Management and Orchestration of Network Slices in 5G, Fog, Edge and Clouds

Network slicing allows network operators to build multiple isolated virtual networks on a shared physical network to accommodate a wide variety of services and applications. With network slicing, service providers can provide a cost-efficient solution towards meeting diverse performance requirements of deployed applications and services. Despite slicing benefits, End-to-End orchestration and management of network slices is a challenging and complicated task. In this chapter, we intend to survey all the relevant aspects of network slicing, with the focus on networking technologies such as Software-defined networking (SDN) and Network Function Virtualization (NFV) in 5G, Fog/Edge and Cloud Computing platforms. To build the required background, this chapter begins with a brief overview of 5G, Fog/Edge and Cloud computing, and their interplay. Then we cover the 5G vision for network slicing and extend it to the Fog and Cloud computing through surveying the state-of-the-art slicing approaches in these platforms. We conclude the chapter by discussing future directions, analyzing gaps and trends towards the network slicing realization.Comment: 31 pages, 4 figures, Fog and Edge Computing: Principles and Paradigms, Wiley Press, New York, USA, 201

Software-Defined Networking: State of the Art and Research Challenges

Plug-and-play information technology (IT) infrastructure has been expanding very rapidly in recent years. With the advent of cloud computing, many ecosystem and business paradigms are encountering potential changes and may be able to eliminate their IT infrastructure maintenance processes. Real-time performance and high availability requirements have induced telecom networks to adopt the new concepts of the cloud model: software-defined networking (SDN) and network function virtualization (NFV). NFV introduces and deploys new network functions in an open and standardized IT environment, while SDN aims to transform the way networks function. SDN and NFV are complementary technologies; they do not depend on each other. However, both concepts can be merged and have the potential to mitigate the challenges of legacy networks. In this paper, our aim is to describe the benefits of using SDN in a multitude of environments such as in data centers, data center networks, and Network as Service offerings. We also present the various challenges facing SDN, from scalability to reliability and security concerns, and discuss existing solutions to these challenges

A Survey of Energy Efficiency in SDN Software Based Methods and Optimization Models

Software Defined Networking (SDN) paradigm has the benefits of programmable network elements by separating the control and the forwarding planes, efficiency through optimized routing and flexibility in network management. As the energy costs contribute largely to the overall costs in networks, energy efficiency has become a significant design requirement for modern networking mechanisms. However, designing energy efficient solutions is non-trivial since they need to tackle the trade-off between energy efficiency and network performance. In this article, we address the energy efficiency capabilities that can be utilized in the emerging SDN. We provide a comprehensive and novel classification of software-based energy efficient solutions into subcategories of traffic aware, end system aware and rule placement. We propose general optimization models for each subcategory, and present the objective function, the parameters and constraints to be considered in each model. Detailed information on the characteristics of state-of-the-art methods, their advantages, drawbacks are provided. Hardware-based solutions used to enhance the efficiency of switches are also described. Furthermore, we discuss the open issues and future research directions in the area of energy efficiency in SDN.Comment: 17 double column pages, 3 figures, 6 table

Survey on Network Virtualization Hypervisors for Software Defined Networking

Software defined networking (SDN) has emerged as a promising paradigm for making the control of communication networks flexible. SDN separates the data packet forwarding plane, i.e., the data plane, from the control plane and employs a central controller. Network virtualization allows the flexible sharing of physical networking resources by multiple users (tenants). Each tenant runs its own applications over its virtual network, i.e., its slice of the actual physical network. The virtualization of SDN networks promises to allow networks to leverage the combined benefits of SDN networking and network virtualization and has therefore attracted significant research attention in recent years. A critical component for virtualizing SDN networks is an SDN hypervisor that abstracts the underlying physical SDN network into multiple logically isolated virtual SDN networks (vSDNs), each with its own controller. We comprehensively survey hypervisors for SDN networks in this article. We categorize the SDN hypervisors according to their architecture into centralized and distributed hypervisors. We furthermore sub-classify the hypervisors according to their execution platform into hypervisors running exclusively on general-purpose compute platforms, or on a combination of general-purpose compute platforms with general- or special-purpose network elements. We exhaustively compare the network attribute abstraction and isolation features of the existing SDN hypervisors. As part of the future research agenda, we outline the development of a performance evaluation framework for SDN hypervisors.Comment: IEEE Communications Surveys and Tutorials, in print, 201

iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments

Internet of Things (IoT) aims to bring every object (e.g. smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive amounts of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need a evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in terms of latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit in terms of RAM consumption and execution time is verified under different circumstances.Comment: Cloud Computing and Distributed Systems Laboratory, The University of Melbourne, June 6, 201

