Satellite edge computing has emerged as a promising paradigm to support global-scale low-latency services by enabling remote code execution in orbit. However, unlike terrestrial networks, satellite communication is fundamentally vulnerable to eavesdropping due to its wide broadcast range, high accessibility, and long-distance propagation, which is further exacerbated by the high altitudes allowing a vast set of potential eavesdropper positions. Despite these unique risks, most prior works have either ignored security or treated it as a uniform concern across services. In this paper, we take an initial step toward secure satellite edge computing by explicitly modeling and optimizing for security heterogeneity, i.e., diverse security demands across different services and dynamic jamming strategies. We formulate a novel optimization problem that jointly minimizes average information leakage for security-sensitive services and overall energy consumption while meeting latency constraints. To solve it, we propose a stochastic optimization-based algorithm, namelySOS, which dynamically controls code offloading, resource allocation, and jamming strategy with respect to service-specific security requirements. Extensive simulations show thatSOSoutperforms existing schemes by achieving up to a 92% reduction in average information leakage with only an 18% increase in energy consumption at the same latency compared to an existing algorithm. Moreover,SOSmaintains high jamming efficiency and leakage robustness under both colluding and non-colluding attacks. Our findings highlight a fundamental yet overlooked security-energy-latency trade-off and positionSOSas a pioneering step toward security-aware satellite edge computing.

Mobile services such as Augmented Reality (AR) and online gaming require high computing and storage resources with real-time data exchange with servers. However, mobile devices are resource-constrained and cloud computing incurs additional network latency which makes them dissatisfactory to use such mobile services. To tackle this challenge, service caching has been emerging as a critical technology that provides services by leasing resources from mobile edge computing (MEC) servers located close to the users. Unlike content caching, which stores data only for delivery, service caching utilizes both computing and storage resources, and thereby should be jointly considered with code offloading which offloads the processing workloads of mobile devices to servers. Therefore, the conventional caching strategy that caches the most popular services may not be the best choice, and the intertwined nature of service caching and code offloading policies makes the system operation complex. In this paper, we formulate an average cost (energy consumption, offloading payment) minimization problem of mobile devices constrained by service queue stability in a three-layer (mobile/MEC/cloud) network architecture. We apply a statistic-based dynamic optimization framework to derive an online-fashioned interTWined Interplay between Storage and Transmission, namely TWIST algorithm that jointly makes decisions of service caching, CPU clock frequency of mobile devices, and code offloading. Next, via theoretical analysis, we show the trade-off relationships between the average cost of mobile devices and service delay. Finally, extensive simulations demonstrate that TWIST can save up to 51% of cost for the same delay of 2.41sec. compared to the existing algorithms.

Due to the broadcasting nature, satellite signals are vulnerable to potential eavesdropping attacks, which pose a significant security risk for users. Physical layer security (PLS) and cryptography technologies have been independently developed to address this security risk. However, the independent use of each technology in the power-limited satellite systems results in a trade-off problem between onboard power consumption and security performance due to additional encryption costs and dependence of PLS on the performance of eavesdroppers (Eves). In this paper, we integrate the PLS and cryptography considering the complementary properties of the adaptability to wireless channel characteristics and inherent message confidentiality for secure multibeam satellite networks. To this end, we estimate the eavesdropping risk as a function of the given number of Eves for independent and collaborative attacks. Moreover, we design an onboard power model utilized for transmission and computation, and Gaussian beamforming based on the eavesdropping risk. Then, we derive solutions for onboard power allocation, beam scheduling, and security algorithm selection in the non-orthogonal multiple access (NOMA) systems. Finally, we demonstrate that the secure transmission performance improves even under the increment of the eavesdropping risk, and the trade-off performance between cryptography and PLS is provided through analytical and simulation results.

