Energy Optimization and Management in Cloud Computing

Energy Optimization and
Management in Cloud
Computing
Full Name
Student ID
Submitted for unit NIT6042 (Thesis 2)
Date

INTRODUCTION
• The tremendous growth in the technological front,
starting from server virtualization to multi-tenant cloud
models, the increasing number of cloud data centers is
consuming an enormous amount of power.

Contd..
• Cloud computing typically supports scalable resource
utilization, virtualization and offers various services
including IaaS (Infrastructure as a Service), PaaS
(Platform as a Service) and SaaS (Software as a Service).
In spite of the tremendous advantages of cloud computing,
it still has certain shortcomings.

Background
• The energy consumption by the cloud data centers increasing at an exponential rate is becoming a
huge challenge for the cloud service providers.
• The cloud users are no more tied to the physical infrastructure as their applications, software and
data everything is access through virtual services.

RESEARCH OBJECTIVES
• To identify the major challenges of cloud energy
consumption
• To investigate the impact of energy consumption in the
cloud
• To understand the various approaches and mechanism
employed for optimizing and managing energy
consumption in cloud computing
• To propose recommendations from secondary sources to
address the energy consumption issue

RESEARCH QUESTIONS
• What are the major challenges of cloud energy
consumption?
• What is the impact of energy consumption in the cloud?
• What are the various approaches and mechanisms
employed for optimizing, and managing energy
consumption in cloud computing?
• What solutions are there to address the energy
consumption issue?

Energy Consumption Problem in the
Cloud
• There are two techniques for saving the energy consumed
by the data center hardware and servers. DVFS (dynamic
voltage or frequency scaling) is one method and the other
one is by shutting down the idle servers. To be more
precise, in the method of dynamic voltage and frequency
scaling, energy is typically saved by means of adjusting
the operating clock for the purpose of scaling down the
supply voltages that is based in adaptive algorithm.

Contd..
• The DVFS approach effectively reduces potential power
consumption even if it is dependent on the hardware
components for executing the scaling job. On the other
hand, shutting down the servers that are not in use
effectively conserve more amount energy as in a highly
dynamic environment, turning off the system can
potentially create high overhead that in turn leads to a
strong degradation performance.