Abstract

The Internet of Things (IoT) leads to the next phase of human interaction with technology. With the help of the IoT, physical objects can be given the ability to generate, receive, and seamlessly trade data with one another. The IoT includes a wide variety of applications, each of which focuses on automating a specific task and works to give inanimate objects the ability to act independently of human intervention. The currently available and upcoming IoT applications hold a great deal of promise for enhancing the level of convenience, productivity, and automation enjoyed by users. High levels of security, privacy, authentication, and the ability to recover from attacks are required for the implementation of such a world in a manner that is constantly expanding. In this light, it is necessary to make the necessary adjustments to the architecture of IoT applications to accomplish end-to-end security in IoT environments. In this article, a comprehensive review of the security-related challenges and potential sources of danger posed by IoT applications is provided. Following a discussion of security concerns, a variety of new and established technologies that are focused on achieving a high degree of trust in the applications of the IoT are covered. Machine learning, fog computing, edge computing, and blockchain are just a few of the technologies that help the IoT provide greater security.

Introduction

The Internet of Things (IoT) is one of the disruptive technologies of the 21st century that has revolutionized every business dimension across the globe. The Internet has become an essential component in daily activities such as e-commerce, e-learning, e-conferences, etc. According to [1], the number of IoT devices deployed may exceed 21.3 billion by 2022. Intelligent interfaces connect IoT devices in order to collaborate, gather, communicate, and store data. Deploying IoT devices enhances business productivity by optimizing operational procedures and maximizing the utilization of available resources. IoT brings innovation in the way things are monitored and managed remotely with the help of real-time data acquired from the sensors. With the unrestrained volumes of data generated by billions of connected devices, storage, management, and security are arduous. Security aspects such as authentication, authorization, privacy, confidentiality, availability, and integrity form the basis for information exchange in a trusted environment. Authentication is the process of identifying the genuineness of an entity. The authorization process allows only authenticated users to access the resources. The protection of privacy prevents hackers from accessing the user’s private information. Data confidentiality is an outcome of authentication and authorization. It prevents sensitive data from being reclaimed and exploited. A device’s availability confirms that IoT devices and services are resilient to attacks and are accessible all the time. Data integrity is the process of providing a tamper-proof platform for information communication, exchange, and storage.

Conclusion and future directions

In this paper, the methods that are currently available in the literature are discussed, and this paper also presents the methods that are currently available. The subsequent step is an in-depth discussion of the numerous security risks and the importance of threat modeling. The subsequent topic is a discussion of the various IoT applications’ necessary security precautions. It reviews the prior research on the numerous security issues and the solutions put forth by various researchers. The holes that were found while conducting this literature review served as inspiration for the current research work.

7.1. Research challenges
The billions of IoT devices deployed across the globe collect personal and sensitive data and exchange it with other networks using intelligent interfaces. Providing device authentication, authorization, data privacy, and security in such an un trusted environment is a challenge. Centralized trusted infrastructure cannot scale to the dynamic and ever intensifying traffic and, thus, leading to bottlenecks in the network. Distributed authentication can scale to the increasing demands of the IoT networks, but requires distributed trusted systems. The majority of the work in the IoT security domain focuses on embracing the security mechanisms of WSNs and conventional Internet-based applications. However, these mechanisms are far from being implemented in real-time scenarios due to the specific challenges of IoT, such as

Abstract

Keywords

1. Introduction

2. System model

3. Our approach

3.1. Basic idea

3.2. Trajectory modeling

3.3. Trajectory parameter estimation

3.4. Object location prediction

4. Performance evaluation

5. Extension

6. Related works

7. Conclusion

References

Abstract

Mobile object tracking, which has broad applications, utilizes a large number of Internet of Things (IoT) devices to identify, record, and share the trajectory information of physical objects. Nonetheless, IoT devices are energy constrained and not feasible for deploying advanced tracking techniques due to significant computing requirements. To address these issues, in this paper, we develop an edge computing-based multivariate time series (EC-MTS) framework to accurately track mobile objects and exploit edge computing to offload its intensive computation tasks. Specifically, EC-MTS leverages statistical technique (i.e., vector auto regression (VAR)) to conduct arbitrary historical object trajectory data revisit and fit a best-effort trajectory model for accurate mobile object location prediction. Our framework offers the benefit of offloading computation intensive tasks from IoT devices by using edge computing infrastructure. We have validated the efficacy of EC-MTS and our experimental results demonstrate that EC-MTS framework could significantly improve mobile object tracking efficacy in terms of trajectory goodness-of-fit and location prediction accuracy of mobile objects. In addition, we extend our proposed EC-MTS framework to conduct multiple objects tracking in IoT systems.

