The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.

Edge Computing: Vision and Challenges

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

Mobile phones or smartphones are rapidly becoming the central computer and communication device in people's lives. Application delivery channels such as the Apple AppStore are transforming mobile phones into App Phones, capable of downloading a myriad of applications in an instant. Importantly, today's smartphones are programmable and come with a growing set of cheap powerful embedded sensors, such as an accelerometer, digital compass, gyroscope, GPS, microphone, and camera, which are enabling the emergence of personal, group, and communityscale sensing applications. We believe that sensor-equipped mobile phones will revolutionize many sectors of our economy, including business, healthcare, social networks, environmental monitoring, and transportation. In this article we survey existing mobile phone sensing algorithms, applications, and systems. We discuss the emerging sensing paradigms, and formulate an architectural framework for discussing a number of the open issues and challenges emerging in the new area of mobile phone sensing research.

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A survey of mobile phone sensing

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.

Together with an explosive growth of the mobile applications and emerging of cloud computing concept, mobile cloud computing (MCC) has been introduced to be a potential technology for mobile services. MCC integrates the cloud computing into the mobile environment and overcomes obstacles related to the performance (e.g., battery life, storage, and bandwidth), environment (e.g., heterogeneity, scalability, and availability), and security (e.g., reliability and privacy) discussed in mobile computing. This paper gives a survey of MCC, which helps general readers have an overview of the MCC including the definition, architecture, and applications. The issues, existing solutions, and approaches are presented. In addition, the future research directions of MCC are discussed. Copyright © 2011 John Wiley & Sons, Ltd.

A survey of mobile cloud computing: architecture, applications, and approaches

A server may be configured to receive first pose data from a virtual reality device, generate second pose data, including a pose prediction, based at least on the first pose data, render a map representation based at least on the pose prediction, determine regions of the map representation based on a likelihood of view of each of the regions, perform foveation in each of the regions and generate an encoded frame based on the regions and a network parameter, and send the encoded frame and second pose data to the device The encoding may associate each of the regions with a quality level and base the foveation on each region's associated quality level The foveation may use different quantization parameters for each of the regions and/or use rendering foveation for each of regions Network conditions such as latency, bandwidth, and/or jitter may be considered for bit allocation in the foveation

Virtual Reality with Interactive Streaming Video and Likelihood-Based Foveation

Programmable consumer devices have placed computation within arm's reach at all times and in all places. Unfortunately, researchers interested in investigating this phenomenon often struggle with the expense, inconvenience, and limited scale of existing experimental platforms. In this paper, we introduce a new experimental platform for mobile and pervasive computing based on JellyNets, an abstraction for exposing experiments to arbitrary mobile social contexts.

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Experimenting in mobile social contexts using JellyNets

Immersive virtual reality has long been an aspiration of many. For a truly immersive VR experience, three properties are essential: quality, responsiveness and mobility. By quality, we mean that realistic and life-like visual portrayals in a virtual environment heighten our sense of immersion. By responsiveness, we mean that any motion, especially of the head, must be reflected as quickly as possible in visual feedback because ocular proprioception sensitivity is very high. By mobility, we mean that we ought to be able to move untethered in physical space, free to explore our virtual world. Unfortunately mobile devices like phones are tablets are power-constrained and cannot produce acceptable framerate. While cloud offloading allows them to deliver high-quality and framerate it places high bandwidth requirements and introduces an unacceptable amount of latency. This demo introduces Matia, a stereo HMD system that simultaneously attains quality, responsiveness and mobility. Matia achieves this by offloading rendering work to a highend GPU across the WAN. The HMD client is mobile and only requires a low-end GPU, yet receives high quality imagery. To overcome WAN latencies, Matia borrows speculative execution techniques inspired by previous work Outatime [1]. However, speculative execution alone is insufficient. This is because HMD sensitivities are substantially more stringent along several key dimensions: responsiveness and quality.These factors suggest that we ought to greatly benefit from maximally utilizing all available bandwidth between client and server to deliver the highest resolution imagery possible. At the same time, any latency or bandwidth changes must be handled very responsively, lest they deteriorate the user experience and induce simulator sickness. To get the highest possible resolution while remaining responsive to network changes, Matia employs panoramic stereo video and likelihood-based foveation. Specifically, Matia renders a wide field-of-view (FOV) panoramic stereo video. In turn, any possible stereo view (e.g., due to unexpected head movement or network fluctuations) can be generated. Matia then foveates this panorama by reallocating pixels to areas where the user is most likely to look (Maximum Likelyhood View). We divide our panoramic video into three regions described by their image quality: High, Medium and Low. Embedded within is a convex-like optimizer that constantly adapts to real-time data analysis of user head movement and network conditions to figure out the most fruitful foveation configuration, on expectation of these regions. As a result, we expect to achieve high quality immersiveness in the common case, yet still deliver gracefully degraded experiences in case of network degradation. At the same time, Matia greatly expands the scope of viable HMD content while requiring only modest fixed function computation on the HMD device. In this demo we will show Matia running on an Intel Compute Stick driving an Oculus DK2. Our demo shows a scene so graphically detailed that the compute stick is unable to handle it fast enough without offloading it to a server. During the demo, users will be able to observe the high responsiveness and quality of the image even after introducing variable amounts of bandwidth and latency.

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Poster: Mobile Virtual Reality for Head-mounted Displays With Interactive Streaming Video and Likelihood-based Foveation

Head-Mounted Displays (HMDs) have finally reached the point where they can comfortably and affordably transport people to virtual worlds or transform their physical reality by feeding timely information straight to their eyes. However, the technology behind HMDs is still not fully mature. There are plenty of open challenges in wireless networking, cloud computing, security, and privacy, as well as opportunities to create novel applications.

Eduardo Cuervo

Papers

Virtual Reality with Interactive Streaming Video and Likelihood-Based Foveation

Experimenting in mobile social contexts using JellyNets

Poster: Mobile Virtual Reality for Head-mounted Displays With Interactive Streaming Video and Likelihood-based Foveation

BEYOND REALITY: Head-Mounted Displays for Mobile Systems Researchers

Outatime: Using Speculation to Enable Low-Latency Continuous Interaction for Mobile Cloud Gaming