Green Data Centers

There are various initiatives used nowadays to achieve data
centres energy efficient systems, which are covered in this paper. We will introduce ten free software tools and techniques launched by Research Institutions or companies, in order to measure, monitor, simulate or implement energy savings, developed in 2009’-2015’ period. We will compare them by their benefits and limitations. This paper is mainly addressed to researchers of the field in order to give an overview of the alternatives they can choose from to implement experimental studies on the field of energy management computing systems.

In the era of information explosion, cloud technology is the backbone of data storage. The role of a continuous and reliable cloud system is crucial in managing the widespread emergence of "big data". Electronic data devices are generating data at an unprecedented rate that needs to be stored and managed. Data Centers (DCs) provide a place that is primarily designed for data storage and processing purposes. The progressive demand for cloud storage requires larger and more efficient data centers. The constant growth in the number of data centers created subsequent environmental impacts including carbon emissions, resource depletion, and energy and material wastes. Assessing data centers sustainability is now provided by a few third-party verification systems including the US Green Building Council (USGBC) Leadership in Energy and Environmental Design (LEED). The USGBC LEED as the most prominent green building assessment system in the US, added a category for data centers in the current version LEED version 4 (LEED v4). A comprehensive review of the LEED v4 scoring criteria reveals that it fails to properly address and assess the unique attributes of data centers. LEED focuses on improving sustainability measures mainly in "Energy and Atmosphere" but pays less attention to other categories. A major flaw in LEED v4 approach to data centers is that LEED assesses similar scoring criteria to data centers as other building types. This study concludes with discussing the potential areas of improvement in LEED v4 for assessing sustainability measures in data centers.

Cloud computing means storing and accessing data and programs over the Internet instead of your computer's hard drive. The cloud is just a metaphor for the Internet. The elements involved in cloud computing are clients, data center and distributed server. One of the main problems in cloud computing is load balancing. Balancing the load means to distribute the workload among several nodes evenly so that no single node will be overloaded. Load can be of any type that is it can be CPU load, memory capacity or network load. In this paper we presented an architecture of load balancing and algorithm which will further improve the load balancing problem by minimizing the response time. In this paper, we have proposed the enhanced version of existing regulated load balancing approach for cloud computing by comping the Randomization and greedy load balancing algorithm. To check the performance of proposed approach, we have used the cloud analyst simulator (Cloud Analyst). Through simulation analysis, it has been found that proposed improved version of regulated load balancing approach has shown better performance in terms of cost, response time and data processing time.

CFD technology enables designers to predict operating characteristics, validate performance, and visualize flow and/or thermal phenomena which is difficult to capture in the real world. This is critical for AEC applications as prototyping buildings are not practical and retrofitting existing designs is expensive. The impact of CFD on AEC applications is directly related to the interpretation of simulation results; the ability to assess performance, identifies opportunities for improvement, and quantifies the impact of modification. Results visualization provides the opportunity to optimize design performance characteristics such as energy consumption, amount of contaminant entrained, thermal comfort, solar influence, hot air recirculation, and various other metrics of AEC application design performance.

As the scale of cloud data centers becomes larger
and larger, the energy consumption of data centers also grows
rapidly. Dynamic consolidation of Virtual Machines (VMs)
presents a significant opportunity to save energy by turning off
idle or under-utilized Physical Machines (PMs) in data centers.
In this paper, we present a multi-agent based architecture for
performing dynamic VM consolidation task. The architecture
uses a local agent in each PM to decide when a PM becomes
overloaded using reinforcement learning approach. Moreover, a
global agent is proposed as a supervisor to dynamically optimize
the VM placement based on the local agents’ decisions. Therefore,
agents cooperate together to minimize the number of active PMs
according to the current resource requirements. Experimental
results on the real workload traces from more than a thousand
PlanetLab virtual machines show that the proposed architecture
can reduce the energy consumption and maintains the required
performance level in a large-scale data center.

