Internet of Things (IoT) domains generate large volumes of high-velocity event streams from sensors, which need to be analyzed with low latency to drive decisions. Complex Event Processing (CEP) is a Big Data technique to enable such analytics and is traditionally performed on Cloud Virtual Machines (VM). Leveraging captive IoT edge resources in combination with Cloud VMs can offer better performance, flexibility, and monetary costs for CEP. Here, we formulate an optimization problem for energy-aware placement of CEP queries, composed as an analytics dataflow, across a collection of edge and Cloud resources, with the goal of minimizing the end-to-end latency for the dataflow. We propose a Genetic Algorithm (GA) meta-heuristic to solve this problem and compare it against a brute-force optimal algorithm (BF). We perform detailed real-world benchmarks on the compute, network, and energy capacity of edge and Cloud resources. These results are used to define a realistic and comprehensive simulation study that validates the BF and GA solutions for 45 diverse CEP dataflows, LAN and WAN setup, and different edge resource availability. We compare the GA and BF solutions against random and Cloud-only baselines for different configurations for a total of 1,764 simulation runs. Our study shows that GA is within 97 of the optimal BF solution that takes hours, maps dataflows with 4--50 queries in 1--26s, and only fails to offer a feasible solution ≤20 of the time.
There are three primary related research areas relevant to our article: mobile Clouds, Fog and Peer-to-Peer (P2P) computing, and query processing in sensor networks. @cite_41 has grown as a research area that lies at the intersection of mobile devices such as smart phones and the Cloud. The key idea is to use these personal devices as a thin client to access rich services hosted on Clouds, forming a variation of a client-server model. In addition, the concept of Cloudlets has been proposed as an additional layer that sits between the edge and the Cloud to help reduce latencies while offering superior computing power than the edge alone @cite_8 . This is conceptually the computing equivalent of Content Distribution Networks that move data closer to the edge. Both these paradigms conceive of interactions between a single client and a remote Cloud Cloudlet, which is in contrast to our approach of leveraging the collective capabilities of distributed edge devices and the Cloud.
@cite_41 @cite_8
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Handoff mechanisms allow mobile users to move across multiple wireless access points while maintaining their voice and or data sessions. A traditional handoff process is concerned with smoothly transferring a mobile device session from its current access point (or cell) to a target access point (or cell). These handoff characteristics are sufficient for voice calls and background data transfers, however nowadays many mobile applications are heavily based on data and processing capabilities from the cloud. Such applications, especially those that require greater interactivity, often demand not only a smooth session transfer, but also the maintenance of quality of service requirements that impact a user's experience. In this context, the Fog Computing paradigm arises to overcome delays encountered when applications need low latency to access data or offload processing to the cloud. Fog computing introduces a distributed cloud layer, composed of cloudlets (i.e., "small clouds" with lower computational capacity), between the user and the cloud. Cloudlets allow low latency access to data or processing capabilities, which can be accomplished by offering a VM to the user. An overview of Fog computing is first providing, relating it to general concepts in Cloud-based systems, followed by a general architecture to support virtual machine migration in this emerging paradigm -- discussing both the benefits and challenges associated with such migration. The recent advances in cloud services technology are fueling a plethora of information technology innovation, including networking, storage, and computing. Today, various flavors have evolved of IoT, cloud computing, and so-called fog computing, a concept referring to capabilities of edge devices and users’ clients to compute, store, and exchange data among each other and with the cloud. Although the rapid pace of this evolution was not easily foreseeable, today each piece of it facilitates and enables the deployment of what we commonly refer to as a smart scenario, including smart cities, smart transportation, and smart homes. As most current cloud, fog, and network services run simultaneously in each scenario, we observe that we are at the dawn of what may be the next big step in the cloud computing and networking evolution, whereby services might be executed at the network edge, both in parallel and in a coordinated fashion, as well as supported by the unstoppable technology evolution. As edge devices become richer in functionality and smarter, embedding capacities such as storage or processing, as well as new functionalities, such as decision making, data collection, forwarding, and sharing, a real need is emerging for coordinated management of fog-to-cloud (F2C) computing systems. This article introduces a layered F2C architecture, its benefits and strengths, as well as the arising open and research challenges, making the case for the real