Xueying Guo

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.

Delay-Constrained Energy-Optimal Base Station Sleeping Control

Base station (BS) sleeping is an effective way to improve the energy-efficiency of cellular networks. However, it may bring extra user-perceived delay. We conduct a theoretical study into the impact of BS sleeping on both energy-efficiency and user-perceived delay. We consider hysteresis sleep and three typical wake-up schemes, namely single sleep, multiple sleep, and N-limited schemes. We model the system as an M/G/1 vacation queue, which captures the setup time, the mode-changing cost, as well as the counting or detection cost during the sleep mode. Closed-form expressions for the average power and the Laplace-Stieltjes transform of delay distribution are obtained. The impacts of system parameters on these expressions are analyzed. We then formulate an optimization problem to design delay-constrained energy-optimal BS sleeping policies. We show that the optimal solutions possess a special structure, thereby allowing us to obtain them explicitly or numerically by simple bisection search. In addition, the relationship between the optimal power consumption and the mean delay constraint is analyzed, so as to answer the fundamental question: how much energy can be saved by trading off a certain amount of delay? It is shown that this optimal relationship is linear only when the delay constraint is lower than a threshold. Numerical studies are also conducted, where the impact of detection or counting cost during the sleep mode is explored, and the delay distribution under the optimal policy is obtained.

A Cooperative Scheduling Scheme of Local Cloud and Internet Cloud for Delay-Aware Mobile Cloud Computing

With the proliferation of mobile applications, Mobile Cloud Computing (MCC) has been proposed to help mobile devices save energy and improve computation performance. To further improve the quality of service (QoS) of MCC, cloud servers can be deployed locally so that the latency is decreased. However, the computational resource of the local cloud is generally limited. In this paper, we design a threshold-based policy to improve the QoS of MCC by cooperation of the local cloud and Internet cloud resources, which takes the advantages of low latency of the local cloud and abundant computational resources of the Internet cloud simultaneously. This policy also applies a priority queue in terms of delay requirements of applications. The optimal thresholds depending on the traffic load is obtained via a proposed algorithm. Numerical results show that the QoS can be greatly enhanced with the assistance of Internet cloud when the local cloud is overloaded. Better QoS is achieved if the local cloud order tasks according to their delay requirements, where delay- sensitive applications are executed ahead of delay- tolerant applications. Moreover, the optimal thresholds of the policy have a sound impact on the QoS of the system.

Optimal wake-up mechanism for single base station with sleep mode

Base station (BS) sleeping is an effecting way to improve the energy-efficiency of cellular networks. Considering BS sleep mode operation under different scenarios, we focus on three wake-up policies: single vacation (SV) policy, multiple vacation (MV) policy and N policy. A hysteresis time is also considered to avoid frequent BS mode-changing operation. By modeling the systems as M/G/1 vacation queues, we derive two performance measures of interest, expected system response time and energy consumption per bit. The impacts of sleep mode operation parameters and setup time are also studied. In order to determine the optimal parameter settings, which allows for a flexible tradeoff between energy-efficiency and mean delay, a two-step optimization method is proposed. We numerically analyze and investigate the energy-delay tradeoff for different policies, and find that both the MV policy and the N policy have better performance than the SV policy if the sniffing cost is not considered. However, these advantages diminish when the sniffing cost increases.

On energy-delay tradeoff in base station sleep mode operation

Base station (BS) sleep mode operation is one of the effective ways to save energy, but it may lead to longer delay to the customers. In order to evaluate the tradeoffs between energy consumption and customer delay, we model the BS sleep mode operation as an N-policy 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 time after the queue becomes empty and it starts to setup when it sees N customer arrivals during its sleep period. Several closed-form formulas are derived to demonstrate the tradeoffs between energy consumption and mean delay by changing the close-down time and N. It is shown that the relationship between the energy consumption and the mean delay is linear by changing the close-down time. Besides, larger N reduces the energy consumption, but there may exist N >; 1 that minimizes the mean delay. We also investigate the bound on given percentile of overall delay. We observe 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.

