Cost and Latency Optimized Edge Computing Platform

Big Data-Enabled Internet of Things, 2019

Emerging technologies that generate a huge amount of data such as the Internet of Things (IoT) services need latency aware computing platforms to support time-critical applications. Due to the on-demand services and scalability features of cloud computing, Big Data application processing is done in the cloud infrastructure. Managing Big Data applications exclusively in the cloud is not an efficient solution for latency-sensitive applications related to smart transportation systems, healthcare solutions, emergency response systems and content delivery applications. Thus, the Fog computing paradigm that allows applications to perform computing operations in-between the cloud and the end devices has emerged. In Fog architecture, IoT devices and sensors are connected to the Fog devices which are located in close proximity to the users and it is also responsible for intermediate computation and storage. Most computations will be done on the edge by eliminating full dependencies on the cloud resources. In this chapter, we investigate and survey Fog computing architectures which have been proposed over the past few years. Moreover, we study the requirements of IoT applications and platforms, and the limitations faced by cloud systems when executing IoT applications. Finally, we review current research works that particularly focus on Big Data application execution on Fog and address several open challenges as well as future research directions.

Lecture notes on data engineering and communications technologies, 2022

Resource management is the principal factor to fully utilize the potential of Edge/Fog computing to execute real-time and critical IoT applications. Although some resource management frameworks exist, the majority are not designed based on distributed containerized components. Hence, they are not suitable for highly distributed and heterogeneous computing environments. Containerized resource management frameworks such as FogBus2 enable efficient distribution of framework's components alongside IoT applications' components. However, the management, deployment, health-check, and scalability of a large number of containers are challenging issues. To orchestrate a multitude of containers, several orchestration tools are developed. But, many of these orchestration tools are heavyweight and have a high overhead, especially for resource-limited Edge/Fog nodes. Thus, for hybrid computing environments, consisting of heterogeneous Edge/Fog and/or Cloud nodes, lightweight container orchestration tools are required to support both resource-limited resources at the Edge/Fog and resource-rich resources at the Cloud. Thus, in this paper, we propose a feasible approach to build a hybrid and lightweight cluster based on K3s, for the FogBus2 framework that offers containerized resource management framework. This work addresses the challenge of creating lightweight computing clusters in hybrid computing environments. It also proposes three design patterns for the deployment of the FogBus2 framework in hybrid environments, including 1) Host Network, 2) Proxy Server, and 3) Environment Variable. The performance evaluation shows that the proposed approach improves the response time of real-time IoT applications up to 29% with acceptable and low overhead.

2018

Data management nowadays is being focused a lot in many areas in the industry, the reason for this is attributed to the speed we are obtaining the data, the volume of it and the new technologies. So, when we transfer this data through the network, we obviously need the most efficient way to send and retrieve data in our network with less response time, also known as Edge-Fog-Cloud computing. In this work, a framework is implemented in such a way that it provides minimum latency to User devices at the Edge of the network based on the SLA agreement between a Service Provider and the Network Provider. It provides an algorithm for Service chain Placement and monitors the network for potential bottlenecks that may be created in future and removes it.

Proceedings of the 2nd Workshop on Flexible Resource and Application Management on the Edge

We assume a computing infrastructure that implements a cloudto-edge continuum by federating heterogeneous resources. This infrastructure enables next generation (nextgen) QoE-intensive applications, by facilitating one of their critical requirements: latency. As such each node is identified primarily in terms of latency and secondarily in terms of, more traditional, computational capacity and availability characteristics. Since this infrastructure is dependent on the existing, local, and potentially third-party owned resources, we must address the pertinent problem of resource heterogeneity. This paper delineates the technical specifications of the solution that was designed to tackle the problem, creating a software stack that comprises a complete environment for the orchestration of containerized and VM-based workflows. The solution, dubbed "Minicloud", allows the underlying resource to be federated from a higher-level continuum management layer and be used for the deployment of nextgen apps based on latency requirements. Based on lightweight Kubernetes-oriented technologies and tools, it maintains minimum dependencies allowing a wide range of underlying OSs and system architectures to be supported.

2016

The “Cloud” is considered as the powerhouse that will fuel and support the expansion of IoT. The Internet of Things (IoT) continues to gain momentum as vendors and enterprises begin to embrace the opportunities this market presents. According to new research from International Data Corporation (IDC), the worldwide Internet of Things market will grow from $655.8 billion in 2014 to $1.7 trillion in 2020 with a compound annual growth rate (CAGR) of 16.9% [11] with devices, connectivity and IT services taking a majority stake in the market of IoT. This emerging wave of end-computing deployment requires mobility support, geo-distribution, location awareness and most notably very low latency. Will the Cloud be able to provide these features? Or maybe, the right question to ask is if it will be able to sustain the expected growth of IoT, with billions of devices communicating over data shared across inter-clouds while providing the kind of quality of service that we have come to expect over time. In this paper, we pore over the existing Cloud Computing landscape and contemplate its place in the “Things to Come” era of computing. We look at new hierarchical distributed architectures that extend from the edge of the network to the core of the cloud and delve into the idea of extending out the cloud and bringing it to the edge of the compute endpoints. Something that is now being called “The Fog”

