Yeongjin Kim

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.

Practicalizing Delay-Tolerant Mobile Apps with Cedos

Delay-tolerant Wi-Fi offloading is known to improve overall mobile network bandwidth at low delay and low cost. Yet, in reality, we rarely find mobile apps that fully support opportunistic Wi-Fi access. This is mainly because it is still challenging to develop delay-tolerant mobile apps due to the complexity of handling network disruptions and delays. In this work, we present Cedos, a practical delay-tolerant mobile network access architecture in which one can easily build a mobile app. Cedos consists of three components. First, it provides a familiar socket API whose semantics conforms to TCP while the underlying protocol, D2TP, transparently handles network disruptions and delays in mobility. Second, Cedos allows the developers to explicitly exploit delays in mobile apps. App developers can express maximum user-specified delays in content download or use the API for real-time buffer management at opportunistic Wi-Fi usage. Third, for backward compatibility to existing TCPbased servers, Cedos provides D2Prox, a protocol-translation Web proxy. D2Prox allows intermittent connections on the mobile device side, but correctly translates Web transactions with traditional TCP servers. We demonstrate the practicality of Cedos by porting mobile Firefox and VLC video streaming client to using the API. We also implement delay/disruption-tolerant podcast client and run a field study with 50 people for eight weeks. We find that up to 92.4% of the podcast traffic is offloaded to Wi-Fi, and one can watch a streaming video in a moving train while offloading 48% of the content to Wi-Fi without a single pause.

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.

PhonePool: On energy-efficient mobile network collaboration with provider aggregation

Energy consumption for cellular communication is increasingly gaining importance in smartphone battery lifetime as the bandwidth of wireless communication and the demand for mobile traffic increase. For energy-efficient cellular communication, we tackle two energy characteristics of cellular networks: (1) transmission energy highly varies upon channel condition, and (2) transmission of a packet accompanies unnecessary tail energy waste. Under the objective of transmitting packets when the best channel is provided as well as a number of packets are accumulated, we propose a new mobile collaboration framework “PhonePool” that aggregates smart devices across multiple cellular providers. Compared to the standalone operation, even without a buffering delay, PhonePool allows better channel and reduces more tail energy in a statistical point of view. To maximize the energy benefit while maintaining the fairness among the nodes in collaboration, we further develop a dynamic programming framework providing the optimal algorithm of PhonePool and its approximated heuristic. Trace-driven simulations on our experimental HSPA/EVDO/LTE network traces show that PhonePool of 5 devices achieves up to 42% of energy reduction.

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.

CarrierMix: How Much Can User-side Carrier Mixing Help?

Energy consumption for cellular communication is increasingly gaining importance in smartphone battery lifetime as the bandwidth of wireless communication and the demand for mobile traffic increase. For energy-efficient cellular communication, we tackle two energy characteristics of cellular networks: (1) transmission energy highly varies upon channel condition, and (2) transmission of a packet accompanies unnecessary tail energy waste. Under the objective of transmitting packets when the best channel is provided as well as a number of packets are accumulated, we propose a new mobile collaboration framework “CarrierMix” that aggregates smart devices across multiple heterogeneous cellular carriers. Compared to the standalone operation, even without a buffering delay, CarrierMix allows better channel and reduces more tail energy in a statistical point of view. To maximize the energy benefit while maintaining the fairness among the nodes in collaboration, we further develop a dynamic programming framework providing the optimal algorithm of CarrierMix and its approximated heuristic. Trace-driven simulations on our experimental HSPA/EVDO/LTE network traces show that CarrierMix of five devices achieves up to 42 percent of energy reduction.

Cedos: A Network Architecture and Programming Abstraction for Delay-Tolerant Mobile Apps

Delay-tolerant Wi-Fi offloading is known to improve overall mobile network bandwidth at low delay and low cost. Yet, in reality, we rarely find mobile apps that fully support opportunistic Wi-Fi access. This is mainly because it is still challenging to develop delay-tolerant mobile apps due to the complexity of handling network disruptions and delays. In this paper, we present Cedos, a practical delay-tolerant mobile network access architecture in which one can easily build a mobile app. Cedos consists of three components. First, it provides a familiar socket API whose semantics conforms to TCP, while the underlying protocol, <inline-formula> <tex-math notation=LaTeX>

Multi-flow rate control in delayed Wi-Fi offloading systems

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

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