Energy-delay Tradeoff Research Articles

Energy Delay Tradeoff in Multichannel Full-Duplex Wireless LANs

Although full-duplex transmission can be helpful for enhancing wireless link capacity, it may require extra energy to overcome the residual self-interference. In this paper, we investigate the tradeoff between energy consumption and delay in a multichannel full-duplex wireless LAN. The goal is to minimize the energy consumption while keeping the traffic queues stable. With Lyapunov optimization, we develop a throughput optimal online scheme to achieve the goals with optimized channel assignment, transmission scheduling, and transmission mode selection. We study the influence of full-duplex transmissions on the network capacity region, prove the optimality of the proposed algorithm, and derive upper bounds for the average queue length and energy consumption, which demonstrate the energy-delay tradeoff in such systems. The proposed algorithm and the capacity region analysis are validated with simulations.

Energy Minimization in HARQ-I Relay-Assisted Networks With Delay-Limited Users

This paper investigates a practical energy minimization problem for multiuser relay-assisted downlink cellular networks. The system adopts a hybrid-automatic-repeat-request of type I (HARQ-I) protocol, and each user has an average delay constraint to be satisfied under a total power constraint for the system. The contribution of this paper lies in several folds. 1) By introducing new constraints and new variables in the original problem, the solution is analytically obtained by the dual method. 2) An algorithm that jointly allocates for every user the optimal downlink power at base station and relay station (RS) and selects the RS (if cooperation is decided) is proposed. 3) Another binary integer programming algorithm that selects the optimal modulation and coding scheme (MCS) and optimizes the number of time-frequency resource blocks (RBs) between users is proposed. In order to corroborate our findings, simulation results in terms of energy-delay tradeoff, average starvation user rates, and selected MCS statistics are presented. Our analyses show that relay-assisted strategy improves up to 30% the energy consumption, compared to a nonrelay-assisted one.

A control-chart-based queueing approach for service facility maintenance with energy-delay tradeoff

Maintenance planning and energy consumption control are critical issues in facility operations management. In practice, the energy consumption of a facility, which will be affected by the operation condition, is closely connected with the associated maintenance policy. Specifically, for an energy-consuming service system, though a frequent maintenance activity can keep the facility in a good condition with low energy consumption, it makes the delay time longer and leads to a poor customer experience. In this paper, we study a single-server queueing system with different energy consumption levels in the associated running states to address the conflict between energy consumption and customer delay. Two types of maintenance activities are implemented for the server, i.e., the planned maintenance and the reactive maintenance. The planned maintenance is adopted based on a frequency parameter at the beginning of an idle period, and the reactive maintenance is initialized by the Shewhart’s individual control chart (condition-based maintenance). To capture the energy-delay tradeoff, our objective is to develop an optimal maintenance policy that minimizes the long-run expected total cost of the system under a customer waiting time constraint. Numerical experiments are conducted to analyze the problem, in which useful managerial insights are obtained for the optimal maintenance policy. The results demonstrate the robustness of the proposed maintenance model, its advantage over the model without control chart, and its applicability in general situations.

Uplink Energy-Delay Trade-Off under Optimized Relay Placement in Cellular Networks

Relay nodes-enhanced architectures are deemed a viable solution to enhance coverage and capacity of nowadays cellular networks. Besides a number of desirable features, these architectures reduce the average distance between users and network nodes, thus allowing for battery savings for users transmitting on the uplink. In this paper, we investigate the extent of these savings, by optimizing relay nodes deployment in terms of uplink energy consumption per transmitted bit, while taking into account a minimum uplink average user delay that has to be guaranteed. A novel performance evaluation framework for uplink relay networks is first proposed to study this energy-delay trade-off. A simulated annealing is then run to find an optimized relay placement solution under a delay constraint; exterior penalty functions are used in order to deal with a difficult energy landscape, in particular when the constraint is tight. Finally, results show that relay nodes deployment consistently improve users uplink energy efficiency, under a wide range of traffic conditions and that relays are particularly efficient in non-uniform traffic scenarios.

