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

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

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A Break in the Clouds: Towards a Cloud Definition

The cloud heralds a new era of computing where application services are provided through the Internet. Cloud computing can enhance the computing capability of mobile systems, but is it the ultimate solution for extending such systems' battery lifetimes?

Cloud Computing for Mobile Users: Can Offloading Computation Save Energy?

Mobile systems have limited resources, such as battery life, network bandwidth, storage capacity, and processor performance. These restrictions may be alleviated by computation offloading: sending heavy computation to resourceful servers and receiving the results from these servers. Many issues related to offloading have been investigated in the past decade. This survey paper provides an overview of the background, techniques, systems, and research areas for offloading computation. We also describe directions for future research.

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A Survey of Computation Offloading for Mobile Systems

Mobile robots are widely used for computation-intensive tasks such as surveillance, moving object recognition and tracking. Existing studies perform the computation entirely on robot processors or on dedicated servers. The robot processors are limited by their computation capability; real-time performance may not be achieved. Even though servers can perform tasks faster, the communication time between robots and servers is affected by variations in wireless bandwidths. In this paper, we present a system for realtime moving object recognition and tracking using computation offloading. Offloading migrates computation to servers to reduce the computation time on the robots. However, the migration consumes additional time, referred as communication time in this paper. The communication time is dependent on data size exchanged and the available wireless bandwidth. We estimate the computation and communication needed for the tasks and choose to execute them on robot processors or servers to minimize the total execution time, in order to satisfy real-time constraints.

Real-time moving object recognition and tracking using computation offloading

This paper presents a method to allocate resources for real-time tasks using the "Infrastructure as a Service" model offered by cloud computing. Real-time tasks have to be completed before deadlines, and cloud computing offers selection of resources with different speeds and costs. In cloud computing, resource allocations can be scaled up based on the requirements; this is called elasticity and is the key difference from existing multiprocessor task allocation. Scalable resources make economical allocation of resources an important problem. We analyze the problem of allocating resources for a set of realtime tasks such that the economic cost is minimized and all the deadlines are met. We formulate the problem as a constrained optimization problem and propose a polynomial-time solution to allocate resources efficiently. We compare the economic costs and performance provided by our solution with the optimal solution and an EDF (earliest deadline first) method. We show how the cost varies based on the distribution of the tasks.

Resource Allocation for Real-Time Tasks Using Cloud Computing

This paper presents a method to save energy for mobile systems that perform content-based image retrieval (CBIR). CBIR is a computation-intensive application and a resource-constrained mobile system may save energy by offloading computation to a grid-powered server. We develop three offloading schemes based on different conditions to save energy for CBIR on mobile systems. We implement a CBIR algorithm on an HP iPAQ hw6945 and physically measure the energy savings.

Kumar Karthik

Papers

Cloud Computing for Mobile Users: Can Offloading Computation Save Energy?

A Survey of Computation Offloading for Mobile Systems

Real-time moving object recognition and tracking using computation offloading

Resource Allocation for Real-Time Tasks Using Cloud Computing

Energy efficient content-based image retrieval for mobile systems