1. Introduction

With the advance of Internet of Things (IoT) and big data sharing and analytics, massive number of IoT devices (sensors, actuators, etc.) are deployed to enable the monitoring and control of things under minor or no human intervention [1], [2], [3], [4], [5], [6], [7]. Mobile object tracking, a typical IoT application, along with other smart-world applications, such as smart grid, smart transportation, smart health, and smart manufacturing, are involving more and more IoT devices so that automatic monitoring and tracking on physical objects, including moving targets, vehicles, assets, etc., can be supported [8], [9], [10], [11], [12].

Nonetheless, it is a common practice that IoT devices are energy and computing constrained, and they are not competent to consistently handle complex mobile object tracking tasks, which are computationally intensive and consume lots of energy resources. Thus, computation offloading in IoT systems is a critical issue for accurate and efficient mobile object tracking. Our proposed scheme in this paper, designated Edge Computing-based Multivariate Time Series (EC-MTS) framework, endeavors to apply complex tracking technique to improve mobile object tracking performance in IoT systems and employs edge computing to offload computation intensive tasks that energy constrained IoT devices cannot handle. With an intent to understand how to track mobile objects in IoT systems and design corresponding solutions, a number of research efforts have been conducted [13], [14], [15], [16], [17], [18], [19]. The existing efforts demonstrate that the advance of mobile object tracking could support a diverse array of smart-world IoT applications (e.g., video surveillance, robot navigation, etc.) in dynamic environments.

Abstract

     Vehicular Ad Hoc Networks (VANET) is an emerging technology that enables a comfortable, safe, and efficient travel experience by providing mechanisms to execute applications related to traffic congestions, road accidents, autonomous driving, and entertainment. The mobile vehicles in VANET are characterized by low computational and storage capabilities. In such scenarios, to meet applications’ performance requirements, requests from vehicles are offloaded to edge and cloud servers. The high energy consumption of these servers increases operating costs and threatens the environment. Energy-aware offloading strategies have been introduced to tackle this problem. Existing works on computation offloading focus on optimizing the energy consumption of either the IoT devices/mobile/vehicles and/or the edge servers. This paper proposes a novel offloading algorithm that optimizes the energy of edge–cloud integrated computing platforms based on Evolutionary Genetic Algorithm (EGA) while maintaining applications’ Service Level Agreement (SLA). The proposed algorithm employs an adaptive penalty function to incorporate the optimization constraints within EGA. Comparative analysis and numerical experiments are carried out between the proposed algorithm, random and genetic algorithm-based offloading, and no offloading baseline approaches.

Introduction

     Vehicular Ad Hoc Networks (VANET) [1] is an emerging technology where vehicles acting as network nodes are equipped with computational resources and connectivity such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-roadside (V2R), vehicle-to-sensors (V2S), vehicle-to-pedestrian (V2P), and vehicle-to-everything (V2X) communications. It enables a comfortable, safer, convenient, and efficient travel experience for users by using applications such as sending alerts for congestions and accidents, autonomous driving, video-enabled real-time navigation, interactive gaming, and entertainment [2], [3], [4]. These applications often require high computation and storage resources, and low latency to process complex operations. However, mobile vehicles have limited onboard computing and storage capabilities to process resource-intensive applications while maintaining the Quality of Services (QoS). To address this issue, a cloud-based vehicular network has been introduced. Cloud computing [5], [6] provides on-demand computational and storage resources to mobile vehicles over the Internet. The remote cloud servers have high computation capabilities that would satisfy applications processing times. However, a high latency between the vehicle and cloud resources hinders the deployment of time-critical applications such as autonomous driving. In addition, a delayed response for applications such as traffic congestion and interactive gaming becomes less reliable. To overcome vehicles-to-cloud latency issues, Vehicular Edge Computing (VEC) [7] has been introduced.

