Queue Backlog Research Articles

Online Service-Time Allocation Strategy for Balancing Energy Consumption and Queuing Delay of a MEC Server

MEC servers (MESs) support multiple queues to accommodate the delay requirements of tasks offloaded from end devices or transferred from other MESs. The service time assigned to each queue trades off the queue backlog and energy consumption. Because multiple queues share the computational resources of a MES, optimally scheduling the service time among them is important, reducing the energy consumption of a MES and ensuring the delay requirement of each queue. To achieve a balance between these metrics, we propose an online service-time allocation method that minimizes the average energy consumption and satisfies the average queue backlog constraint. We employ the Lyapunov optimization framework to transform the time-averaged optimization problem into a per-time-slot optimization problem and devise an online service-time allocation method whose time complexity is linear to the number of queues. This method determines the service time for each queue at the beginning of each time slot using the observed queue length and expected workload. We adopt a long short-term memory (LSTM) deep learning model to predict the workload that will be imposed on each queue during a time slot. Using simulation studies, we verify that the proposed method strikes a better balance between energy consumption and queuing delay than conventional methods.

Energy and Delay Guaranteed Joint Beam and User Scheduling Policy in 5G CoMP Networks

Massive Multi-Input Multi-Output (MIMO) and Coordinated MultiPoint (CoMP) technologies in Cloud-RAN (C-RAN) architecture become inevitable trend due to the advent of next-generation mobile applications, which are traffic-intensive, such as ultra high definition (UHD) video. In this paper, we study a joint beam activation and user scheduling problem in a 5G cellular network with massive MIMO and CoMP utilizing orthogonal random beamforming technique. This paper aims to minimize total Remote Radio Heads’ (RRHs’) energy expenditure in a dynamic C-RAN architecture while ensuring finite service time for all user traffic arrivals in the communication coverage. We leverage Lyapunov drift-plus-penalty framework to transform an original long-term average problem into a series of per-slot modified problems. Since the provided per-slot problem is combinatorial and nonlinear optimization problem, we are inspired by a greedy algorithm to design energy and delay guaranteed joint beam activation and user scheduling policy, namely BEANS. We prove that the proposed BEANS ensures finite upper bounds of average RRH energy consumption and average queue backlogs for all traffic arrival rates within constant ratio of capacity region and all energy-delay tradeoff parameters. These proofs are the first attempt to theoretically demonstrate guarantees of energy and queue bounds in a framework consisting of possibly negative submodular objective function and non-matriod constraints. Finally, via extensive simulations, we compare the capacity region and energy-queue backlog tradeoff of BEANS with optimal and existing algorithms, and show that BEANS attains up to 65% of energy saving for the same average queue backlog compared to the algorithms which do not take traffic dynamics and energy consumption into considerations.

RL-QN: A Reinforcement Learning Framework for Optimal Control of Queueing Systems

With the rapid advance of information technology, network systems have become increasingly complex and hence the underlying system dynamics are often unknown or difficult to characterize. Finding a good network control policy is of significant importance to achieve desirable network performance (e.g., high throughput or low delay). In this work, we consider using model-based reinforcement learning (RL) to learn the optimal control policy for queueing networks so that the average job delay (or equivalently the average queue backlog) is minimized. Traditional approaches in RL, however, cannot handle the unbounded state spaces of the network control problem. To overcome this difficulty, we propose a new algorithm, called RL for Queueing Networks (RL-QN), which applies model-based RL methods over a finite subset of the state space while applying a known stabilizing policy for the rest of the states. We establish that the average queue backlog under RL-QN with an appropriately constructed subset can be arbitrarily close to the optimal result. We evaluate RL-QN in dynamic server allocation, routing, and switching problems. Simulation results show that RL-QN minimizes the average queue backlog effectively.

TCP-WBQ: a backlog-queue-based congestion control mechanism for heterogeneous wireless networks

In heterogeneous wireless networks, random packet loss and high latency lead to conventional TCP variants performing unsatisfactorily in the case of competing communications. Especially on high-latency wireless links, conventional TCP variants are unable to estimate congestion degrees accurately for fine-grained congestion control because of the effects of random packet loss and delay oscillations. This paper proposes a TCP variant at the sender side to identify congestion degrees, namely TCP-WBQ, which quickly responses to the real congestion and effectively shields against random packet loss and oscillations of latency time. The proposed algorithm of congestion control firstly constructs a backlog-queue model based on the dynamics of the congestion window, and deduces the two bounds of the model which delimit oscillations of the backlog queue for non-congestion and random packet loss respectively. TCP-WBQ detects congestion degrees more accurately and thus implements the corresponding schemes of adjusting the congestion window, maintaining a tradeoff between high throughputs and congestion avoidance. The comprehensive simulations show that TCP-WBQ works efficiently in bandwidth utilization with single and multiple bottleneck scenarios, and achieves high performance and competitive fairness in heterogeneous wireless networks.

