Fog computing reduces the latency induced by distant clouds by enabling the deployment of some application components at the edge of the network, on fog nodes, while keeping others in the cloud. Application components can be implemented as Virtual Network Functions (VNFs) and their execution sequences can be modeled by a combination of sub-structures like sequence, parallel, selection, and loops. Efficient placement algorithms are required to map the application components onto the infrastructure nodes. Current solutions do not consider the mobility of fog nodes, a phenomenon which may happen in real systems. In this paper, we use the random waypoint mobility model for fog nodes to calculate the expected makespan and application execution cost. We then model the problem as an Integer Linear Programming (ILP) formulation which minimizes an aggregated weighted function of the makespan and cost. We propose a Tabu Search-based Component Placement (TSCP) algorithm to find sub-optimal placements. The results show that the proposed algorithm improves the makespan and the application execution cost.

No abstract

No abstract

4G systems have been continuously evolving to cope with the emerging challenges of human-centric and machine-tomachine (M2M) applications. Research has also now started on 5G systems. Scenarios have been proposed and initial requirements derived. 4G and beyond systems are expected to easily deliver a wide range of human-centric and M2M applications and services in a scalable, elastic, and cost efficient manner. The 3GPP IP multimedia subsystem (IMS) was standardized as the service delivery platform for 3G networks. Unfortunately, it does not meet several requirements for provisioning applications and services in 4G and beyond systems. However, cloudifying it will certainly pave the way for its use as a service delivery platform for 4G and beyond. This article presents a critical overview of the architectures proposed so far for cloudifying the IMS. There are two classes of approaches; the first focuses on the whole IMS system, and the second deals with specific IMS entities. Research directions are also discussed. IMS granularity and a PaaS for the development and management of IMS functional entities are the two key directions we currently foresee. 4G, 4G and beyond, 5G, cloud computing, Keywords- elasticity, infrastructure as a service (IaaS), IP multimedia subsystem (IMS), network function virtualization (NFV), platform as a service (PaaS), scalability, software as a service (SaaS), virtualization.I.

Mobile video surveillance applications have become ubiquitous and are used in both civilian and military settings. They enable the capture, storage and transmission of video images over the Internet or other networks. This paper proposes a novel system architecture for mobile video surveillance applications. We leverage the 3GPP 4G Evolved Packet Core (EPC) to enable features such as guaranteed quality of service (QoS), which is not possible with the Internet, and even differentiated QoS, which is not possible with the other networks currently used in mobile video applications. The key components of our proposed architecture are the service development platform (SDP) and the machine to machine (M2M) gateway. The SDP enables the development and management of QoSenabled mobile video surveillance applications. The M2M gateway enables interactions with the M2M devices, such as motion detectors and cameras. We have built a prototype with the Fraunhofer Fokus OpenEPC as 3GPP 4G EPC infrastructure and three AXIS network cameras as M2M devices. The architecture is presented along with the prototype. Related work is also reviewed.

No abstract

Virtualization enables multiple applications to share the same wireless sensor and actuator network (WSAN). However, in heterogeneous environments, virtualized wireless sensor and actuator networks (VWSAN) raise new challenges, such as the need for on-the-fly, dynamic, elastic, and scalable provisioning of gateways. Network Functions Virtualization (NFV) is a paradigm emerging to help tackle these new challenges. It leverages standard virtualization technology to consolidate special-purpose network elements on commodity hardware. This article presents NFV architecture for VWSAN gateways, in which software instances of gateway modules are hosted in NFV infrastructure operated and managed by a VWSAN gateway provider. We consider several VWSAN providers, each with its own brand or combination of brands of sensors and actuators/robots. These sensors and actuators can be accessed by a variety of applications, each may have different interface and QoS (i.e., latency, throughput, etc.) requirements. The NFV infrastructure allows dynamic, elastic, and scalable deployment of gateway modules in this heterogeneous VWSAN environment. Furthermore, the proposed architecture is flexible enough to easily allow new sensors and actuators integration and new application domains accommodation. We present a prototype that is built using the OpenStack platform. Besides, the performance results are discussed. 2

Abstract-The European Telecommunications Standards Institute (ETSI) developed Network Functions Virtualization (NFV)Management and Orchestration (MANO) framework. Within that framework, NFV orchestrator (NFVO) and Virtualized Network Function (VNF) Manager (VNFM) functional blocks are responsible for managing the lifecycle of network services and their associated VNFs. However, they face significant scalability and performance challenges in large-scale and geo-distributed NFV systems. Their number and location have major implications for the number of VNFs that can be accommodated and also for the overall system performance. NFVO and VNFM placement is therefore a key challenge due to its potential impact on the system scalability and performance. In this paper, we address the placement of NFVO and VNFM in large-scale and geo-distributed NFV infrastructure. We provide an integer linear programming formulation of the problem and propose a two-step placement algorithm to solve it. We also conduct a set of experiments to evaluate the proposed algorithm.