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Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

A Survey on Mobile Edge Computing: The Communication Perspective

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

コンピュータ・サイエンス : ACM computing surveys

Recently, machine learning has been used in every possible field to leverage its amazing power. For a long time, the networking and distributed computing system is the key infrastructure to provide efficient computational resources for machine learning. Networking itself can also benefit from this promising technology. This article focuses on the application of MLN, which can not only help solve the intractable old network questions but also stimulate new network applications. In this article, we summarize the basic workflow to explain how to apply machine learning technology in the networking domain. Then we provide a selective survey of the latest representative advances with explanations of their design principles and benefits. These advances are divided into several network design objectives and the detailed information of how they perform in each step of MLN workflow is presented. Finally, we shed light on the new opportunities in networking design and community building of this new inter-discipline. Our goal is to provide a broad research guideline on networking with machine learning to help motivate researchers to develop innovative algorithms, standards and frameworks.

Machine Learning for Networking: Workflow, Advances and Opportunities

Recent advances in cloud technology have turned the idea of cloud gaming into a reality. Cloud gaming, in its simplest form, renders an interactive gaming application remotely in the cloud and streams the scenes as a video sequence back to the player over the Internet. This is an advantage for less powerful computational devices that are otherwise incapable of running high-quality games. Such industrial pioneers as Onlive and Gaikai have seen success in the market with large user bases. In this article, we conduct a systematic analysis of state-of-the-art cloud gaming platforms, and highlight the uniqueness of their framework design. We also measure their real world performance with different types of games, for both interaction latency and streaming quality, revealing critical challenges toward the widespread deployment of cloud gaming.

/pdf/cloud-gaming-architecture-and-performance-2nwl5risqq.pdf

Cloud gaming: architecture and performance

In this paper, we propose a novel compressive sensing (CS) based approach for sparse target counting and positioning in wireless sensor networks. While this is not the first work on applying CS to count and localize targets, it is the first to rigorously justify the validity of the problem formulation. Moreover, we propose a novel greedy matching pursuit algorithm (GMP) that complements the well-known signal recovery algorithms in CS theory and prove that GMP can accurately recover a sparse signal with a high probability. We also propose a framework for counting and positioning targets from multiple categories, a novel problem that has never been addressed before. Finally, we perform a comprehensive set of simulations whose results demonstrate the superiority of our approach over the existing CS and non-CS based techniques.

/pdf/sparse-target-counting-and-localization-in-sensor-networks-j30pzycvs5.pdf

Sparse target counting and localization in sensor networks based on compressive sensing

In this paper, we perform a systematic design study of the "elephant in the room" facing the VR industry -- is it feasible to enable high-quality VR apps on untethered mobile devices such as smartphones? Our quantitative, performance-driven design study makes two contributions. First, we show that the QoE achievable for high-quality VR applications on today's mobile hardware and wireless networks via local rendering or offloading is about 10X away from the acceptable QoE, yet waiting for future mobile hardware or next-generation wireless networks (e.g. 5G) is unlikely to help, because of power limitation and the higher CPU utilization needed for processing packets under higher data rate. Second, we present Furion, a VR framework that enables high-quality, immersive mobile VR on today's mobile devices and wireless networks. Furion exploits a key insight about the VR workload that foreground interactions and background environment have contrasting predictability and rendering workload, and employs a split renderer architecture running on both the phone and the server. Supplemented with video compression, use of panoramic frames, and parallel decoding on multiple cores on the phone, we demonstrate Furion can support high-quality VR apps on today's smartphones over WiFi, with under 14ms latency and 60 FPS (the phone display refresh rate).

https://dl.acm.org/doi/pdf/10.1145/3117811.3117815

Furion: Engineering High-Quality Immersive Virtual Reality on Today's Mobile Devices

Data center clusters that run DNN training jobs are inherently heterogeneous. They have GPUs and CPUs for computation and network bandwidth for distributed training. However, existing distributed DNN training architectures, all-reduce and Parameter Server (PS), cannot fully utilize such heterogeneous resources. In this paper, we present a new distributed DNN training architecture called BytePS. BytePS can leverage spare CPU and bandwidth resources in the cluster to accelerate distributed DNN training tasks running on GPUs. It provides a communication framework that is both proved optimal and unified – existing all-reduce and PS become two special cases of BytePS. To achieve the proved optimality in practice, BytePS further splits the functionalities of a parameter optimizer. It introduces a Summation Service abstraction for aggregating gradients, which is common for all the optimizers. Summation Service can be accelerated by AVX instructions and can be efficiently run on CPUs, while DNN model-related optimizer algorithms are run on GPUs for computation acceleration. BytePS can accelerate DNN training for major frameworks including TensorFlow, PyTorch and MXNet. For representative DNN training jobs with up to 256 GPUs, BytePS outperforms the state-of-the-art open source all-reduce and PS by up to 84% and 245%, respectively.

/pdf/a-unified-architecture-for-accelerating-distributed-dnn-1fieju6pz9.pdf

Yong Cui

Papers

Machine Learning for Networking: Workflow, Advances and Opportunities

Cloud gaming: architecture and performance

Sparse target counting and localization in sensor networks based on compressive sensing

Furion: Engineering High-Quality Immersive Virtual Reality on Today's Mobile Devices

A Unified Architecture for Accelerating Distributed {DNN} Training in Heterogeneous GPU/CPU Clusters