ITC571 Emerging Technologies: Green Cloud Computing Bibliography

This annotated bibliography critically examines recent scholarly research on Green Cloud Computing. It covers topics such as energy efficiency, data center design, virtualization, and novel algorithms for optimization. The annotations highlight the research significance, originality, literature review, research gaps, and aims of each study. Articles discuss data center energy consumption, e-waste recycling, telecommunications, and ethical computer practices. The bibliography also points out the limitations of simulation-based research and calls for more practical, comprehensive approaches to achieving sustainable energy savings in cloud computing environments. Desklib provides this and many other resources for students.

ANNOTATED BIBLIOGRAPHY 1
Annotated Bibliography
Name
Date

ANNOTATED BIBLIOGRAPHY 2
Introduction
Agrawal, K., Chowdhury, A., & Tripathi, P. (2015). Scrutiny of Energy Efficiency for Green
Cloud Computing. International Journal of Computer Applications, 121(3), 25-28.
doi:10.5120/21521-4499
Research Significance
The author makes critical observations on energy efficiency issues in cloud computing and
discusses some of the research done s far to tackle the issue. The author does a comprehensive
analysis that provides a better understanding of green cloud computing energy efficiency
Literature Review
Discusses ways green clouding computing can be achieved, including data center design,
virtualization, use of algorithms for optimization, and power management. The authors also look at
research on energy efficiency for green cloud computing and put forth mechanisms ad approaches
for achieving green cloud computing
Research Gap
The research is too general and has a wide scope; research aimed at solving problems ought
to be specific and to the point (Spradlin, 2012)
Aim of Research
This research is focused on practices starting from design: specifically data center design, e-
waste recycling, telecommunications, and best practices in energy management
Dabbagh, M., Hamdaoui, B., Guizani, M., & Rayes, A. (2015). Toward energy-efficient cloud
computing: Prediction, consolidation, and overcommitment. IEEE Network, 29(2), 56-
61. http://dx.doi.org/10.1109/mnet.2015.7064904
Research Significance
The research recognizes the challenges faced by cloud service providers in the context of
energy efficiency and goes further to highlight some of the problems and propose novel solutions
Originality of Approach
The research proposes two unique methods for ensuring energy efficiency that would not
require significant capital investments: it proposes two novel approaches of workload prediction,
Workload consolidation and VM Placement, and resource overcommitment. The researchers
demonstrate their effectiveness using data and graphs
Literature Review

ANNOTATED BIBLIOGRAPHY 3
The researchers sought to evaluate the energy challenges faced by cloud service providers
and also looked at the available opportunities, before proposing solutions based on evidence. The
researchers propose solutions to solve the problem of limited growth in the performance of cloud
data centers due to the ever increasing consumption of energy of the computing systems
(Beloglazov, Buyya, Lee & Zomaya, 2011). The approach is original in that it proposes practices
that will not add significant costs to energy saving. The authors show how great energy savings can
be achieved in data centers through practices that include turning more servers to a low power state
and increasing utilization of existing servers. These are made possible by workload prediction,
resource overcommitment, and VM consolidation and placement with graphical evidence based on
experimental data showing reduced energy use.
Research Gap
The researchers present a model but this may not be applicable to small data centers; further,
their approaches have limitations when used on their own, such as resource over-utilization. What is
needed is a comprehensive approach rather than basic suggestions, for instance, a new algorithm in
data processing.
Aim of Research
This research aims at proposing a model that achieves continuous energy savings with a
comprehensive approach that is sustainable even as workloads increase. The proposed strategies
include e-waste recycling (can the wasted data center energy be recycled?), follow basic ethics in
computing, and energy efficient design for data centers, as well as telecommunication
Shu, W., Wang, W., & Wang, Y. (2014). A novel energy-efficient resource allocation algorithm
based on immune clonal optimization for green cloud computing. EURASIP Journal
On Wireless Communications And Networking, 2014(1).
http://dx.doi.org/10.1186/1687-1499- 2014-64
Research Significance
It proposes a clonal algorithm as a way of that is based on energy consumption and time cost
models to ensure dynamic energy savings and efficient utilization.
Literature Review
The proposed solution is interesting because it is an improved algorithm that goes beyond
traditional approaches to data center energy efficiency and they use a simulated experimental
research approach that shows immense potential. The model can also meet SLA agreements in data
center agreements, given that SLA agreements are very important in cloud computing

