17 research outputs found
SDN-based virtual machine management for cloud data centers
Software-Defined Networking (SDN) is an emerging paradigm to logically centralize the network control plane and automate the configuration of individual network elements. At the same time, in Cloud Data Centers (DCs), even though network and server resources converge over the same infrastructure and typically over a single administrative entity, disjoint control mechanisms are used for their respective management. In this paper, we propose a unified server-network control mechanism for converged ICT environments. We present a SDN-based orchestration framework for live Virtual Machine (VM) management where server hypervisors exploit temporal network information to migrate VMs and minimize the network-wide communication cost of the resulting traffic dynamics. A prototype implementation is presented and Mininet is used to evaluate the impact of diverse orchestration algorithms
SDN-based Virtual Machine Management for Cloud Data Centers
Software-Defined Networking (SDN) is an emerging paradigm to logically centralize the network control plane and automate the configuration of individual network elements. At the same time, in Cloud Data Centers (DCs), although network and server resources are collocated and managed by a single administrative entity, disjoint control mechanisms are used for their respective management. In this article, we propose a unified server-network resource management for such converged Information and Communication Technology (ICT) environments. We present a SDN-based orchestration framework for live Virtual Machine (VM) management that exploits temporal network information to migrate VMs and minimize the network-wide communication cost of the resulting traffic dynamics. A prototype implementation is presented, and a Cloud DC testbed is used to evaluate the impact of diverse orchestration algorithms. Our live VM management has been shown to reduce the network-wide communication cost, especially for the high-cost and congestionprone core and aggregation layers of the DC. Our results show an increase in network-wide throughput by over 6 times, as well as over 70% communication cost reduction by migrating less than 50% of the VMs
SDN-based Virtual Machine Management for Cloud Data Centers
Software-Defined Networking (SDN) is an emerging paradigm to logically centralize the network control plane and automate the configuration of individual network elements. At the same time, in Cloud Data Centers (DCs), even though network and server resources converge over the same infrastructure and typically under a single administrative entity, disjoint control mechanisms are used for their respective management. In this paper, we propose a unified server-network control mechanism for converged ICT environments. We present a SDN-based orchestration framework for live Virtual Machine (VM) management where server hypervisors exploit temporal network information to migrate VMs and minimize the network-wide communication cost of the resulting traffic dynamics. A prototype implementation is presented and Mininet is used to evaluate the impact of diverse orchestration algorithms
NetO-App: A Network Orchestration Application for Centralized Network Management in Small Business Networks
Software-defined networking (SDN) is reshaping the networking paradigm.
Previous research shows that SDN has advantages over traditional networks
because it separates the control and data plane, leading to greater flexibility
through network automation and programmability. Small business networks require
flexibility, like service provider networks, to scale, deploy, and self-heal
network infrastructure that comprises of cloud operating systems, virtual
machines, containers, vendor networking equipment, and virtual network
functions (VNFs); however, as SDN evolves in industry, there has been limited
research to develop an SDN architecture to fulfill the requirements of small
business networks. This research proposes a network architecture that can
abstract, orchestrate, and scale configurations based on small business network
requirements. Our results show that the proposed architecture provides enhanced
network management and operations when combined with the network orchestration
application (NetO-App) developed in this research. The NetO-App orchestrates
network policies, automates configuration changes, and manages internal and
external communication between the campus networking infrastructure.Comment: 12 pages, 4 figures, To appear in the Proceedings of the 4th
International Conference on Networks & Communications, 28-29 July 2018,
Sydney, Australi
PLAN: Joint policy- and network-aware VM management for cloud data centers
Policies play an important role in network configuration and therefore in offering secure and high performance services especially over multi-tenant Cloud Data Center (DC) environments. At the same time, elastic resource provisioning through virtualization often disregards policy requirements, assuming that the policy implementation is handled by the underlying network infrastructure. This can result in policy violations, performance degradation and security vulnerabilities. In this paper, we define PLAN, a PoLicy-Aware and Network-aware VM management scheme to jointly consider DC communication cost reduction through Virtual Machine (VM) migration while meeting network policy requirements. We show that the problem is NP-hard and derive an efficient approximate algorithm to reduce communication cost while adhering to policy constraints. Through extensive evaluation, we show that PLAN can reduce topology-wide communication cost by 38 percent over diverse aggregate traffic and configuration policies
Recommended from our members
QoS-Aware dynamic RRH allocation in a Self-Optimised cloud radio access network with RRH proximity constraint
An inefficient utilisation of network resources in a
time-varying traffic environment often leads to load imbalances,
high call-blocking events and degraded Quality of Service
(QoS). This paper optimises the QoS of a Cloud Radio Access
Network (C-RAN) by investigating load balancing solutions.
