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By Mobile Edge Computing (MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution.

A promising research area that has recently emerged, is on how to use index coding to improve the communication efficiency in distributed computing systems, especially for data shuffling in iterative computations. In this paper, we posit that pliable index coding can offer a more efficient framework for data shuffling, as it can better leverage the many possible shuffling choices to reduce the number of transmissions. We theoretically analyze pliable index coding under data shuffling constraints, and design a hierarchical data-shuffling scheme that uses pliable coding as a component. We find benefits up to $O(ns/m)$ over index coding, where $ns/m$ is the average number of workers caching a message, and $m$ , $n$ , and $s$ are the numbers of messages, workers, and cache size, respectively.

A series of 4-phenyl-2,2′; 6′,2″-terpyridyl Pt(II) complexes bearing different σ-alkynyl ancillary ligands (1a–1f) were synthesized and characterized. All complexes exhibit strong 1π,π* absorption bands in the UV region; and broad, structureless metal-to-ligand charge transfer (1MLCT)/ligand-to-ligand charge transfer (1LLCT) absorption bands in the visible region. When excited at the charge-transfer absorption band, the complexes exhibit yellow to red luminescence (λmax = 553–608 nm) in CH3CN at room temperature, which is attributed to the 3MLCT/3LLCT state, except 1c. The emitting state of 1c exhibits a significant intraligand 3π,π* character, which is originated from the naphthalimide acetylide ligand. Complexes 1a–1c exhibit moderate triplet transient absorptions from visible to NIR region, where reverse saturable absorption (RSA) occurs. The photophysical, electrochemical and nonlinear absorption properties of these Pt(II) complexes can be tuned drastically by the different acetylide ligand, which would be useful for rational design of broadband nonlinear absorption materials.

Mobile edge computing is a novel technique in which mobile devices offload computation-intensive tasks with stringent delay requirements to the edge cloud. However, the limited computational resource in the edge cloud may result in the Quality of Service degradation. In this paper, we address this issue by coordinating the heterogeneous cloud which includes the edge cloud and the remote cloud. Considering the offloading of delay-bounded tasks, we study into the scheduling of heterogeneous cloud in order to maximize the probability that tasks can have the delay requirements met. The problem formulation is proved to be concave, and an optimal algorithm is proposed accordingly. The optimal policy with heterogeneous cloud is notably different from the policy merely using the edge cloud. With only the edge cloud, the system serves tasks with loose delay bounds and drops tasks with stringent delay bounds when the traffic load is heavy. However, with the heterogeneous cloud, tasks with stringent delay bounds are offloaded to the edge cloud and tasks with loose delay bounds are offloaded to the remote cloud. In numerical results, the probability that the delay bounds of tasks are satisfied can be improved by about 40% with the assistance of the remote cloud.

Mobile edge computing is a novel technique to offer cloud-based computation offloading services to mobile users with short delay. However, the Cloud Service Providers (CSPs) of the edge cloud are generally different from the CSPs of remote Internet cloud. Considering the competition between the heterogeneous clouds, we study the optimal provisioning of the computational resource in the edge cloud. We firstly analyze the Nash equilibrium prices of the cloud market when the amount of edge computational resource is given. Based on the pricing policy, we further design an algorithm to optimize the edge computational resource capacity so that the profit of the edge cloud is maximized. We also derive the lower and upper bound of the optimal edge computational resource. The numerical results indicate that the profit of the edge cloud is greatly influenced by the price of the remote cloud when users care more about the price than the delay.

Edge cloud is a promising architecture in order to address the latency problem in mobile cloud computing. However, as compared with remote clouds, edge clouds have limited computational resources, and higher operating costs. In this paper, we design policies which carry out the assignment of tasks that are generated at the mobile subscribers with edge clouds in an online fashion. The proposed policies achieve an optimal power-delay trade-off in the system. Here, the delay experienced by a mobile computing task includes the time spent waiting for transmission to the edge cloud, and the execution time at the edge cloud servers. We perform a theoretical analysis after modeling the system as a continuous-time queueing system. The contribution of this paper is two-fold: Firstly, the algorithm to determine the optimal policy is obtained by proposing an equivalent discrete-time Markov decision process. Secondly, an easily implementable index policy is proposed by analyzing the dual of the original problem. Extensive simulations illustrate the effectiveness of the proposed policies.

Mobile cloud computing enables the resource-poor mobile terminals to deal with the resource-hungry applications thanks to the extra storage and computational resources at the cloud side. However, the advantages of mobile cloud computing cannot be fully exploited without proper collaboration of radio transmission and computing, which is challenging under multiuser scenarios due to the coupling of radio and computational resources. In this paper, targeting at reducing the terminal energy consumption, we study the joint optimization of radio and computational resources for multiple users in mobile cloud computing and propose a heuristic strategy, based on the latency constraint and the application type of each MT, for resource allocation of low computational complexity. Numerical results show that our proposed task offloading strategy can significantly reduce the total energy at the mobile terminal side by 40% with 3 mobile terminals in the system, compared with the non-offloading mobile computing scheme, while at the same time satisfying the delay constraints. Moreover, it performs fairly close to the optimum.

With the proliferation of mobile applications, Mobile Cloud Computing (MCC) has been proposed to help mobile devices save energy and improve computation performance. To further improve the quality of service (QoS) of MCC, cloud servers can be deployed locally so that the latency is decreased. However, the computational resource of the local cloud is generally limited. In this paper, we design a threshold-based policy to improve the QoS of MCC by cooperation of the local cloud and Internet cloud resources, which takes the advantages of low latency of the local cloud and abundant computational resources of the Internet cloud simultaneously. This policy also applies a priority queue in terms of delay requirements of applications. The optimal thresholds depending on the traffic load is obtained via a proposed algorithm. Numerical results show that the QoS can be greatly enhanced with the assistance of Internet cloud when the local cloud is overloaded. Better QoS is achieved if the local cloud order tasks according to their delay requirements, where delay-sensitive applications are executed ahead of delay-tolerant applications. Moreover, the optimal thresholds of the policy have a sound impact on the QoS of the system., Comment: 6 pages, 7 figures, accepted by GlobeCom'15 Workshop

The photocatalytic degradation of methylene blue solution by 8 photocatalytic diphenylanthrazoline compounds was investigated. All diphenylanthrazoline compounds exhibited a good photocatalytic activity towards the methylene blue solution. The removal rate for chemical oxygen demand (CODCr) in the methylene blue solution at 12 hours was ~54.1% to 96.3%. 2,8-Bis(4-triphenylamino)-4,6-diphenyl-1,9-anthrazoline (TM-2-d) was selected for further investigation because of its better photocatalytic activity. To study the optimal reaction conditions for the photocatalytic degradation of dye wastewater, photocatalyst was applied to degrade methylene blue solution. The decolorization rate for simulated dye solutions can exceed 99% in 10 hours, and the CODCr removal rate exceeded 91%. These organic semiconductor materials, diphenylanthrazoline compounds, displayed comparative photocatalytic properties to the inorganic semiconductor materials, which can be used in the photocatalytic degradation of organic pollutants.