Index policies for optimal mean-variance trade-off of inter-delivery times in real-time sensor networks

A problem of much current practical interest is the replacement of the wiring infrastructure connecting approximately 200 sensor and actuator nodes in automobiles by an access point. This is motivated by the considerable savings in automobile weight, simplification of manufacturability, and future upgradability. A key issue is how to schedule the nodes on the shared access point so as to provide regular packet delivery. In this and other similar applications, the mean of the inter-delivery times of packets, i.e., throughput, is not sufficient to guarantee service-regularity. The time-averaged variance of the inter-delivery times of packets is also an important metric. So motivated, we consider a wireless network where an Access Point schedules real-time generated packets to nodes over a fading wireless channel. We are interested in designing simple policies which achieve optimal mean-variance tradeoff in interdelivery times of packets by minimizing the sum of time-averaged means and variances over all clients. Our goal is to explore the full range of the Pareto frontier of all weighted linear combinations of mean and variance so that one can fully exploit the design possibilities. We transform this problem into a Markov decision process and show that the problem of choosing which nodes packet to transmit in each slot can be formulated as a bandit problem. We establish that this problem is indexable and explicitly derive the Whittle indices. The resulting Index policy is optimal in certain cases. We also provide upper and lower bounds on the cost for any policy. Extensive simulations show that Index policies perform better than previously proposed policies.fading wireless channel.

An index based task assignment policy for achieving optimal power-delay tradeoff in edge cloud systems

Edge cloud is a promising architecture in order to address the latency problem in mobile cloud computing. However, as compared with remote clouds, edge clouds have limited computational resources, and higher operating costs. In this paper, we design policies which carry out the assignment of tasks that are generated at the mobile subscribers with edge clouds in an online fashion. The proposed policies achieve an optimal power-delay trade-off in the system. Here, the delay experienced by a mobile computing task includes the time spent waiting for transmission to the edge cloud, and the execution time at the edge cloud servers. We perform a theoretical analysis after modeling the system as a continuous-time queueing system. The contribution of this paper is two-fold: Firstly, the algorithm to determine the optimal policy is obtained by proposing an equivalent discrete-time Markov decision process. Secondly, an easily implementable index policy is proposed by analyzing the dual of the original problem. Extensive simulations illustrate the effectiveness of the proposed policies.

Tasks scheduling and resource allocation in heterogeneous cloud for delay-bounded mobile edge computing

Mobile edge computing is a novel technique in which mobile devices offload computation-intensive tasks with stringent delay requirements to the edge cloud. However, the limited computational resource in the edge cloud may result in the Quality of Service degradation. In this paper, we address this issue by coordinating the heterogeneous cloud which includes the edge cloud and the remote cloud. Considering the offloading of delay-bounded tasks, we study into the scheduling of heterogeneous cloud in order to maximize the probability that tasks can have the delay requirements met. The problem formulation is proved to be concave, and an optimal algorithm is proposed accordingly. The optimal policy with heterogeneous cloud is notably different from the policy merely using the edge cloud. With only the edge cloud, the system serves tasks with loose delay bounds and drops tasks with stringent delay bounds when the traffic load is heavy. However, with the heterogeneous cloud, tasks with stringent delay bounds are offloaded to the edge cloud and tasks with loose delay bounds are offloaded to the remote cloud. In numerical results, the probability that the delay bounds of tasks are satisfied can be improved by about 40% with the assistance of the remote cloud.

Optimal energy-efficient regular delivery of packets in cyber-physical systems

In cyber-physical systems such as in-vehicle wireless sensor networks, a large number of sensor nodes continually generate measurements that should be received by other nodes such as actuators in a regular fashion. Meanwhile, energy-efficiency is also important in wireless sensor networks. Motivated by these, we develop scheduling policies which are energy efficient and simultaneously maintain “regular” deliveries of packets. A tradeoff parameter is introduced to balance these two conflicting objectives. We employ a Markov Decision Process (MDP) model where the state of each client is the time-since-last-delivery of its packet, and reduce it into an equivalent finite-state MDP problem. Although this equivalent problem can be solved by standard dynamic programming techniques, it suffers from a high-computational complexity. Thus we further pose the problem as a restless multi-armed bandit problem and employ the low-complexity Whittle Index policy. It is shown that this problem is indexable and the Whittle indexes are derived. Also, we prove the Whittle Index policy is asymptotically optimal and validate its optimality via extensive simulations.

Pricing policy and computational resource provisioning for delay-aware mobile edge computing

Mobile edge computing is a novel technique to offer cloud-based computation offloading services to mobile users with short delay. However, the Cloud Service Providers (CSPs) of the edge cloud are generally different from the CSPs of remote Internet cloud. Considering the competition between the heterogeneous clouds, we study the optimal provisioning of the computational resource in the edge cloud. We firstly analyze the Nash equilibrium prices of the cloud market when the amount of edge computational resource is given. Based on the pricing policy, we further design an algorithm to optimize the edge computational resource capacity so that the profit of the edge cloud is maximized. We also derive the lower and upper bound of the optimal edge computational resource. The numerical results indicate that the profit of the edge cloud is greatly influenced by the price of the remote cloud when users care more about the price than the delay.