1,383 research outputs found
An Algorithm for Network and Data-aware Placement of Multi-Tier Applications in Cloud Data Centers
Today's Cloud applications are dominated by composite applications comprising
multiple computing and data components with strong communication correlations
among them. Although Cloud providers are deploying large number of computing
and storage devices to address the ever increasing demand for computing and
storage resources, network resource demands are emerging as one of the key
areas of performance bottleneck. This paper addresses network-aware placement
of virtual components (computing and data) of multi-tier applications in data
centers and formally defines the placement as an optimization problem. The
simultaneous placement of Virtual Machines and data blocks aims at reducing the
network overhead of the data center network infrastructure. A greedy heuristic
is proposed for the on-demand application components placement that localizes
network traffic in the data center interconnect. Such optimization helps
reducing communication overhead in upper layer network switches that will
eventually reduce the overall traffic volume across the data center. This, in
turn, will help reducing packet transmission delay, increasing network
performance, and minimizing the energy consumption of network components.
Experimental results demonstrate performance superiority of the proposed
algorithm over other approaches where it outperforms the state-of-the-art
network-aware application placement algorithm across all performance metrics by
reducing the average network cost up to 67% and network usage at core switches
up to 84%, as well as increasing the average number of application deployments
up to 18%.Comment: Submitted for publication consideration for the Journal of Network
and Computer Applications (JNCA). Total page: 28. Number of figures: 15
figure
Balancing the Migration of Virtual Network Functions with Replications in Data Centers
The Network Function Virtualization (NFV) paradigm is enabling flexibility,
programmability and implementation of traditional network functions into
generic hardware, in form of the so-called Virtual Network Functions (VNFs).
Today, cloud service providers use Virtual Machines (VMs) for the instantiation
of VNFs in the data center (DC) networks. To instantiate multiple VNFs in a
typical scenario of Service Function Chains (SFCs), many important objectives
need to be met simultaneously, such as server load balancing, energy efficiency
and service execution time. The well-known \emph{VNF placement} problem
requires solutions that often consider \emph{migration} of virtual machines
(VMs) to meet this objectives. Ongoing efforts, for instance, are making a
strong case for migrations to minimize energy consumption, while showing that
attention needs to be paid to the Quality of Service (QoS) due to service
interruptions caused by migrations. To balance the server allocation strategies
and QoS, we propose using \emph{replications} of VNFs to reduce migrations in
DC networks. We propose a Linear Programming (LP) model to study a trade-off
between replications, which while beneficial to QoS require additional server
resources, and migrations, which while beneficial to server load management can
adversely impact the QoS. The results show that, for a given objective, the
replications can reduce the number of migrations and can also enable a better
server and data center network load balancing
- …