When the wireless networks become large, it is hard to connect all base stations (BSs) to the core networks via the wired backhaul due to the increased infrastructure cost. Therefore, the integrated access and backhaul (IAB) networks, where the BS connects to the core networks via the wireless backhaul, has been emerged. In this paper, we consider a millimeter wave (mmWave) beamforming design and backhaul rate assignment in IAB networks. On top of this system model, we derive a closed form expression of the expected sum data rate. Moreover. we formulate the expected sum data rate maximization problem concerning the beamforming design and backhaul rate assignment. Since the formulated problem is a complex form and coupled with the optimization variables, we define a slack variable and divide the original problem into two subproblems. Then, we propose an iterative algorithm to obtain the optimal solution. Moreover, we provide a low-complex algorithm for large-scale networks by using a genetic algorithm (GA)-based approach. Finally, from numerical simulations, we show that the proposed solutions achieve a higher achievable sum data rate than the baseline schemes. We also explore the impacts of the bandwidth partitioning ratio, the biased factor ratio, the density of the user and the number of macro base station (MBS)’s transmit antennas on the achievable sum data rate.

To enhance the performance of autonomous driving, recent studies have been incorporating various tasks that require increasingly more computation. As computational demands increase, it is often difficult to achieve timely execution with the limited performance of onboard computing units alone. To address this issue, Vehicle Edge Computing (VEC), which offloads computational workloads to the edge and retrieves the results back to the vehicle, is gaining significant attention. To achieve efficient offloaded analytics via VEC, it is crucial to comprehensively consider both of the computing and network conditions of the V2X systems, as well as the vehicle energy consumption and timely execution. However, current studies have not sufficiently addressed the comprehensive modeling of computational and network loads in these V2X systems. To deal with this, we propose a Cooperative Network-Computation Load Balancing Simulator for VEC.

This paper proposes an integrated simulator combining WiLabV2Xsim, a MATLAB-based open-source simulator, and Virtual Test Drive (VTD), a traffic generation and vehicle dynamics simulator developed by HEXAGON, to evaluate Vehicle to Everything (V2X) communication performance in real road and driving environments. WiLabV2Xsim is a system-level simulator that implements Cellular-V2X and New Radio-V2X communication protocol stacks. Through VTD, it is possible to implement usecases standardized in 3GPP Technical Report (TR) 22.886 or evaluate V2X communication performance in real environments using virtual environments based on the Association for Standardization of Automation and Measuring Systems (ASAM) Open Scenario. Future research plans include evaluating various scenarios based on the 5G Automotive Association (5GAA) TR documents through the integrated simulator and conducting studies on wireless resource allocation and decentralized congestion control to enhance V2X communication performance.

As deep learning technology becomes advanced, mobile vision applications, such as augmented reality (AR) or autonomous vehicles, are prevalent. The performance of such services highly depends on computing capability of different mobile devices, dynamic service requests, stochastic mobile network environment, and learning models. Existing studies have independently optimized such mobile resource allocation and learning model design with given other side of parameters and computing/network resources. However, they cannot reflect realistic mobile environments since the time-varying wireless channel and service requests are assumed to follow specific distributions. Without these unrealistic assumptions, we propose an algorithm that jointly optimizes learning models and process/network resources adapting to system dynamics, namely, VisionScaling by leveraging the state-of-the-art online convex optimization (OCO) framework. This VisionScaling jointly makes decisions on 1) the learning model and the size of input layer at learning-side and 2) the GPU clock frequency, the transmission rate, and the computation offloading policy at resource-side every time slot. We theoretically show that VisionScaling asymptotically converges to an offline optimal performance with satisfying sublinearity. Moreover, we demonstrate that VisionScaling saves at least 24% of dynamic regret which captures energy consumption and processed frames per second (PFPS) under mean average precision (mAP) constraint via real trace-driven simulations. Finally, we show that VisionScaling attains 30.8% energy saving and improves 39.7% PFPS while satisfying the target mAP on the testbed with Nvidia Jetson TX2 and an edge server equipped with high-end GPU.

