Vehicular Adhoc Networks (VANETs) have been attracted a lot of research recent years. Although VANETs are deployed in reality offering several services, the current architecture has been facing many difficulties in deployment and management because of poor connectivity, less scalability, less flexibility and less intelligence. We propose a new VANET architecture called FSDN which combines two emergent computing and network paradigm Software Defined Networking (SDN) and Fog Computing as a prospective solution. SDN-based architecture provides flexibility, scalability, programmability and global knowledge while Fog Computing offers delay-sensitive and location-awareness services which could be satisfy the demands of future VANETs scenarios. We figure out all the SDN-based VANET components as well as their functionality in the system. We also consider the system basic operations in which Fog Computing are leveraged to support surveillance services by taking into account resource manager and Fog orchestration models. The proposed architecture could resolve the main challenges in VANETs by augmenting Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Base Station communications and SDN centralized control while optimizing resources utility and reducing latency by integrating Fog Computing. Two use-cases for non-safety service (data streaming) and safety service (Lane-change assistance) are also presented to illustrate the benefits of our proposed architecture.

/pdf/software-defined-networking-based-vehicular-adhoc-network-2x6ue27788.pdf

Software defined networking-based vehicular Adhoc Network with Fog Computing

When users flood in cloud data centers, how to efficiently manage hardware resources and virtual machines (VMs) in a data center to both lower economical cost and ensure a high service quality becomes an inevitable work for cloud providers. VM migration is a cornerstone technology for the majority of cloud management tasks. It frees a VM from the underlying hardware. This feature brings a plenty of benefits to cloud providers and users. Many researchers are focusing on pushing its cutting edge. In this paper, we first give an overview of VM migration and discuss both its benefits and challenges. VM migration schemes are classified from three perspectives: 1) manner; 2) distance; and 3) granularity. The studies on non-live migration are simply reviewed, and then those on live migration are comprehensively surveyed based on the three main challenges it faces: 1) memory data migration; 2) storage data migration; and 3) network connection continuity. The works on quantitative analysis of VM migration performance are also elaborated. With the development and evolution of cloud computing, user mobility becomes an important motivation for live VM migration in some scenarios (e.g., fog computing). Thus, the studies regarding linking VM migration to user mobility are summarized as well. At last, we list the open issues which are waiting for solutions or further optimizations on live VM migration.

A Survey on Virtual Machine Migration: Challenges, Techniques, and Open Issues

We propose a novel roadside unit (RSU) cloud, a vehicular cloud, as the operational backbone of the vehicle grid in the Internet of Vehicles (IoV). The architecture of the proposed RSU cloud consists of traditional and specialized RSUs employing software-defined networking (SDN) to dynamically instantiate, replicate, and/or migrate services. We leverage the deep programmability of SDN to dynamically reconfigure the services hosted in the network and their data forwarding information to efficiently serve the underlying demand from the vehicle grid. We then present a detailed reconfiguration overhead analysis to reduce reconfigurations, which are costly for service providers. We use the reconfiguration cost analysis to design and formulate an integer linear programming (ILP) problem to model our novel RSU cloud resource management (CRM). We begin by solving for the Pareto optimal frontier (POF) of nondominated solutions, such that each solution is a configuration that minimizes either the number of service instances or the RSU cloud infrastructure delay, for a given average demand. Then, we design an efficient heuristic to minimize the reconfiguration costs. A fundamental contribution of our heuristic approach is the use of reinforcement learning to select configurations that minimize reconfiguration costs in the network over the long term. We perform reconfiguration cost analysis and compare the results of our CRM formulation and heuristic. We also show the reduction in reconfiguration costs when using reinforcement learning in comparison to a myopic approach. We show significant improvement in the reconfigurations costs and infrastructure delay when compared to purist service installations.

Software-Defined Networking for RSU Clouds in Support of the Internet of Vehicles

The power consumption of presently available Internet servers and data centers is not proportional to the work they accomplish. The scientific community is attempting to address this problem in a number of ways, for example, by employing dynamic voltage and frequency scaling, selectively switching off idle or underutilized servers, and employing energy-aware task scheduling. Central to these approaches is the accurate estimation of the power consumption of the various subsystems of a server, particularly, the processor. We distinguish between power consumption measurement techniques and power consumption estimation models. The techniques refer to the art of instrumenting a system to measure its actual power consumption whereas the estimation models deal with indirect evidences (such as information pertaining to CPU utilization or events captured by hardware performance counters) to reason about the power consumption of a system under consideration. The paper provides a comprehensive survey of existing or proposed approaches to estimate the power consumption of single-core as well as multicore processors, virtual machines, and an entire server.

