As digital services are moving out and about with the growing computer centers across the globe, their energy is not all because of the courtesy of cloud computing. Cloud computing has overcome the most feared trait of all the rising computer centers, the fear of high energy production from digital devices. The major trait behind improving efficiency and effectiveness is the shift to cloud computing. The big cloud data centers use virtual machine software, high-density storage, tailored chips, ultrafast networking, and customized airflow systems- all to increase computing firepower with the least electricity.
Data centers can be considered as the `brains of the internet`. Their role is to process, store and communicate the data behind the huge and ordinary information we depend upon in our regular everyday lives whether it be social media, emails, video streaming, or scientific computing.
Data centers utilize different information technology devices to provide these services, all of them which are powered by electricity. Servers provide computations and logic in response to information requests, while storage drives house the files and data needed to meet those requests. Network devices connect all of the data centers to the internet, enabling incoming and outgoing data flows. The electricity used by these IT devices is ultimately converted into heat, which must be removed from the data centers by cooling equipment that also works on electricity.
On average, servers and cooling systems account for the greatest shares of use of direct electricity in data centers, followed by storage drives and network devices. Some of the world’s largest data centers can contain many ten- thousands of IT devices and require more than 100 megawatts of power capacity—enough to power around 80,000 households. The growth trends are strongly expected to continue as the world is consuming more and more data energy every day and new forms of information services such as artificial intelligence (AI), (which are particularly computationally-intensive), might accelerate demand growth further in the future.
The substantial electricity use by data centers also gives rise to concerns over their carbon dioxide (CO2) emissions in the atmosphere. Only a handful of companies, including Apple, Google, Facebook, and Switch, publicly reported such data, indicating a growing trend among some of the world’s largest data center operators toward renewable energy procurement. Knowing the electricity used by the global data centers provides a useful benchmark for testing claims about the CO2 implications of data center services. Apart from this, the average coal-fired power plant, (the most carbon-intensive option available,) has an emissions intensity that is less than one-fourth of this value around one kilogram CO2 per kWh.
TECHNIQUES USED TO SAVE ENERGY
One of the most important techniques that are commonly being used in the cloud environment is virtualization. Virtualization helps in decreasing the hardware and operating cost by assignments of multiple virtual machines (VMs) to a single server and the assignment of multiple VMs helps to consolidate the task by turning off other physical machines by lowering the consumption of energy. The following are certain techniques used through virtualization-
– MCC method- MCC method is utilized to provide a balance between power consumption and the SLA. It measures the total energy consumption and SLA violation. Hence, this algorithm requires information from the hardware level.
– Balancing of physical resources in Virtual Machines Placement- This migrates the virtual machines to heavily loaded servers and the algorithm showed that multi-dimensional resources have well-balanced utilization and good power savings.
– Workload Scheduling- It`s a Java-Based dedicated tool that has an exponential relationship between power cost and server utilization.
– DVFS, (Virtual Machine), consolidation and server power switching- They reduce power consumption to minimize the performance loss of cloud computing.
– Resource allocation and scheduling adaptive utilization- They provide high- the quality of services to minimize energy consumption, provide satisfactory performances, and green resource allocator, The DVFS.
– Virtual Machine Scheduling and Migration- The VMs` algorithms are capable of performing the scheduling of Virtual Machines in non-federated homogeneous and heterogeneous data centers and they also tend to improve power consumption in loads and high amounts.
– The BNF and BCFS policies- Cater to the already consumed energy. The average elapsed time to schedule a Virtual Machine and the average waiting time of Virtual Machines in the running queue are later measured.
– Load balancing Algorithm– It uses CloudSim and is good in reducing energy, and it also determines the energy`s pricing and time.
– Virtual consolidation method- It is utilized by FF and BF bin packing. It reduces the energy consumption of inhomogeneous data centers by minimizing the number of active servers in data centers.
– Energy Consumption modeling and analysis approaches- It helped to identify the relationship between energy consumption and running tasks in cloud environments, as well as system configuration and performance. The analytical results which have the correlated system performance and energy consumed which shall be important for developing an energy-efficient mechanism shortly.
– Workload Allocation- Efficiency can be achieved by minimizing the packet loss and efficiency using the residual server capacity concerning traffic patterns and optimization can be achieved by selecting the server that can match the speed with the packet arrival rate.
Efficient and effective use of computing resources in the cloud is to make it Green Cloud computing. By reducing carbon emission and energy consumption in cloud computing data centers make a challenge and orient toward making all the data centers green. The study reveals that there are many energy-efficient frameworks for cloud computing and data centers that make cloud computing the Green cloud computing.
The rapidly growing demand for information services—and compute-intensive applications like Artificial Intelligence in particular (which shall begin to outpace the efficiency gains that have historically kept data center energy use in check.) Futuristic remains in substantial efficiency gains generate significant risk. Investments in next-generation storage, computing, and heat removal technologies shall be required to avoid potentially steep energy use growth later in this or future decades.
High and advanced modeling capabilities are required for decision-makers to confidently evaluate mitigation options and future efficiency, so by developing more predictive methods that increase the frequency of bottom-up ideologies which overcome the limitations of the forecasts that are a key priority for the energy analysis community.