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

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

Mobile applications are becoming increasingly ubiquitous and provide ever richer functionality on mobile devices. At the same time, such devices often enjoy strong connectivity with more powerful machines ranging from laptops and desktops to commercial clouds. This paper presents the design and implementation of CloneCloud, a system that automatically transforms mobile applications to benefit from the cloud. The system is a flexible application partitioner and execution runtime that enables unmodified mobile applications running in an application-level virtual machine to seamlessly off-load part of their execution from mobile devices onto device clones operating in a computational cloud. CloneCloud uses a combination of static analysis and dynamic profiling to partition applications automatically at a fine granularity while optimizing execution time and energy use for a target computation and communication environment. At runtime, the application partitioning is effected by migrating a thread from the mobile device at a chosen point to the clone in the cloud, executing there for the remainder of the partition, and re-integrating the migrated thread back to the mobile device. Our evaluation shows that CloneCloud can adapt application partitioning to different environments, and can help some applications achieve as much as a 20x execution speed-up and a 20-fold decrease of energy spent on the mobile device.

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CloneCloud: elastic execution between mobile device and cloud

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.

Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

We describe a new approach to power saving and battery life extension on an untethered laptop through wireless remote processing of power-costly tasks. We ran a series of experiments comparing the power consumption of processes run locally with that of the same processes run remotely. We examined the trade-off between communication power expenditures and the power cost of local processing. This paper describes our methodology and results of our experiments. We suggest ways to further improve this approach, and outline a software design to support remote process execution.

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Saving portable computer battery power through remote process execution

Recent research has demonstrated that portable computer users can save battery power by migrating tasks over wireless networks to server machines. Making this technique generally useful requires considerable automation. This paper describes a framework for automatically migrating tasks from a portable computer over a wireless network to a server and migrating the results back. The paper presents the framework's architecture, discusses key issues in creating the framework, and presents performance results that demonstrate that the framework is both useful and power-inexpensive.

http://fmg-www.cs.ucla.edu/ficus-members/arudenko/PUBLICATIONS/SAC.pdf

The remote processing framework for portable computer power saving

End-to-end connections experience a high level of diversity in network characteristics. At one extreme, an application may receive highly degraded service, leaving the application unusable. At another extreme, the costs to guarantee some level of service may be undesirably high to the user or overall system. Open architecture networks help applications push adaptation technology into the network and reduce the effects of poor network characteristics. Automated planning is important for the services that are supported by the open architecture. The remedies that modify an application’s data stream to adjust it to unfavorable network conditions should be located and ordered to provide good data transfer and network resources use. The search for good plans is a hard AI problem and requires additional research.

https://www.lasr.cs.ucla.edu/reiher/papers/planning.pdf

Automated Planning for Open Architectures

Computer networks are becoming more complex and diverse. Increasingly, an end-to-end connection will traverse several links with orders of magnitude differences in characteristics such as bandwidth, latency, error rate, jitter, and monetary cost. At the same time, most applications assume a level of network characteristics below which they either provide no service, or service at a cost higher than the user is willing to pay. Conductor is an adaptation framework that moves the responsibility for network complexity out of the application and into the network. Conductor allows an application’s use of the network to be tailored to the needs of the user in a manner that is transparent to the application. Conductor allows adaptation to be distributed to the points in the network where it is required. Further, Conductor allows arbitrary adaptations without compromising reliability. Finally, Conductor requires minimal changes to existing systems. All of these characteristics are incorporated into a single, integrated framework. Conductor has been shown to be effective in important classes of problems in mobile computing, where network complexity is most prevalent today. This paper describes the Conductor architecture and presents experimental results indicating that transparent, distributed adaptation can significantly improve the user’s experience in complex networks.

https://lasr.cs.ucla.edu/yarvis/Conductor/papers/CSD-TR-990042.pdf

Conductor: Distributed Adaptation for Complex Networks

Panda is middleware designed to bring the benefits of active networks to applications not written with active networks in mind. This paper describes the architecture and implementation of Panda, and provides data on the overheads incurred and performance benefits achieved. The paper also discusses some of the key issues of automatically and transparently intercepting data streams and converting them into active streams, including interception mechanisms, automated planning facilities, and allowing user and application control of the middleware.

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Alexey Rudenko

Papers

Saving portable computer battery power through remote process execution

The remote processing framework for portable computer power saving

Automated Planning for Open Architectures

Conductor: Distributed Adaptation for Complex Networks

Panda: middleware to provide the benefits of active networks to legacy applications