The growing demands and energy consumption have put us to think about the hardware, software languages, data centres and the algorithm we use to manage the data as this has emerged as the need of the time.
This has led to focus on reducing energy consumption, leading to two primary concerns:
- The data centres’ ever-increasing need for energy.
- The explosive growth in the demand for personal computing devices like smartphones, laptops, batteries and others.
This article discusses the static and dynamic portions of cloud computing and solutions to mitigate their environmental effect and energy consumption.
The use of efficient programming language and algorithms are also discussed here.
Introduction:
A Data Centre is a physical facility that organizations use to house their critical applications and data.
Since IT services are essential to business sustainability, redundant or backup components and power supply systems, data transmission links, environmental controls and various protection devices are typically included.
A scalable and efficient information technology(IT) infrastructure, storage, network bandwidth, staff and enormous capital expenditure, operating costs are required for the vast and vivid increase in computing capacity.
The power of today’s demanding IT infrastructure is cloud data centres, and there is a critical need to boost their performance.
Production flows, Social Media, Big Data, Bitcoin, Artificial Intelligence and even Businesses processes. These and other trends are leading to more and more data being stored and processed in data centres. Datacenter capacity is growing dynamically.
The IT company Cisco assumes in its analyses that the worldwide computing capacities of data centres measured in workloads and compute instances will more than double between 2016 and 2021 (2.3 fold), the data storage capacities in the data centres will even grow by a factor of almost 4 to 2.6 ZB in the same period.
However, most available analyses assume a more or less substantial increase in energy consumption.
The wide-scale potential of online banking, social networking, e-commerce, e-government, processing of information, and others, results in large-scale and vast workloads.
Meanwhile, the capacity of many private companies and public institutions to process computing and communication, ranging from transportation to banking and manufacturing to housing, has been multiplying.
Measuring Energy efficiency of a programming language
To understand the energy efficiency across various programming languages, it is necessary to obtain comparable implementations with a good representation of different problems and solutions.
– So to understand, can we compare the energy efficiency of software languages?
It will allow us to have results in which we can reach the energy efficiency of popular programming languages. These results can also explore the relations between energy consumption, execution time, and memory usage.
So, to cope with the issue of efficiency and to reduce energy consumption, we have discussed four solutions over here.
Energy Efficiency:
To improve the efficiency of a data centre while reducing the consumption of energy is the need of the time, but at the same, there is a need for looking for nullifying the effect of it on data servers.
- Replace hard drives with solid-state disks
- Refresh your storage array equipment
- Equip your data centre with a direct current (DC) power supply
- Reduce RAID levels to increase capacity and save power
- Implement better storage software
- Use the cloud
- Replace older hard drives
- Turn on thin provisioning
- Don’t forget deduplication and compression
Data Redundancy:
Need for Deduping and Compression. Both of these storage efficiency techniques also allow you to store more data with less power.
A typical data centre consumes a large amount of electricity due to its redundantly built architecture.
Environment:
Of course, selecting a site location that is physically secure and has reliable access to power, water, and communications is an essential first step.
Cooling plants efficient operation, without compromising reliability Improvement of CRAC system: reduce overcooling, decrease server temperatures, increase server reliability and density Re-used energy from the Environment (air, waste heat, water..) is needed.
Organizations can successfully build and implement green data centre initiatives that maximize efficiency and return on investment by following these five steps:
- Conduct a Baseline Energy Audit
- Select Green-Friendly Materials and Environmental Attributes
- Prioritize the Reduction of Data Center Power Usage
- Optimize Data Center Cooling
- Design Modular Data Centers
Software Language:
– Is briskly language always the foremost energy-effective?
Rightly understanding this may address if energy effectiveness is only a performance problem and permit inventors to retain a lesser understanding of how energy and time relate during and between languages.
– How does memory operation relate to energy consumption?
Sapience on how memory operation affects energy consumption will allow inventors to know more about managing memory if their concern is energy consumption.
– Can we automatically decide the most straightforward programing language considering energy, time, and memory operation?
Frequently inventors are concerned with relatively one ( conceivably limited) resource. For case, energy and time, time and memory space, and energy and memory space are all three.
Assaying these trade-offs will allow inventors to understand which programming languages are best during a given script.
– Is Faster, Greener?
When assaying energy consumption in software, a standard misconception is that it will bear within the same way prosecution time does. In other words, reducing the prosecution time of a program would beget an original quantum of energy reduction.
– How does memory operation affect the memory’s energy consumption?
Two main possible scripts may impact this energy consumption nonstop memory operation and peak memory operation.
The top 5 languages, which demanded the lowest quantum of memory space (on average) to execute the results were Pascal (66Mb), Go (69Mb), C (77Mb), Fortran (82Mb), and C (88Mb); these are all collected languages.
The nethermost five languages were JRuby (1309Mb), Dart (570Mb), Erlang (475Mb), Lua (444Mb), and Perl (437Mb); of those, only Erlang is not an interpreted language.
On average, the collected languages demanded 125Mb, the virtual machine languages demanded 285Mb, and thus the interpreted demanded 426Mb.
Still, the imperative languages demanded 116Mb, the object- acquainted 249Mb, If sorted by their programming paradigm.
Also, the loftiest five languages which consumed the lowest quantum of DRAM energy (average) were C (5J), Rust (6J), C (8J), Ada (10J), and Java (11J); of those, only Java is not a collected language.
The nethermost five languages were Lua (430J), JRuby (383J), Python (356J), Perl (327J), and Ruby (295J); all are interpreted languages.
On average, the collected languages consumed 14J, the virtual machine languages consumed 52J, and thus the interpreted languages consumed 236J.
Conclusion:
Although the various studies presented, assume more significant or smaller increases in energy consumption of data centres in recent years, estimates of both the absolute amount of energy consumed and the increases in energy consumption differ significantly.
The forecasts for the development of future energy consumption of data centres differ even further. Improving energy efficiency will therefore continue to be of great importance.
The focus here will be more on improving the energy efficiency of IT components in the future, as significant improvements in infrastructure such as cooling and uninterruptible power supply have already been achieved in the past.
Measures that do not directly affect the energy efficiency of data centres, such as the use of waste heat and operation with (fluctuating) regenerative energy, will also become increasingly important in the future.
Initial approaches, e.g., considering the 17 Sustainable Development Goals of the UN, already exist today.
Finally, as often developers have limited resources and may be concerned with more than one efficiency characteristic, we calculated which were the best/worst languages according to a combination of the previous three factors: Energy & Time, Energy & Peak Memory, Time & Peak Memory, and Energy & Time & Peak Memory.
