Energy use and efficiency in modern communication systems: a review

We can clearly differentiate two different trends: there are constant power value of about 3.5 kW for the morning and the night time, it is an oscillating trend, with an average value of about 5 kW for the fieriest hours and the late evening. This is a not a shocking trend if we consider that in the first case the consumption is due only to the transmission functions, but in the second case besides the transmission functions there is the conditioning energy consumption, with a behavior called “saw tooth” made by the on/off switching mechanism of the air-conditioning systems.

The energy consumption can thus be separated in two parts: approx. 1/3 of the energy is used to run the conditioning systems, 2/3 of the daily consumed energy is due to the transmission. The variation of the energy consumptions against the external temperature are plotted, for different BTS typology in 3 situation shelter, room, outdoor and BTS technology UMTS and GSM, for 77 sites, for which there were dependable temperature information.

An “universal trend” of the energy consumption against the temperature can be recognized and it seems to be independent both from the technology used and from the typology of the BTS. Also, the upper dispersion of the consumption data in the shelter and room typologies, when we compared to the outdoor one, it can be clarified if we believe that in the first two cases there is a consequent increase of the consumption in the higher thermal dispersion – which is needed to state the equipment as well as the environment where they are installed.

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Comparing the graphs for the room and shelter typology, it is sharp that the UMTS technology has on average, GSM technology has a lower energy consumption since the different characteristic of the mobile communication standards. There are no different size was found for energy consumptions for the 3 BTSs typologies. Consequently, we conclude: i) When there is no difference between the internal and external shelter temperature, so an energy consumption of about 110 kWh/day; ii) we can see it increases 2.1 kWh/day for each degree of temperature, which means that the maximum registered difference in temperature is 25°C, it gives an excursion in consumption of about 50 kWh/day. 4.Improvement This part will focus on the improvement of data center and base station. Also, I will talk about should green energy apply on the above equipment and how did their efficiency improve.

4.1 Data center

4.1.1 Improvement without green energy

4.1.1.1Replace hard drives with solid state disks SSDs consume less power and generate less heat if more expensive per Gigabyte to purchase than spinning disks. They also have better performance than high performance relatively high energy consuming in 15K disks. A normal scenario where SSDs can lead to big energy savings is in a condition where a big number of relatively inefficient disks are being short stroked to reach a desired level of IOPS, and thus their capacity is only very partially applied. ‘If you have 1,000 15k disks that are only 37% applied, then it looks like you have a strong need for SSD drives,’ (Greg Schulz, a consultant at Server and Storage IO Group)

4.1.1.2 Use storage tiering to increase utilization Since illustrated when SSDs are used to replace short stroked disks for high performance, increased utilization drives up your data stored per Watt of power consumed. However, tiering is not just about increasing energy efficiency for high performance purposes: there are 3 important things are lower performance spinning drives efficiency, moving archive data to offline tape systems, moving the appropriate data to high. ‘Tiering can help IT organizations to do more with less,’ and ‘Use low power SSDs for performance reduces the quantity of 15K spindles used for performance workloads, and the ability to tier the lower performance data to let the high capacity drives lower the number of spindles used to provide capacity ‘ ESG’s Scott Sinclair said that. 4.1.1.3 Refresh your storage array equipment Newer storage systems provide more storage per Watt of power than older ones, says StorageIO’s Greg Schulz. Of course the new one is better. How to let the company to charge the old equipment to the new one. It is one of the big problem will face in this. No company is going to buy an expensive new thing when the old one still can use.

4.1.1.4 Equip your data center with a direct current (DC) power supply most computing and storage equipment uses DC,but Electricity is generally supplied as alternating current (AC). That means that the AC supply has to be changed to DC for each unit. If we converting the supply at a device level is inefficient: The Electric Power Research Institute has try to tests and show that DC power systems can apply in energy savings ranging from 14.9 per cent when servers and storage arrays are run at full power to 15.8 per cent when systems are idling. Finally, it shown that DC supplies can develop energy efficiency by 15.3 per cent over AC systems.

