Live migration in green virtualized networks

Procedia Computer Science, 2020

The Information and Communications Technology sector produces approximately 2% of the global carbon footprint every year. Estimations show that by the year 2020, this will grow up to 4% if the communication industry continues on this current path, which will be disastrous for the environment and hence the time for change towards green computing is now. Green networking refers to the processes used to optimize networking functions to make it more energy efficient. Datacenter networking infrastructures rely on power hungry devices to operate. This study will explore some modern enabling technologies such as Software Defined Networking, Edge Computing and Virtualization, and how these distinct concepts can fit together to enable more efficient green network solutions. Simulations carried out in a CloudSim testing environment using an energy cost model measure saving from virtualization and Edge technologies in datacenters.

2015 Global Information Infrastructure and Networking Symposium (GIIS), 2015

A major concern for cloud infrastructure providers is to decrease both their carbon footprint and their energy consumption. In this paper, we propose an elastic and energyaware virtual machine migration mechanism that decreases power consumption by turning off physical machines and turns them on according to the demand. We develop heuristics that reallocate virtual machines in a smaller number of physical machines, turns off the idle physical machines and, consequently, saves energy. When the demand increases, the proposal turns on physical machines to supply the required resources. The contributions of the paper are four-fold: i) an efficient and elastic mechanism for energy saving on data centers; ii) a simple formal model for minimization of idle physical machines; iii) an implementation in a real environment, and iv) a simulation of the optimization algorithm for larger scenarios. The mechanism was developed and tested in Future Internet Testbed with Security (FITS). The results show the effectiveness of the mechanism on reallocating virtual machines to decrease power consumption.

Computer Networks, 2017

Next generation 5G networks will rely on virtualized Data Centers (vDC) to host virtualized network functions on commodity servers. Such Network Function Virtualization (NFV) will lead to significant savings in terms of infrastructure cost and reduced management complexity. However, green strategies for networking and computing inside data centers, such as server consolidation or energy aware routing, should not negatively impact the quality and service level agreements expected from network operators. In this paper, we study how robust strategies that place virtual network functions (VNF) inside vDC impact the energy savings and the protection level against resource demand uncertainty. We propose novel optimization models that allow the minimization of the energy of the computing and network infrastructure which is hosting a set of service chains that implement the VNFs. The model explicitly provides for robustness to unknown or imprecisely formulated resource demand variations, powers down unused routers, switch ports and servers, and calculates the energy optimal VNF placement and network embedding also considering latency constraints on the service chains. We propose both exact and heuristic methods. Our experiments were carried out using the virtualized Evolved Packet Core (vEPC), which

2014 IEEE International Conference on Advanced Networks and Telecommuncations Systems (ANTS), 2014

International Journal of Communication Systems, 2020

The evolution of 5th Generation wireless technology introduced Mobile Edge Computing, where edge servers are placed at the edge of the network, and are associated with evolved Node Base Stations (eNBs). This enables mobile users to offload their resource-intensive tasks to these servers and improve network performance by reducing end-to-end delay. However, frequent user mobility leads to frequent re-planning of network and increases network load. This demands dynamic Virtual Machine (VM) migration in Mobile Edge paradigm for an improved Quality of Service (QoS). For an enhanced VM migration process, an optimal pair of migrating VMs and destination edge servers needs to be chosen. In this paper, we propose an optimized decision-making policy that chooses such optimal pairs. Several decision parameters such as average wait time, processing delay, migration delay, transmission power, and processing power are modeled. A profit function is developed using these modeled decision parameters that chooses the optimal pairs. This function is maximized using the proposed hybrid evolutionary algorithm, which combines the advantages of PSO and GA. The pairs are chosen in such a manner, that the selection guarantees high network throughput, reduced service delay, and energy consumption which is reflected in the simulation.