ANNOTATED BIBLIOGRAPHY 4
(Marudhadevi, Dhatchayani & Sriram, 2014). The research proposes DVFS (dynamic voltage and
frequency scaling) as a model for energy efficiency optimization. Further, the authors propose a
make-span optimization model as well as a multi-objective optimization model and use these to
generate an improved clonal selection algorithm, a very popular, nature inspired artificial immune
system model (Sharma & Sharma, 2011).
Research Gap
The proposed solutions are based on simulations and not real tests; simulations has its limits
and may not give similar outcomes when applied in real life (Coale, 2012)
Aim of Research
This research aims at proposing a mixed solution system that can be practically applied and
not just based on simulations. This paper proposes e-waste recycling, telecommunication, follow
basic ethics in computing, and energy efficient design for data centers
Gai, K., Qiu, M., Zhao, H., Tao, L., & Zong, Z. (2016). Dynamic energy-aware cloud let-based
mobile cloud computing model for green computing. Journal Of Network And
Computer Applications, 59, 46-54. http://dx.doi.org/10.1016/j.jnca.2015.05.016
Research Significance
The research seeks to address the important and emerging issue of energy waste and latency
delays in mobile cloud computing and solves one of the myriad challenges in the increasing use of
mobile cloud computing (Shahzad & Hussain, 2013)
Literature Review
The researchers propose a novel approach that leverages dynamic cloud-lets using a
dynamic energy aware cloud-let based model for cloud computing DECM. The solution is unique,
given the increased use of mobile devices (Arun & Jaiganesh, 2016)
Research Gap
The proposed solution, while novel and unique, also uses simulation of a real life situation
which has limitations I the context of scalability and credibility (Rampfl, 2013)
Aim of Research
My research aims at using telecommunication, ethical computer practices, energy efficient
design for data centers, and e-waste recycling as practical solutions for data center energy efficiency
for all forms pf cloud computing, not just mobile cloud computing

ANNOTATED BIBLIOGRAPHY 5
Wu, C., Chang, R., & Chan, H. (2014). A green energy-efficient scheduling algorithm using
the DVFS technique for cloud data centers. Future Generation Computer Systems, 37,
141-147. http://dx.doi.org/10.1016/j.future.2013.06.009
Research Significance
The paper seeks to improve energy efficiency and reduce CO2 emissions associated with
data centers using a unique scheduling algorithm to increase resource utilization and achieve
efficiency.
Literature Review
The researchers create models for the problem of servers, taking note of differences in
hardware configurations for servers which mean a single solution cannot fit all. They then propose a
unique scheduling algorithm and use simulation to prove their model, which shows promising
results and could significantly reduce data center energy use
Research Gap
While the approach is novel, algorithms alone are not sufficient in reducing data
consumption in data centers, and applying an algorithm to different data center environments with
different configurations may be complex and not guaranteed (Coale, 2012)
Aim of Research
This research seeks to ensure data center and cloud computing energy efficiency from its
foundation based on design with additional measures such as e-waste recycling to ensure further
savings (Debnath, Roychoudhuri & Ghosh, 2016)
Boru, D., Kliazovich, D., Granelli, F., Bouvry, P., & Zomaya, A. (2015). Energy-efficient data
replication in cloud computing data centers. Cluster Computing, 18(1), 385-402.
http://dx.doi.org/10.1007/s10586-014-0404-x
Research Significance
The authors propose a unique model in which data is replicated in cloud resources as a way
of ensuring provisioning is maintained and energy efficiency is achieved; it considers both energy
efficiency concerns as well as bandwidth consumption. The model is very useful for cloud
computing where data centers are located in geographical dispersed regions
Literature Review
Using mathematical models, the authors propose the use of data replication that use a three
tier architecture for the data center. This is based on a replication algorithm that takes into account

ANNOTATED BIBLIOGRAPHY 6
power consumption and use experimentation through simulation to demonstrate that the model
works better when compared to other systems.
Research Gap
The solution is novel and seeks to achieve energy efficiency from the design of the data
center; however, its weakness is based on a flawed assumption that there is uniform distribution of
data center traffic as well as jobs among the computing servers; this seldom happens (Singh et al.,
2015)
Aim of Research
To propose a solution based on efficient data center architecture and e-waste recycling as a
way of reducing energy consumption in cloud computing
Farahnakian, F., Ashraf, A., Pahikkala, T., Liljeberg, P., Plosila, J., Porres, I., & Tenhunen, H.
(2015). Using Ant Colony System to Consolidate VMs for Green Cloud Computing.
IEEE Transactions on Services Computing, 8(2), 187-198. doi:10.1109/tsc.2014.2382555
Research Significance
The proposed solution uses an innovative method of dynamically consolidating virtual
machines as a way of solving the high energy consumption in data centers used for cloud computing
Literature Review
The proposed method uses dynamic virtual machines (VM’s) consolidation through live
migration of VM’s so physical machines that are under loaded can be switched off. The authors
proposes a distributed system architecture based on an online ACS (ant colony system) meta
heuristic algorithm that achieves a near optimal solution. The meta heuristic algorithm approach is a
suitable model that is a hybrid of a meta heuristic and other algorithm based optimization
approaches (Yang, 2011) as a first resort in large scale stochastic problems of optimization (Chica &
Pprez, 2017)
Research Gap
While the solution is novel, it only attains near optimization and not complete optimization;
the meta heuristic model has inbuilt limitations in its efficacy (Chica & Pprez, 2017)
Aim of Research
In place of developing or using algorithms, this research will use practical methods to
achieve power efficiency in cloud computing such as through e-waste recycling and efficient
architecture

