Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities

The Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

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Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

Internet of Things offloading: Ongoing issues, opportunities, and future challenges

In the IoT-based systems, the fog computing allows the fog nodes to offload and process tasks requested from IoT-enabled devices in a distributed manner instead of the centralized cloud servers to reduce the response delay. However, achieving such a benefit is still challenging in the systems with high rate of requests, which imply long queues of tasks in the fog nodes, thus exposing probably an inefficiency in terms of latency to offload the tasks. In addition, a complicated heterogeneous degree in the fog environment introduces an additional issue that many of single fogs can not process heavy tasks due to lack of available resources or limited computing capabilities. To cope with the situation, this article introduces FRATO (Fog Resource aware Adaptive Task Offloading) - a framework for the IoT-fog-cloud systems to offer the minimal service provisioning delay through an adaptive task offloading mechanism. Fundamentally, FRATO is based on the fog resource to select flexibly the optimal offloading policy, which in particular includes a collaborative task offloading solution based on the data fragment concept. In addition, two distributed fog resource allocation algorithms, namely TPRA and MaxRU are developed to deploy the optimized offloading solutions efficiently in cases of resource competition. Through the extensive simulation analysis, the FRATO-based service provisioning approaches show potential advantages in reducing the average delay significantly in the systems with high rate of service requests and heterogeneous fog environment compared with the existing solutions.

FRATO: Fog Resource Based Adaptive Task Offloading for Delay-Minimizing IoT Service Provisioning

Edge computing and its role in Industrial Internet: Methodologies, applications, and future directions

The exponential growth in smart device usage and mobile applications markets, coupled with the increased complexity of these applications has outpaced the storage typically available on these devices. As a result, developers and end-users have both started pursuing cloud solutions for the storage and caching of application data, as well as personal user data. Such solutions, however, require a great deal of manual intervention from the user to first determine which files need to be offloaded, and then perform the actual task of offloading them. Given the time, attention, and effort required for such manual tasks, there is a need for automated storage management systems that make these decisions in an intelligent manner. In this paper, we analyze the access traces of 28 different mobile device users, and show that there are discernible access patterns to both popular and unpopular files on a user's device. We leverage this knowledge of access patterns to develop our Pattern Based Popularity Assessor (PBPA) algorithm. Our evaluation shows that PBPA can predict file popularity with high accuracy, making it a promising candidate for future automated mobile storage offloading systems.

Towards Intelligent Edge Storage Management: Determining and Predicting Mobile File Popularity

The recent growth in smart phone adoption coupled with social networking has lead to an increase in user generated content (UGC). The majority of UGC, captured in the form of images, videos, and audio clips, have significantly grown in size as a result of advancements in multimedia technology with higher definition phone cameras. Online clouds are generally the go-to solution in order to accommodate this increase in storage requirements. However, online clouds raise privacy concerns, are not fully automated, and do not adapt to different networking environments. We propose an edge based automated storage management system, EdgeStore, that utilizes user-owned devices at the edge, to automatically offload and retrieve files. EdgeStore determines file popularity based on the user's access patterns and offloads unpopular files. It accounts for different networking infrastructures, ranging from rural areas to metropolitan areas, in order to serve users in different environments. We implement and evaluate EdgeStore showing that users can offload up to 90% of storage to underutilized edge devices, and still have a high percentage of low latency access to offloaded files within 10 seconds.

EdgeStore: Leveraging Edge Devices for Mobile Storage Offloading

The digital world is expanding rapidly and advances in networking technologies such as wireless broadband (WiBro), low-power wide area networks (LPWANs), 4G/5G, LiFi, and so on, are paving the way for the emergence of sophisticated services. The number of online and mobile applications leveraging sophisticated smart devices are increasing in complexity requiring more computation and communication. While current smart phones and other IoT devices are becoming more powerful, the support gap is widening when compared to the demand of current and future compute-intensive tasks such as those often required for smart health care, ambient assisted living (AAL), virtual/augmented reality, intelligent vehicular communication, and so on. For many of these applications, computational or data storage tasks can not be entirely performed locally and have thus exploited different offloading techniques. The focus in such techniques has traditionally been on minimizing delay when supporting such applications. In this paper, we argue for the opportunity to tap into idle compute resources to support another dimension of applications characterized with high task demand, but can tolerate larger delays. We present delay tolerant computing (DTC) paradigm, and define where it falls within the compute ecosystem. We highlight the potential behind DTC with various applications, and propose a generalized architecture for how this paradigm can be realized. Finally, we show, using a couple of case studies, the potential behind DTC when compared to other compute paradigms by providing preliminary experimental results based on monetary cost, computational requirements, and delay.

Delay Tolerant Computing: The Untapped Potential

Fog and cloud computing are becoming increasingly popular as computational offloading platforms. However, many areas throughout the world either do not have a stable enough network connection to effectively use cloud computing, or do not have the required infrastructure or resources to set up fog nodes. Moreover, many application categories such as cognitive assistance and augmented reality applications require single digit latency, which both cloud and fog cannot provide. As such, these areas can benefit tremendously from edge computing. To ensure single digit latency, Edge nodes are typically other co-located devices within single-hop proximity. Having said that, due to user mobility, single edge devices set up at home will not provide the required latency if the user is on the move. Therefore, we introduce Teddybear, a Docker based system that seamlessly and efficiently migrates server containers between edge computing platforms by utilizing both the Internet and the user's mobile device as a carrier for the container. We show how Teddybear can continue to provide ultra-low latency services to users on the move, with minimal service downtime as they change locations.

Enabling Seamless Container Migration in Edge Platforms

Fog and cloud computing are becoming increasingly popular as computational offloading platforms. However, many areas throughout the world either do not have a stable enough network connection to effectively use cloud computing, or do not have the required infrastructure or resources to set up fog nodes. Moreover, many application categories such as cognitive assistance and augmented reality applications require single digit latency, which both cloud and fog cannot provide. As such, these areas can benefit tremendously from edge computing. To ensure single digit latency, Edge nodes are typically other co-located devices within single-hop proximity. Having said that, due to user mobility, single edge devices set up at home will not provide the required latency if the user is on the move. Therefore, we introduce Teddybear, a Docker based system that seamlessly and efficiently migrates server containers between edge computing platforms by utilizing both the Internet and the user's mobile device as a carrier for the container. We show how Teddybear can continue to provide ultra-low latency services to users on the move in areas with poor network bandwidths/infrastructures, with minimal service downtime as they change locations.

Ali Elgazar

Papers

Towards Intelligent Edge Storage Management: Determining and Predicting Mobile File Popularity

EdgeStore: Leveraging Edge Devices for Mobile Storage Offloading

Delay Tolerant Computing: The Untapped Potential

Enabling Seamless Container Migration in Edge Platforms

Teddybear: Enabling Efficient Seamless Container Migration in User-Owned Edge Platforms