Towards developing a compact model for magnetization switching in straintronics magnetic random access memory devices

Strain-mediated magnetization switching in a magnetic tunneling junction (MTJ) by exploiting a combination of piezoelectricity and magnetostriction has been proposed as an energy efficient alternative to spin transfer torque (STT) and field induced magnetization switching methods in MTJ-based magnetic random access memories (MRAM). Theoretical studies have shown the inherent advantages of strain-assisted switching, and the dynamic response of the magnetization has been modeled using the Landau-Lifshitz-Gilbert (LLG) equation. However, an attempt to use LLG for simulating dynamics of individual elements in large-scale simulations of multi-megabyte straintronics MRAM leads to extremely time-consuming calculations. Hence, a compact analytical solution, predicting the flipping delay of the magnetization vector in the nanomagnet under stress, combined with a liberal approximation of the LLG dynamics in the straintronics MTJ, can lead to a simplified model of the device suited for fast large-scale simulations of multi-megabyte straintronics MRAMs. In this work, a tensor-based approach is developed to study the dynamic behavior of the stressed nanomagnet. First, using the developed method, the effect of stress on the switching behavior of the magnetization is investigated to realize the margins between the underdamped and overdamped regimes. The latter helps the designer realize the oscillatory behavior of the magnetization when settling along the minor axis, and the dependency of oscillations on the stress level and the damping factor. Next, a theoretical model to predict the flipping delay of the magnetization vector is developed and tested against LLG-based numerical simulations to confirm the accuracy of findings. Lastly, the obtained delay is incorporated into the approximate solutions of the LLG dynamics, in order to create a compact model to liberally and quickly simulate the magnetization dynamics of the MTJ under stress. Using the developed delay equation, the efficiency of the straintronics switching over the STT method is highlighted by analytically investigating the energy-delay trade-off of both methodologies.

Optimal sleeping: models and experiments for energy-delay tradeoff

ABSTRACTMulti-core architectures have supplanted single core schemes, in part because the maximum clock speed of a single core is limited by its energy consumption. They provide the additional, less exploited benefit of allowing a finer trade-ff between energy consumption and delay by turning off subsets of cores. We investigate how this tradeoff varies with the number of cores, and whether heterogeneity brings additional benefits to outweigh its increased complexity. We study optimal sleep policies in two settings: switching of homogeneous cores on a fast timescale, which models multiple cores in a CPU, and switching of heterogeneous cores on a slow timescale, which models different generation servers in a data centre. In the homogeneous-core case, we show the optimal policy is monotone hysteretic, and that the performance is less sensitive to load estimation errors. By implementing this policy on a real experimental testbed, we show that theory makes a good prediction of the resource pooling benefits, and extend our study to realistic bursty job arrivals and heavy-tailed job sizes. In the heterogeneous-core case, we provide a low complexity algorithm to find a combination of servers that have approximately the minimum power draw while providing a specified minimum processing speed.

Processor-Network Speed Scaling for Energy–Delay Tradeoff in Smartphone Applications

Many smartphone applications, e.g., file backup, are intrinsically delay-tolerant so that data processing and transfer can be delayed to reduce smartphone battery usage. In the literature, these energy--delay tradeoff issues have been addressed independently in the forms of Dynamic Voltage and Frequency Scaling (DVFS) problems and network selection problems when smartphones have multiple wireless interfaces. In this paper, we jointly optimize the CPU speed and network speed to determine how much more energy can be saved through the joint optimization when applications can tolerate delays. We propose a dynamic speed scaling scheme called SpeedControl that jointly adjusts the processing and networking speeds using four controls: application scheduling, CPU speed control, wireless interface selection, and transmit power control. Through invoking the Lyapunov drift-plus-penalty technique, the scheme is demonstrated to be near optimal because it substantially reduces energy consumption for a given delay constraint. This paper is the first to reveal the energy--delay tradeoff relationship from a holistic perspective for smartphones with multiple wireless interfaces, DVFS, and multitasking capabilities. The trace-driven simulations based on real measurements of CPU power, network power, WiFi/3G throughput, and CPU workload demonstrate that SpeedControl can reduce battery usage by more than 42% through trading a 10 minutes delay when compared with the same delay in existing schemes; moreover, this energy conservation level increases as the WiFi coverage extends.

