With the increasing demand of high-speed mobile data transmission, densely deployed small cell base stations capable of caching popular contents have recently emerged as a promising technique to improve the quality of service for mobile users. In this paper, we investigate the cooperative transmission and power management problem for a set of ``off-grid'' base stations in a cellular network hierarchy that are powered solely by on-site renewable energy sources. The network throughput maximization problem is mathematically formulated as a mixed-integer non-linear programming problem. In the proposed formulation, a base station can adjust its transmission power in a coordinated multipoint communication scheme and/or switch to a "sleep mode" for energy saving. Based on the Lyapunov optimization theory, an efficient near-optimal solution method is proposed with provable bound of the optimality gap. Experimental results on a realistic setup show that the proposed algorithm can achieve up to 2.96x download throughput per user compared to some baseline algorithms.

Plug-in electric vehicles (PEVs) are considered the key to reducing fossil fuel consumption and an important part of the smart grid. The plug-in electric vehicle-to-grid (V2G) technology in the smart grid infrastructure enables energy flow from PEV batteries to the power grid so that the grid stability is enhanced and the peak power demand is shaped. PEV owners will also benefit from V2G technology, as they will be able to reduce energy cost through proper PEV charging and discharging scheduling. Moreover, power regulation service (RS) reserves have been playing an increasingly important role in modern power markets. It has been shown that by providing RS reserves, the power grid achieves a better match between energy supply and demand in presence of volatile and intermittent renewable energy generation. This article starts with the problem of PEV charging under dynamic energy pricing, properly taking into account the degradation of battery state-of-health (SoH) during V2G operations as well as RS provisioning. An overall optimization throughout the whole parking period is proposed for the PEV and an adaptive control framework is presented to dynamically update the optimal charging/discharging decision at each hour to mitigate the effect of RS tracking error. As more and more PEVs are being plugged into the power grid, the control or management issue of PEV charging arises, since mass unregulated charging processes of PEVs may result in degradation of power quality and damage utility equipments and customer appliances. To solve this problem, this article also presents an SoH-aware charging aggregator design, which decides the control sequences of a group of PEVs. An energy storage system is used in the charging aggregator to do a peak power shaving, and future parking PEVs are properly taken care of. Experimental results show that the proposed optimal charging algorithm minimizes the combination of electricity cost and battery aging cost in the RS provisioning power market. Experimental results also show that the introduction of charging aggregator can significantly reduce the peak power consumption caused by simultaneous PEV charging.

The usage of energy storage devices in data centers has been widely studied for the purpose of peak shaving in the context of dynamic utility pricing. To effectively achieve peak shaving in a data center with energy storage capability, a joint optimization framework is proposed to solve the task assignment problem and the energy storage management problem. The power hierarchy of a data center is modeled as a tree with battery arrays connected to each node, which is a generalization of common energy storage deployment topologies including the centralized structure and the rack/server-level distributed structure. A multi-dimensional resource request model is adopted based on released cluster traces in order to capture the complex dynamics of the workload of a data center. In addition, the rate capacity effect and aging effect are considered when modeling the batteries. A progressive refinement approach is used to solve the optimization problem. Experimental results show that the proposed algorithm consistently outperforms some baseline algorithms.

The heating, ventilation and air conditioning (HVAC) system accounts for half of the energy consumption of a typical building. Additionally, the need for HVAC changes over hours and days as does the electric energy price. Level of comfort of the building occupants is, however, a primary concern, which tends to overwrite pricing. Dynamic HVAC control under a dynamic energy pricing model while meeting an acceptable level of occupants' comfort is thus critical to achieving energy efficiency in buildings in a sustainable manner. Finally, there is the possibility that the building is equipped with some renewable source of power such as solar panels mounted on the rooftop. The presence of a battery energy storage system in a target building would enable peak power shaving by adopting a suitable charge and discharge schedule for the battery, while simultaneously meeting building energy efficiency and user satisfaction. Achieving this goal requires detailed information (or predictions) about the amount of local power generation from the renewable source plus the power consumption load of the building. This paper addresses the coscheduling problem of HVAC control and battery management to achieve energy-efficient buildings, while also accounting for the degradation of the battery state-of-health during charging and discharging operations (which in turn determines the amortized cost of owning and utilizing a battery storage system)aa cč A time-of-use dynamic pricing scenario is assumed and various energy loss components are considered including power dissipation in the power conversion circuitry as well as the rate capacity effect in the battery. A global optimization framework targeting the entire billing cycle is presented and an adaptive co-scheduling algorithm is provided to dynamically update the optimal HVAC air flow control and the battery charging/discharging decision in each time slot during the billing cycle to mitigate the prediction error of unknown parameters. Experimental results show that the proposed algorithm achieves up to 15% in the total electric utility cost reduction compared with some baseline methods.