Lyapunov Optimization-Based Trade-Off Policy for Mobile Cloud Offloading in Heterogeneous Wireless Networks

In order to improve mobile users’ service experience, mobile cloud computing (MCC) is promoted. Although MCC can alleviate the burdens of Smart mobile devices (SMDs) by offloading computation-intensive applications to the cloud, it also aggravates computing and storage overheads in cloud centers and bandwidth overhead on wireless links for offloading workloads of mobile users. Therefore, we should carefully design the offloading policy to decrease these overheads while easing the burdens of SMDs. To this end, we investigate the offloading policy in heterogeneous wireless networks. In this paper, a queue model is built to formulate the mobile users’ workload offloading problem and Lyapunov optimization framework is proposed to make trade-off between system offloading utility and queue backlog. For deterministic WiFi connections, a Lagrangian optimization method is proposed to decide the optimal offloading workloads. Furthermore, considering random WiFi connection durations, a multi-stage stochastic programming method is proposed. The experimental results show effectiveness of the Lagrangian optimization offloading method for deterministic WiFi connection and the multi-stage stochastic programming method for random WiFi connection.

Service-Oriented LSTM Multi-Criteria RAT Selection Scheme for Vehicle-to-Infrastructure Communication

The evolution towards future Intelligent Transportation Systems (ITS) is significantly influenced by enabling telematics and safety services for vehicular use-cases, especially for autonomous driving. These vehicular use-cases have their own network requirements, with a variety of instantaneous requirements and dynamic characterizations that make delivering data via a single Radio Access Technology (RAT) severely challenging. By utilizing current and future network context and vehicular use-case requirements, the RAT selection scheme must select the optimum network that guarantees the continuity and high throughput data transfer. Moreover, the consideration of on-time stochastic queue backlog of all use-cases in each RATs, while maximizing throughput is important for a reliable vehicular use-case deployment. In this paper, we developed a Service-Oriented Joint LSTM Multi-Criteria (SOLMC) RAT selection scheme for Vehicle-to-Infrastructure (V2I) networks. The objective of this scheme is to maximize the overall network throughput while reducing the current stochastic queue backlog in deployed RATs. LSTM prediction technique is used as a network filter process that rejects the worst channel quality before the initialization RAT selection scheme. Transmission capacity, connectivity time, data delivery cost, and queue backlog are the decision criteria in our proposed SOLMC RAT scheme. In particular, to characterize the importance of these criteria factors, we construct Analytical Hierarchy Process (AHP) for each use-case. Extensive simulations are carried out under different network context conditions. The results demonstrate that the SOLMC scheme can significantly achieve up to 47.5% network throughput maximization and 20.42% packet delivery ratio improvement, using lower queue length compared to nearest-RAT-based selection. In addition, implementing LSTM in SOLMC improves the total average throughput by up to 3.1% and 6% packet delivery ratio.

Organizational Resource Allocation by Mobile Edge Computing in the Context of the Internet of Things