A Survey on Software-Defined VANETs: Benefits, Challenges, and Future Directions

The evolving of Fifth Generation (5G) networks isbecoming more readily available as a major driver of the growthof new applications and business models. Vehicular Ad hocNetworks (VANETs) and Software Defined Networking (SDN)represent the key enablers of 5G technology with the developmentof next generation intelligent vehicular networks and applica-tions. In recent years, researchers have focused on the integrationof SDN and VANET, and look at different topics related to thearchitecture, the benefits of software-defined VANET servicesand the new functionalities to adapt them. However, securityand robustness of the complete architecture is still questionableand have been largely negleted. Moreover, the deployment andintegration of novel entities and several architectural componentsdrive new security threats and vulnerabilities.In this paper, first we survey the state-of-the-art SDN basedVehicular ad-hoc Network (SDVN) architectures for their net-working infrastructure design, functionalities, benefits, and chal-lenges. Then we discuss these SDVN architectures against majorsecurity threats that violate the key security services such asavailability, confidentiality, authentication, and data integrity.We also propose different countermeasures to these threats.Finally, we discuss the lessons learned with the directions offuture research work towards provisioning stringent security andprivacy solutions in future SDVN architectures. To the best of ourknowledge, this is the first comprehensive work that presents sucha survey and analysis on SDVNs in the era of future generationnetworks (e.g., 5G, and Information centric networking) andapplications (e.g., intelligent transportation system, and IoT-enabled advertising in VANETs).Comment: 17 pages, 2 figure

All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey

With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.Comment: 48 pages, 7 tables, 11 figures, 450 references. The data (categories and features/objectives of the papers) of this survey are now available publicly. Accepted by Elsevier Journal of Systems Architectur

Integrated NFV/SDN Architectures: A Systematic Literature Review

Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) are new paradigms in the move towards open software and network hardware. While NFV aims to virtualize network functions and deploy them into general purpose hardware, SDN makes networks programmable by separating the control and data planes. NFV and SDN are complementary technologies capable of providing one network solution. SDN can provide connectivity between Virtual Network Functions (VNFs) in a flexible and automated way, whereas NFV can use SDN as part of a service function chain. There are many studies designing NFV/SDN architectures in different environments. Researchers have been trying to address reliability, performance, and scalability problems using different architectural designs. This Systematic Literature Review (SLR) focuses on integrated NFV/SDN architectures, with the following goals: i) to investigate and provide an in-depth review of the state-of-the-art of NFV/SDN architectures, ii) to synthesize their architectural designs, and iii) to identify areas for further improvements. Broadly, this SLR will encourage researchers to advance the current stage of development (i.e., the state-of-the-practice) of integrated NFV/SDN architectures, and shed some light on future research efforts and the challenges faced.Comment: Accepted for publication at ACM Computing Survey

Efficient Virtual Network Function Placement Strategies for Cloud Radio Access Networks

The new generation of 5G mobile services places stringent requirements for cellular network operators in terms of latency and costs. The latest trend in radio access networks (RANs) is to pool the baseband units (BBUs) of multiple radio base stations and to install them in a centralized infrastructure, such as a cloud, for statistical multiplexing gains. The technology is known as Cloud Radio Access Network (CRAN). Since cloud computing is gaining significant traction and virtualized data centers are becoming popular as a cost-effective infrastructure in the telecommunication industry, CRAN is being heralded as a candidate technology to meet the expectations of radio access networks for 5G. In CRANs, low energy base stations (BSs) are deployed over a small geographical location and are connected to a cloud via finite capacity backhaul links. Baseband processing unit (BBU) functions are implemented on the virtual machines (VMs) in the cloud over commodity hardware. Such functions, built-in software, are termed as virtual functions (VFs). The optimized placement of VFs is necessary to reduce the total delays and minimize the overall costs to operate CRANs. Our study considers the problem of optimal VF placement over distributed virtual resources spread across multiple clouds, creating a centralized BBU cloud. We propose a combinatorial optimization model and the use of two heuristic approaches, which are, branch-and-bound (BnB) and simulated annealing (SA) for the proposed optimal placement. In addition, we propose enhancements to the standard BnB heuristic and compare the results with standard BnB and SA approaches. The proposed enhancements improve the quality of the solution in terms of latency and cost as well as reduce the execution complexity significantly.Comment: E-preprin

Dynamic Environments for Virtual Machine Placement considering Elasticity and Overbooking

Cloud computing datacenters provide millions of virtual machines in actual cloud markets. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering the large number of possible optimization criteria and different formulations that could be studied. Considering the on-demand model of cloud computing, the VMP problem should be solved dynamically to efficiently attend typical workload of modern applications. This work proposes a taxonomy in order to understand possible challenges for Cloud Service Providers (CSPs) in dynamic environments, based on the most relevant dynamic parameters studied so far in the VMP literature. Based on the proposed taxonomy, several unexplored environments have been identified. To further study those research opportunities, sample workload traces for each particular environment are required; therefore, basic examples illustrate a preliminary work on dynamic workload trace generation.Comment: arXiv admin note: text overlap with arXiv:1507.0009