need for their coordinated management. Our architecture, the illustrative use case presented, and a comparative performance analysis, albeit conceptual, all clearly show the way forward toward a new IoT scenario with a set of existing and unforeseen services provided on highly distributed and dynamic compute, storage, and networking resources, bringing together heterogeneous and commodity edge devices, emerging fogs, as well as conventional clouds. Introduction: The Scenario Cloud services to smart things face latency and intermittent connectivity issues. Fog devices are positioned between cloud and smart devices. Their high speed Internet connection to the cloud, and physical proximity to users, enable real time applications and location based services, and mobility support. Cisco promoted fog computing concept in the areas of smart grid, connected vehicles and wireless sensor and actuator networks. This survey article expands this concept to the decentralized smart building control, recognizes cloudlets as special case of fog computing, and relates it to the software defined networks (SDN) scenarios. Our literature review identifies a handful number of articles. Cooperative data scheduling and adaptive traffic light problems in SDN based vehicular networks, and demand response management in macro station and micro-grid based smart grids are discussed. Security, privacy and trust issues, control information overhead and network control policies do not seem to be studied so far within the fog computing concept. Internet of Things (IoT) has accelerated the deployment of millions of sensors at the edge of the network, through Smart City infrastructure and lifestyle devices. Cloud computing platforms are often tasked with handling these large volumes and fast streams of data from the edge. Recently, Fog computing has emerged as a concept for low-latency and resource-rich processing of these observation streams, to complement Edge and Cloud computing. In this paper, we review various dimensions of system architecture, application characteristics and platform abstractions that are manifest in this Edge, Fog and Cloud eco-system. We highlight novel capabilities of the Edge and Fog layers, such as physical and application mobility, privacy sensitivity, and a nascent runtime environment. IoT application case studies based on first-hand experiences across diverse domains drive this categorization. We also highlight the gap between the potential and the reality of Fog computing, and identify challenges that need to be overcome for the solution to be sustainable. Taken together, our article can help platform and application developers bridge the gap that remains in making Fog computing viable. Cloud technology is moving towards multi-cloud environments with the inclusion of various devices. Cloud and IoT integration resulting in so-called edge cloud and fog computing has started. This requires the combination of data centre technologies with much more constrained devices, but still using virtualised solutions to deal with scalability, flexibility and multi-tenancy concerns. Lightweight virtualisation solutions do exist for this architectural setting with smaller, but still virtualised devices to provide application and platform technology as services. Containerisation is a solution component for lightweight virtualisation solution. Containers are furthermore relevant for cloud platform concerns dealt with by Platform-as-a-Service (PaaS) clouds like application packaging and orchestration. We demonstrate an architecture for edge cloud PaaS. For edge clouds, application and service orchestration can help to manage and orchestrate applications through containers. In this way, computation can be brought to the edge of the cloud, rather than data from the Internet-of-Things (IoT) to the cloud. We show that edge cloud requirements such as cost-efficiency, low power consumption, and robustness can be met by implementing container and cluster technology on small single-board devices like Raspberry Pis. This architecture can facilitate applications through distributed multi-cloud platforms built from a range of nodes from data centres to small devices, which we refer to as edge cloud. We illustrate key concepts of an edge cloud PaaS and refer to experimental and conceptual work to make that case. Despite increasing usage of mobile computing, exploiting its full potential is difficult due to its inherent problems such as resource scarcity, frequent disconnections, and mobility. Mobile cloud computing can address these problems by executing mobile applications on resource providers external to the mobile device. In this paper, we provide an extensive survey of mobile cloud computing research, while highlighting the specific concerns in mobile cloud computing. We present a taxonomy based on the key issues in this area, and discuss the different approaches taken to tackle these issues. We conclude the paper with a critical analysis of challenges that have not yet been fully met, and highlight directions for future work. Highlights? Problems in mobile computing can be solved when combined with cloud computing. ? Three types of mobile clouds are; offloading to a remote server, to a local cloudlet, and sharing work in a mobile p2p network. ? Existing research on mobile cloud computing is presented as a taxonomy of issues. ? We identify issues in operational, end user, and service levels, and in