Conclusions and future work

     Computation offloading is important in edge–cloud integrated vehicular networks to execute computationally intensive applications having strict SLA requirements. However, the energy consumption of the edge–cloud integrated computing platform should be considered energy-efficiency is crucial. In this paper, an Energy-SLA-Aware evolutionary genetic algorithm is proposed for edge–cloud computation offloading in a vehicular network that executes a vehicle’s request either on the edge server to which the request is submitted or offloads the request to one of the cloud servers. The offloading decision is made in a way that the total energy consumption of a set of requests is minimized and the SLA requirements of each request are maintained in terms of latency and processing time. The SLA constraints in the proposed algorithm are handled using the adaptive penalty function. This is the first work to propose an energy-SLA-aware offloading in the vehicular network using EGA that optimizes the energy consumption of the edge and cloud servers simultaneously, while adhering to the latency and processing time constraints. Comparative analysis and numerical experiments carried out revealed that the proposed algorithm outperforms no offloading and random offloading approaches in terms of energy consumption, and no offloading, random and energy-non-SLA-aware genetic-based baseline approaches in terms of percentage of SLA violations.

1- Introduction

2- Related work

3- Proposed the cognitive agent paradigm and design issues

4- Problem formulation and solution

5- Performance analysis

6- Conclusion

References

Abstract

The service of mobile network develops rapidly nowadays, which generates various computing and resource-intensive applications, such as Internet of vehicles and virtual reality. Mobile edge computing (MEC) is close to data source and users, so terminals can execute tasks at the edge of network. In this way, the heavy load on core network can be relieved and tasks can be executed effectively. However, the demands of users vary from each other and users move all the time. It is difficult for the existing way of service supply to meet demands of all users. Cognitive Agent (CA) is put forward in this paper to help users cache and execute tasks on MEC in advance. In detail, CA is used to build personalized model combined with users’ behavior data. At the same time, it uses Long short-term memory neural network to forecast the moving trajectory of terminal equipment and the service types to be requested, uses the prediction result to generate caching strategy, cache business and shorten the delay of task execution. Besides, to further reduce the stress on MEC, we propose the collaboration of computing, communicating and caching resource with neighboring users’ equipment. To verify the effectiveness of CA, we build a model that assesses the performance of the system. Finally, we design a simulation experiment to execute resource request and resource collaboration. The result of the experiments show that CA can improve the efficiency of communication network, relieve the stress on network and improve the quality of services to users.

Introduction

At present, emerging information services and applications increase rapidly with the development of wireless communication and Internet of Things (IoT) [1,2]. More and more intelligent devices, such as wearable devices, remote medical system and virtual/augmented reality (VR/AR), need advanced communication technology and computing ability to guarantee ultra-reliable low-latency communication (URLLC), which is a great challenges for network suppliers [3,4]. Besides, wireless access device in large quantity outputs massive quantity of data all the time [5]. The reasonable use of valuable information is greatly significant for improving the quality of services supplied to users [6]. Mobile edge computing (MEC) supplies infrastructures of communication, computing and caching at the network edge that is close to data source or users [7]. It offers cloud services and information technology environment for edge applications and supplies high-quality communication services to users [8–13]. The advantages of the emerging service model MEC can be summarized below: (i). Due to be close to users or data source, some problems can be solved, such as long delay and heavy data traffic. It well supports the real-time and bandwidth-dense Internet of Things facilities, especially for the emotion-aware multimedia systems [14–16]. Taking unmanned driving for example, automobile needs to calculate running speed and the distance from others and sense surroundings in real time.

The pervasive integration of ‘things‘ in the Internet of Things together with state-of-the-art computer systems provide a stimulating environment for creativity and business opportunities, but also a large range of security challenges. Engineering the security of such systems must acknowledge the peculiar conditions under which such systems operate: low computational capacity, distributed decision-making, significant node churn, etc. These conditions must, therefore, be supported by the techniques and methodologies for building secure and robust IoT systems. With CAAVI-RICS methodology we explore credibility, authentication, authorization, verification, and integrity of IoT and edge computing systems, through explaining the rationale, influence, concerns, and security solutions that accompany them. Our contribution is a complete and detailed systematic categorization and streamlining of security problems, covering the security environment of IoT and edge computing systems. Besides, we contribute to the debate on key aspects of edge computing security and state-of-the-art solutions.

