Online VNF Scaling in Datacenters

Network Function Virtualization (NFV) is a promising technology that promises to significantly reduce the operational costs of network services by deploying virtualized network functions (VNFs) to commodity servers in place of dedicated hardware middleboxes. The VNFs are typically running on virtual machine instances in a cloud infrastructure, where the virtualization technology enables dynamic provisioning of VNF instances, to process the fluctuating traffic that needs to go through the network functions in a network service. In this paper, we target dynamic provisioning of enterprise network services - expressed as one or multiple service chains - in cloud datacenters, and design efficient online algorithms without requiring any information on future traffic rates. The key is to decide the number of instances of each VNF type to provision at each time, taking into consideration the server resource capacities and traffic rates between adjacent VNFs in a service chain. In the case of a single service chain, we discover an elegant structure of the problem and design an efficient randomized algorithm achieving a e/(e-1) competitive ratio. For multiple concurrent service chains, an online heuristic algorithm is proposed, which is O(1)-competitive. We demonstrate the effectiveness of our algorithms using solid theoretical analysis and trace-driven simulations.Comment: 9 pages, 4 figure

Flexible coordination techniques for dynamic cloud service collaboration

The provision of individual, but also composed services is central in cloud service provisioning. We describe a framework for the coordination of cloud services, based on a tuple‐space architecture which uses an ontology to describe the services. Current techniques for service collaboration offer limited scope for flexibility. They are based on statically describing and compositing services. With the open nature of the web and cloud services, the need for a more flexible, dynamic approach to service coordination becomes evident. In order to support open communities of service providers, there should be the option for these providers to offer and withdraw their services to/from the community. For this to be realised, there needs to be a degree of self‐organisation. Our techniques for coordination and service matching aim to achieve this through matching goal‐oriented service requests with providers that advertise their offerings dynamically. Scalability of the solution is a particular concern that will be evaluated in detail

An Elasticity-aware Governance Platform for Cloud Service Delivery

In cloud service provisioning scenarios with a changing demand from consumers, it is appealing for cloud providers to leverage only a limited amount of the virtualized resources required to provide the service. However, it is not easy to determine how much resources are required to satisfy consumers expectations in terms of Quality of Service (QoS). Some existing frameworks provide mechanisms to adapt the required cloud resources in the service delivery, also called an elastic service, but only for consumers with the same QoS expectations. The problem arises when the service provider must deal with several consumers, each demanding a different QoS for the service. In such an scenario, cloud resources provisioning must deal with trade-offs between different QoS, while fulfilling these QoS, within the same service deployment. In this paper we propose an elasticity-aware governance platform for cloud service delivery that reacts to the dynamic service load introduced by consumers demand. Such a reaction consists of provisioning the required amount of cloud resources to satisfy the different QoS that is offered to the consumers by means of several service level agreements. The proposed platform aims to keep under control the QoS experienced by multiple service consumers while maintaining a controlled cost.Junta de Andalucía P12--TIC--1867Ministerio de Economía y Competitividad TIN2012-32273Agencia Estatal de Investigación TIN2014-53986-RED

Dynamic Resource Management in Clouds: A Probabilistic Approach

Dynamic resource management has become an active area of research in the Cloud Computing paradigm. Cost of resources varies significantly depending on configuration for using them. Hence efficient management of resources is of prime interest to both Cloud Providers and Cloud Users. In this work we suggest a probabilistic resource provisioning approach that can be exploited as the input of a dynamic resource management scheme. Using a Video on Demand use case to justify our claims, we propose an analytical model inspired from standard models developed for epidemiology spreading, to represent sudden and intense workload variations. We show that the resulting model verifies a Large Deviation Principle that statistically characterizes extreme rare events, such as the ones produced by "buzz/flash crowd effects" that may cause workload overflow in the VoD context. This analysis provides valuable insight on expectable abnormal behaviors of systems. We exploit the information obtained using the Large Deviation Principle for the proposed Video on Demand use-case for defining policies (Service Level Agreements). We believe these policies for elastic resource provisioning and usage may be of some interest to all stakeholders in the emerging context of cloud networkingComment: IEICE Transactions on Communications (2012). arXiv admin note: substantial text overlap with arXiv:1209.515

Prediction Based Efficient Resource Provisioning and Its Impact on QoS Parameters in the Cloud Environment

The purpose of this paper is to provision the on demand resources to the end users as per their need using prediction method in cloud computing environment. The provisioning of virtualized resources to cloud consumers according to their need is a crucial step in the deployment of applications on the cloud. However, the dynamical management of resources for variable workloads remains a challenging problem for cloud providers. This problem can be solved by using a prediction based adaptive resource provisioning mechanism, which can estimate the upcoming resource demands of applications. The present research introduces a prediction based resource provisioning model for the allocation of resources in advance. The proposed approach facilitates the release of unused resources in the pool with quality of service (QoS), which is defined based on prediction model to perform the allocation of resources in advance. In this work, the model is used to determine the future workload prediction for user requests on web servers, and its impact toward achieving efficient resource provisioning in terms of resource exploitation and QoS. The main contribution of this paper is to develop the prediction model for efficient and dynamic resource provisioning to meet the requirements of end users

Little Boxes: A Dynamic Optimization Approach for Enhanced Cloud Infrastructures

The increasing demand for diverse, mobile applications with various degrees of Quality of Service requirements meets the increasing elasticity of on-demand resource provisioning in virtualized cloud computing infrastructures. This paper provides a dynamic optimization approach for enhanced cloud infrastructures, based on the concept of cloudlets, which are located at hotspot areas throughout a metropolitan area. In conjunction, we consider classical remote data centers that are rigid with respect to QoS but provide nearly abundant computation resources. Given fluctuating user demands, we optimize the cloudlet placement over a finite time horizon from a cloud infrastructure provider's perspective. By the means of a custom tailed heuristic approach, we are able to reduce the computational effort compared to the exact approach by at least three orders of magnitude, while maintaining a high solution quality with a moderate cost increase of 5.8% or less