Recently, applications using artificial intelligence (AI) technique in mobile devices such as augmented reality have been extensively pervasive. The hardware specifications of mobile devices, dynamic service demands, stochastic network states, and characteristics of DNN (Deep Neural Network) models affect the quality of experience (QoE) of such applications. In this paper, we proposeCutEdge, that leverages a virtual queue-based Lyapunov optimization framework to jointly optimize DNN model partitioning between a mobile device and a mobile edge computing (MEC) server and processing/networking resources in a mobile device with respect to internal/external system dynamics. Specifically,CutEdgemakes decisions of(i)the partition point of DNN model between the mobile device and MEC server,(ii)GPU clock frequency, and(iii)transmission rates in a mobile device, simultaneously. Then, we theoretically show the optimal trade-off curves among energy consumption, throughput, and end-to-end latency yielded byCutEdgewhere such QoE metrics have not been jointly addressed in the previous studies. Moreover, we show the impact of joint optimization of three control parameters on the performances via real trace-driven simulations. Finally, we show the superiority ofCutEdgeover the existing algorithms by experiment on top of implemented testbed using an embedded AI device and an MEC server.

Service caching has been emerging as a key technology that can overcome the hardware limitations of mobile devices by leasing resources from mobile edge computing (MEC) servers. This technology is inherently different from content caching where caching performance can be maximized by caching the most popular contents since most of the services require workload processing from CPU/GPU in servers. Thereby caching the most popular services on the MEC server may not be the best choice. Besides, when the service caching system is designed together with code offloading policy, the problem becomes more difficult because two control parameters are tightly intertwined with each other. In this paper, we formulate an average cost minimization problem of mobile devices constrained by service queue stability in a three-layer (mobile/MEC/cloud) network architecture. We apply a statistic-based dynamic optimization framework to derive an online-fashioned interTWined Interplay between Storage and Transmission, namely TWIST algorithm that jointly makes decisions of service caching, CPU/GPU clock frequency of mobile devices, and code offloading. Next, via theoretical analysis, we show the trade-off relationships between average cost of mobile devices and service delay. Finally, extensive simulations demonstrate that TWIST can save up to 51% of cost for the same delay 2.41sec. compared to the existing algorithms.

Computational resources are increasingly virtualized to enable computational tasks to be offloaded to remote facilities along the route between the source and destination. The principle that underlies traditional routing, i.e., that only networking resources need to be considered, may no longer be true in a virtualized environment. In this paper, we propose a framework for the efficient utilization of multi-resource infrastructures in which computational resources can be used via the network. Such a framework intrinsically calls for the joint consideration of networking and computational resources. In particular, we focus on unifying the controls in dynamic service chaining and multiple resource management, which are the key technologies in an integrated SDN/NFV architecture. We formulate a multi-path problem for choosing the resources to use in different services. The problem can be viewed as variational inequality using the Lagrange duality and saddle point theory. Based on this, we develop an extragradient-based algorithm that controls and splits the sending rate of each service. We prove that the algorithm converges to the optimal, minimizing the system cost while maximizing service utility. Simulations for diverse scenarios demonstrate that our algorithm achieves high QoS while reducing the system cost by jointly considering dual-resource coupling and service characteristics.

Wireless technologies, such as WirelessHART, are being adopted in industrial wireless sensor–actuator networks (IWSAN), which are required to provide reliable quality of control (QoC). This article focuses on adaptively selecting the best network path for reliable QoC in the IWSAN. The main challenge is estimating the time-varying packet delivery ratio (PDR) of each path. The IWSAN path selection problem in a multi-armed bandit (MAB) framework is formulated. A novel algorithm criticality-aware adaptive path learning (CAPL) is proposed, which determines the criticality of each packet according to the degree of QoC degradation if it is lost. The key novelty of CAPL is that it simultaneously considers the fundamental exploration–exploitation trade-off in MAB and QoC in the IWSAN. CAPL uses low-criticality packets for exploration to measure the PDR so that it can minimize the impact of exploration on QoC degradation. CAPL with extensive simulation and empirical studies for DC motor position control is validated.