In recent years, the number of electronic devices and users has increased sharply with the rapid development and progress of electronic information technology. The motivation of this paper is to optimize the organization’s resource allocation strategy in the Internet of Things (IoT) environment. The optimal path planning and information processing efficiency are improved through Unmanned Aerial Vehicle (UAV) technology and migration optimization algorithm. The research method is migration optimization algorithm and UAV dynamic network. The information processing capability of traditional wireless communication systems has gradually been unable to meet the actual information processing needs. Therefore, a static network migration algorithm is constructed based on multiple-user multilateral edge computing servers. It migrates each user’s information processing task to the neighboring edge confidence processing server and uses the information processing server to perform auxiliary calculations. The simulation model adds a utility function that simulates energy consumption, delay weighting, and maximum extreme value, combined with the allocation strategy of optimizing each user’s information processing task to achieve the optimization goal. The static network migration algorithm established in this simulation has better results than other benchmark algorithms. Both scenario 1 and scenario 2 in the simulation show very close performance to the optimal solution. Meanwhile, a migration algorithm that can provide wireless charging for UAVs is built by a dynamic edge computing model based on the time associated with the UAV base station and multiple end users. Combined with completing the information processing tasks in each time slot, the energy arrival is also non-directional. The dynamic network migration algorithm can optimize the number of tasks absorbed by the end-user based on the current online status of the system without knowing the global information. The optimized target equation is related to the queue stability, and the parameter V has a linear relationship with the queue backlog length. Here, the problem of computing migration is studied in Mobile Edge Computing (MEC). The results show that the utility function of the weighted sum has an approximately linear relationship with the weights. As the value of the utility function increases, so does the weight function. The optimal data throughput of the proposed model is 70,000 bits, while the optimal data throughput of the state-of-the-art model is 68,000 bits. Therefore, the data transmission performance of the model presented here is better than that of other models. MEC can be significantly improved the efficiency of organizational resource allocation. Combining UAV and wireless charging technology, the computing and communication resource allocation issues of the UAV’s edge computing system are comprehensively discussed to improve the performance and efficiency of the network.

Latency-Aware Highly-Reliable mmWave Systems via Multi-Point Connectivity

The sensitivity of millimeter-wave (mmWave) radio channel to blockage is a fundamental challenge in achieving low-latency and ultra-reliable connectivity. In this paper, we explore the viability of using coordinated multi-point (CoMP) transmission for a delay bounded and reliable mmWave communication. We propose a novel blockage-aware algorithm for the sum-power minimization problem under the user-specific latency requirements in a dynamic mobile access network. We use the Lyapunov optimization framework, and provide a dynamic control algorithm, which efficiently transforms a time-average stochastic problem into a sequence of deterministic subproblems. A robust beamformer design is then proposed by exploiting the queue backlogs and channel information, that efficiently allocates the required radio and cooperation resources, and proactively leverages the multi-antenna spatial diversity according to the instantaneous needs of the users. Further, to adapt to the uncertainties of the mmWave channel, we consider a pessimistic estimate of the rates over link blockage combinations and an adaptive selection of the CoMP serving set from the available remote radio units (RRUs). Moreover, after the relaxation of coupled and non-convex constraints via the Fractional Program (FP) techniques, a low-complexity closed-form iterative algorithm is provided by solving a system of Karush-Kuhn-Tucker (KKT) optimality conditions. The simulation results manifest that, in the presence of random blockages, the proposed methods outperform the baseline scenarios and provide power-efficient, high-reliable, and low-latency mmWave communication.

Ultra-Reliable and Low-Latency Wireless Communications in the High SNR Regime: A Cross-Layer Tradeoff

Ultra-Reliable and Low-Latency Communications (URLLC) has attracted considerable attention because of its potential applications in factory automation, automated driving, and telesurgery anticipated for the era of the sixth-Generation (6G) networks. In URLLC with random channel gains and a hard delay constraint, the scheduling of backlogged queues and finite blocklength coding in the physical layer will make it very challenging to specify its performance limits. In this paper, we focus our attention on the asymptotic cross-layer analysis of URLLC when the Signal-to-Noise Ratio (SNR) is sufficiently high. More specifically, we find that a fundamental tradeoff exists among the service capability, latency, and error probability in the high SNR regime, which is characterized by a gain conservation equation. The main result of this work reveals that the sum of our defined service rate gain, real-time gain, and reliability gain is equal to one under the optimal policy. Numerical simulations are also exploited to validate that the derived gain conservation equation holds even with bounded random arrival.

Flow control oriented forwarding and caching in cache-enabled networks

The cache-enabled network architecture is very promising to improve the efficiency of content distribution and reduce the network congestion. In this paper, we propose a maximizing weighted throughput (MWT) algorithm for joint request forwarding, cache placement and flow control to dynamically optimize network performance. Specifically, a dual queue system that includes requests and data is established to retrieve the global content demands and traffic congestion information. In order to improve throughput performance as well as stabilize the queue, we formulate the flow-level throughput and design the request-level control policy by optimizing the throughput function and Lyapunov drift. The request forwarding policy adaptively allocates request forwarding rates for every link according to the differences among adjacent request queue backlogs. A novel cache priority function and a threshold-based request-dropping policy are used in the caching policy and flow control respectively to alleviate network overload. In addition, we prove the MWT algorithm achieves throughput near-optimal performance, and testify the dual queue system is stable by deducing the upper bound of the all queue backlogs. The experimental results verify the MWT algorithm stability and demonstrate the superiority of the MWT algorithm, compared to the state-of-the-art caching algorithms which are combined with back-pressure algorithm.