A Time-Adaptive Heuristic for Cognitive Cloud Offloading in Multi-RAT Enabled Wireless Devices

We introduce the concept of cognitive cloud offloading where all viable wireless interfaces of a multiple radio enabled device are used for computation offloading. We propose a time and wireless adaptive heuristic for offloading computationally intensive applications to a remote cloud with goals of reducing the energy consumption on the mobile device, execution time of the application, and efficient use of the multiple radio interfaces available at the device. The proposed algorithms simultaneously determine: 1) execution place of each application component (mobile/cloud); 2) amount of the associated data to be sent via each available interface of the multiple radio access technology device; and 3) scheduling order of the application components. We define a net utility function that trades off mobile device resources (battery, CPU, and memory) with realtime communication costs, such as latency and communication energy, subject to constraints that ensure queue stability of radio interfaces. Simulations using real data from an HTC smartphone running multi-component applications with Amazon EC2 as the cloud, and two radios, LTE and WiFi, show that cognitive cloud offloading provides higher net utility in comparison to the best-interface protocol. Scalability of the proposed heuristic is further analyzed using various levels for component dependency graphs and energy-delay trade-off factors.

Delay-Energy Tradeoff in Multicast Scheduling for Green Cellular Systems

Multicast transmission based on real-time network state information is a resource-friendly technique to improve the energy efficiency and reduce the traffic burden for cellular systems. This paper evaluates the effectiveness of this technique for downlink transmissions. In particular, a scenario is considered in which multiple mobile users (MUs) asynchronously request to download one common message locally cached at a base station (BS). Due to the randomness of both the channel conditions and the request arrivals from the MUs, the BS may choose to intelligently hold the arrived requests, especially when the channel conditions are bad or the number of requests is small, and then serve them in one shot later via multicasting. Clearly it is of great interest to balance the delay (incurred by holding the requests) and the energy efficiency (EE, defined as the energy cost per request), and this motivates us to quantify the fundamental tradeoff for the proposed “hold-then-serve” scheme. For the scenario with single channel and unit message sizes, it is shown that for a fixed channel bandwidth, the delay-EE tradeoff reduces to judiciously choosing the optimal stopping rule for when to serve all the arrived requests, where the effect of the bandwidth on the achievable delay-EE region is discussed further. By using optimal stopping theory, it is shown that the optimal stopping rule exists for general Markov channel models and request arrival processes. Particularly, for the hard deadline and proportional delay penalty cases, it is shown that the optimal stopping rule exhibits a threshold structure, and the corresponding threshold in the former case is time varying while in the latter case it is a constant. Finally, for the more general scenario with multiple channels and arbitrary message sizes, the optimal scheduling is formulated as a Markov decision process problem, where some efficient suboptimal scheduling algorithms are proposed.

Base-Station Sleeping Control and Power Matching for Energy–Delay Tradeoffs With Bursty Traffic

In this paper, we study sleeping control (SC) and power matching (PM) for a single cell in cellular networks with bursty traffic. The base station (BS) sleeps whenever the system is empty and wakes up when N users are assembled during the sleep period. The service capacity of the BS in the active mode is controlled by adjusting its transmit power. The total power consumption and average delay are analyzed, and based on this, the impact of parameter N and transmit power on the energy-delay tradeoff is studied. It is shown that, given the average traffic load, the more bursty the traffic is, the less total power consumed, although the delay performance of more bursty traffic is better only under certain circumstances. The optimal energy-delay tradeoff is then obtained through joint SC and PM optimization. The relationship between the optimal control parameters and the asymptotic performance are also provided. Moreover, the influence of the traffic autocorrelation is explored, which shows less impact on the system performance compared with that of the burstiness. Numerical results show the energy saving gain of the joint SC and PM scheme, as well as the impact of burstiness on the optimal energy-delay tradeoff.

Energy Efficiency and Delay Tradeoff in Device-to-Device Communications Underlaying Cellular Networks

This paper investigates the problem of revealing the tradeoff between energy efficiency (EE) and delay in device-to-device (D2D) communications underlaying cellular networks. Considering both stochastic traffic arrivals and time-varying channel conditions, we formulate it as a stochastic optimization problem, which optimizes EE subject to the average power, interference-control, and network stability constraints. With the help of fractional programming and the Lyapunov optimization technique, we develop an algorithm, referred to as the TRADEOFF, to solve the problem. To deal with the nonconvex and NP-hard power allocation subproblem in the TRADEOFF, we adopt the prismatic branch and bound algorithm to find its globally optimal solution, where only a linear programming needs to be solved in each iteration. Thus, the TRADEOFF serves as an important benchmark to evaluate performance of other heuristic algorithms and is usually cost-efficient. The theoretical analysis and simulation results show that the TRADEOFF achieves an EE-delay tradeoff of $[O(1/V),O(V)]$ with $V$ being a control parameter and can strike a flexible balance between them by simply tuning $V$ .

