In cellular networks, it is important to conserve energy while at the same time satisfying different user performance requirements. In this paper, we first propose a comprehensive metric to capture the user performance cost due to task delay, deadline violation, different application profiles, and user preferences. We prove that finding the energy-optimal scheduling solution while meeting the requirements on the performance cost is NP-hard. Then, we design an adaptive online scheduling algorithm PerES to minimize the total energy cost on data transmissions subject to user performance constraints. We prove that PerES can make the energy consumption arbitrarily close to that of the optimal scheduling solution. Further, we develop offline algorithms to serve as the evaluation benchmark for PerES. The evaluation results demonstrate that PerES achieves average 2.5 times faster convergence speed compared to state-of-art static methods, and also higher performance than peers under various test conditions. Using 821 million traffic flows collected from a commercial cellular carrier, we verify our scheme could achieve on average 32-56 percent energy savings over the total transmission energy with different levels of user experience.

Ideally, the throughput of a Multipath TCP (MPTCP) connection should be as high as that of multiple disjoint single-path TCP flows. In reality, the throughput of MPTCP is far lower than expected. In this paper, we conduct an extensive simulation-based study on this phenomenon, and the results indicate that a subflow experiencing high delay and loss severely affects the performance of other subflows, thus becoming the bottleneck of the MPTCP connection and significantly degrading the aggregate goodput. To tackle this problem, we propose Fountain code-based Multipath TCP (FMTCP), which effectively mitigates the negative impact of the heterogeneity of different paths. FMTCP takes advantage of the random nature of the fountain code to flexibly transmit encoded symbols from the same or different data blocks over different subflows. Moreover, we design a data allocation algorithm based on the expected packet arriving time and decoding demand to coordinate the transmissions of different subflows. Quantitative analyses are provided to show the benefit of FMTCP. We also evaluate the performance of FMTCP through ns-2 simulations and demonstrate that FMTCP outperforms IETF-MPTCP, a typical MPTCP approach, when the paths have diverse loss and delay in terms of higher total goodput, lower delay, and jitter. In addition, FMTCP achieves high stability under abrupt changes of path quality.

In cellular networks, it is important to conserve energy while at the same time ensuring users to have good transmission experiences. The energy cost can result from tail energy due to the radio resource control strategies designed in cellular networks and data transmission. Existing efforts generally consider one of the energy issues, and also ignore the adverse impact on user transmission performance due to energy conservation. In addition, many existing algorithms are based on prediction and knowledge on future traffic, which are hard to apply in a practical wireless system with dynamic user traffic and channel condition. The goal of this work is to design an efficient online scheduling algorithm to minimize energy consumption both due to tail energy and transmissions while meeting user performance expectation. We prove the problem to be NP-hard, and design a practical online scheduling algorithm PerES to minimize the total energy cost of multiple mobile applications subject to user performance constraints. We propose a comprehensive performance cost metric to capture the impacts due to task delay, deadline violation, different application profiles and user preferences. We prove that our proposed scheduling algorithm can make the energy consumption arbitrarily close to that of the optimal scheduling solution. The evaluation results demonstrate the effectiveness of our scheme and its higher performance than peers. Moreover, by supporting dynamic performance requirement by mobile users, PerES can achieve 2 times faster convergence to both the performance degradation bound and optimal energy conversation bound than those of traditional static methods. Using 821 million traffic flows collected from a commercial cellular carrier, we verify our scheme could achieve on average 32%-56% energy savings with different levels of user experience.

Unbalanced traffic demands of different data center applications are an important issue in designing data center networks (DCN). In this paper, we present our exploratory investigation on a hybrid DCN solution of utilizing wireless transmissions in DCNs. Our work aims to solve the congestion problem caused by a few hot nodes to improve the global performance. We model the wireless transmissions in DCN by considering both the wireless interference and the adaptive transmission rate. Besides, both throughput and job completion time are considered to measure the impact of wireless transmissions on the global performance. Based on the model, we formulate the problem of channel allocation as an optimization problem. We also design an approximation algorithm with an approximation bound of 1/2 and a genetic algorithm to address the scheduling problem. A series of simulations are performed to evaluate and demonstrate the effectiveness of our wireless DCN scheme.

Ideally, the throughput of a Multipath TCP (MPTCP) connection should be as high as that of multiple disjoint single-path TCP flows. In reality, the throughput of MPTCP is far lower than expected. This is fundamentally caused by the fact that a sub flow with high delay and loss affects the performance of other sub flows, and thus becomes the bottleneck of the MPTCP connection and significantly degrades the aggregate good put. To tackle this problem, we propose Fountain code-based Multipath TCP (FMTCP), which effectively mitigates the negative impact of the heterogeneity of different paths. FMTCP takes advantage of the random nature of the fountain code to flexibly transmit encoded symbols from the same or different data blocks over different sub flows. Moreover, we design a data allocation algorithm based on the expected packet arriving time and decoding demand to coordinate the transmissions of different sub flows. Quantitative analyses are provided to show the benefit of FMTCP. We also evaluate the performance of FMTCP through ns-2 simulations and demonstrate that FMTCP can outperform IETF-MPTCP, a typical MPTCP approach, when the paths have diverse loss and delay in terms of higher total good put, lower delay and jitter. In addition, FMTCP achieves much more stable performance under abrupt changes of path quality.

Unbalanced traffic demands of different data center applications are an important issue in designing Data center networks (DCNs). In this paper, we present our exploratory investigation of utilizing wireless transmissions in DCNs. Our work aims to solve the congestion problem caused by a few hot nodes to improve the global performance. We model the wireless transmissions in a DCN by considering both the wireless interference and the adaptive transmission rate. Moreover, both throughput and job completion time are taken into account to evaluate the impact of wireless transmissions on the global performance. Based on this model, we formulate the channel allocation in wireless DCNs as an optimization problem and design a genetic algorithm (GA) based approach to address it. To demonstrate the effectiveness of wireless transmissions as well as our GA-based algorithm in a wireless DCN, extensive simulation study is carried out and the results validate our design.