Demand for using cloud object storage has been increasing in order to efficiently manage a large number of binary large objects (BLOBs), including videos, photos and documents. Although many companies and institutions are currently trying to utilize public cloud object storage services such as AWS Simple Storage Service (S3), most of existing encoding systems for safe storage of data have not been optimized for current cloud object storage architecture. In this article, we propose a novel dynamic extreme erasure encoding algorithm, namelyDexEncodingaiming to maximize the utility of clients where the encoding locations in the cloud storage architecture are dynamically optimized between gateway and storage servers with respect to the time-varying cloud environment. Here, the utility captures the satisfaction of clients for the speed of data storage and fairness among clients.DexEncodingefficiently resolves resource bottlenecks by adapting to the dynamic network, processing and storage resource availability and storage request. Real measurement-driven simulations demonstrate that the proposedDexEncodingalgorithm drastically outperforms that applied in the state-of-the-art object storage systems in a perspective of clients’ satisfaction.

Due to introduction of the non-terrestrial network (NTN) and satellite-air-terrestrial integrated network (SATIN), many applications using satellites are being expected and this will result in a severe security issue. In this paper, we propose a satellite communication security method that jointly considers signal transmission and security computation power based on the orthogonal multiple access (OMA). Before transmission, satellite estimates a security threat based on the number of eavesdroppers (Eves) in the large satellite beam coverage as wide as 50 km for LEO satellites at the L-band. With the security threat, the satellite splits onboard power for signals and security, and controls a beam size. Through the sum capacity optimization problem, we derive the optimal onboard power allocation and security algorithm selection with respect to security threats and channel conditions.

For beyond 5G and 6G communications, the satellite terrestrial integrated network (STIN) is expected to provide diverse services with seamless coverage. The first step for implementing the STIN is to make the satellite network capable of supporting advanced functions that the terrestrial counterpart is providing. In this paper, network virtualization with network slices, which is actively studied in the terrestrial network, is analyzed for the satellite network. The main difference between satellite and terrestrial networks is the mobility of satellites. Since the slice services require end-to-end connectivity, the satellite network topology change due to the mobility of satellites can give a huge impact to the slices. The latency is analyzed with time-varying satellite topology with an assumption that the virtual network resource for slice is reserved for low latency. For simulations, the minimum number of handovers is assumed and the end-to-end latency is analyzed for its initial latency, average latency, minimum latency, and maximum latency during the service time of slices in the satellite network.

For 5G and 6G communications, satellites are drawing great attention for global coverage extension and 3-D mobility enhancement. With advancements of satellite hardware, functional satellites are expected to be applied for 6G Internet of Things (IoT) services. In particular, because IoT service has a relatively low computational burden, it is more feasible for satellite edge computing (SatEC) with limited power, making IoT supportable SatEC one of the economically feasible applications for future satellite networks. In this article, an architecture of IoT supportable SatEC is analyzed, and the corresponding network slice scheduling is proposed. First, a multiobjective optimization problem for IoT supportable SatEC is formulated with respect to latency, computational power, and transmission power attenuation. The problem is solved for the satellite offloading rate and altitude by using a heuristic algorithm in low time complexity with time-varying satellite constellation topology and various service requirements for simulations. Next, to analyze the expandability of the SatEC IoT network, a sliced SatEC IoT scheduling problem is formulated in the normalized weighted sum of latency, computational power, and transmission power attenuation. Scheduling rules are proposed to prioritize various applications with the results of the scheduling problem and with the proper SatEC offloading rates predefined in the Pareto optimality of the satellite edge multiobjective Tabu search (SE-MOTS). Finally, efficient satellite constellations are determined by comparing low-Earth orbit (LEO) and very LEO (VLEO) satellite networks, in terms of proper satellite altitudes and offloading strategies for IoT supportable SatEC. Based on simulation results, a scheduling rule for sliced satellite network and a proper offloading strategy of different slices are proposed, and an appropriate altitude of the satellite network for sliced SatEC is discussed.

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, namelyBEANS. We prove that the proposedBEANSensures 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 possiblynegative submodular objective functionandnon-matriod constraints. Finally, via extensive simulations, we compare the capacity region and energy-queue backlog tradeoff ofBEANSwith optimal and existing algorithms, and show thatBEANSattains 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.