Stable Sleep Mode Optimization for Energy Efficient DSL

In this paper, we optimize the use of existing DSL low-power sleeping modes (L2 and L3) in order to improve the energy efficiency of DSL access networks. Given that switching on a DSL line can cause instability by increasing the amount of time-varying crosstalk in the cable bundle, and that it takes energy and time to switch a DSL line on and off , we develop a method to optimally choose the appropriate sleeping state based on the line and traffic characteristics. We further develop and prove the structural properties of the optimal policy for switching to the appropriate sleeping state, allowing transitions between submodes with different power and transmit rate characteristics. We also present techniques that guarantee stable sleep mode operation. Using a realistic DSL simulator, we demonstrate the three-way tradeoff among energy consumption, delay performance, and stability. The increased flexibility of control introduced by our approach improves the energy-delay Pareto optimal tradeoff, and results in a more energy efficient and stable DSL operation compared to existing power saving policies.

A Fluid model based Heuristic for Optimal Speed-scaling of Multi-class Single Server Queues

We investigate the energy-delay tradeoff in multi-class queues in which the server can regulate its speed according to the load of the system. Assuming that the queue is initially congested, we investigate the rate allocation to the classes that drains out the queue with minimum total energy and delay cost. We propose to solve this stochastic problem using a deterministic fluid approximation. We show that the optimal-fluid solution follows the well-known c? rule and obtain an explicit expression for the optimal speed. Numerical results show the utility and the applicability of the fluid-optimal policy.

Analysis of the Energy-Response Time Tradeoff for Delayed Mobile Cloud Offloading

We develop a delayed offloading model to leverage the complementary strength of WiFi and cellular networks when choosing heterogeneous wireless interfaces for offloading. Optimality analysis of the energy-delay tradeoff is carried out by using a queueing model with impatient jobs and service interruptions, which captures both energy and performance metrics and also intermittently available access links.

A novel energy efficiency algorithm in green mobile networks with cache

With more devices and emerging data-intensive services, mobile data traffic grows explosively, which makes the energy efficiency issue in current and future wireless networks be a growing concern. Recently, the advantages of bringing caching scheme in wireless networks have been widely investigated. Although additional power consumption is incurred by deploying caching equipments, the traffic loading balance of radio access network (RAN) and total network delay will be improved. In this paper, we study the energy efficiency problem in future wireless network. Particularly, we give an energy-delay tradeoff algorithm by using the sleeping control and power matching scheme. In the proposed algorithm, we consider a N-based sleeping control scheme, where the base station (BS) is in the sleep state whenever there is no arriving user, and works again when N users arrive. Moreover, the proposed algorithm is suitable for multi-BS scenario; to simplify the analysis, we mainly consider the single case. Simulation results shows that the proposed algorithm can obviously reduce the network power and delay in heterogeneous network conditions. Moreover, we find that a larger cache size does not always obtain a less network cost, because more cache power is consumed as cache size increases.

Characterizing Energy–Delay Tradeoff in Hyper-Cellular Networks With Base Station Sleeping Control

Base station (BS) sleeping operation is one of the effective ways to save energy consumption of cellular networks, but it may lead to longer delay to the customers. The fundamental question then arises: How much energy can be traded off by a tolerable delay? In this paper, we characterize the fundamental tradeoffs between total energy consumption and overall delay in a BS with sleep mode operations by queueing models. Here, the BS total energy consumption includes not only the transmitting power but also basic power (for baseband processing, power amplifier, etc.) and switch-over power of the BS working mode, and the overall delay includes not only transmission delay but also queueing delay. Specifically, the BS is modeled as an M/G/1 vacation queue with setup and close-down times, where the BS enters sleep mode if no customers arrive during the close-down (hysteretic) time after the queue becomes empty. When asleep, the BS stays in sleep mode until the queue builds up to N customers during the sleep period ( N-Policy) . Several closed-form formulas are derived to demonstrate the tradeoffs between the energy consumption and the mean delay for different wake-up policies by changing the close-down time, setup time, and the parameter N. It is shown that the relationship between the energy consumption and the mean delay is linear in terms of mean close-down time, but non-linear in terms of N. The explicit relationship between total power consumption and average delay with varying service rate is also analyzed theoretically, indicating that sacrificing delay cannot always be traded off for energy saving. In other words, larger N may lead to lower energy consumption, but there exists an optimal N* that minimizes the mean delay and energy consumption at the same time. We also investigate the maximum delay (delay bound) for certain percentage of service and find that the delay bound is nearly linear in mean delay in the cases tested. Therefore, similar tradeoffs exist between energy consumption and the delay bound. In summary, the closed-form energy-delay tradeoffs cast light on designing BS sleeping and wake-up control policies that aim to save energy while maintaining acceptable quality of service.