security and context awareness. Cloud technology is moving towards more distribution across multi-clouds and the inclusion of various devices, as evident through IoT and network integration in the context of edge cloud and fog computing. Generally, lightweight virtualisation solutions are beneficial for this architectural setting with smaller, but still virtualised devices to host application and platform services, and the logistics required to manage this. Containerisation is currently discussed as a lightweight virtualisation solution. In addition to having benefits over traditional virtual machines in the cloud in terms of size and flexibility, containers are specifically relevant for platform concerns typically dealt with Platform-as-a-Service (PaaS) clouds such as application packaging and orchestration. For the edge cloud environment, application and service orchestration can help to manage and orchestrate applications through containers as an application packaging mechanism. We review edge cloud requirements and discuss the suitability container and cluster technology of that arise from having to facilitate applications through distributed multi-cloud platforms build from a range of networked nodes ranging from data centres to small devices, which we refer to here as edge cloud. The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network “edge,” near the end users. Thus, a number of computation nodes distributed across the network can offload the computational stress away from the centralized data center, and can significantly reduce the latency in message exchange. In addition, the distributed structure can balance network traffic and avoid the traffic peaks in IoT networks, reducing the transmission latency between edge cloudlet servers and end users, as well as reducing response times for real-time IoT applications in comparison with traditional cloud services. Furthermore, by transferring computation and communication overhead from nodes with limited battery supply to nodes with significant power resources, the system can extend the lifetime of the individual nodes. In this paper, we conduct a comprehensive survey, analyzing how edge computing improves the performance of IoT networks. We categorize edge computing into different groups based on architecture, and study their performance by comparing network latency, bandwidth occupation, energy consumption, and overhead. In addition, we consider security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and propose a framework for security evaluation of IoT networks with edge computing. Finally, we compare the performance of various IoT applications (smart city, smart grid, smart transportation, and so on) in edge computing and traditional cloud computing architectures. Mobile computing continuously evolve through the sustained effort of many researchers. It seamlessly augments users' cognitive abilities via compute-intensive capabilities such as speech recognition, natural language processing, etc. By thus empowering mobile users, we could transform many areas of human activity. This article discusses the technical obstacles to these transformations and proposes a new architecture for overcoming them. In this architecture, a mobile user exploits virtual machine (VM) technology to rapidly instantiate customized service software on a nearby cloudlet and then uses that service over a wireless LAN; the mobile device typically functions as a thin client with respect to the service. A cloudlet is a trusted, resource-rich computer or cluster of computers that's well-connected to the Internet and available for use by nearby mobile devices. Our strategy of leveraging transiently customized proximate infrastructure as a mobile device moves with its user through the physical world is called cloudlet-based, resource-rich, mobile computing. Crisp interactive response, which is essential for seamless augmentation of human cognition, is easily achieved in this architecture because of the cloudlet's physical proximity and one-hop network latency. Using a cloudlet also simplifies the challenge of meeting the peak bandwidth demand of multiple users interactively generating and receiving media such as high-definition video and high-resolution images. Rapid customization of infrastructure for diverse applications emerges as a critical requirement, and our results from a proof-of-concept prototype suggest that VM technology can indeed help meet this requirement. Fog computing, an extension of cloud computing services to the edge of the network to decrease latency and network congestion, is a relatively recent research trend. Although both cloud and fog offer similar resources and services, the latter is characterized by low latency with a wider spread and geographically distributed nodes to support mobility and real-time interaction. In this paper, we describe the fog computing architecture and review its different services and applications. We then discuss security and privacy issues in fog computing, focusing on service and resource availability. Virtualization is a vital technology in both fog and cloud computing that enables virtual machines (VMs) to coexist in a physical server (host) to share resources. These VMs could be subject to malicious attacks or the physical server hosting it could experience system failure, both of which result in unavailability of services and resources. Therefore, a conceptual smart pre-copy