ANNOTATED BIBLIOGRAPHY 7
Farahnakian, F., Pahikkala, T., Liljeberg, P., Plosila, J., & Tenhunen, H. (2015). Utilization
Prediction Aware VM Consolidation Approach for Green Cloud Computing. 2015
IEEE 8th International Conference on Cloud Computing, 15, 2159-6190.
doi:10.1109/cloud.2015.58
Research Significance
Provides a dynamic approach to Virtual machine consolidation that also takes into
consideration of future energy demands, rather than just looking at present demands as most other
solutions do
Literature Review
The researchers acknowledge that most present solutions use heuristic algorithms in VM
migration, resulting I unnecessary VM migrations that violate SLA agreements and do not consider
future energy demands. The researchers address these problems using bin-packing approach and
accurately predict future energy demands using a k-nearest neighbor regression model and use real
workload traces in an experimental setup. The results show the algorithm and model offers
significant improvements in energy efficiency in the cloud over other models and minimizes
unnecessary VM migrations and SLA violations
Research Gap
The solution provide is dynamic and novel, but is strictly algorithm based, rather than
providing easy solutions that are implemented at the initial phase
Aim of Research
This research shies off from the use of algorithms and instead focuses on hardware and
design approaches, best practices, and e-waste recycling to achieve green cloud computing: the aim
is to solve the problem from the initial phase and use other methods to continually improve
efficiency (Jin, Wen, Chen, & Zhu, 2013, p. 982)
Singh, S., Swaroop, A., Kumar, A., & A. (2016). A survey on techniques to achieve energy
efficiency in cloud computing. 2016 International Conference on Computing,
Communication and Automation (ICCCA). doi:10.1109/ccaa.2016.7813915
Research Significance
The research provides valuable techniques that can be used to achieve energy efficiency in
cloud computing data centers: it uses a multiple approach rather than a single model
Literature Review

ANNOTATED BIBLIOGRAPHY 8
The researchers proposes various methods that can be applied based on need to achieve
energy efficiency in cloud computing, including hardware optimization aspects, virtualization,
scheduling, energy efficient networks, and clustering. Just a single method is not sufficient, multiple
approaches are needed to attain energy efficiency in data centers (Bishop, 2013)
Research Gap
The paper fails to discuss the fundamental aspect of data center design to achieve energy
efficiency as there is a strong link between data center design and energy efficiency (Masanet et al.,
2013)
Aim of Research
To base energy efficiency in databases on energy efficient architecture and e-waste
recycling, as well as use of telecommunications and ethical computer practices
Pierson, J., & Hlavacs, H. (2015). Introduction to Energy Efficiency in Large-Scale
Distributed Systems. Large-Scale Distributed Systems and Energy Efficiency, 1-16.
doi:10.1002/9781118981122.ch1
Research Significance
The article provides a suitable background on energy efficiency and the concept of large
scale distributed systems to help understand the complex nature of cloud systems and energy
dynamics
Literature Review
The article defined large distributed systems and their components which include thousands
of heterogeneous elements that are able to communicate with each other and offer different
capabilities of memory, processing, and storage. He article explains how cloud centers, data centers,
and data grids operate, the need for attaining energy efficiency, and how they consume energy. This
provides a useful background on the problem and details of how data-centers operate, crucial for
understanding how to propose solutions for cloud computing energy efficiency (Rizvandi &
Zomaya, 2012)
Research Gap
It provides a useful background useful for understanding the concept but falls short in
proposing solutions
Aim of Research
This paper aims at offering workable solutions to ensure data center energy efficiency, in
addition to discussing the operational aspects of cloud computing

