies for resolving the problem that they generally face due to the utilization of standard programming paradigm for calculating, storing as well as retrieving data. It is found that the development of trading storage not only helps in resolving the issue but also assists in proper storage of data in a very much cost-effective manner.

The research questions that are associated with the project are reflected below:

• How trading storage for computation is useful for the business organizations?
• How the cloud computing technology is advantageous in mitigating the issue that is created by the standard programming paradigm?
• What are steps that are required to be followed by the business organizations for developing trading storage for computation?

According to Lebjioui, Eckert and Earl (2016) in traditional computing, storage is mainly utilized for holding the results of computation. In the simple model, where the final computation state is preserved, the results are simply read from the storage each time they are generally required.  It is found that it not only stores a result but it is more much more efficient for storing the provenance of data as well as input for processing along with proper means of recalculating the entire result if it is generally required. On the other hand, Egan & Cagan, (2016), stated  that recomputation is mainly defined as a replacement of storage that generally fits well into the holistic model of computing that is mainly described into the architecture of cloud. It is found that cloud computing mainly aims to abstract always all the details that are associated with the underlying infrastructure. In addition to this, it is identified that in both private and public clouds, the users are mainly encouraged for thinking in terms of service not structure.

It is opined by Bertoni, Chowdhery and Bellini (2018) that while the decision between the computation as well as storage properly appears in a very much cost-effective trade-off, there are number of issues and challenges that needs to be consider. A system generally aims to enable computation which further require additional meta data as well as performance facilities for ensuring that all the re-computation methods are generally known and the result regeneration are generally successful (Babaei & Mollayi, 2016). It is very much important to understand the factors that helps in determining when it is very much sufficient for re-computing the results as opposed to storing it including the likelihood of reuse as well as various types of potential penalties if the data is not unavailable. It generally needs both the system knowledge as well as user in order to understand the factors as understanding this factor helps in ensuring that the decision maker generally have all types of information that is generally needed for making proper informed decision.

Problem domain

The basic relationship that generally governs the results computation can be generally expressed with the help of three entity model. It is found that the computation is mainly rooted within one more input which generally can be the results for the computation that is done previously. These types of inputs are generally not transformed with the help of process and the output of that procedure is considered to be the result. It is stated by Watson and Kaldor (2015) that cloud computing provides holistic view of storage as well as computation that is quite suited for selecting which results to store and which one to recompute as required. The efficiency generally helps in gaining the approach that can be likened to the compression of file that helps in trading some computation costs for enhancing the storage computation.

According to Kaldor and Watson (2015), the first step that helps in determining the desirability of re-computing the results as opposed to storing them reflects that that it is very much significant to understand the conditions that helps in making re-computation very much possible. It is identified that the inputs that are being stored by regenerating results then it is found that the corresponding procedure is needed (Babaei & Mollayi, 2016). This is considered to be true for results that are strict constraints of integrity. If the entire procedure is stored as well as reused then there are number of types of requirements that are generally needed to be met.

It is identified that the project utilizes agile development methodology in order to develop the trading storage for computation. It is found that the agile methodology is mainly utilized for supporting continuous as well as rapid development of the Trading storage (Spundak, 2014). It is found that the agile methodology not only helps in developing the trading storage but also assists in enhancing the quality of the project, customer satisfaction, enhancing project control as well as assists in reducing the risks that are associated with the project. Additionally, it is found that the utilization of agile methodology helps in adopting the changing circumstances and therefore it generally serves the customers with proper technique of project delivery.

It is found that the paper uses secondary method of data collection for collecting information that are mainly related with the development of Trading storage. It is found that the researcher reviews number of journal articles, eBooks as well as other types of published papers in order to gather information that is mainly required for the development of the Trading Storage.

  The different types of ethical issues that generally can occur during the development of the trading storage is provided below:

• Pressurizing the project workers for working after their shift timing so that they can be able to complete the research.
• Carelessness of the employees of the organization for managing the information as well as confidential data.

It is found that the organization must follow proper occupational health and safety acts in order to work properly within the organizational environment so that the Trading Storage for computation can be developed properly within the organization. It generally assists in minimizing the chances of accidents that generally occur within the workplace and further helps in reducing the chances of injuries.

After the development of the Trading storage for computation it is found that 80% of the organizations that uses Trading storage instead of programming paradigm has able to maximize their efficiency of storing, retrieving as well as computing data. The utilization of Trading storage for computation not only improves the efficiency but also assists in making the procedure of storing data quite cost effective so that the organizations can easily afford it without facing much problem.

The deliverables of the project are as follows:

• Development of cloud-based trading system for storage
• Developing proper trading routine
• Selection of right trading tools for the project
• Proper trade management
• Understanding money and risk management
• Development of profitable mindset
• Proper strategies for trading

Figure 1: Work breakdown Structure

(Source: Created by Author)

Risk	Description	Impact	Probability	Mitigation
Lack of budget	Due to absence of proper budget, the project manager faces difficulty in completing the work of the project within the expected budget and time.	High	High	It is very much necessary for the project manager to undertake earned value analysis in order to make sure that the estimated budget is appropriate for the execution of the project.
Technological risk	Due to improper programming, the project manager faces lot of issues and challenges in developing the trading storage for computation.	High	Medium	It is very much necessary to hire experienced programmer so that they can  code properly for successful development of the project.
Slippage of project schedule	If the schedule of the project is not managed while development of the trading storage for computation then it will be quite difficult to finish the entire project work within the expected budget and time.	High	High	It is very much necessary to manage the schedule of the project quite effectively so that it can get completed within the expected time and budget without creating any challenges.