Introduction

Since its appearance, cloud computing has provided the easiest way to remotely store and access data and services. Cloud computing has rapidly brought about a revolution in how we develop our services and applications by providing on-demand self-service, multi-tenant processing resources, broad network access, pooling of resources, fast provisioning, and rapid elasticity. Despite its capacity, the cloud-based application building model does not extend to use-cases where disrupting time-sensitive functionalities and inducing higher latency can result in catastrophic events (e.g. vehicle-to-vehicle communication). Even though the cloud offers a range of advantages, it introduces new concerns about security, privacy, availability of data and services, reliability, and performance. Hence the concepts of the Internet of Things (IoT) and Edge Computing (ECP) systems. IoT cyber-physical systems are enabled through a multitude of technological innovations: on-demand adaptive resource management frameworks, lightweight communication, and data protection protocols, etc. ECP is a computational framework/deployment methodology where data analytics and decision-making processes are moved from cloud closer to data sources, i.e. to the edge of the network. ECP vastly reduces the volume of data that is sent through the network, improves overall system security, responsiveness, and latency.

Abstract
The Internet of mobile things is a burgeoning technique that generates, stores and processes big real-time data to render rich services for mobile users. In order to mitigate conflicts between the resource limitation of mobile devices and users’ demands of decreasing processing latency as well as prolonging battery life, it spurs a popular wave of offloading mobile applications for execution to centralized and decentralized data centers, such as cloud and edge servers. Due to the complexity and difference of mobile big data, arbitrarily offloading the mobile applications poses a remarkable challenge to optimizing the execution time and the energy consumption for mobile devices, despite the improved performance of Internet of Things (IoT) in cloud-edge computing. To address this challenge, we propose a computation offloading method, named COM, for IoT-enabled cloud-edge computing. Specifically, a system model is investigated, including the execution time and energy consumption for mobile devices. Then dynamic schedules of data/control-constrained computing tasks are confirmed. In addition, NSGA-III (non-dominated sorting genetic algorithm III) is employed to address the multi-objective optimization problem of task offloading in cloud-edge computing. Finally, systematic experiments and comprehensive simulations are conducted to corroborate the efficiency of our proposed method.

Metagenomic studies are becoming increasingly widespread, yielding important insights into microbial communities covering diverse environments from terrestrial to aquatic ecosystems. This also because genome sequencing is likely to become a routinely and ubiquitous analysis in a near future thanks to a new generation of portable devices, such as the Oxford Nanopore MinION. The main issue is however represented by the huge amount of data produced by these devices, whose management is actually challenging considering the resources required for an efficient data transfer and processing. In this paper we discuss these aspects, and in particular how it is possible to couple Edge and Cloud computing in order to manage the full analysis pipeline. In general, a proper scheduling of the computational services between the data center and smart devices equipped with low-power processors represents an effective solution.

Introduction

Genome sequencing is one of the most effective analysis technique to monitor both the human body, in physiological settings and pathological conditions, as well as the bacterial communities of different environments. Developed in the 1970s with a cost of hundred million dollars, its impressive progress [1] reduced the cost down to about $1000 dollars, and the perspective is a further reduction to about $100 for genome1 . In particular, the MinION by Oxford Nanopore [2], a miniaturized sequencing instrument device with a weight under 100g powered by its USB port, represents one of the most promising tool belonging to the thirdgeneration DNA sequencing technology [3]. Coupled with a laptop, MinION can be used on the field [4] to obtain genomic sequences, thus providing essential information for tracing back the organisms present in the environment [5]. These devices have been widely used for microbiology studies 2 , for water monitoring3 and in agriculture 4 . Portable sequencer can also be used to monitor bacteria in air-filters of hospitals, food industries, and pharmaceutical companies in order to give alarms in case pathogens are identified [6, 7]. More extreme usages of Oxford Nanopore devices have also been experimented [8, 9, 10]. This new trend is sometimes referred as Internet of Living Things (IoLT) [11]. The combination of the MinION sequencers with remote platform for data integration is still in its infancy, although some attempts have been reported in conference talks and blogs [12, 13]. Moreover, some prototypes of IoT platforms for monitoring clean water [14, 15], precision farming [16, 17, 18], livestock [19, 20] and more generally to improve agricultural productivity [21, 22, 23] have been presented. The two major obstacles affecting the utility of this kind of devices are the access to suitable computational capabilities and bandwidth, since in principle it is possible to stream a couple of gigabytes of raw data per day. Even if the raw data analysis software works reliably on most of the current laptops, it is a very resource-intensive task. The adoption of a Cloud-based approach can mitigate such issue, though it comes at the expense of broadband connections.

