Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider an MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources—the transmit precoding matrices of the MUs—and the computational resources—the CPU cycles/second assigned by the cloud to each MU—in order to minimize the overall users’ energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to compute the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. We then show that the proposed algorithmic framework naturally leads to a distributed and parallel implementation across the radio access points, requiring only a limited coordination/signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms.

Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing

Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider a MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources-the transmit precoding matrices of the MUs-and the computational resources-the CPU cycles/second assigned by the cloud to each MU-in order to minimize the overall users' energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to express the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. Then, we reformulate the algorithm in a distributed and parallel implementation across the radio access points, requiring only a limited coordination/signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms.

To provide fine-grained access to different dimensions of the physical world, the data uploading in smart cyber-physical systems suffers novel challenges on both energy conservation and privacy preservation. It is always critical for participants to consume as little energy as possible for data uploading. However, simply pursuing energy efficiency may lead to extreme disclosure of private information, especially when the uploaded contents from participants are more informative than ever. In this article, we propose a novel mechanism for data uploading in smart cyber-physical systems, which considers both energy conservation and privacy preservation. The mechanism preserves privacy by concealing abnormal behaviors of participants, while still achieves an energy-efficient scheme for data uploading by introducing an acceptable number of extra contents. To derive an optimal uploading scheme is proved to be NP-hard. Accordingly, we propose a heuristic algorithm and analyze its effectiveness. The evaluation results towards a real-world dataset demonstrate that the performance of the proposed algorithm is comparable with the optimal results.

A Private and Efficient Mechanism for Data Uploading in Smart Cyber-Physical 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.

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

Cloud computing enables customers with limited computational resources to outsource large-scale computational tasks to the cloud, where massive computational power can be easily utilized in a pay-per-use manner. However, security is the major concern that prevents the wide adoption of computation outsourcing in the cloud, especially when end-user's confidential data are processed and produced during the computation. Thus, secure outsourcing mechanisms are in great need to not only protect sensitive information by enabling computations with encrypted data, but also protect customers from malicious behaviors by validating the computation result. Such a mechanism of general secure computation outsourcing was recently shown to be feasible in theory, but to design mechanisms that are practically efficient remains a very challenging problem. Focusing on engineering computing and optimization tasks, this paper investigates secure outsourcing of widely applicable linear programming (LP) computations. In order to achieve practical efficiency, our mechanism design explicitly decomposes the LP computation outsourcing into public LP solvers running on the cloud and private LP parameters owned by the customer. The resulting flexibility allows us to explore appropriate security/efficiency tradeoff via higher-level abstraction of LP computations than the general circuit representation. In particular, by formulating private data owned by the customer for LP problem as a set of matrices and vectors, we are able to develop a set of efficient privacy-preserving problem transformation techniques, which allow customers to transform original LP problem into some random one while protecting sensitive input/output information. To validate the computation result, we further explore the fundamental duality theorem of LP computation and derive the necessary and sufficient conditions that correct result must satisfy. Such result verification mechanism is extremely efficient and incurs close-to-zero additional cost on both cloud server and customers. Extensive security analysis and experiment results show the immediate practicability of our mechanism design.

Secure and practical outsourcing of linear programming in cloud computing

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.

/pdf/fmtcp-a-fountain-code-based-multipath-transmission-control-5bdtpclilw.pdf

FMTCP: a fountain code-based multipath transmission control protocol

Data centers play a key role in the expansion of cloud computing. However, the efficiency of data center networks is limited by oversubscription. The typical unbalanced traffic distributions of a DCN further aggravate the problem. Wireless networking, as a complementary technology to Ethernet, has the flexibility and capability to provide feasible approaches to handle the problem. In this article, we analyze the challenges of DCNs and articulate the motivations of employing wireless in DCNs. We also propose a hybrid Ethernet/wireless DCN architecture and a mechanism to dynamically schedule wireless transmissions based on traffic demands. Our simulation study demonstrates the effectiveness of the proposed wireless DCN.

Wireless data center networking

The emergence of mobile cloud computing (MCC) indicates that increasingly abundant applications are available, thus deeming energy problems even more significant. To achieve energy optimization in mobile systems, power consumption involved with each component or application need to be estimated prior to execution. In this paper, we present a survey on the universal energy estimation model for mobile devices. Additionally, due to the significance of wireless network interface card (WNIC) in the power use of mobile devices, considerable researches have been devoted to a low-power design of the WNIC (i.e., Cellular and WiFi). These efforts have allowed us to provide a comprehensive summary of recent work on transmission energy savings. Finally, we conclude the survey and discuss the future research directions.

A Survey of Energy Efficient Wireless Transmission and Modeling in Mobile Cloud Computing

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.

FMTCP: A Fountain Code-Based Multipath Transmission Control Protocol

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.

/pdf/channel-allocation-in-wireless-data-center-networks-56zk3e6p2r.pdf

Hongyi Wang

Papers

FMTCP: a fountain code-based multipath transmission control protocol

Wireless data center networking

A Survey of Energy Efficient Wireless Transmission and Modeling in Mobile Cloud Computing

FMTCP: A Fountain Code-Based Multipath Transmission Control Protocol

Channel allocation in wireless data center networks