Optical wireless communication (OWC) is a promising technology that can provide high data rates while supporting multiple users. The optical wireless (OW) physical layer has been researched extensively, however, less work was devoted to multiple access and how the OW front end is connected to the network. In this paper, an OWC system which employs a wavelength division multiple access (WDMA) scheme is studied, for the purpose of supporting multiple users. In addition, a cloud/fog architecture is proposed for the first time for OWC to provide processing capabilities. The cloud/fog-integrated architecture uses visible indoor light to create high data rate connections with potential mobile nodes. These OW nodes are further clustered and used as fog mini servers to provide processing services through the OW channel for other users. Additional fog-processing units are located in the room, the building, the campus and at the metro level. Further processing capabilities are provided by remote cloud sites. Two mixed-integer linear programming (MILP) models were proposed to numerically study networking and processing in OW systems. The first MILP model was developed and used to optimize resource allocation in the indoor OWC systems, in particular, the allocation of access points (APs) and wavelengths to users, while the second MILP model was developed to optimize the placement of processing tasks in the different fog and cloud nodes available. The optimization of tasks placement in the cloud/fog-integrated architecture was analysed using the MILP models. Multiple scenarios were considered where the mobile node locations were varied in the room and the amount of processing and data rate requested by each OW node was varied. The results help to identify the optimum colour and AP to use for communication for a given mobile node location and OWC system configuration, the optimum location to place processing and the impact of the network architecture. This article is part of the theme issue ‘Optical wireless communication’.

In this paper, we address the optimization of wavelength resource allocation in multi-user WDM Visible Light Communication (VLC) systems. A Mixed Integer Linear Programming (MILP) model that maximizes the sum of Signal-to-Interference-plus-Noise-Ratio (SINR) for all users is utilized. The results show that optimizing the wavelength allocation in multi-user WDM VLC systems can reduce the impact of the interference and improve the system throughput in terms of the sum of data rates for up to 7 users.

In this paper, we utilize Mixed Integer Linear Programming (MILP) models to compare the energy efficiency and performance of a server-centric Passive Optical Networks (PON)-based data centers design with different data centers networking topologies for the use in fog computing. For representative MapReduce workloads, completion time results indicate that the server-centric PON-based design achieves 67% reduction in the energy consumption compared to DCell with equivalent performance.

This is a repository copy of PON-Based Connectivity for Fog Computing.

This paper aims to quantitatively measure the impact of different data centers networking topologies on the performance and energy efficiency of shuffling operations in MapReduce. Mixed Integer Linear Programming (MILP) models are utilized to optimize the shuffling in several data center topologies with electronic, hybrid, and all-optical switching while maximizing the throughput and reducing the power consumption. The results indicate that the networking topology has a significant impact on the performance of MapReduce. They also indicate that with comparable performance, optical-based data centers can achieve an average of 54% reduction in the energy consumption when compared to electronic switching data centers. Keywords: Data Center Networking (DCN), MapReduce, energy efficiency, completion time. INTRODUCTIONThe MapReduce programming model and its widely-used platform, Hadoop, are enabling several costeffective cloud-based big data services [1]. These services typically require extensive all-to-all communications between hosting servers leading to increased congestion and power consumption in data centers. Moreover, they result in the East-West traffic dominating over the South-North traffic. This new traffic trend has become the focus in designing state-of-art production data centers [2]. These challenges are increasingly motivating the consideration of all-optical networking in future data centers to cope with the increasing demands of big data applications while improving the data centers performance and decreasing their power consumption [3].The processing in MapReduce is composed of map, shuffle, and reduce phases. The input data is stored in several servers' local disks and is globally managed by a distributed file system (DFS) [1]. The processing starts by assigning map slots according to the number of input data chunks and available computing resources, and reduce slots according to the user's configurations. If chunks are more than map slots, the map phase will run in several waves according to their scheduling [4]. Each map slot processes it assigned chunks, preferably available locally, and generates intermediate results in the form of < key,value> pairs. The intermediate results are shuffled to reduce slots according to their keys where each slot is assigned to process a unique set of keys [1]. Finally, each reduce slot sorts its inputs, calculates final results, and saves them in the DFS.Several optimization studies have been carried out by both academia and industry to enhance the performance and energy efficiency of big data applications (e.g. [2], [4]- [21]). The performance of big data applications and frameworks such as MapReduce is associated with a wide range of factors and parameters such as the cluster specifications (e.g. CPU, memory, networking, and disk I/O resources [9]), framework used or version, in addition to selected configurations and mechanisms for data and jobs placements and tasks scheduling [4]- [8]. Moreover, as the deployments of big data applications are evolvin...