This paper addresses the problem of profit maximization for a data center with battery banks deployed at various levels of the power hierarchy. An optimization framework that covers the request dispatch, server resource allocation, and battery charging management is proposed. Instead of controlling the input/output power of the batteries after knowing the power profile of all other components of the data center as in a set of prior work, an optimal management policy is proposed which adjusts the power consumption (or supply) of servers and the battery banks at the same time. A response time dependent revenue model is adopted based on the delay estimation using the generalized processor sharing model. The rate capacity effect and the state of health degradation of the batteries, as well as the conversion and transmission loss in the power delivery network, are considered for the purpose of accurate power modeling and utility cost estimation. It is shown that the problem can be transformed into a series of convex optimization problems and then solved using standard solvers.

A cross-layer framework (spanning device and circuit levels) is presented for designing robust and energy-efficient SRAM cells, made of deeply-scaled FinFET devices. In particular, 7nm FinFET devices are designed and simulated by using Synopsys TCAD tool suite, Sentaurus. Next, 6T and 8T SRAM cells, which are composed of these devices, are designed and optimized. To enhance the cell stability and reduce leakage energy consumption, the dual (i.e., front and back) gate control feature of FinFETs is exploited. This is, however, done without requiring any external signal to drive the back gates of the FinFET devices. Subsequently, the effect of process variations on the aforesaid SRAMs is investigated and steps are presented to protect the cells against these variations. More precisely, the SRAM cells are first designed to minimize the expected energy consumption (per clock cycle) subject to the non-destructive read and successful write requirements under worst-case process corner conditions. These SRAM cells, which are overly pessimistic, are then refined by selectively adjusting some transistor sizes, which in turn reduces the expected energy consumption while ensuring that the parametric yield of the cells remains above some prespecified threshold. To do this efficiently, an analytical method for estimating the yield of SRAM cells under process variations is also presented and integrated in the refinement procedure. A dual-gate controlled 6T SRAM cell operating at 324mV (in the near-threshold supply regime) is finally presented as a high-yield and energy-efficient memory cell in the 7nm FinFET technology.

Plug-in electric vehicles (PEVs) are considered the key to reducing the fossil fuel consumption and an important part of the smart grid. The plug-in electric vehicle-to-grid (V2G) technology in the smart grid infrastructure enables energy flow from PEV batteries to the power grid so that the grid stability is enhanced and the peak power demand is shaped. PEV owners will also benefit from V2G technology as they will be able to reduce energy cost through proper PEV charging and discharging scheduling. Moreover, power regulation service (RS) reserves have been playing an increasingly important role in modern power markets. It has been shown that by providing RS reserves, the power grid achieves a better match between energy supply and demand in presence of volatile and intermittent renewable energy generation. This paper addresses the problem of PEV charging under dynamic energy pricing, properly taking into account the degradation of battery state-of-health (SoH) during V2G operations as well as RS provisioning. An overall optimization throughout the whole parking period is proposed for the PEV and an adaptive control framework is presented to dynamically update the optimal charging/discharging decision at each time slot to mitigate the effect of RS tracking error. Experimental results show that the proposed optimal PEV charging algorithm minimizes the combination of electricity cost and battery aging cost in the RS provisioning power market.

Cloud computing and storage have attracted a lot of attention due to the ever increasing demand for reliable and cost-effective access to vast resources and services available on the Internet. Cloud services are typically hosted in a set of geographically distributed data centers, which we will call the cloud infrastructure. To minimize the total cost of ownership of this cloud infrastructure (which accounts for both the upfront capital cost and the operational cost of the infrastructure resources), the infrastructure owners/operators must do a careful planning of data center locations in the targeted service area (for example the US territories), data center capacity provisioning (i.e., the total CPU cycles per second that can be provided in each data center). In addition, they must have flow control policies that will distribute the incoming user requests to the available resources in the cloud infrastructure. This paper presents an approach for solving the unified problem of data center placement and provisioning, and request flow control in one shot. The solution technique is based on mathematical programming. Experimental results, using Google cluster data and placement/provisioning of up to eight data center sites demonstrate the cost savings of the proposed problem formulation and solution approach.