1- Introduction

2- System model

3- Problem formulation

4- Algorithm design

5- Performance evaluation

6- Related work

7- Conclusion

References

Abstract

The ever-increasing computation tasks and communication traffic have imposed a heavy burden on cloud data centers and also resulted in a significantly high energy consumption. To ease such burden, edge computing is proposed to explore the distributed resources of edge devices (e.g., base stations) to provision the cloud services for latency-sensitive applications at the network edge. Owing to the geo-distribution of edge devices, edge computing is also an ideal energy efficient platform to leverage the distributed green energy for energy efficient computing. Thus, it is natural to integrate Energy Internet (EI) technology into edge computing for customizable energy scheduling. In such EI supported edge computing, both the green energy generation rates and the data processing demands vary in different time and space. To pursue high energy efficiency, it is desirable to maximize the utilization of green energy so as to reduce the brown energy consumption. This requires careful task allocation and energy scheduling to match the energy provision and demand. In this paper, we investigate the energy cost minimization problem with joint consideration of VM migration, task allocation and green energy scheduling and prove its NP-hardness. To tackle the computation complexity, a heuristic algorithm approximating the optimal solution is proposed. Through extensive simulations, we show that the proposed algorithm can efficiently reduce brown energy consumption and perform much close to the optimal solution.

Introduction

Nowadays, cloud computing provides various services to global users with high resource utilization, strong computing ability, and high service reliability. According to Cisco’s Global Cloud Index Report (2015–2020), 92% of global computational workloads is processed in cloud. However, with the rapid increasing of user tasks, the bulk data transmission and processing impose a heavy burden on the communication bandwidth and computation resource of cloud, bringing unbearable service delay to end users [1– 3]. Moreover, the significantly high energy consumption of cloud also becomes another critical issue with growing concerns [4,5]. To handle this challenge, the concept of edge computing, which extends cloud computing to the network edge, is proposed. Edge computing utilizes the distributed resources in routers, gateways, base stations and even mobile devices to offer ‘‘cloud’’ services to the users in proximity [6,7], as shown in Fig. 1. By directly processing user tasks in local edge devices, the transmission delay and the traffic congestion on the Internet can be effectively reduced, thereby improving the user experience [8]. Furthermore, edge computing platform is formed by geo-distributed edge devices, which can harvest green energy from the environment. Such paradigm naturally provides an ideal platform to utilize the local green energy [9]. Actually, many studies [10–13] have already mentioned the possibility and advantages of utilizing green energy in edge cloud to reduce the brown energy consumption.

I. INTRODUCTION

II. RELATED WORK

III. SYSTEM MODEL

IV. ALGORUTHM DESIGN

V. EVALUATE THE PERFORMANCE

VI. CONCLUSION AND FUTURE WORK

REFERENCES

Mobile terminal users applications, such as smartphones or laptops, have frequent computational task demanding but limited battery power. Edge computing is introduced to offload terminals’ tasks to meet the quality of service requirements such as low delay and energy consumption. By offloading computation tasks, edge servers can enable terminals to collaboratively run the highly demanding applications in acceptable delay requirements. However, existing schemes barely consider the characteristics of the edge server, which leads to random assignment of tasks among servers and big tasks with high computational intensity (named as ‘‘big task’’) may be assigned to servers with low ability. In this paper, a task is divided into several subtasks and subtasks are offloaded according to characteristics of edge servers, such as transmission distance and central processing unit (CPU) capacity. With this multi-subtasks-to-multi-servers model, an adaptive offloading scheme based on Hungarian algorithm is proposed with low complexity. Extensive simulations are conducted to show the efficiency of the scheme on reducing the offloading latency with low energy consumption.

INTRODUCTION

Mobile terminal devices are connected through the internet to accomplish many different applications and services, such as smartphones, laptops, sensors, machines, and vehicles, etc[1]. To extract valuable information from the huge amount of users’ data, local computation with terminal devices are no longer provide demanding quality of services such as low latency and energy consumption[2], [3], especially for video image stream data processing[4]–[6]. In-vehicle networks, tasks with high latency sensitivity require lower processing time. Otherwise, message propagation among vehicles may fail [7]. Therefore, light-weighted servers are deployed on the edge around terminals to bring computation and storage resource from the centralized cloud (CC), which is called as Mobile Edge Computing (MEC) [8]. Tasks generated by terminals can be offloaded and processed on edge servers [9]–[10] instead of being transferred to CC with large delay, and tasks or applications can effectively meet the delay requirements [11]–[13]. As privacy and security become more important in our daily life [14]–[17], a low delay would be particularly important in privacy and security issues for mobile edge computing systems.