live migration approach is presented for VM migration. Using this approach, we can estimate the downtime after each iteration to determine whether to proceed to the stop-and-copy stage during a system failure or an attack on a fog computing node. This will minimize both the downtime and the migration time to guarantee resource and service availability to the end users of fog computing. Last, future research directions are outlined. Major interest is currently given to the integration of clusters of virtualization servers, also referred to as ‘cloudlets’ or ‘edge clouds’, into the access network to allow higher performance and reliability in the access to mobile edge computing services. We tackle the edge cloud network design problem for mobile access networks. The model is such that the virtual machines (VMs) are associated with mobile users and are allocated to cloudlets. Designing an edge cloud network implies first determining where to install cloudlet facilities among the available sites, then assigning sets of access points, such as base stations to cloudlets, while supporting VM orchestration and considering partial user mobility information, as well as the satisfaction of service-level agreements. We present link-path formulations supported by heuristics to compute solutions in reasonable time. We qualify the advantage in considering mobility for both users and VMs as up to 20 less users not satisfied in their SLA with a little increase of opened facilities. We compare two VM mobility modes, bulk and live migration, as a function of mobile cloud service requirements, determining that a high preference should be given to live migration, while bulk migrations seem to be a feasible alternative on delay-stringent tiny-disk services, such as augmented reality support, and only with further relaxation on network constraints. Mobile users typically have high demand on localized and location-based information services. To always retrieve the localized data from the remote cloud, however, tends to be inefficient, which motivates fog computing. The fog computing, also known as edge computing, extends cloud computing by deploying localized computing facilities at the premise of users, which prestores cloud data and distributes to mobile users with fast-rate local connections. As such, fog computing introduces an intermediate fog layer between mobile users and cloud, and complements cloud computing toward low-latency high-rate services to mobile users. In this fundamental framework, it is important to study the interplay and cooperation between the edge (fog) and the core (cloud). In this paper, the tradeoff between power consumption and transmission delay in the fog-cloud computing system is investigated. We formulate a workload allocation problem which suggests the optimal workload allocations between fog and cloud toward the minimal power consumption with the constrained service delay. The problem is then tackled using an approximate approach by decomposing the primal problem into three subproblems of corresponding subsystems, which can be, respectively, solved. Finally, based on simulations and numerical results, we show that by sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing. With smart devices, particular smartphones, becoming our everyday companions, the ubiquitous mobile Internet and computing applications pervade people daily lives. With the surge demand on high-quality mobile services at anywhere, how to address the ubiquitous user demand and accommodate the explosive growth of mobile traffics is the key issue of the next generation mobile networks. The Fog computing is a promising solution towards this goal. Fog computing extends cloud computing by providing virtualized resources and engaged location-based services to the edge of the mobile networks so as to better serve mobile traffics. Therefore, Fog computing is a lubricant of the combination of cloud computing and mobile applications. In this article, we outline the main features of Fog computing and describe its concept, architecture and design goals. Lastly, we discuss some of the future research issues from the networking perspective. As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures, which bring network functions and contents to the network edge, are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks, including definition, architecture, and advantages. Next, a comprehensive survey of issues on computing, caching, and communication techniques at the network edge is presented. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks, such as cloud technology, SDN NFV, and smart devices are discussed. Finally, open research challenges and future directions are presented as well. The technological advances in mobile devices are pushing cloud computing to the network edge , where services such as data storage and processing can be offered by mobile devices locally with improved quality. In this letter, we identify named data networking (NDN) as a key enabler to support “by design” the peculiarities of decentralized edge clouds at the network layer . We extend NDN beyond its original scope of content retrieval facilitator, by letting names to address, not only “contents” but also “cloud services,” and enhancing the semantics of NDN primitives to efficiently and reliably support both the provider discovery and the service provisioning phases. An early evaluation is performed to showcase