Due to the limited battery capacity in mobile devices, the concept of mobile cloud computing (MCC) is proposed where some applications are offloaded from the local device to the cloud for higher energy efficiency. The portion of applications or tasks to be offloaded for remote processing should be judiciously determined. In this paper, the problem of optimal task dispatch, transmission, and execution in the MCC system is considered. Dynamic voltage and frequency scaling (DVFS) is applied to the local mobile processor, whereas the RF transmitter of the mobile device can choose from multiple modulation schemes and bit rates. The power consumptions of the mobile components that cannot be directly controlled, e.g., the touch screen, GPU, audio codec, and I/O ports, are also accounted for through capturing their correlation with the mobile processor and RF transmitter. Finally, a realistic and accurate battery model is adopted in this work in order to estimate the battery energy loss rate in a more accurate way. This paper presents a semi-Markov decision process (SMDP)-based optimization framework, with the actions of different DVFS levels and modulation schemes/transimission bit rates and the objective of minimizing both the energy drawn from the battery and the average latency in request servicing. This paper derives the optimal solution, including the optimal DVFS policy, offloading rate, and transmission scheme, using linear programming combined with a heuristic search. Experiments are conducted on Qualcomm Snapdragon Mobile Development Platform MSM8660 to find the correlations among the power consumptions of the CPU, RF components, and other components. Simulation results show that the proposed optimal solution consistently outperforms some baseline algorithms.

Power gating is a very effective method in reducing the leakage energy during the standby mode in VLSI circuits at the cost of increased circuit delay. This method has been well studied and widely used for circuits fabricated by using traditional CMOS technology nodes operating at super-threshold supply voltage regime. However, for advanced technology nodes with small feature sizes and low supply voltages, the propagation delay becomes very sensitive to the high process-induced variations. Therefore, this paper first analyzes how the circuit delay depends on the size of the sleep transistor under the process-induced variation for the 7nm gate length FinFET technology. Then a joint optimization problem is formulated to minimize the total energy consumption, while both supply voltage and sleep transistor size are considered as optimization variables. A near-optimal heuristic is presented to solve the optimization problem and determine the energy-optimal supply voltage and sleep transistor size. Experimental results based on HSPICE simulations show that more than 98% energy reduction for applications with relaxed deadline constraints after applying the joint optimization technique, compared to FinFET circuits without using the power gating method.

As cloud computing is becoming the new generation of computing paradigm because of its many attractive attributes, huge data centers are built and operated to host the cloud services. Since these data centers usually incur a high electricity bill, the problem of reducing the electricity cost and maximizing the profit for a data center operator arises naturally. Because of the trend of dynamic pricing policies in the energy market, in which the electricity price changes across different hours of a day, the use of energy storage devices, such as batteries and supercapacitors, in a data center can be extended in addition to judicious computing/memory/storage resource management policies as another way to cut down on the operational cost. In this paper, we formulate a generalized optimization problem to minimize the linear combination of the electricity cost and the average request response time in a data center with energy storage devices. Solutions based on convex optimization techniques are proposed and the experimental results are discussed to demonstrate the effectiveness of the proposed formulation and the solution methods.

The finite and rather small battery energy capacity in today's mobile devices has limited the functionality that can be integrated into these platforms or the performance and quality of applications that can be delivered to the users. In the last few years, there is a trend toward offloading certain computation-intensive and latency-tolerant local applications and service requests to a mobile cloud computing (MCC) system so as to save the precious battery life while providing the services requested by the users. Each mobile application can be thought of as a sequence of tasks that are executed locally or remotely. In this paper, the problem of optimal task dispatch, transmission, and execution onto the MCC system is considered. To achieve a good balance between the application execution time and power consumption, dynamic voltage and frequency scaling (DVFS) is applied to the local processor in the mobile device, while the transmitter can choose among multiple modulation schemes and bit rates. The rate capacity effect of a battery and power conversion losses in the mobile device are also accounted for so as to have a more realistic model of the remaining battery life. The mobile device is modeled as a semi-Markov decision process (SMDP) and the optimization problem to set the DVFS level and the transmission rate is effectively solved by linear programming combined with a one-dimensional heuristic search. Experimental results show that the proposed algorithm consistently outperforms some baseline algorithms.