Multi-Timescale Multi-Dimension Resource Allocation for NOMA-Edge Computing-Based Power IoT With Massive Connectivity

Integrating edge computing and non-orthogonal multiple access (NOMA) into the power Internet of Things (PIoT) provides a promising way to meet the stringent demands of communication delay, energy consumption, and massive connectivity. The service tasks can be partitioned into independent subtasks, and can be processed locally by PIoT devices or offloaded to the edge servers through reusing radio resource blocks (RBs). In this paper, we propose a Multi-timescale multi-dimEnsion Resource allocatIon and Task Splitting algorithm, namely MERITS, for NOMA-edge computing-based PIoT. First, Lyapunov optimization is exploited to decompose the long-term stochastic optimization problem into three short-term deterministic subproblems, i.e., RB allocation in a large timescale, task splitting and computation resource allocation in a small timescale. The first subproblem is modeled as a one-to-many matching problem with externalities, and solved by the proposed swap matching-based RB allocation algorithm. The second subproblem is relaxed into a continuous convex problem which is easily to be solved, and the third subproblem is solved by Lagrange dual decomposition. Simulation results demonstrate that MERITS outperforms the existing resource allocation and task splitting algorithms in terms of energy consumption, queuing delay, queue backlog, and connection success ratio in the massive connectivity scenario.

Online algorithm for migration aware Virtualized Network Function placing and routing in dynamic 5G networks

The goal of Network Function Virtualization (NFV) is to build agile and service-aware networks by building a new paradigm of provisioning network services where physical network functions are deployed as Virtualized Network Functions (VNFs). Due to the advantages of NFV, 5G networks have adopted NFV as one of its key enabling technologies. One of the most important challenges for realizing NFV-enable 5G Networks is VNF placing and routing. However, most existing studies neglected flow rate fluctuation of service requests, which could cause service migration and fluctuation of queues (buffers) in the networks. In this paper, we study the cost-minimizing problem of VNF placing and routing in this dynamic environment while considering service migration and queue backlog stability. We first formulate the problem as a stochastic optimization problem. Then we propose an online algorithm, named MACRO, to solve the problem. In order to bound the worst-case delay encountered by requests we propose WEB-MACRO. MACRO and WEB-MACRO base on Lyapunov optimization and make decisions without knowing future information. The theoretical analysis suggests that MACRO and WEB-MACRO achieve an optimality gap of O(1V), where V is a tunable parameter that controls the tradeoff between cost and backlogs. In addition, the queue backlogs maintained by MACRO are bounded by O(V) and the queue backlogs maintained by WEB-MACRO are upper bounded which accordingly bounds the worst-case delay encountered by service requests. The experiment results suggest that MACRO and WEB-MACRO achieve queue stability and reduce the total cost by 36.49% when V=2 and 45.84% when V=6, compared with the state-of-the-art method.

Service-Oriented Dynamic Resource Slicing and Optimization for Space-Air-Ground Integrated Vehicular Networks

In this paper, we study Space-Air-Ground integrated Vehicular Network (SAGVN), and propose an online control framework to dynamically slice the SAG spectrum resource for isolated vehicular services provisioning. In particular, at a given time slot, the system makes online decisions on the request admission and scheduling, UAV dispatching, and resource slicing for different services. To characterize the impact of those parameters, we construct a time-averaged queue stability criteria by taking queue backlogs of all services into consideration, and formulate a system revenue function which incorporates the time-averaged system throughput and UAV dispatching cost. The objective is to maximize the system revenue while stabilizing the time-averaged queue, which falls into the scope of Lyapunov optimization theory. By bounding the drift-plus-penalty, the original problem can be decoupled into four independent subproblems, each of which is readily solved. The merits of our control framework are three-fold: 1) the system is able to admit and process as many requests as possible (i.e., maximizing the time-averaged throughput); 2) the time-averaged UAV dispatching cost is minimized; and 3) service queues are stabilized in the long-term. Extensive simulations are carried out, and the results demonstrate that the control framework can effectively achieve the system revenue maximization and queueing stabilization. Moreover, it can balance the trade-off among system throughput, UAV dispatching cost, and queueing states via parameter tuning. Compared with the fixed slicing, our dynamic slicing can react to the vehicular environment rapidly and achieve an average 26% of throughput improvement.