The dynamic re-mapping ability of C-RAN is exploited to
configure the Remote Radio Heads (RRHs) to proper Base
Band Unit (BBU) sectors in a time-varying traffic environment.
RRH-sector configuration redistributes the network capacity
over a given geographical area. A Self-Optimised Cloud
Radio Access Network (SOCRAN) is considered to enhance
the network QoS by traffic load balancing with minimum
possible handovers in the network. QoS is formulated as an
optimisation problem by defining it as a weighted combination
of new key performance indicators (KPIs) for the number
of blocked users and handovers in the network subject to
RRH sectorisation constraint. A Genetic Algorithm (GA) and
Discrete Particle Swarm Optimisation (DPSO) are proposed
as evolutionary algorithms to solve the optimisation problem.
Computational results based on three benchmark problems
demonstrate that GA and DPSO deliver optimum performance
for small networks, whereas close-optimum is delivered for large
networks. The results of both GA and DPSO are compared to
Exhaustive Search (ES) and K-mean clustering algorithms. The
percentage of blocked users in a medium sized network scenario
is reduced from 10.523% to 0.421% and 0.409% by GA and
DPSO, respectively. Also in a vast network scenario, the blocked
users are reduced from 5.394% to 0.611% and 0.56% by GA
and DPSO, respectively. The DPSO outperforms GA regarding
execution, convergence, complexity, and achieving higher levels
of QoS with fewer iterations to minimise both handovers and
blocked users. Furthermore, a trade-off between two critical
parameters for the SOCRAN algorithm is presented, to achieve
performance benefits based on the type of hardware utilised for
C-RAN
Recommended from our members
Semi-Static Cell Differentiation and Integration with Dynamic BBU-RRH Mapping in Cloud Radio Access Network
Abstract—In this paper, a Self-Organising Cloud Radio Access
Network is proposed, which dynamically adapt to varying network
capacity demands. A load prediction model is considered
for provisioning and allocation of Base Band Units (BBUs) and
Remote Radio Heads (RRHs). The density of active BBUs and
RRHs is scaled based on the concept of cell differentiation and
integration (CDI) aiming efficient resource utilisation without
sacrificing the overall QoS. A CDI algorithm is proposed in
which a semi-static CDI and dynamic BBU-RRH mapping for
load balancing are performed jointly. Network load balance is
formulated as a linear integer-based optimisation problem with
constraints.The semi-static part of CDI algorithm selects proper
BBUs and RRHs for activation/deactivation after a fixed CDI cycle,
and the dynamic part performs proper BBU to RRH mapping
for network load balancing aiming maximum Quality of Service
(QoS) with minimum possible handovers. A Discrete Particle
Swarm Optimisation (DPSO) is developed as an Evolutionary
Algorithm (EA) to solve network load balancing optimisation
problem. The performance of DPSO is tested based on two
problem scenarios and compared to Genetic Algorithm (GA) and
the Exhaustive Search (ES) algorithm. The DPSO is observed to
deliver optimum performance for small-scale networks and near
optimum performance for large-scale networks. The DPSO has
less complexity and is much faster than GA and ES algorithms.
Computational results of a CDI-enabled C-RAN demonstrate
significant throughput improvement compared to a fixed C-RAN,
i.e., an average throughput increase of 45.53% and 42.102%, and
an average blocked users reduction of 23.149%, and 20.903% is
experienced for Proportional Fair (PF) and Round Robin (RR)
schedulers, respectivel