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

Today’s smartphone operating systems frequently fail to provide users with visibility into how third-party applications collect and share their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid enables realtime analysis by leveraging Android’s virtualized execution environment. TaintDroid incurs only 32p performance overhead on a CPU-bound microbenchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, in our 2010 study we found 20 applications potentially misused users’ private information; so did a similar fraction of the tested applications in our 2012 study. Monitoring the flow of privacy-sensitive data with TaintDroid provides valuable input for smartphone users and security service firms seeking to identify misbehaving applications.

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TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones

Today's smartphone operating systems frequently fail to provide users with adequate control over and visibility into how third-party applications use their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid provides realtime analysis by leveraging Android's virtualized execution environment. TaintDroid incurs only 14% performance overhead on a CPU-bound micro-benchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of potential misuse of users' private information across 20 applications. Monitoring sensitive data with TaintDroid provides informed use of third-party applications for phone users and valuable input for smartphone security service firms seeking to identify misbehaving applications.

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TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones

The popularity and adoption of smart phones has greatly stimulated the spread of mobile malware, especially on the popular platforms such as Android. In light of their rapid growth, there is a pressing need to develop effective solutions. However, our defense capability is largely constrained by the limited understanding of these emerging mobile malware and the lack of timely access to related samples. In this paper, we focus on the Android platform and aim to systematize or characterize existing Android malware. Particularly, with more than one year effort, we have managed to collect more than 1,200 malware samples that cover the majority of existing Android malware families, ranging from their debut in August 2010 to recent ones in October 2011. In addition, we systematically characterize them from various aspects, including their installation methods, activation mechanisms as well as the nature of carried malicious payloads. The characterization and a subsequent evolution-based study of representative families reveal that they are evolving rapidly to circumvent the detection from existing mobile anti-virus software. Based on the evaluation with four representative mobile security software, our experiments show that the best case detects 79.6% of them while the worst case detects only 20.2% in our dataset. These results clearly call for the need to better develop next-generation anti-mobile-malware solutions.

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Dissecting Android Malware: Characterization and Evolution

Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method-level computation offloading. Advancing on previous work, it focuses on the elasticity and scalability of the cloud and enhances the power of mobile cloud computing by parallelizing method execution using multiple virtual machine (VM) images. We implement ThinkAir and evaluate it with a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for a N-queens puzzle application and one order of magnitude for a face detection and a virus scan application. We then show that a parallelizable application can invoke multiple VMs to execute in the cloud in a seamless and on-demand manner such as to achieve greater reduction on execution time and energy consumption. We finally use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements.

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ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading

Android is the first mass-produced consumer-market open source mobile platform that allows developers to easily create applications and users to readily install them. However, giving users the ability to install third-party applications poses serious security concerns. While the existing security mechanism in Android allows a mobile phone user to see which resources an application requires, she has no choice but to allow access to all the requested permissions if she wishes to use the applications. There is no way of granting some permissions and denying others. Moreover, there is no way of restricting the usage of resources based on runtime constraints such as the location of the device or the number of times a resource has been previously used. In this paper, we present Apex -- a policy enforcement framework for Android that allows a user to selectively grant permissions to applications as well as impose constraints on the usage of resources. We also describe an extended package installer that allows the user to set these constraints through an easy-to-use interface. Our enforcement framework is implemented through a minimal change to the existing Android code base and is backward compatible with the current security mechanism.

Apex: extending Android permission model and enforcement with user-defined runtime constraints

Smartphones have been becoming ubiquitous and mobile users are increasingly relying on them to store and handle personal information. However, recent studies also reveal the disturbing fact that users' personal information is put at risk by (rogue) smartphone applications. Existing solutions exhibit limitations in their capabilities in taming these privacy-violating smartphone applications. In this paper, we argue for the need of a new privacy mode in smartphones. The privacy mode can empower users to flexibly control in a fine-grained manner what kinds of personal information will be accessible to an application. Also, the granted access can be dynamically adjusted at runtime in a fine-grained manner to better suit a user's needs in various scenarios (e.g., in a different time or location). We have developed a system called TISSA that implements such a privacy mode on Android. The evaluation with more than a dozen of information-leaking Android applications demonstrates its effectiveness and practicality. Furthermore, our evaluation shows that TISSA introduces negligible performance overhead.

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Taming information-stealing smartphone applications (on Android)

Techniques for detecting unauthorized use (e.g., malicious attacks) of the computing systems (e.g., computing devices) are disclosed. Unauthorized use can be detected based on patterns of use (e.g., behavioral patterns of use typically associated with a human being) of the computing systems. Acceptable behavioral pattern data can be generated for a computing system by monitoring the use of a support system (e.g., an operating system, a virtual environment) operating on the computing system. For example, a plurality of system support provider components of a support system (e.g., system calls, device drivers) can be monitored in order to generate the acceptable behavioral pattern data in a form which effectively defines an acceptable pattern of use (usage pattern) for the monitored system support provider components, thereby allowing detection of unauthorized use of a computing system by detecting any deviation from the acceptable pattern of use of the monitored system support provider components.

Detecting unauthorized use of computing devices based on behavioral patterns

We propose a new elastic application model that enables seamless and transparent use of cloud resources to augment the capability of resource-constrained mobile devices. The salient features of this model include the partition of a single application into multiple components called weblets, and a dynamic adaptation of weblet execution configuration. While a weblet can be platform independent (e.g., Java or .Net bytecode or Python script) or platform dependent (native code), its execution location is transparent--it can be run on a mobile device or migrated to the cloud, i.e., run on one or more nodes offered by an IaaS provider. Thus, an elastic application can augment the capabilities of a mobile device including computation power, storage, and network bandwidth, with the light of dynamic execution configuration according to device's status including CPU load, memory, battery level, network connection quality, and user preferences. This paper presents the motivation behind developing elastic applications and their architecture including typical elasticity patterns and cost models that are applied to determine the elasticity patterns. We implement a reference architecture and develop a set of elastic applications to validate the augmentation capabilities for smartphone devices. We demonstrate promising results of the proposed application model using data collected from one of our example elastic applications.

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Xinwen Zhang

Papers

ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading

Apex: extending Android permission model and enforcement with user-defined runtime constraints

Taming information-stealing smartphone applications (on Android)

Detecting unauthorized use of computing devices based on behavioral patterns

Towards an Elastic Application Model for Augmenting the Computing Capabilities of Mobile Devices with Cloud Computing