/pdf/power-consumption-estimation-models-for-processors-virtual-4qowjyk1sp.pdf

Power Consumption Estimation Models for Processors, Virtual Machines, and Servers

Energy efficiency has grown into a latest exploration area of virtualized cloud computing paradigm. The increase in the number and the size of the cloud data centers has propagated the need for energy efficiency. An extensively practiced technology in cloud computing is live virtual machine migration and is thus focused in this work to save energy. This paper proposes an energy-aware virtual machine migration technique for cloud computing, which is based on the Firefly algorithm. The proposed technique migrates the maximally loaded virtual machine to the least loaded active node while maintaining the performance and energy efficiency of the data centers. The efficacy of the proposed technique is exhibited by comparing it with other techniques using the CloudSim simulator. An enhancement in the average energy consumption of about 44.39 % has been attained by reducing an average of 72.34 % of migrations and saving 34.36 % of hosts, thereby, making the data center more energy-aware.

Energy-aware Virtual Machine Migration for Cloud Computing - A Firefly Optimization Approach

One of the mechanisms to achieve energy efficiency in virtualized environments is to consolidate the workload (virtual machines) of underutilized servers and to switch-off these servers all together. Similarly, the workloads of overloaded servers can be distributed onto other servers for a load balancing reason. Central to this approach is the migration of virtual machines at runtime, which may introduce its own overhead in terms of energy consumption and service execution latency. This paper experimentally investigates the magnitude of this overhead. We use the Kernel-based Virtual Machine (KVM) hypervisor and a custom-made benchmark for our experiments. We will demonstrate that the workload of a virtual machine does not have any bearing on the power consumption of the destination server during migration but it has on the source server. Moreover, the available network bandwidth and the size of the virtual machine do indeed. introduce a non-negligible energy overhead and migration latency on both the source and the destination server.

/pdf/investigation-into-the-energy-cost-of-live-migration-of-14lw6070xd.pdf

Investigation into the energy cost of live migration of virtual machines

Due to the increasing number of Web Services, which provide similar functionality, the non-functional properties are becoming more important during the selection of the best available service. Non-functional properties describe Quality of Service (QoS) as well as context of service execution. Although there are many approaches considering only QoS or context during service discovery and selection, there is a lack of systems taking both non-functional categories into account. In this paper we introduce ConQo our approach for a contextand QoS-aware service discovery and selection.

ConQo – A Context- And QoS-Aware Service Discovery

The energy consumption of ICT infrastructures has increased considerably in the recent years. This has resulted in extensive research on dynamic power management strategies as well as data centre design and placement. The main problem with most of the proposed or existing approaches is that they do not fully take the distributed nature of and strong logical dependencies between executed services into account. However, without a comprehensive knowledge of the wider relationships between services, local power management strategies may be ineffectual or can even result in high aggregate energy cost. Understanding this relationship is useful for fine-grained energy-aware computing. For example, services that run on underutilised servers can be stopped or seamlessly migrated to other servers, so that the underutilised servers can be turned off. Alternatively, a re-binding process can be used if the cost of service migration is high. Such advantages can be fully exploited if the dependency between services is properly understood and meaningfully modelled. This paper introduces a conceptual architecture for an energy-aware service execution platform and compares three optimisation mechanisms to support dynamic service migration and rebinding.

/pdf/energy-aware-service-execution-1mj918mrar.pdf

Energy-aware service execution

The evolution of service-oriented architectures towards digital ecosystems comprehends a number of challenges According to Papazoglou et al, one challenge is to develop services in a dynamic environment with high uncertainties, and in collaboration with other companies The Integrated Service Engineering (ISE) Workbench is a step in this direction in that it supports a model-driven approach and different stakeholders This paper briefly introduces the ISE Framework, presents the ISE Workbench and its functionality, and proposes a demonstration of a conducted case study in the IT outsourcing domain

/pdf/integrated-service-engineering-workbench-service-engineering-3c1pnuf9yj.pdf

Integrated service engineering workbench: service engineering for digital ecosystems

Recently, industry has adopted multimodal, context-aware applications. However, addressing various modalities on heterogeneous platforms implies a demanding development effort. Therefore, we present a task-centric methodology and a tool chain leveraging the development of adaptive multimodal applications.

In order to improve efficiency the tool chain is based on the Model Driven Architecture approach emphasizing two key principles: model-to-model transformations and tool integration.

MDA applied: a task-model driven tool chain for multimodal applications

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