The advent of elastic Content Delivery Networks (CDNs) enable Content Providers (CPs) to lease cache capacity on demand and at different cloud and edge locations in order to enhance the quality of their services. This article addresses key challenges in this context, namely how to invest an available budget in cache space in order to match spatio-temporal fluctuations of demand, wireless environment and storage prices. Specifically, we jointly consider dynamic cache rental, content placement, and request-cache association in wireless scenarios in order to provide just-in-time CDN services. The goal is to maximize the an aggregate utility metric for the CP that captures both service benefits due to caching and fairness in servicing different end users. We leverage the Lyapunov drift-minus-benefit technique and Jensen's inequality to transform our infinite horizon problem into hour-by-hour subproblems which can be solved without knowledge of future file popularity and transmission rates. For the case of non-overlapping small cells, we provide an optimal subproblem solution. However, in the general overlapping case, the subproblem becomes a mixed integer non-linear program (MINLP). In this case, we employ a randomized cache lease method to derive a scalable solution. We show that the proposed algorithm guarantees the theoretical performance bound by exploiting the submodularity property of the objective function and pick-and-compare property of the randomized cache lease method. Finally, via real dataset driven simulations, we find that the proposed algorithm achieves 154% utility compared to similar static cache storage-based algorithms in a representative urban topology.

We begin this paper by asking a simple question: All beams can be simultaneously activated thanks to the ignorable inter-beam interference and sharp beam shape in mmWave networks? This paper provides a counter-intuitive observation that interference between one-hop adjacent beams still significantly affects the network performance in mmWave networks. Leveraging this observation, we revisit an optimization of interbeam scheduling problem in a network-wide mmWave system on top of a physical layer precoding technique and suggest practical and low-complex beam on/off scheduling and corresponding user scheduling algorithms. Finally, via simulations in a real mmWave network environment, we reveal that the proposed algorithm attains close to the performance of an optimal policy which has much higher computational complexity.

The proliferation of advanced mobile devices has enabled the wide-spread adoption of computation-intensive mobile applications. Nevertheless, the execution of these applications is still constrained by the limited battery and processing capacity of the devices. These limitations have led to the mobile cloud/edge computation offloading paradigm, and several such architectures have been proposed in recent years. However, we currently lack a clear understanding of the benefits of these diverse offloading solutions in terms of computation delay and energy cost savings. In this paper, we implement a hierarchical mobile-edge-cloud computing system that is comprised of a mobile device, an edge server and a cloud server, and conduct a series of experiments to measure the device's energy consumption, as well as the computation and transmission delay for different tasks. Our experiments reveal an interesting relation between the mobile's CPU clock frequency and the transmission delay for the offloaded tasks. Based on this finding, we propose a dynamic greedy algorithm that selects the CPU frequency, the tasks to be offloaded, and the network path (cellular or WiFi), in order to reduce the total energy cost and execution delay. We fully implement this multi-tier architecture and verify experimentally that the proposed algorithm can save up to 50% of the device battery energy, at the expense of transmission delay. Our results motivate the design of offloading policies that optimize jointly the CPU clock and network capacity.

More network operators have recently provided content delivery network (CDN) services, where traffic engineering techniques, such as the base station (BS) association, are jointly employed with content delivery. This deployment attempts to reduce the network operating cost and enhance the quality of service (QoS) of end users. Toward this end, we study the BS association and file caching problem considering the spatial diversity of the file popularity and the realistic time-scale separation between the file caching and the BS association decisions in this paper. Our design aims to minimize the file delivery latency and operating power consumption in the cellular networks, where the tradeoff between these conflicting objectives is controlled by a single parameter. The short time-scale BS association problem is solved by using the convex optimization technique for a given file caching solution. However, the long time-scale file caching problem considering the varying BS association decisions taken at the short time-scale is difficult to tackle. To solve this file caching problem, we prove and leverage the submodularity property of the underlying objective function to develop a greedy content caching algorithm that guarantees a constant approximation ratio of the optimal objective value. Via simulations using real-world datasets, we show that the proposed algorithms outperform file caching and BS association algorithms that do not consider the spatial diversity of the file popularity in terms of the power consumption and delay performance in the geographically heterogeneous file popularity scenario.

