7 research outputs found
Probabilistic QoS-aware Placement of VNF chains at the Edge
Deploying IoT-enabled Virtual Network Function (VNF) chains to Cloud-Edge
infrastructures requires determining a placement for each VNF that satisfies
all set deployment requirements as well as a software-defined routing of
traffic flows between consecutive functions that meets all set communication
requirements. In this article, we present a declarative solution, EdgeUsher, to
the problem of how to best place VNF chains to Cloud-Edge infrastructures.
EdgeUsher can determine all eligible placements for a set of VNF chains to a
Cloud-Edge infrastructure so to satisfy all of their hardware, IoT, security,
bandwidth, and latency requirements. It exploits probability distributions to
model the dynamic variations in the available Cloud-Edge infrastructure, and to
assess output eligible placements against those variations
Hierarchical Multi-Agent Optimization for Resource Allocation in Cloud Computing
In cloud computing, an important concern is to allocate the available
resources of service nodes to the requested tasks on demand and to make the
objective function optimum, i.e., maximizing resource utilization, payoffs and
available bandwidth. This paper proposes a hierarchical multi-agent
optimization (HMAO) algorithm in order to maximize the resource utilization and
make the bandwidth cost minimum for cloud computing. The proposed HMAO
algorithm is a combination of the genetic algorithm (GA) and the multi-agent
optimization (MAO) algorithm. With maximizing the resource utilization, an
improved GA is implemented to find a set of service nodes that are used to
deploy the requested tasks. A decentralized-based MAO algorithm is presented to
minimize the bandwidth cost. We study the effect of key parameters of the HMAO
algorithm by the Taguchi method and evaluate the performance results. When
compared with genetic algorithm (GA) and fast elitist non-dominated sorting
genetic (NSGA-II) algorithm, the simulation results demonstrate that the HMAO
algorithm is more effective than the existing solutions to solve the problem of
resource allocation with a large number of the requested tasks. Furthermore, we
provide the performance comparison of the HMAO algorithm with the first-fit
greedy approach in on-line resource allocation
Softwarization of Large-Scale IoT-based Disasters Management Systems
The Internet of Things (IoT) enables objects to interact and cooperate with each other for reaching common objectives. It is very useful in large-scale disaster management systems where humans are likely to fail when they attempt to perform search and rescue operations in high-risk sites. IoT can indeed play a critical role in all phases of large-scale disasters (i.e. preparedness, relief, and recovery). Network softwarization aims at designing, architecting, deploying, and managing network components primarily based on software programmability properties. It relies on key technologies, such as cloud computing, Network Functions Virtualization (NFV), and Software Defined Networking (SDN). The key benefits are agility and cost efficiency. This thesis proposes softwarization approaches to tackle the key challenges related to large-scale IoT based disaster management systems.
A first challenge faced by large-scale IoT disaster management systems is the dynamic formation of an optimal coalition of IoT devices for the tasks at hand. Meeting this challenge is critical for cost efficiency. A second challenge is an interoperability. IoT environments remain highly heterogeneous. However, the IoT devices need to interact. Yet another challenge is Quality of Service (QoS). Disaster management applications are known to be very QoS sensitive, especially when it comes to delay.
To tackle the first challenge, we propose a cloud-based architecture that enables the formation of efficient coalitions of IoT devices for search and rescue tasks. The proposed architecture enables the publication and discovery of IoT devices belonging to different cloud providers. It also comes with a coalition formation algorithm. For the second challenge, we propose an NFV and SDN based - architecture for on-the-fly IoT gateway provisioning. The gateway functions are provisioned as Virtual Network Functions (VNFs) that are chained on-the-fly in the IoT domain using SDN. When it comes to the third challenge, we rely on fog computing to meet the QoS and propose algorithms that provision IoT applications components in hybrid NFV based - cloud/fogs. Both stationary and mobile fog nodes are considered. In the case of mobile fog nodes, a Tabu Search-based heuristic is proposed. It finds a near-optimal solution and we numerically show that it is faster than the Integer Linear Programming (ILP) solution by several orders of magnitude