The duration of the project for developing the Trading Storage for Computation is provided in the table below:

WBS	Task Name	Duration	Start	Finish
0	Development of trading storage for computation	32 days	Mon 06-08-18	Tue 18-09-18
1	Initiation phase	9 days	Mon 06-08-18	Thu 16-08-18
1.1	Development of business case	3 days	Mon 06-08-18	Wed 08-08-18
1.2	Undertaking feasibility study	2 days	Thu 09-08-18	Fri 10-08-18
1.3	Establishing project charter	3 days	Mon 13-08-18	Wed 15-08-18
1.4	Appointing team members of the project	4 days	Mon 13-08-18	Thu 16-08-18
1.5	Milestone 1: Completion of project initiation phase	0 days	Wed 15-08-18	Wed 15-08-18
2	Planning phase	10 days	Thu 16-08-18	Wed 29-08-18
2.1	Creation of project plan	3 days	Thu 16-08-18	Mon 20-08-18
2.2	Development of resource plan	2 days	Thu 16-08-18	Fri 17-08-18
2.3	Creating financial plan	3 days	Tue 21-08-18	Thu 23-08-18
2.4	Creating development plan	2 days	Tue 21-08-18	Wed 22-08-18
2.5	Developing quality plan	3 days	Mon 20-08-18	Wed 22-08-18
2.6	Developing risk management plan	4 days	Fri 24-08-18	Wed 29-08-18
2.7	creation of communication plan	3 days	Fri 24-08-18	Tue 28-08-18
2.8	Milestone 2: Completion of planning phase	0 days	Wed 22-08-18	Wed 22-08-18
3	Execution phase	16 days	Thu 23-08-18	Thu 13-09-18
3.1	Development of cloud-based trading system for storage	6 days	Thu 23-08-18	Thu 30-08-18
3.2	Developing proper trading routine	5 days	Thu 23-08-18	Wed 29-08-18
3.3	Selection of right trading tools for the project	6 days	Fri 31-08-18	Fri 07-09-18
3.4	Proper trade management	5 days	Thu 30-08-18	Wed 05-09-18
3.5	Understanding money and risk management	7 days	Thu 30-08-18	Fri 07-09-18
3.6	Development of profitable mindset	4 days	Mon 10-09-18	Thu 13-09-18
3.7	Proper strategies for trading	5 days	Thu 06-09-18	Wed 12-09-18
3.8	Milestone 3: Completion of execution phase	0 days	Thu 13-09-18	Thu 13-09-18
4	Closure phase	3 days	Fri 14-09-18	Tue 18-09-18
4.1	Post project review	2 days	Fri 14-09-18	Mon 17-09-18
4.2	Stakeholder sign off	1 day	Fri 14-09-18	Fri 14-09-18
4.3	Documentation	1 day	Tue 18-09-18	Tue 18-09-18
4.4	Milestone 4: Completion of closure phase	0 days	Fri 14-09-18	Fri 14-09-18

Figure 2: Gantt chart

(Source: Created by Author)

References

Babaei, M., & Mollayi, M. (2016). Multi-objective optimization of reinforced concrete frames using NSGA-II algorithm. Engineering Structures and Technologies, 8(4), 157-164.

Bertoni, M., Chowdhery, S. A., & Bellini, A. (2018). Model-driven value assessment: a case from the food packaging industry. In International Design Conference (DESIGN 2018), Dubrovnik (Vol. 1, pp. 161-170). The Design Society.

Clegg, C. W., Robinson, M. A., Davis, M. C., Bolton, L. E., Pieniazek, R. L., & McKay, A. (2017). Applying organizational psychology as a design science: A method for predicting malfunctions in socio-technical systems (PreMiSTS). Design Science, 3.

Egan, P., & Cagan, J. (2016). Human and computational approaches for design problem-solving. In Experimental Design Research (pp. 187-205). Springer, Cham.

Hornstein, H. A. (2015). The integration of project management and organizational change management is now a necessity. International Journal of Project Management, 33(2), 291-298.

Joslin, R., & Müller, R. (2015). Relationships between a project management methodology and project success in different project governance contexts. International Journal of Project Management, 33(6), 1377-1392.

Kaldor, L., & Watson, N. R. (2015). Improving wellbeing for victims of crime. In The 20th International Conference for Engineering Design (ICED15): Design for Life. The Design Society.

Kerzner, H. (2017). Project management metrics, KPIs, and dashboards: a guide to measuring and monitoring project performance. John Wiley & Sons.

Lebjioui, S., Eckert, C. M., & Earl, C. (2016). Understanding the relationship between design margins and trade-offs.

McMAHON, C. (2014). Design research: current status and future challenges. In The 3rd Internacional Conference on Design Enginering and Science, ICDES 2014.

Mir, F. A., & Pinnington, A. H. (2014). Exploring the value of project management: linking project management performance and project success. International journal of project management, 32(2), 202-217.

Spundak, M. (2014). Mixed agile/traditional project management methodology–reality or illusion?. Procedia-Social and Behavioral Sciences, 119, 939-948.

Svejvig, P., & Andersen, P. (2015). Rethinking project management: A structured literature review with a critical look at the brave new world. International Journal of Project Management, 33(2), 278-290.

Watson, N. R., & Kaldor, L. (2015). Designing with crime prevention–Creating community wellbeing through design. In International Conference on Engineering Design (ICED), The Design Society. The Design Society.