ANNOTATED BIBLIOGRAPHY 9
Mehta, H., Singhal, S., & Doshi, J. (2016). A Comparative Analysis of Green Cloud
Computing Mechanisms. International Journal of Computer Science and Mobile
Computing, 5(11), 90-93.
Research Significance
The paper makes a comparison of the methods by which green cloud computing can be
achieved in a bid to overcome the exponential power consumption in cloud computing data centers
and evaluates three main methods, DVFS, green algorithms, and VM migration
Literature Review
The paper uses a comparative analysis of existing methods of attaining green cloud
computing: it evaluates and compares DVFS, green algorithms, and VM migration and proposes the
best method among those evaluated. After a meticulous evaluation, the paper concludes that the best
approach is thermal and power- aware VM allocation mechanism is the best method compared to
others. Comparative analysis is an important and useful way for making multi criteria decisions
(Polios, Mytilinou, Lozano-Minguez, & Salonitis, 2016)
Research Gap
The paper uses a comparative analysis and uses a theoretical approach rather than an
experimental or simulated method. Further, the chosen is wide and generalized
Aim of Research
This research will propose specific methods for achieving green cloud computing using a
variety of practical methods and practices, including e-waste recycling and efficient design for
cloud computing data centers.
Qiu, C., Shen, H., & Chen, L. (2015). Towards green cloud computing: Demand allocation and
pricing policies for cloud service brokerage. 2015 IEEE International Conference on
Big Data (Big Data), 23, 203-211. doi:10.1109/bigdata.2015.7363757
Research Significance
The researchers provide a completely different approach to ensuring energy efficiency based
on financial incentives and benefits as a means of encouraging cloud service vendors to attain
greater energy efficiency
Literature Review

ANNOTATED BIBLIOGRAPHY 10
It proposes an LP-relaxation approximation algorithm based pricing model to achieve
constant performance guarantee. Using an experimental approach with the algorithms, the
researchers establish that their model led to efficient energy utilization and resource allocation, and
so can be used with incentives for cloud service brokers (CSB’s) to achieve green cloud computing
objectives
Research Gap
the method is novel but is based upon pricing and an incentive model to CSB’s
Aim of Research
This paper aims at achieving green cloud computing from the very foundations of the cloud
computing through an energy efficient cloud and data center architecture and to recycle e-waste,
including wasted energy

ANNOTATED BIBLIOGRAPHY 11
References
Bishop, L. (2013, March 23). 4 Ways to Reduce Energy Consumption in Any Data Center. Retrieved
April 22, 2018, from https://blog.schneider-electric.com/datacenter/2013/02/01/4-ways-to-
reduce-energy-consumption-in-any-data-center/
Beloglazov, A., Buyya, R., Lee, Y. C., & Zomaya, A. (2011). A Taxonomy and Survey of Energy-
Efficient Data Centers and Cloud Computing Systems. Advances in Computers, 1, 47-111.
doi:10.1016/b978-0-12-385512-1.00003-7
Chica, M., & Pprez, A. A. (2017). Why Simheuristics? Benefits, Limitations, and Best Practices
When Combining Metaheuristics with Simulation. SSRN Electronic Journal.
doi:10.2139/ssrn.2919208
Jin, Y., Wen, Y., Chen, Q., & Zhu, Z. (2013). An Empirical Investigation of the Impact of Server
Virtualization on Energy Efficiency for Green Data Center. The Computer Journal, 56(8),
977-990. doi:10.1093/comjnl/bxt017
Kolios, A., Mytilinou, V., Lozano-Minguez, E., & Salonitis, K. (2016). A Comparative Study of
Multiple-Criteria Decision-Making Methods under Stochastic Inputs. Energies, 9(7), 566.
doi:10.3390/en9070566
Masanet, E., Walker, B., Liang, J., Ma, X., Walker, B., Shehabi, A., . . . Mantha, P. (2013, June).
The Energy Efficiency Potential of Cloud-Based Software: A U.S. Case Study [PDF].
Berkeley: Lawrence Berkeley National Laboratory.
Singh, A., Germano, P., Kanagala, A., Provost, J., Simmons, J., & Tanda, E. et al. (2015). Jupiter
Rising. ACM SIGCOMM Computer Communication Review, 45(5), 183-197.
http://dx.doi.org/10.1145/2829988.2787508
Rizvandi, N., & Zomaya, A. (2012). A Primarily Survey on Energy Efficiency in Cloud
and Distributed Computing Systems. ARXIV
Spradlin, D. (2012, September 1). Are You Solving the Right Problem? Retrieved from
https://hbr.org/2012/09/are-you-solving-the-right-problem
Yang, X. (2010). Nature-inspired metaheuristic algorithms. Frome (U.K.): Luniver Press.