Joint Energy Efficient and QoS-aware Path Allocation and VNF Placement for Service Function Chaining

Service Function Chaining (SFC) allows the forwarding of a traffic flow along a chain of Virtual Network Functions (VNFs, e.g., IDS, firewall, and NAT). Software Defined Networking (SDN) solutions can be used to support SFC reducing the management complexity and the operational costs. One of the most critical issues for the service and network providers is the reduction of energy consumption, which should be achieved without impact to the quality of services. In this paper, we propose a novel resource (re)allocation architecture which enables energy-aware SFC for SDN-based networks. To this end, we model the problems of VNF placement, allocation of VNFs to flows, and flow routing as optimization problems. Thereafter, heuristic algorithms are proposed for the different optimization problems, in order find near-optimal solutions in acceptable times. The performance of the proposed algorithms are numerically evaluated over a real-world topology and various network traffic patterns. The results confirm that the proposed heuristic algorithms provide near optimal solutions while their execution time is applicable for real-life networks.Comment: Extended version of submitted paper - v7 - July 201

Software-Defined Cloud Computing: Architectural Elements and Open Challenges

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi, Indi

A Framework for QoS-aware Execution of Workflows over the Cloud

The Cloud Computing paradigm is providing system architects with a new powerful tool for building scalable applications. Clouds allow allocation of resources on a "pay-as-you-go" model, so that additional resources can be requested during peak loads and released after that. However, this flexibility asks for appropriate dynamic reconfiguration strategies. In this paper we describe SAVER (qoS-Aware workflows oVER the Cloud), a QoS-aware algorithm for executing workflows involving Web Services hosted in a Cloud environment. SAVER allows execution of arbitrary workflows subject to response time constraints. SAVER uses a passive monitor to identify workload fluctuations based on the observed system response time. The information collected by the monitor is used by a planner component to identify the minimum number of instances of each Web Service which should be allocated in order to satisfy the response time constraint. SAVER uses a simple Queueing Network (QN) model to identify the optimal resource allocation. Specifically, the QN model is used to identify bottlenecks, and predict the system performance as Cloud resources are allocated or released. The parameters used to evaluate the model are those collected by the monitor, which means that SAVER does not require any particular knowledge of the Web Services and workflows being executed. Our approach has been validated through numerical simulations, whose results are reported in this paper

Quality of service modeling for green scheduling in Clouds

International audienceBest known Cloud providers propose services under constraints of Service Level Agreement (SLA) definitions.The SLAs are composed of different Quality of Service (QoS) rules promised by the provider. Thus, the QoSin Clouds becomes more and more important. Precise definitions and metrics have to be explained. Thisarticle proposes an overview of Cloud QoS parameters as well as their classification, but also it defines usablemetrics to evaluate QoS parameters. Moreover, the defined QoS metrics are measurable and reusable in anyscheduling approach for Clouds. Energy consumption is an inherent objective in Cloud Computing, thus, it isalso considered. For evaluation purposes, two uncommon QoS parameters: Dynamism and Robustness are takeninto account in different Cloud virtual machines scheduling approaches. Validation is done through comparisonof common scheduling algorithms, including a genetic algorithm (GA), in terms of QoS parameters evolutionin time. Simulation results have shown that including various QoS parameters allows a deeper schedulingalgorithms analysi

QVIA-SDN: Towards QoS-Aware Virtual Infrastructure Allocation on SDN-based Clouds

International audienceVirtual Infrastructures (VIs) emerged as a potential solution for network evolution and cloud services provisioning on the Internet. Deploying VIs, however, is still challenging mainly due to a rigid management of networking resources. By splitting control and data planes, Software-Defined Networks (SDN) enable custom and more flexible management, allowing for reducing data center usage , as well as providing mechanisms to guarantee bandwidth and latency control on switches and endpoints. However, reaping the benefits of SDN for VI embedding in cloud data centers is not trivial. Allocation frameworks require combined information from the control plan (e.g., isolation policies, flow identification) and data (e.g., storage capacity, flow table configuration) to find a suitable solution. In this context, the present work proposes a mixed integer programming formulation for the VI allocation problem that considers the main challenges regarding SDN-based cloud data centers. Some constraints are then relaxed resulting in a linear program, for which a heuristic is introduced. Experimental results of the mechanism, termed as QVIA-SDN, highlight that an SDN-aware allocation solution can reduce the data center usage and improve the quality-of-service perceived by hosted tenants