Given a set of moving objects as well as their friend relationships, a time-aware road network, and a time threshold per friend pair, the proximity detection problem in time-aware road networks is to find each pair of moving objects such that the time distance (defined as the shortest time needed for two moving objects to meet each other) between them is within the given threshold. The problem of proximity detection is often encountered in autonomous driving and traffic safety related applications, which require low-latency, real time proximity detection with relatively low communication cost. However, (i) most existing proximity detection solutions focus on the Euclidean space which cannot be used in road network space, (ii) the solutions for road networks focus on static road networks and do not consider time distance and thus cannot be applied in time-aware road networks, and (iii) there are no works aiming to simultaneously reduce the communication cost, the communication latency, and computational cost. Motivated by these, we first design a low-latency proximity detection architecture based on Mobile Edge Computing (MEC) with the purpose of achieving low communication latency, then propose a proximity detection method including a client-side algorithm and a server-side algorithm, aiming at reducing the communication cost, and subsequently propose server-side computational cost optimization techniques to reduce the computational cost. Experimental results show that our MEC enhanced proximity detection architecture, our proximity detection method, and the server-side computational cost optimization techniques can reduce the communication latency, the communication cost, and the computational cost effectively.

Introduction

In a road network, how to effectively detect whether the moving users are within proximity or not, is referred to as the problem of proximity detection in road networks. In a dynamically changing road network, proximity detection among a large number of moving users plays an important role in ensuring traffic safety, guaranteeing assisted driving, and realizing the future large-scale autonomous driving. With the spreading of modern mobile devices like smart phones, PDAs or car navigation systems and the development of positioning technologies such as GPS, WiFi, cellular base station positioning or A-GPS, users can conveniently obtain their positions and send their location information to control center servers or other users’ mobile devices. Mobile users communicate with servers or other users frequently, leading to a large number of communication messages referred to as communication cost, consuming a lot of network bandwidth. Given a large number of mobile users and time distance T , time-aware proximity detection problem in road networks is to find a solution which not only can continuously detect which pairs of users among all users are within proximity based on time distance, but also can achieve the objectives of reducing communication cost, communication latency, and computational cost, so as to save the network bandwidth and improve the reliability and efficiency of proximity detection. Here, time distance refers to the shortest time needed for two users to meet each other.

1- Introduction

2- Overview of edge paradigms

3- Analysis of features and synergies

4- Security threats

5- Security challenges and opportunities

6- Conclusions

References

Abstract

For various reasons, the cloud computing paradigm is unable to meet certain requirements (e.g. low latency and jitter, context awareness, mobility support) that are crucial for several applications (e.g. vehicular networks, augmented reality). To fulfill these requirements, various paradigms, such as fog computing, mobile edge computing, and mobile cloud computing, have emerged in recent years. While these edge paradigms share several features, most of the existing research is compartmentalized; no synergies have been explored. This is especially true in the field of security, where most analyses focus only on one edge paradigm, while ignoring the others. The main goal of this study is to holistically analyze the security threats, challenges, and mechanisms inherent in all edge paradigms, while highlighting potential synergies and venues of collaboration. In our results, we will show that all edge paradigms should consider the advances in other paradigms.

Introduction

Cloud computing has taken the world by storm. In this category of utility computing, a collection of computing resources (e.g. network, servers, storage) are pooled to serve multiple consumers, using a multi-tenant model. These resources are available over a network, and accessed through standard mechanisms [1]. The cloud computing paradigm provides a variety of deployment models and service models, from public clouds (organizations provide cloud computing services to any customer) to private clouds (organizations deploy their own private cloud computing platform), and from Infrastructure as a Service models (IaaS, where fundamental computing resources are offered as a capability) to Software as a Service models (SaaS, where applications are offered as a capability), among other things. The benefits of cloud computing – minimal management effort, convenience, rapid elasticity, pay per use, ubiquity – have given birth to a multi-billion industry that is growing worldwide [2]. Despite its benefits, cloud computing is not a panacea. Generally, public cloud vendors have built a few large data centers in various parts of the world. These large-scale, commodity-computer data centers have enough computing resources to serve a very large number of users. However, this centralization of resources implies a large average separation between end user devices and their clouds, which in turn increases the average network latency and jitter [3]. Because of this physical distance, cloud services are not able to directly access local contextual information, such as precise user location, local network conditions, or even information about users’ mobility behaviour. For various delay-sensitive applications, such as vehicular networks and augmented reality, these requirements (low latency and jitter, context awareness, mobility support) are needed.