2019

Network virtualization is an inherent component of future internets. Network resources are virtualized and provisioned to users on demand. The virtual network embedding entails two processes: node mapping and link mapping. However, efficient and practical solutions to the link mapping problem in software-defined networks (SDN) and data centers are still lacking. This paper proposes a solution to the link mapping process that can dynamically interact with the routing protocols of the substrate network to allocate virtual link requests to the underlying substrate links and satisfies optimizing cost, minimizing energy consumption, and avoiding congestion (CEVNE LiM) concurrently. CEVNE LiM is realized as a composite application on top of the SDN controller running the Segment Routing (SR) application. The performance of the CEVNE LiM algorithm is compared with the k-shortest path (link mapping) algorithm and shows its superior performance in terms of the overall runtime, the average path length, the average node stress, the average link stress and the overall energy consumption.

Globecom 2014

Data centers power consumption corresponds to near 2% of the total world wide power consumption, with constantly increasing greenhouse effect and CO2 footprints. Virtualization techniques improve the efficiency of data cen-ters infrastructure sharing a same physical hardware among several Virtual Machines (VMs). An efficient VM placement can minimize even further the hardware and energy needs. In contrast to existing VM placement algorithms that usually focus on a single resource or assumes that resources demands are deterministic, this paper proposes and compares four energy-aware algorithms that consider multiple stochastic resources, including network bandwidth. We first formulate the problem as a multi objective optimization problem with stochastic resources and we present two algorithms based on this approach. We also formulate the problem as an evolutionary computation problem and we present two algorithms based on this approach. The objective is a joint strategy: minimize the required hardware to maximize the allocated VMs satisfying the resource requirements. Through simulations, we compare our algorithms using real VMs workloads from the PlanetLab project and showed the significant improvements on power consumption and network utilization. In average, the algorithms reduce power consumption by 87.90% and the network utilization by 9.94%.

2022

The use of virtual machines increases as more people switch to intelligent gadgets that require a lot of computing power to function correctly. With virtualization, different resources from existing physical infrastructure can be created. Virtualization strategies effectively address the increasing need for computers, storage, and communication resources in large Cloud Data Centers (CDC). It also achieves various resource management objectives such as load balancing, Internet system maintenance, speed, and tolerance with Virtual Machine migration. VM migration techniques would be helpful for autonomic resource management. So, in this paper, VM migration techniques, applications and their open challenges have been discussed.

IEEE Transactions on Network and Service Management, 2015

The increasing adoption of server virtualization has recently favored three key technology advances in data-center networking: the emergence at the hypervisor software level of virtual bridging functions, between virtual machines and the physical network; the possibility to dynamically migrate virtual machines across virtualization servers in the data-center network (DCN); a more efficient exploitation of the large path diversity by means of multipath forwarding protocols. In this paper, we investigate the impact of these novel features in DCN optimization by providing a comprehensive mathematical formulation and a repeated matching heuristic for its resolution. We show, in particular, how virtual bridging and multipath forwarding impact common DCN optimization goals, traffic engineering (TE) and energy efficiency (EE), and assess their utility in the various cases of four different DCN topologies. We show that virtual bridging brings a high performance gain when TE is the primary goal and should be deactivated when EE becomes important. Moreover, we show that multipath forwarding can bring relevant gains only when EE is the primary goal and virtual bridging is not enabled.

Computing, 2018

Network virtualization is widely considered as a mainstay for overcoming the Internet's ossification problem, and virtual network embedding (VNE) is a critical issue. Over recent years, growing energy costs and increased ecological awareness have stimulated the interest in reducing energy consumption by Internet service providers (ISP). Dependability is also an important requirement, as it involves metrics such as reliability and availability, which directly impact quality of service (QoS). Prior works on virtual network embedding have focused mainly on maximizing revenue for Internet service providers (ISPs), and they did not consider energy consumption and dependability metrics jointly in the mapping. This paper presents an energy-efficient mapping of dependable virtual networks. The approach considers a problem formulation that concomitantly takes into account energy consumption and availability constraints for virtual network embedding problem, and an algorithm based on Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic is adopted. The algorithm utilizes B Victor Lira