Jeongho Kwak

DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems

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.

Processor-network speed scaling for energy: delay tradeoff in smartphone applications

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

Dynamic speed scaling for energy minimization in delay-tolerant smartphone applications

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.

Impact of spatio-temporal power sharing policies on cellular network greening

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 CO2 emission 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.

Dual-side dynamic controls for cost minimization in mobile cloud computing systems

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.

Dynamic Pricing, Scheduling, and Energy Management for Profit Maximization in PHEV Charging Stations

Recently, as plug-in hybrid electric vehicles (PHEVs) take center stage for the eco-friendly and cost-effective transportation, commercial PHEV charging stations will be widely prevalent in the future. However, previous studies in the fields of the management of PHEV charging stations have not synthetically taken practical charging systems into account. In this paper, we study the profit-optimal management of a PHEV charging station under the realistic environment addressing not only various types of vehicles but waiting time guarantee for PHEV customers as well. This paper is first to jointly take into account pricing for charging services, scheduling of reserved vehicles to PHEV chargers, dropping of reserved vehicles, and management of the energy storage in a unified framework that contains key features of a practical PHEV charging station. Based on this framework, we develop an algorithm to find the parameters required for charging management by invoking the “Lyapunov drift-plus-penalty” technique. Through theoretical analysis, we prove that the proposed algorithm achieves close-to-optimal performance under particular conditions by exploiting opportunism of time-varying arrival of charging vehicles, price of electricity, and renewable energy generation, but it requires no probabilistic future information. Finally, we find several significant messages via trace-driven simulation of the proposed algorithm.

Dual-side Optimization for Cost-Delay Tradeoff in Mobile Edge Computing

Mobile code offloading (MCO) is a technology that offloads computing tasks from mobile devices to remote servers managed by code offload service providers (CSPs). Nowadays, it is recommended that the remote servers be placed on the edge of the network to support modern applications, e.g., augmented reality, which demand stringent and ultralow latency or high bandwidth. To date, previous studies have independently addressed code offloading policy in mobile devices and pricing/server provisioning policies in the CSP; moreover, the system models for both user side and CSP side have not adequately reflected their practical aspects. This paper designs a practical model for the both sides and takes account of them in an integrated MCO framework simultaneously. By leveraging Lyapunov drift-plus-penalty technique, we propose code offloading, local CPU clock frequency, and network interface selection policies for mobile users, and propose MCO service pricing and server provisioning policies for the CSP in each of a competition scenario and a cooperation scenario. In the competition scenario, we propose a Com-UC algorithm for mobile users and a Com-PC algorithm for the CSP with the aim to minimize each cost for each queue stability constraint. In the cooperation scenario, we propose a Coo-JC algorithm with the aim to minimize their sum cost for both mobile users and CSP. Via trace-driven simulations, we demonstrate that Com-UC saves at most 71% of its cost and Com-PC attains 82% profit gain for the same delay compared to existing algorithms; moreover, the cooperation between mobile users and CSP additionally reduces costs and delays.

Robust Power Allocation in Cognitive Radio Networks With Uncertain Knowledge of Interference

We study the secondary user power allocation problem in cognitive radio networks with uncertain knowledge of interference information. We capture the channel uncertainty in the chance-constrained formulation where interference to primary users is guaranteed to be below a given threshold with high probability. However, the formulation is nonconvex, hence, we approximate the nonconvex problem with a safe convex problem by invoking robust optimization theory. Via extensive simulations under various channel conditions, we verify that our power allocation design based on the chance-constrained formulation guarantees interference constraint with high probability while enlarging the feasible region of secondary users power.

Hybrid content caching for low end-to-end latency in cloud-based wireless networks

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.

Competition-based distributed BS power activation and user scheduling algorithm

Existing cellular technologies in unlicensed band such as license assisted access (LAA)-LTE do not capture inter-cell interference (ICI) management which becomes more important in modern small cell network environments. Moreover, existing ICI management techniques not only can be operated in only licensed frequency band due to their centralized properties, but also have high computational complexities. In this paper, by invoking distributed optimization, we propose a fully distributed base station (BS) activation and user scheduling framework which can be operated in even unlicensed band because of its competition properties. Our simulation results demonstrate that (i) proposed competitionbased BS activation and user scheduling framework (CBA) increases throughput of cell edge users by 112%–335% compared to conventional algorithms, (ii) the CBA properly catches up with the performance of optimal algorithm up to 93% in terms of overall performance and up to 95% in terms of edge user throughput, and (iii) the CBA also provides higher performance gains in the larger ratio of edge users and the smaller cell size, which indicates that the CBA well adapts to cellular network trend where cells are gradually smaller and densely deployed.