Control-Aware Energy-Efficient Transmissions for Wireless Control Systems With Short Packets

In this article, we investigate control-aware energy-efficient transmission strategies for wireless control systems with short packets (WCSs), in which remote state estimation error, system stability, transmission energy consumption, and communication packets with finite-length coding are all taken into account. Specifically, we formulate the transmission strategy design problem as a multiobjective optimization problem, which minimizes the remote state estimation error and transmission energy consumption simultaneously under the constraints of system stability and short packet communications. To solve the multiobjective optimization problem, we first introduce a novel objective function that encapsulates two different objective functions into a single one by using weight parameters, and further prove that the solution of the new stochastic optimization problem is a nondominated solution of the original one. Moreover, to solve the new stochastic optimization, we propose a dynamic control-aware energy-efficient transmission (DCET) algorithm that pushes the objective cost close to the optimal with a tradeoff in virtual queue backlogs for constraints. In particular, to tackle the nonconvexity constraint due to short packet communications, we introduce an additional constraint, with which the optimization problem is convex. Finally, simulation results verified the superiority of our proposed transmission strategy as compared with schemes of TDMA and Aloha-RAM multi access.

Hone: Mitigating Stragglers in Distributed Stream Processing With Tuple Scheduling

Low latency stream processing on large clusters consisting of hundreds to thousands of servers is an increasingly important challenge. A crucial barrier to tackling this challenge is stragglers, i.e., tasks that are significantly straggling behind others in processing the stream data. However, prior straggler mitigation solutions have significant limitations. They balance streaming workloads among tasks but may incur imbalanced backlogs when the workloads exhibit variance, causing stragglers as well. Fortunately, we observe that carefully scheduling the outgoing tuples of different tasks can yield benefits for balancing backlogs, and thus avoids stragglers. To this end, we present Hone, a tuple scheduler that aims to minimize the maximum queue backlog of all tasks over time. Hone leverages an online Largest-Backlog-First (LBF) algorithm with a provable good competitive ratio to perform efficient tuple scheduling. We have implemented Hone based on Apache Storm and evaluated it extensively via both simulations and testbed experiments. Our results show that under the same workload balancing strategy-shuffle grouping, Hone outperforms the original Storm significantly, with the end-to-end tuple processing latency reduced by 78.7 percent on average.

Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks

Thanks to flexible deployment and excellent maneuverability, autonomous drones are regarded as an effective means to enable aerial data capture in large-scale wireless sensor networks with limited to no cellular infrastructure, e.g., smart farming in a remote area. A key challenge in drone-assisted sensor networks is that the autonomous drone’s maneuvering can give rise to buffer overflows at the ground sensors and unsuccessful data collection due to lossy airborne channels. In this paper, we propose a new deep deterministic policy gradient based maneuver control (DDPG-MC) scheme which minimizes the overall data packet loss through online training instantaneous headings and patrol velocities of the drone, and the selection of the ground sensors for data collection in a continuous action space. Moreover, the maneuver control of the drone and communication schedule is formulated as an absorbing Markov chain, where network states consist of battery energy levels, data queue backlogs, timestamps of the data collection, and channel conditions between the ground sensors and the drone. An experience replay memory is utilized onboard at the drone to store the training experiences of the maneuver control and communication schedule at each time step. Numerical results demonstrate that the proposed DDPG-MC achieves 15.2 and 47.6 percent lower packet loss rate than deep Q-learning-based flight control and non-learning scheduling policies, respectively.

Application of Adaptive Threshold Algorithm with Selected Modified Parameters for the Detection of Flooding Based Denial-of-Service (DoS) Attack in Mobile AdHoc Network

Mobile AdHoc Networks (MANET) were self- organizing networks formed without pre-installed infrastructure. Nodes in these networks extensively relied on intermediate nodes to communicate with the destination node. Due to the limited network and host resources, Denial-of-Service (DoS) attacks were the major problem faced by MANET. Flooding based DoS attacks aimed to overwhelm certain resources which resulted in destabilizing an entire network which further denying the access of services to legitimate users. SYN flooding attack was a flooding attack launched from the transport layer of TCP/IP protocol stack in which the attacker forwarded a prodigious number of spoofed SYN packets to the victim node in order to overflow its back log queue. Fixed Threshold Algorithm (FTA) or Adaptive Threshold Algorithm (ATA) was a commonly used technique for detecting these attacks in the network in which an alarm was raised whenever the number of SYN packets increased abnormally. But these algorithms were suffered from a higher false positive rate (FPR) and lower accuracy.There as on for these limitations was that network traffic statistics were updated regularly during normal traffic conditions as well as for attack traffic. In order to overcome these limitations,a modified version of the Adaptive Threshold Algorithm was proposed.Based on the comprehensive evaluation using DARPA data set,it was shown that the present work could successfully detect flooding attacks with higher accuracy and lower false positive rate.