Dynamic Virtual Machine (VM) consolidation is one
of the most promising solutions to reduce energy consumption
and improve resource utilization in data centers. Since VM
consolidation problem is strictly NP-hard, many heuristic algorithms
have been proposed to tackle the problem. However,
most of the existing works deal only with minimizing the
number of hosts based on their current resource utilization and
these works do not explore the future resource requirements.
Therefore, unnecessary VM migrations are generated and the
rate of Service Level Agreement (SLA) violations are increased
in data centers. To address this problem, our VM consolidation
method which is formulated as a bin-packing problem considers
both the current and future utilization of resources. The future
utilization of resources is accurately predicted using a k-nearest
neighbor regression based model. In this paper, we investigate
the effectiveness of VM and host resource utilization predictions
in the VM consolidation task using real workload traces. The
experimental results show that our approach provides substantial
improvement over other heuristic algorithms in reducing energy
consumption, number of VM migrations and number of SLA
violations.

Efficient energy use has become a critical issue for
designing and managing of cloud data centers. Virtualization
is a key technology for reducing energy cost and improving
resource utilization in data centers. One of the challenges faced
by virtualized data centers is to decide how to pack VMs on the
least number of physical machines. This paper presents a VM
management framework which is based on a multi-agent system
to minimize energy consumption and Service Level Agreement
(SLA) violations. The proposed agents are arranged in a threelevel
hierarchical structure to perform VM assignment, VM
placement and VM consolidation in a data center efficiently.
Experimental results demonstrate that the framework achieves
high quality solution in spite of its simplicity and scalability.

This CEO of a cutting edge high tech company advocates managing innovation with the same patience and forbearance called for in the process of birthing and bringing up children. It"s his way of making the point that long-term corporate survival depends upon setting the right priorities and mastering the crucial skills to convert technological head starts into rewarding offerings. By equating innovative corporate development of new products and services with rearing ""children"", he dramatizes how most companies fail as ""parents"". His metaphor warns companies that they will repeatedly miss their opportunities to bring valuable innovation to market unless they learn to manage the parent/child relationship with infant businesses.

As the scale of a cloud data center becomes larger
and larger, the energy consumption of the data center also
grows rapidly. Dynamic consolidation of Virtual Machines (VMs)
presents a significant opportunity to save energy by turning
off unused Physical Machines (PMs) in data centers. In this
paper, we present a distributed controller to perform dynamic
VM consolidation to improve the resource utilizations of PMs
and to reduce their energy consumption. Moreover, we use
the ant colony system to find a near-optimal VM placement
solution based on the specified objective function. Experimental
results on the real workload traces from more than a thousand
PlanetLab VMs show that the proposed approach reduces energy
consumption and maintains required performance levels in a
large-scale data center.

Advances in information technology (IT) have provided a continuous and reliable connection to the Internet for everyone. This factor, coupled with the widespread use of digital devices like laptops, cellphones and tablets, have led to the emergence of " big data ". Nowadays, society is producing and using data at an unprecedented rate. Data centers are the primary place for storing, and processing data. Another important trend in IT is the emergence of cloud technology. The rapid rise of IT, the emergence of big data, and the shift to cloud technology have led to a high demand for newer and larger data centers. Data centers are industrial facilities that provide space for servers, hard drives, and batteries, where data is stored and processed. Having numerous servers that consume large quantities of energy makes heat management a critical issue. Heat, generated mostly by servers, needs to be controlled carefully and temperature needs to be maintained within a specified range. Removing excess heat means extensive use of energy resources by the HVAC systems. This constant high-cooling demand marks data centers as buildings that are especially problematic from an environmental standpoint. The LEED version 4 (v4) rating system now has a category for data centers. However, a review of LEED v4 reveals that it fails to address many sustainability factors related to data centers. By focusing only on specific issues, LEED attempts to make significant improvements to sustainability measures mainly in the sections of Energy and Atmosphere and Indoor Environmental Quality. In other sections of LEED, such as Water and Site Selection, points are not allocated to specifically address data centers' needs. In LEED v4, data centers have the same criteria as other building types. This paper identifies the potential areas for improvement in LEED v4 for data centers.