A fundamental question in the study of location-based mobile sensing is how much energy can be saved while still guaranteeing the reliable localization accuracy. In this paper, we analyze key features of human proximity and find a motivation which implies that the energy-efficient and accurate localization is possible by sharing the locations of nearby mobile devices. From the location-based collaborative sensing idea, we formulate an optimization problem which aims to minimize the total number of location measurements for a given fairness criterion. Then, we propose a practical and distributed location sharing (DLS) protocol and an optimal parameter control algorithm (OWD) which makes the DLS protocol attain an asymptotic optimal performance. Via extensive simulations under various environments including real mobility traces, we verify that the proposed DLS+OWD policy significantly reduces the average power consumption of mobile devices with a higher fairness compared to the existing algorithms.

With elastic CDNs, content providers can rent cache space on demand at different cloud locations in order to enhance their offered quality of service (QoS). This paper addresses a key challenge in this context, namely how to invest an available budget in cache space in order to match spatio-temporal fluctuations of file demand and storage price. Specifically, we consider jointly dynamic cache rental, file placement, and request-cache association in a wireless scenario in order to provide a just-in-time CDN service. The objective is to maximize the benefit in average download delay obtained by the rented caches, while ensuring that the time-average rental cost is less than a fixed budget. We leverage a Lyapunov drift-minus-benefit technique to transform our infinite horizon problem into day-by-day subproblems which can be solved without knowledge of distant future file popularity and transmission rates. For the case of non-overlapping small cells (also wired case) we provide an efficient subproblem solution, referred to as JCC. However, in the general overlapping case, the subproblem becomes a mixed integer non-linear program (MINLP). In this case, we employ a dual decomposition method to derive a scalable solution, namely the JCCA algorithm. Finally, via extensive simulations, we reveal that the proposed JCCA algorithm attains 82.66 % higher delay benefit than existing static cache storage-based algorithms when available average cache budget is 20% of entire file library; moreover, the benefit becomes higher as the average cache budget gets tighter.

Most existing content caching designs require accurate estimation of content popularity, which can be challenging in the dynamic mobile network environment. Moreover, emerging hierarchical network architecture enables us to enhance the content caching performance by opportunistically exploiting both cloud-centric and edge-centric caching. In this paper, we propose a hybrid content caching design that does not require the knowledge of content popularity. Specifically, our design optimizes the content caching locations, which can be original content servers, central cloud units (CUs) and base stations (BSs) where the design objective is to support as high average requested content data rates as possible subject to the finite service latency. We fulfill this design by employing the Lyapunov optimization approach to tackle an NP-hard caching control problem with the tight coupling between CU caching and BS caching control decisions. Toward this end, we propose algorithms in three specific caching scenarios by exploiting the submodularity property of the sum-weight objective function and the hierarchical caching structure. Moreover, we prove the proposed algorithms can achieve finite content service delay for all arrival rates within the constant fraction of capacity region using Lyapunov optimization technique. Furthermore, we propose practical and heuristic CU/BS caching algorithms to address a general caching scenario by inheriting the design rationale of the aforementioned performance-guaranteed algorithms. Trace-driven simulation demonstrates that our proposed hybrid CU/BS caching algorithms outperform the general popularity based caching algorithm and the independent caching algorithm in terms of average end-to-end service latency and backhaul/fronthaul load reduction ratios.

In this paper, we develop a content caching and flow level BS-user association framework in a network environment with the spatial variation of content popularity. Because the studied content caching and BS-user association functions are tightly intertwined with each other, and their decision time scales can be very different in practice, our design considers the time-scale separation of these network functions to tackle and develop the BS-user association and content caching policies. Specifically, we propose an optimal BS-user association algorithm, namely OptUA, operating in the short time scale for a given content caching solution, and a greedy content caching algorithm, namely GCC, operating in the long time scale. The GCC algorithm exploits the submodularity characteristics of the objective function which ensures that the GCC algorithm achieves a constant fraction of the optimal performance for most feasible caching sets. Via extensive numerical studies in heterogeneous cellular networks, we demonstrate that proposed OptUA and GCC algorithms outperform other algorithms which do not consider spatial variations of content popularity in terms of average end-to-end delay per content request and average system load per content at each BS.

