An Optimal Allocation of Tracking Tasks in a SW/HW Fog/Cloud Based Distributed Video Surveillance System
01 Jan 2019-Journal of Communications (Engineering and Technology Publishing)-Vol. 14, pp 159-163
TL;DR: A novel and systematic approach for dynamic allocation of tasks in a video surveillance system using smart cameras and based on Cloud/Fog architecture is proposed, guaranteeing the best solution optimizing power consumption and communication cost over the system.
Abstract: In this paper we propose a novel and systematic approach for dynamic allocation of tasks in a video surveillance system using smart cameras and based on Cloud/Fog architecture. Tracking tasks arrive in the system in a random way and must be assigned to the available devices (cameras, Fog nodes and the Cloud). Our approach guarantees the best solution optimizing power consumption and communication cost over the system. The proposed methods uses an integer programming model and its effectiveness is shown on an application example.
••21 Jun 2015
TL;DR: The definition of fog computing and similar concepts are discussed, representative application scenarios are introduced, and various aspects of issues the authors may encounter when designing and implementing fog computing systems are identified.
Abstract: Despite the increasing usage of cloud computing, there are still issues unsolved due to inherent problems of cloud computing such as unreliable latency, lack of mobility support and location-awareness. Fog computing can address those problems by providing elastic resources and services to end users at the edge of network, while cloud computing are more about providing resources distributed in the core network. This survey discusses the definition of fog computing and similar concepts, introduces representative application scenarios, and identifies various aspects of issues we may encounter when designing and implementing fog computing systems. It also highlights some opportunities and challenges, as direction of potential future work, in related techniques that need to be considered in the context of fog computing.
TL;DR: By sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing.
Abstract: 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.
TL;DR: A survey of integration components: Cloud platforms, Cloud infrastructures and IoT Middleware is presented and some integration proposals and data analytics techniques are surveyed as well as different challenges and open research issues are pointed out.
Abstract: The Internet of Things (IoT) is a paradigm based on the Internet that comprises many interconnected technologies like RFID (Radio Frequency IDentification) and WSAN (Wireless Sensor and Actor Networks) in order to exchange information. The current needs for better control, monitoring and management in many areas, and the ongoing research in this field, have originated the appearance and creation of multiple systems like smart-home, smart-city and smart-grid. However, the limitations of associated devices in the IoT in terms of storage, network and computing, and the requirements of complex analysis, scalability, and data access, require a technology like Cloud Computing to supplement this field. Moreover, the IoT can generate large amounts of varied data and quickly when there are millions of things feeding data to Cloud Computing. The latter is a clear example of Big Data, that Cloud Computing needs to take into account. This paper presents a survey of integration components: Cloud platforms, Cloud infrastructures and IoT Middleware. In addition, some integration proposals and data analytics techniques are surveyed as well as different challenges and open research issues are pointed out.
••27 Aug 2014
TL;DR: This paper has discussed the IoT-Cloud computing integration in detail in detail and presented the architecture of Smart Gateway with Fog Computing, which has tested this concept on the basis of Upload Delay, Synchronization Delay, Jitter, Bulk-data upload Delay, and Bulk- data synchronization delay.
Abstract: With the increasing applications in the domains of ubiquitous and context-aware computing, Internet of Things (IoT) are gaining importance. In IoTs, literally anything can be part of it, whether it is sensor nodes or dumb objects, so very diverse types of services can be produced. In this regard, resource management, service creation, service management, service discovery, data storage, and power management would require much better infrastructure and sophisticated mechanism. The amount of data IoTs are going to generate would not be possible for standalone power-constrained IoTs to handle. Cloud computing comes into play here. Integration of IoTs with cloud computing, termed as Cloud of Things (CoT) can help achieve the goals of envisioned IoT and future Internet. This IoT-Cloud computing integration is not straight-forward. It involves many challenges. One of those challenges is data trimming. Because unnecessary communication not only burdens the core network, but also the data center in the cloud. For this purpose, data can be preprocessed and trimmed before sending to the cloud. This can be done through a Smart Gateway, accompanied with a Smart Network or Fog Computing. In this paper, we have discussed this concept in detail and present the architecture of Smart Gateway with Fog Computing. We have tested this concept on the basis of Upload Delay, Synchronization Delay, Jitter, Bulk-data Upload Delay, and Bulk-data Synchronization Delay.
TL;DR: A conceptual smart pre-copy live migration approach is presented for VM migration that 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.
Abstract: 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.