the benefits of the proposal, and also when compared with a traditional TCP IP-based approach. With the proliferation of increasingly powerful mobile devices, mobile users can collaboratively form a mobile cloud to provide pervasive services, such as data collecting, processing, and computing. With this mobile cloud, mobile crowdsourcing, as an emerging service paradigm, can enable mobile users to take over the outsourced tasks. By leveraging the sensing capabilities of mobile devices and integrating humanintelligence and machine-computation, mobile crowdsourcing has the potential to revolutionize the approach of data collecting and processing. In this article we investigate the mobile crowdsourcing architecture and applications, then discuss some research challenges and countermeasures for developing mobile crowdsourcing. Some research orientations are finally envisioned for further studies. Path computing is a new paradigm that generalizes the edge computing vision into a multi-tier cloud architecture deployed over the geographic span of the network. Path computing supports scalable and localized processing by providing storage and computation along a succession of datacenters of increasing sizes, positioned between the client device and the traditional wide-area cloud data-center. CloudPath is a platform that implements the path computing paradigm. CloudPath consists of an execution environment that enables the dynamic installation of light-weight stateless event handlers, and a distributed eventual consistent storage system that replicates application data on-demand. CloudPath handlers are small, allowing them to be rapidly instantiated on demand on any server that runs the CloudPath execution framework. In turn, CloudPath automatically migrates application data across the multiple datacenter tiers to optimize access latency and reduce bandwidth consumption. Due to the increasing number of applications (and their data) being placed on mobile devices, access to dependable storage is becoming a key issue in mobile system design -- and cloud storage is becoming an attractive solution. However, this introduces a number of new issues related to unpredictable wireless network connectivity and data privacy over the network. In this article we present RFS, a wireless-friendly network file system for mobile devices and the cloud. RFS provides deviceaware cache management and client-driven data security and privacy protection. We implement the RFS client in the Linux kernel and the RFS server with Amazon S3 cloud storage, and we employ two new optimizations: server prepush (a server-side data pre-fetching mechanism) and client reintegration (synchronizing a mobile device's cache with the cloud). The empirical results over wired, WiFi and 3G networks show that RFS achieves good performance compared to Coda and FScache, and it reduces network activity visibly. Further, the privacy overhead is acceptable when RFS is run over wireless networks. We present a case study of booting Android over RFS, thereby demonstrating the ability for RFS to host a full mobile system. Overall, RFS can deliver a good user experience under undependable network conditions, allowing mobile users to seamlessly, and safely, use the cloud for data storage. paper describes a research in the area of mobile cloud computing. Cloud computing can be considered as a model that can provide network access to a shared pool of resources, such as storage and computing power, that can be rapidly provisioned and released with minimal management effort. The solutions discussed in this paper focus on different aspects of cloud computing in connection with mobile usage. By combining the different approaches and merging them into a common solution, it might be possible to generate a new solution that covers most of the issues currently experienced. Such a solution might have the chance to finally make cloud computing usable on mobile devices, resulting in new and interesting usage scenarios and offering execution speedups and energy savings to mobile users. Keywordscomputing, mobile devices, smart phones Abstract With the rapid increase in the development of the Internet of Things and 5G networks in the smart city context, a large amount of data (i.e., big data) is expected to be generated, resulting in increased latency for the traditional cloud computing paradigm. To reduce the latency, mobile edge computing has been considered for offloading a part of the workload from mobile devices to nearby edge servers that have sufficient computation resources. Although there has been significant research in the field of mobile edge computing, little attention has been given to understanding the placement of edge servers in smart cities to optimize the mobile edge computing network performance. In this paper, we study the edge server placement problem in mobile edge computing environments for smart cities. First, we formulate the problem as a multi-objective constraint optimization problem that places edge servers in some strategic locations with the objective to make balance the workloads of edge servers and minimize the access delay between the mobile user and edge server. Then, we adopt mixed integer programming to find the optimal solution. Experimental results based on Shanghai Telecom’s base station dataset show that our approach outperforms several representative approaches in terms of access delay and workload balancing.