On the Energy–Delay Tradeoff and Relay Positioning of Wireless Butterfly Networks

This paper considers energy–delay tradeoff (EDT) of data transmission in wireless-network-coded butterfly networks (WNCBNs) where two sources convey their data to two destinations with the assistance of a relay employing either physical-layer network coding (PNC) or analog network coding (ANC). Hybrid automatic repeat request with incremental redundancy (HARQ-IR) is applied for reliable communication. In particular, we first investigate the EDT of both PNC and ANC schemes in WNCBNs to evaluate their energy efficiency. It is found that there is no advantage to using a relay in a high-power regime. However, in a low-power regime, the PNC scheme is shown to be more energy efficient than both the ANC and direct transmission (DT) schemes if the relay is located far from the sources, whereas both the PNC and ANC schemes are less energy efficient than the DT scheme when the relay is located near the sources. Additionally, algorithms that optimize relay positioning (RP) are developed based on two criteria: minimizing total transmission delays and minimizing total energy consumption subject to node location and power allocation constraints. This optimization can be considered a benchmark for RP in either a low-latency or low-energy-consumption WNCBN.

Energy Delay Tradeoff in Cloud Offloading for Multi-Core Mobile Devices

Cloud offloading is considered a promising approach for both energy conservation and storage/computation enhancement for resource-limited mobile devices. In this paper, we present a Lyapunov optimization-based scheme for cloud offloading scheduling, as well as download scheduling for cloud execution output, for multiple applications running in a mobile device with a multi-core CPU. We derive an online algorithm and prove performance bounds for the proposed algorithm with respect to average power consumption and average queue length, which is indicative of delay, and reveal the fundamental tradeoff between the two optimization goals. The performance of the proposed online scheduling scheme is validated with trace-driven simulations.

Modeling Energy-Delay Tradeoffs in Single Base Station with Cache

With the explosive increase of mobile data traffic, the energy efficiency issue in cellular networks is a growing concern. Recently, the advantages of in-network caching in Internet have been widely investigated, for example, speeding up content distribution and improving network resource utilization. In this paper, we analyze the energy-delay tradeoff problem in the context of single base station (BS), which has a cache capacity to buffer the contents through it. Although additional power is consumed by the cache, work load of BS and network delay will be improved, which makes a tradeoff between network power consumption and delay. Simulation results reveal that, by introducing the cache in a BS, the network power and delay can be obviously reduced in different network conditions compared to the scenario without a cache. In addition, we find that a large cache size does not always mean a less network cost because of the more cache power consumption.

Energy Efficiency and Delay Tradeoff for Time-Varying and Interference-Free Wireless Networks

In this paper, we investigate the fundamental tradeoff between energy efficiency (EE) and delay for time-varying and interference-free wireless networks. We formulate the problem as a stochastic optimization model, which optimizes the system EE subject to network stability and the average and peak transmit power constraints. By adopting the fractional programming theory and Lyapunov optimization technique, a general and effective algorithm, referred to as the EE-based dynamic power allocation algorithm (EE-DPAA), is proposed. The EE-DPAA does not require any prior knowledge of traffic arrival rates and channel statistics, yet yields an EE that can arbitrarily approach the theoretical optimum achieved by a system with complete knowledge of future events. Most importantly, we quantitatively derive the EE-delay tradeoff as [O(1/V),O(V)] with V as a control parameter for the first time. This result provides an important method for controlling the EE-delay performance on demand. Simulation results validate the theoretical analysis on the EE-delay tradeoff, as well as show the adaptiveness of the EE-DPAA.