4.1.1.5 Reduce RAID levels to increase capacity and save power Most of people will use a combination of RAID10 with short-stroking to meet performance demands with spinning media and get the good result, and hence only a minor proportion of each drive, maybe just25%, is used to provide additional performance. ‘Compare that to SSD, where RAID 5 with five 400GB SSDs will offer superior performance, more than increse the capacity, and use less power at the same time.’

4.1.2 Improvement with green energy

4.1.2.1 Data center infrastructure management provide real-time insight Data Center Infrastructure Management, or DCIM, it is a process are best viewed and not a particular tool or objective. For data centers to flourish, they must manage, monitor and measure their assets using an integrated, real-time solution. It is important to provide a forum for IT personnel and facilities management to work together. In addition, DCIM software can helps administrators supporting hardware and monitor energy sensors as well as analyzing cooling system efficiency and power usage effectiveness (PUE). DCIM software supports you keep your data center running at peak performance. DCIM can also help take virtualization to another level, for example, cooling, enabling you to “virtualize” power, and space to enhance use and let your data center far more energy efficient.

4.1.2.2 Economics reduce data center cooling cost About the developing processes, choosing the correct technology is a key aspect of overall data center efficiency and use tools to monitor to improve efficiency. As cooling accounts for almost 40 percent of data center energy usage it’s a principal point for cost savings and driving energy. Thermal management equipment available supply significant savings by using economizers to lower data center cooling costs. Economizer systems use either water, pumped refrigerant, outside air to decrease or eliminate mechanical cooling used in data centers. These economizer systems produce significant energy savings around 50 percent, compared to the legacy systems in use today. While indirect economizers transfer heat outside the building, the direct economizers will bring outside air into the data center. Three Types of Indirect Economizers  Air to air heat exchangers  Pumped refrigerant systems  Dry coolers on chilled water systems The Liebert DSE Free Cooling System is a direct-expansion (DX) system that uses an integrated pumped refrigerant economizer to enlarge annual energy savings and provide greater availability without the need for separate economization coils. The integrated refrigerant pump is used to mix the refrigerant in lieu of the compressor to improve the desired supply air temperature, while outdoor ambient temperatures are low enough. The refrigerant pump will apply the fraction of the energy used by the compressor, increasing energy savings. Ease of installation greatly lower the TCO (Total Cost of Ownership) of pumped refrigerant systems.

4.1.2.3 Data center design Green data center design aims for speed, availability, and efficiency. Technology is moving closer to the edge, in a container, a modular approach, or a regional micro data center need more flexibility in data center implementation . Modular data centers involve of pre-fabricated building blocks that can be organized in a fraction of the time it takes to straight a bricks-and-mortar facility. They include all the necessary cooling, IT, power, access control elements and fire protection. Whether in a branch office, closet, or container, small IT spaces often need high availability for quick implementation, efficient operation, critical applications. An good-looking option for small modular data centers is an integrated approach, for example, Vertiv’s SmartRow, with all the necessary components packed together. The SmartRow system use conventional design to reduce energy consumption by up to 27% compared to a data centers. Here is some example that Enhancing energy efficiency of the facility including air conditioning, lighting, power supplies / Using green energy and get the award. 1) A data center of power supply that use a high-voltage direct current (HVDC) and a centralized DC power supply to achieves high efficiency and reduce the number of power supply units of each IT equipment. Moreover, GIPC has worked hard to progress DPPE, a metric for measuring the efficiency of a data center as a whole. High-performance IT equipment will let the data centers to increase their energy consumption per unit and energy overall consumption. Saving energy in each data center is crucial in terms of reducing energy costs. The important point to let the high energy efficiency is to save energy in each data center (reducing energy costs) and to use high-efficiency data centers. 2012 METI Minister’s Awards NTT Data Intellilink Corporation, Japan Radio Co., Ltd., and NTT Data Corporation Example of Energy saving of IT: use high energy efficiency IT equipment and improving the efficiency of operation by virtualization 1) Servers are usually connected for each customer to provide security and reliability. Combining existing servers as a single virtual system substantially decreases the number of servers and power consumption. The company is Fujitsu Limited