Distributed and Dynamic Service Placement in Pervasive Edge Computing Networks

The explosive growth of mobile devices promotes the prosperity of novel mobile applications, which can be realized by service offloading with the assistance of edge computing servers. However, due to limited computation and storage capabilities of a single server, long service latency hinders the continuous development of service offloading in mobile networks. By supporting multi-server cooperation, Pervasive Edge Computing (PEC) is promising to enable service migration in highly dynamic mobile networks. With the objective of maximizing the system utility, we formulate the optimization problem by jointly considering the constraints of server storage capability and service execution latency. To enable dynamic service placement, we first utilize Lyapunov optimization method to decompose the long-term optimization problem into a series of instant optimization problems. Then, a sample average approximation-based stochastic algorithm is proposed to approximate the future expected system utility. Afterwards, a distributed Markov approximation algorithm is utilized to determine the service placement configurations. Through theoretical analysis, the time complexity of our proposed algorithm is linear to the number of users, and the backlog queue of PEC servers is stable. Performance evaluations are conducted based on both synthetic and real trace-driven scenarios, with numerical results demonstrating the effectiveness of our proposed algorithm from various aspects.

Queue-Aware Dynamic Resource Allocation for the Joint Communication-Radar System

The joint communication-radar system (JCRS) is promising in the future wireless network which can provide great advantages, such as structure simplification, interference mitigation and less resource occupation, compared with the traditional separate deployment of communication and radar systems. However, with the large number of users accessing the network both for communication and radar purposes, massive user data packets may arrive at the base station equipped with the JCRS in a short period of time, which can easily cause the backlog of user data packets and affect network stability. To solve this problem, we introduce orthogonal frequency division multiple access (OFDMA) technique into the JCRS to support communication function for massive users. Then, we design a queue scheduling model of user data packets to perceive the status of current queue backlog and schedule the queue dynamically. Different from the existing works, we formulate a transmit power minimization problem, while taking network stability and radar detection performance into account. Since the problem is a non-convex problem, we transform it into the upper-bound minimization of drift-plus-function by using the Lyapunov optimization technique. Further, the successive convex approximation method and parameter transformation method are exploited to convert this problem into a convex problem. Finally, we derive an optimal closed-form power allocation expression by using the Lagrangian dual decomposition method and propose a dynamic subcarrier and power allocation algorithm. Simulation results show that the proposed algorithm can achieve an $[{\rm {O}}(1/\eta),{\rm {O}}(\eta)]$ power-backlog tradeoff and demonstrate the efficacy of the proposed algorithm in reducing transmit power while significantly enhancing network stability.

Coflow-Like Online Data Acquisition from Low-Earth-Orbit Datacenters

Satellite-based communication technology has gained much attention in the past few years, where satellites play mainly the supplementary roles as relay devices to terrestrial communication networks. Unlike previous work, we treat the low-earth-orbit (LEO) satellites as secure data storage mediums. We focus on data acquisition from a LEO satellite based data storage system (also referred to as the LEO based datacenters), which has been considered as a promising and secure paradigm on data storage. Under the LEO based datacenter architecture, one fundamental challenge is to deal with energy-efficient downloading from space to ground while maintaining the system stability. In this paper, we aim to maximize the amount of data admitted while minimizing the energy consumption, when downloading files from LEO based datacenters to meet user demands. To this end, we first formulate a novel optimization problem and develop an online scheduling framework. We then devise a novel coflow-like “Join the first K-shortest Queues (JKQ)” based job-dispatch strategy, which can significantly lower backlogs of queues residing in LEO satellites, thereby improving the system stability. We also analyze the optimality of the proposed approach and system stability. We finally evaluate the performance of the proposed algorithm through conducting emulator based simulations, based on real-world LEO constellation and user demand traces. The simulation results show that the proposed algorithm can dramatically lower the queue backlogs and achieve high energy efficiency.