In this paper, we consider the content caching design without requiring historical content access information or content popularity profiles in a hierarchical cellular network architecture. Our design aims to dynamically select caching locations for different contents where caching locations can be content servers, cloud units (CUs), and base stations (BSs). Our design objective is to support as high content request rates as possible while maintaining the finite service time. To tackle this design problem, we employ the Lyapunov optimization method where the caching algorithm is developed by minimizing the Lyapunov drift of a quadratic Lyapunov function of virtual queue backlogs. This solution approach requires to solve a max-weight problem, which is an NP-hard and difficult problem to solve due to the coupling between CU caching and BS caching decisions. By exploiting the submodularity of the objective function, we propose a hybrid caching algorithm which achieves the constant approximation ratio to the optimal performance. Trace-driven simulation results demonstrate that the proposed joint CU/BS caching algorithm achieves almost the same performance with the exhaustive search and outperforms the independent caching algorithm and heuristic joint caching algorithms in terms of average end-to-end latency and backhaul load reduction ratio.

In this paper, we study dynamic bandwidth slicing and resource allocation problems to support a mixture of IoT (Internet of Things) and video streaming services. By employing Lyapunov optimization method with time-scale separation approach, we develop algorithms for long time-scale bandwidth slicing, and short time-scale IoT device scheduling, power allocation (for IoT service) and quality decision (for video streaming service). We show through simulations that proposed dynamic bandwidth slicing and resource allocation algorithms outperform the static bandwidth slicing and resource allocation policies in terms of average total cost and average total delay.

In this paper, we study a joint beam and user scheduling problem in a cooperative cellular network utilizing orthogonal random beamforming technique. This paper aims to minimize total base stations' average energy expenditure while ensuring finite service time for all traffic arrivals in a given set. We leverage Lyapunov optimization technique to transform original long-term problem into short-term modified max-weight problem without knowledge of future network states such as traffic arrivals. We introduce a parameter which manipulates energy-delay tradeoff in our system as well. Since provided short-term problem is combinatorial and nonlinear optimization problem, we are inspired by a greedy algorithm to design near-optimal joint beam and user scheduling policy, namely BEANS. We prove that proposed BEANS (i) ensures finite service time for all traffic arrival rates within close to 1/2 capacity region and all (energy-delay) tradeoff parameters thanks to submodular characteristics of the objective function, and (ii) attains finite upper bounds of average energy consumption and average queue backlog for all traffic arrival rates within close to 1/4 capacity region and all tradeoff parameters. 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 43% of energy saving for the same average queue backlog compared to the algorithms which do not take traffic dynamics and energy consumption into considerations.

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.

Mobile cloud computing (MCC) has been proposed to offload heavy computing jobs of mobile devices to cloud servers managed by cloud service provider (CSP), which enables the mobile devices to save energy and processing delay. Heretofore, cloud offloading policies in mobile devices and pricing/scheduling in CSP have been independently addressed. This paper is first to jointly account for both sides of mobile users and CSP in a unified mobile cloud computing framework. By invoking “Lyapunov drift-plus-penalty” technique, we propose dual-side control algorithms for the mobile users and CSP in two different scenarios: (i) In non-cooperation scenario, we propose a NC-UC algorithm for the mobile users and a NC-CC algorithm for the CSP to minimize each cost for given delay constraints. (ii) In cooperation scenario, we suggest a CP-JC algorithm for both cloud users and CSP to minimize the sum costs of them for given delay constraints. Trace-driven simulations demonstrate that NC-UC saves minimum 63% of cost by trading 8MB of average queue lengths when compared with the existing algorithms, and NC-CC achieves 71% of profit gain when compared with the same delay of existing scheme; moreover, the cooperation enables them to save additional costs and delays.