4.2 Base Station  BSS In order to enlarge the green power utilization, green energy powered BSs should be properly planned and improved to tackle with the dynamics of green power and mobile data traffic. In order to apply green energy, there are five energy related components may be united into a BS. These components are the green power generator, for example, the DC- AC inverter, the charge controller which regulates the output voltage of the green power generator, the battery, solar panel and the smart meter which enables the power transmission between BSs and the power grid.  By participating green energy into mobile networks, mobile service suppliers may save on-grid power consumption and thus lower their CO2 emissions. But, the size of the green power generator, the battery capacity, and other installation expenses will determine a BS with a green energy system incurs additional capital expenditures (CAPEX). It is desired to minimalize the CAPEX on provisioning green energy for BSs. Now, the green energy provisioning is mentioned to as defining the full capacity of the green power generator and the battery. Though the green energy provisioning problem for off-grid loads are well studied, the existing solutions will not directly relate to provisioning green energy for mobile networks. The process of green energy provisioning involves three basic models: the green power generation model, the load model, and the battery model. The existing solutions usually evaluate the loss of load probability (LOLP) and the loss of energy probability (LOEP) and take the statistical load information as an input. Based on the evaluation results, these methods change the green power generator battery capacity and sizes until the system performance in terms of LOEP and LOLP is mollified. These methods trusting only on the statistical load information do not enhance the energy utilization, and may result in over provisioning. In a mobile network, a BS’s power consumption can be modified according to the availability of green energy. On the one hand, a BS’s transmission strategies can be adjusted to reduce the energy demands without degrading the quality of service of the network. Instead, owing to unified deployment of BSs, a mobile user may be covered by multiple BSs. The traffic load of a BS can be lower by offloading its associated users to neighboring BSs. With using this way, the BS’s power consumption is modified. So, by enhancing the BS’s transmission strategies and mobile network lay- out, the power consumption of the BSs can be adjusted to minimize the size of the green energy system.  Because of renewable energy is highly dynamic, the green power may not be able to always assurance sufficient power supplies to BSs while the green power system is well provisioned. Then, the BS’s resource management including radio resource management and energy management should be enhanced in order to optimize the BS’s performance with constrained green energy. A BS’s optimal resource management will contain five dynamic processes: the battery dynamics, the power grid dynamics, the energy arrival dynamics, the wireless channel condition dynamics and the traffic dynamics. Due to the complex coupling of radio resource allocation and the energy allocation, it is challenging to attain optimal resource management. Packet Scheduling Optimization — While it is mathematically intractable to obtain optimal packet scheduling with consideration of all of these dynamic processes, several contributions have offered some insights on answering this difficult problem. If we consider a single user communication system with energy harvest transmitter, Ulukus and Yang proposed to enhance the packet transmission policy to reduce the packet transmission completion time. For this difficulty, the most challenging aspect is the causality limitation: a packet cannot be transported before it has arrived and the energy cannot be consumed before it is collected. This limitation introduces a tradeoff between the packet transmission time and the energy harvest time in determining the transmission power and rate. In overall, considering a network with a single transmitter, it may enable a higher transmission rate when using more transmission power and thus lower the packet transmission time. Though, since the causality limitation is introduced by green energy, in order to implement a higher transmission rate, the transmitter has to wait until enough green energy is harvested. For a multiuser system, the multiuser diversity in terms of channel conditions can be discovered to improve the green energy utilization. Within a given completion time, we will use less transmit power when transmitting a packet toward a user with better channel condition. Hence, arranging different users at a given time slot may require different amounts of green energy. By exploring the multiuser diversity, a packet scheduling algorithm may shape the BS’s energy demands to match the green power generation. If a BS’s energy demands perfectly match the green power generation, no additional energy sources are required to sustain the traffic demands in the BS.