Trading Storage for Computation: An Analysis of the Process and its Integration with Cloud Computing

This presentation analyzes the process of trading storage for computation and its integration with cloud computing. It discusses the benefits and limitations of the process and its applications in various operational sectors.

School of Computing & Mathematics
ITC571
Emerging Technologies &
Innovation
Research
Seminar Presentation
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School of Computing & Mathematics
Introduction
Trading storage for computation is referred to the process
in which empty storage space is utilized for increasing the
efficiency of computation of numerical calculations.
In this system, the storage space is saved by removing
the results of the calculations and instead, the inputs,
outputs, processes and data are saved.
This system is mainly useful for the systems that use
small volumes of input data but generate huge volumes of
data that is only for temporary use and will not be used or
accessed later.

School of Computing & Mathematics
Literature Review
According to Joshi, Liu and Soljanin (2014), the
combination of cloud computing and tradeoff storage
has been benefitting trans coding in real life scenario
as well as video layer encoding and decoding.
The authors based their work on wireless
technologies like WCDMA and LTE and set out to
evaluate the requirements for implementing trade off
technology for efficient and effective computation
purposes.

School of Computing & Mathematics
Literature Review
As per McEvoy and Correll (2015), the trading storage for computing
process has been significantly benefitted by the integration with
cloud computing because cloud computing is an easily accessible
technology with very less complexity.
They have reported that the cloud computing holistic view is essentially
useful for selecting the specific results that is to be stored for future
accessibility and removing every other results that will lose
relevance after some time.

School of Computing & Mathematics
Literature Review
Chen (2015) said that in addition to saving significant
amount of storage space and increasing
computing efficiency, the storage trade off system
also helps in cutting costs and time during
operations and processing of data.
The efficiency of computation significantly increases
with the implementation of trading storage for
computation.

School of Computing & Mathematics
Literature Review
Zhao et al. (2015) developed certain programming
paradigms in order to manage the storage trade off
process such that it follows a particular framework
for calculation, storage and retrieval of data.
However, they also reported that finding the balance
between storage and computation is extremely
difficult even through the storage is traded off with
the computing processes.

School of Computing & Mathematics
Literature Review
Authors like Sasidharan, Senthoor and Kumar (2014)
developed conceptual frameworks of the storage
trading for computation that have been published in
their papers.
They reported that when a simple model is implemented,
the final computation state is stored and during access
for further reference, the results are directly read from
the final computation state itself.

School of Computing & Mathematics
Literature Review
The authors have used some storage algorithms
that they implemented in python.
A part of the storage algorithm developed by the
authors is shown as follows.

School of Computing & Mathematics
Storage Algorithm
# storage algorithm:
determines the cost of storage and the cost of re-computation for all
nodes in the DAG and stores based on the following procedure:
if node re-computation cost > node storage cost ---> STORE
if node re-computation cost < node storage cost ---> RE-COMPUTE (or
NOT STORE)
"""
print 'calculating costs for storage algorithm\n'
total_rcost = 0
total_scost = 0

School of Computing & Mathematics
Storage Cost Calculation
calculating costs for store-all (reuse = 1)
node ID: 1 node storage cost: 225.28
node ID: 3 node storage cost: 77.0
node ID: 2 node storage cost: 33.0
node ID: 5 node storage cost: 50.336
node ID: 4 node storage cost: 110.0
store-all total storage cost (in $): 495.616
calculating costs for storage algorithm

School of Computing & Mathematics
Discussion and Analysis
Cloud computing is a fast evolving technology that has found extensive
use in almost every field of commercial operations.
In addition to its basic features, it has also been integrated with a large
number of systems in order to enhance their operations as well as
increase the usability of the cloud computing services.
Similarly, cloud computing is now utilized with the storage trading
process in order to enhance its functionality and usability in the
business organizations.

School of Computing & Mathematics
Discussion and Analysis
Implementation of cloud computing in the process has its own
limitations and risks.
It is evident from case studies that cloud computing is often vulnerable
to external threats like cyber attacks, malware injections and others.
While cloud computing is integrated with any other processes, the risks
are also brought to the same.
Hence, it is important to scale the features in such a way that the
chances of the risks are minimized as much as possible.

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Results/Findings
In this entire course of the project, the trading storage process for
computation has been analyzed and its applications in the current
computing world have been determined.
It has been found that combination of trading storage process combined
with cloud computing services has been a very effective technology in
various operational sectors that conduct vast amount of calculations
everyday along with the generation of a vast amount of outputs as well.

School of Computing & Mathematics
Results/Findings
While in some organizations, the outputs need to be stored for future
analysis and reference, in some others, once the outputs are analyzed,
they are not further needed.
In this technology, the results are not stored at all.
Instead, the data used for the computation are kept and the
formulations used for the computation are saved.
In case the results are required in future, recomputation is performed
based on the stored input data and formulations and the results are
generated back as necessary.

School of Computing & Mathematics
Reflection
This entire project was based on the research and analysis of storage
trading for computation process that has become a very popular
technology among the operational sectors where large volumes of data
are computed every day.
Further, cloud computing has been integrated with the storage trade off
to increase the overall efficiency of the computation.
The basic concept of storage trade off is to remove any data results
stored after computing and in case of future requirement, recomputation
is done to retrieve back the results.

School of Computing & Mathematics
Refernces
Chen, X. (2015). Decentralized computation offloading game for mobile cloud computing. IEEE
Transactions on Parallel and Distributed Systems, 26(4), 974-983.
Chen, X., Jiao, L., Li, W., & Fu, X. (2016). Efficient multi-user computation offloading for mobile-
edge cloud computing. IEEE/ACM Transactions on Networking, (5), 2795-2808.
Gangwar, H., Date, H., & Ramaswamy, R. (2015). Understanding determinants of cloud
computing adoption using an integrated TAM-TOE model. Journal of Enterprise Information
Management, 28(1), 107-130.
Joshi, G., Liu, Y., & Soljanin, E. (2014). On the delay-storage trade-off in content download
from coded distributed storage systems. IEEE Journal on Selected Areas in
Communications, 32(5), 989-997.
Liu, C., Chen, J., Yang, L. T., Zhang, X., Yang, C., Ranjan, R., & Kotagiri, R. (2014). Authorized
public auditing of dynamic big data storage on cloud with efficient verifiable fine-grained
updates. IEEE Transactions on Parallel and Distributed Systems, 25(9), 2234-2244.
McEvoy, M. A., & Correll, N. (2015). Materials that couple sensing, actuation, computation, and
communication. Science, 347(6228), 1261689.