To cope with increasing energy consumption in mobile devices, the mobile cloud offloading has received considerable attention from its ability to offload processing tasks of mobile devices to cloud servers, and previous studies have focused on single type tasks in fixed network environments. However, real network environments are spatio-temporally varying, and typical mobile devices have not only various types of tasks, e.g., network traffic, cloud offloadable/nonoffloadable workloads but also capabilities of CPU frequency scaling and network interface selection between WiFi and cellular. In this paper, we first jointly consider the following three dynamic problems in real mobile environments: 1) cloud offloading policy, i.e., determining to use local CPU resources or cloud resources; 2) allocation of tasks to transmit through networks and to process in local CPU; and 3) CPU clock speed and network interface controls. We propose a DREAM algorithm by invoking the Lyapunov optimization and mathematically prove that it minimizes CPU and network energy for given delay constraints. Trace-driven simulation based on real measurements demonstrates that DREAM can save over 35% of total energy than existing algorithms with the same delay. We also design DREAM architecture and demonstrate the applicability of DREAM in practice.

Energy-delay tradeoffs in smartphone applications have been studied independently in dynamic voltage and frequency scaling (DVFS) problem and network interface selection problem. We optimize the two problems jointly to quantify how much energy can be saved further and propose a scheme called SpeedControl which jointly manages application scheduling, CPU speed control and wireless interface selection. The scheme is shown to be near-optimal in that it tends to minimize energy consumption for given delay constraints. This paper is the first to reveal energy-delay tradeoffs in a holistic view considering multiple wireless interfaces, DVFS and multitasking in smartphone. We perform real measurements on WiFi/3G coverage and throughput, power consumption of CPU and WiFi/3G interfaces, and CPU workloads. Trace-driven simulations based on the measurements demonstrate that SpeedControl can save over 30% of battery by trading 10 min delay as compared to existing schemes when WiFi temporal coverage is 65%, moreover, the saving tendency increases as WiFi coverage increases.

Greening effect in interference management (IM), a way of enhancing spectrum sharing via intelligent transmit power control, can be achieved by the fact that as BSs moderately reduce their transmit powers, the performance degradation decreases slower than linearly, yet a considerable overall energy saving is expected due to transmit powers' exerting influence on operational power. This paper investigates the impact of different spatial and/or temporal power sharing policies for a given system-wide power budget in IM schemes. We develop an optimization-theoretic IM framework on cellular network greening, from which we first develop four IM schemes governed by different power sharing: no sharing, only temporal sharing, only spatial sharing, and both spatial and temporal sharing. Through extensive simulations, including a real BS deployment in Manchester city, United Kingdom, we obtain the following interesting observations: (i) the gains both from performance and power saving are obtained by adopting the spatial and/or temporal power sharing policies, (ii) tighter greening regulation (i.e., smaller total power budget) leads to higher spatio-temporal power sharing gain than IM gain, (iii) spatial power sharing significantly excels temporal one in terms of power saving, and (iv) higher greening efficiency can be achieved as the cell size becomes smaller.

Greening effect in interference management (IM), which is a technology to enhance spectrum sharing via intelligent BS transmit power control, can be achieved by the fact that even small reduction in BS transmit powers enables considerable saving in overall energy consumption due to their exerting influence on operational powers. In this paper, we study the impact of power sharing policies in IM schemes on cellular network greening, where different spatio-temporal power sharing policies are considered for a fixed system-wide power budget. This study is of great importance in that the pressure on the CO2emission limit per nation increases, e.g., by Kyoto protocol, which will ultimately affect the power budget of a wireless service provider. We propose optimization theoretic IM frameworks with greening, from which we first develop four IM schemes with different power sharing policies. Through extensive simulations under various configurations, including a real BS deployment in Manchester city, United Kingdom, we obtain the following interesting observations: (i) tighter greening regulation (i.e., the smaller total power budget) leads to higher spatio-temporal power sharing gain than IM gain, (ii) spatial power sharing significantly excels temporal one, and (iii) more greening gain can be achieved as the cell size becomes smaller.