50th International Conference on Parallel Processing, 2021

Applications running in edge computing system seamlessly collect data, process data and take actions accordingly. In many cases, the applications need to assist people in real time, and even have lifeor-death consequences such as heart attack detection in healthcare and object detection in driving, which requires low job latency. Moreover, power and bandwidth are constrained resources in edge computing systems. Therefore, a challenge is how to handle data efficiently to reduce job latency, and meanwhile reduce power and bandwidth consumption. Previous works mainly focus on where to store collected source data to reduce the communication latency for source data sharing. Noticing that intermediate and final processing results may be shared by many applications, we propose to store intermediate and final results for sharing to avoid the duplicated computation. We also propose data collection that reduces data collection frequency based on context-related factors to achieve an optimal tradeoff between the overhead and decision making accuracy. We further propose data redundancy elimination to reduce the redundant data transmitted between edge and fog nodes. Our combined data operation strategies show significant improvement over the state-of-the-art methods in terms job latency, power and bandwidth consumption for experiments on both simulated and real edge environment.

Electronics

Cloud computing has significantly enhanced the growth of the Internet of Things (IoT) by ensuring and supporting the Quality of Service (QoS) of IoT applications. However, cloud services are still far from IoT devices. Notably, the transmission of IoT data experiences network issues, such as high latency. In this case, the cloud platforms cannot satisfy the IoT applications that require real-time response. Yet, the location of cloud services is one of the challenges encountered in the evolution of the IoT paradigm. Recently, edge cloud computing has been proposed to bring cloud services closer to the IoT end-users, becoming a promising paradigm whose pitfalls and challenges are not yet well understood. This paper aims at presenting the leading-edge computing concerning the movement of services from centralized cloud platforms to decentralized platforms, and examines the issues and challenges introduced by these highly distributed environments, to support engineers and researchers wh...

Journal of Systems and Software, 2021

Modern Cyber-physical Systems (CPS) include applications like smart traffic, smart agriculture, smart power grid, etc. Commonly, these systems are distributed and composed of end-user applications and microservices that typically run in the cloud. The connection with the physical world, which is inherent to CPS, brings the need to operate and respond in real-time. As the cloud becomes part of the computation loop, the real-time requirements have to be also reflected by the cloud. In this paper, we present an approach that provides soft real-time guarantees on the response time of services running in cloud and edge-cloud (i.e., cloud geographically close to the end-user), where these services are developed in high-level programming languages. In particular, we elaborate a method that allows us to predict the upper bound of the response time of a service when sharing the same computer with other services. Importantly, as our approach focuses on minimizing the impact on the developer of such services, it does not require any special programming model nor limits usage of common libraries, etc.

Procedia Computer Science, 2018

Internet of Things (IoT) smart places are systems composed of sensors, actuators and computing infrastructure that acquires data about the surrounding environment and uses that data to improve the user experience of the smart place. For instance, RFID readers can detect a tag approaching and, after the event is processed in a dedicated server, open a door automatically. Many IoT applications are latency-sensitive because actions need to be done in a timely manner. To meet this requirement these applications are usually provisioned close to the physical place, which represents an infrastructure burden because it is not always practical to deploy a physical server at a location. Utility computing in the Cloud can solve this issue but the latency requirements must be carefully assessed. Fog computing is a concept that brings the cloud close to devices at the edge of the network, aiming to provide low latency communication for applications and services. The present work implemented a provisioning mechanism to deploy a "smart warehouse" IoT application according in utility computing platforms: Cloud and Fog. We compared the event latency performance of both approaches and the results show that a fog deployment is more adequate for the considered IoT application.

Proceedings of the IEEE

Edge computing is an emerging concept based on distributing computing, storage, and control services closer to end network nodes. Edge computing lies at the heart of the fifth generation (5G) wireless systems and beyond. While current state-of-the-art networks communicate, compute, and process data in a centralized manner (at the cloud), for latency and compute-centric applications, both radio access and computational resources must be brought closer to the edge, harnessing the availability of computing and storage-enabled small cell base stations in proximity to the end devices. Furthermore, the network infrastructure must enable a distributed edge decision-making service that learns to adapt to the network dynamics with minimal latency and optimize network deployment and operation accordingly. This article will provide a fresh look to the concept of edge computing by first discussing the applications that the network edge must provide, with a special emphasis on the ensuing challenges in enabling ultra-reliable and lowlatency edge computing services for mission-critical applications such as virtual reality (VR), vehicle-to-everything (V2X), edge artificial intelligence (AI), and so forth. Furthermore, several case studies where the edge is key are explored followed by insights and prospect for future work.