• Energy Research
• 2016-2025close

With the proliferation of mobile devices (e.g., vehicles and smartphones), rich media content services from massive users lead to high network resource consumption and energy usage. How to effectively allocate heterogeneous network resources and achieve green content delivery is a major challenge to be addressed in urgency, especially when vehicular users are involved and the spatiotemporal distribution of the content requests can change drastically. In this paper, we propose a new deep reinforcement learning (DRL)-aided task offloading and resource allocation scheme, named TORA-DRL, to minimize power consumption in cloud-edge cooperation environments, where in-network caching and request aggregation are incorporated to reduce replicated transmissions of network contents. Based on the history of content requests and available network resources in the system, TORA-DRL jointly optimizes the decisions about task offloading, as well as computing, caching and communication resource allocation, adapting to the changes in network states and user requirements. Simulation results demonstrate that the proposed TORA-DRL solution converges fast and has much higher energy efficiency than the existing popular cloud-edge cooperation schemes under different network environments. IEEE

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 $K$ 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.

The fast development of mobile computing has raised ever-increasing diverse communication needs in wireless networks. To catch up with such needs, cloud radio access networks (CRAN) is proposed to enable efficient radio resource sharing and management. At the same time, the massive deployment of radio access networks has caused huge energy consumption. Incorporating renewable green energy to lower the brown energy consumption also has become a widely concerned topic. In this paper, we are motivated to investigate a green energy aware remote radio head activation problem for coordinated multipoint communications in green energy powered CRAN, aiming at minimizing the network brown energy power consumption. The problem is first formulated into a nonconvex optimization form. By analyzing the characteristics of the formulation, we further propose a heuristic algorithm based on an ordered selection method. Extensive simulation based experiment results show that the proposed green energy aware algorithm provides an effective way to reduce brown energy power consumption, well fitting the goal of developing green communications.

In this letter, we give a cloud-based satellite communication network (C-SCN) architecture to make full utilization of resources in current satellite systems. Since the complex computation and transmission bring huge energy cost, energy minimization is one of the most important problems in the C-SCN. Considering that the energy minimization problem is involved with the delay constraint, we decompose the original problem into two coupling subproblems, virtual machine assignment and power allocation, and then propose a joint optimization algorithm to solve the subproblems. Our simulation results show that the proposed algorithm is superior to the existing algorithms on energy efficiency in the C-SCN.

Wireless Powered Mobile Edge Computing (WPMEC) is an integration of Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) technologies, to both improve computing capabilities of mobile devices and energy compensation for their limited battery capabilities. Generally, energy transmitters, mobile devices, and edge servers form a WPMEC system that realizes a closed loop of sending and collecting energy as well as offloading and receiving task data. Due to constraints of time-varying network environments, time-coupled battery levels, and the half-duplex character of mobile devices, the joint design of computation offloading and resource allocation solutions in WPMEC systems has become extremely challenging, and a great number of studies have been devoted to it in recent years. In this article, we first introduce the basic model of the WPMEC system. Then, we present key issues and techniques related to WPMEC. In addition, we summarize solutions for computation offloading and resource allocation to solve critical issues in WPMEC networks. Finally, we discuss some research challenges and open issues.

The fuel cell is a promising power source for green data centers due to its high energy efficiency, low carbon emissions, and high reliability. However, because of the mechanical limitations related to fuel delivery, fuel cells are slow in adjusting power output when the energy demand quickly changes, which is called limited load following . Many recent work have studied to mitigate the limited load following by using energy storage to adjust energy supply, but achieves limited successes because of the constraint of energy storage size. In this paper, we address this challenge by changing both energy supply and demand, via joint workload scheduling and energy management. Specifically, we consider multiple geo-distributed data centers powered by both fuel cells and energy storage. An online algorithm has been proposed to minimize the gap between energy supply and demand by jointly managing the fuel cells output and migrating workloads among data centers. Simulations results based on real-world traces show that the proposed algorithms can achieve satisfactory performance.

Energy Internet (EI) is proposed as the evolution of smart grid, aiming to integrate various forms of energy into a highly flexible and efficient grid that provides energy packing and routing functions, similar to the Internet. As an essential part in EI system, a scalable and interoperable communication infrastructure is critical in system construction and operation. In this article, we survey the recent research efforts on EI communications. The motivation and key concepts of EI are first introduced, followed by the key technologies and standardizations enabling the EI communications as well as security issues. Open challenges in system complexity, efficiency, reliability are explored and recent achievements in these research topics are summarized as well.

Nowadays, there are two significant tendencies, how to process the enormous amount of data, big data, and how to deal with the green issues related to sustainability and environmental concerns. An interesting question is whether there are inherent correlations between the two tendencies in general. To answer this question, this paper firstly makes a comprehensive literature survey on how to green big data systems in terms of the whole life cycle of big data processing, and then this paper studies the relevance between big data and green metrics and proposes two new metrics, effective energy efficiency and effective resource efficiency in order to bring new views and potentials of green metrics for the future times of big data.

Power-aware task scheduling on processors has been a research hotspot in computing systems. Given an application $G$ G containing a set $N$ N of tasks $\lbrace n_1,\ldots, n_{|N|}\rbrace$ { n 1 , ... , n | N | } , and a system containing a set $U$ U of processors $\lbrace u_1,\ldots, u_{|U|}\rbrace$ { u 1 , ... , u | U | } , the power-aware task scheduling generally refers to finding the appropriate processor and frequency for each task $n_i$ n i , so as to make sure that all the tasks can be finished efficiently and the overall energy consumption is guaranteed. In this article, we study the problem of minimizing the schedule length for energy consumption constrained parallel applications on heterogeneous computing systems, where the schedule length refers to the time interval between starting the first task and finishing the last task. For this problem, existing work adopts a policy that preassigns the minimum energy consumption for each unassigned task. Nevertheless, our analysis reveals that, such a preassignment policy could be unfair for the low priority tasks, and it may not achieve an optimistic schedule length . Thereby, we propose a new task scheduling algorithm that suggests a weight-based mechanism to preassign energy consumption for unassigned tasks, and we provide the rigorous proof to show its feasibility. Further, we show that this idea can be extended to solve reliability maximization problems with energy consumption constraint or with both deadline and energy consumption constraints, where the reliability refers to the probability of executing application $G$ G without failures, and the deadline constraint refers to the “allowable” maximum schedule length. We have conducted extensive experiments based on real parallel applications. The experimental results consistently demonstrate that our proposed algorithms can achieve favourable performance, compared to state-of-the-art algorithms.