Memetic Multi-Objective Particle Swarm Optimization-Based Energy-Aware Virtual Network Embedding

In cloud infrastructure, accommodating multiple virtual networks on a single physical network reduces power consumed by physical resources and minimizes cost of operating cloud data centers. However, mapping multiple virtual network resources to physical network components, called virtual network embedding (VNE), is known to be NP-hard. With considering energy efficiency, the problem becomes more complicated. In this paper, we model energy-aware virtual network embedding, devise metrics for evaluating performance of energy aware virtual network-embedding algorithms, and propose an energy aware virtual network-embedding algorithm based on multi-objective particle swarm optimization augmented with local search to speed up convergence of the proposed algorithm and improve solutions quality. Performance of the proposed algorithm is evaluated and compared with existing algorithms using extensive simulations, which show that the proposed algorithm improves virtual network embedding by increasing revenue and decreasing energy consumption.Comment: arXiv admin note: text overlap with arXiv:1504.0684

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

Energy Efficient Resource Allocation for Virtual Network Services with Dynamic Workload in Cloud Data Centers

Title from PDF of title page, viewed on March 21, 2016Dissertation advisor: Baek-Young ChoiVitaIncludes bibliographical references (pages 126-143)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2016With the rapid proliferation of cloud computing, more and more network services and applications are deployed on cloud data centers. Their energy consumption and green house gas emissions have significantly increased. Some efforts have been made to control and lower energy consumption of data centers such as, proportional energy consuming hardware, dynamic provisioning, and virtualization machine techniques. However, it is still common that many servers and network resources are often underutilized, and idle servers spend a large portion of their peak power consumption. Network virtualization and resource sharing have been employed to improve energy efficiency of data centers by aggregating workload to a few physical nodes and switch the idle nodes to sleep mode. Especially, with the advent of live migration, a virtual node can be moved from one physical node to another physical node without service disrup tion. It is possible to save more energy by shrinking virtual nodes to a small set of physical nodes and turning the idle nodes to sleep mode when the service workload is low, and expanding virtual nodes to a large set of physical nodes to satisfy QoS requirements when the service workload is high. When the service provider explicates the desired virtual network including a specific topology, and a set of virtual nodes with certain resource demands, the infrastructure provider computes how the given virtual network is embedded to its operated data centers with minimum energy consumption. When the service provider only gives some description about the network service and the desired QoS requirements, the infrastructure provider has more freedom on how to allocate resources for the network service. For the first problem, we consider the evolving workload of the virtual networks or virtual applications and residual resources in data centers, and build a novel model of energy efficient virtual network embedding (EE-VNE) in order to minimize energy usage in the physical network consists of multiple data centers. In this model, both operation cost for executing network services’ task and migration cost for the live migrations of virtual nodes are counted toward the total energy consumption. In addition, rather than random generated physical network topology, we use practical assumption about physical network topology in our model. Due to the NP-hardness of the proposed model, we develop a heuristic algorithm for virtual network scheduling and mapping. In doing so, we specifically take the expected energy consumption at different times, virtual network operation and future migration costs, and a data center architecture into consideration. Our extensive evaluation results showthatouralgorithmcouldreduceenergyconsumptionupto40%andtakeuptoa57% higher number of virtual network requests over other existing virtual mapping schemes. However, through comparison with CPLEX based exact algorithm, we identify that there is still a gap between the heuristic solution and the optimal solution. Therefore, after investigation other solutions, we convert the origin EE-VNE problem to an Ant Colony Optimization (ACO) problem by building the construction model and presenting the transition probability formula. Then, ACO based algorithm has been adapted to solve the ACO-EE-VNE problem. In addition, we reduce the space complexity of ACO-EE VNE by developing a novel way to track and update the pheromone. For the second problem, we design a framework to dynamically allocate resources for a network service by employing container based virtual nodes. In the framework,each network service would have a pallet container and a set of execution containers. The pal let container requests resource based on certain strategy, creates execution containers with assigned resources and manage the life cycle of the containers; while the execution containers execute the assigned job for the network service. Formulations are presented to optimize resource usage efficiency and save energy consumption for network services with dynamic workload, and a heuristic algorithm is proposed to solve the optimization problem. Our numerical results show that container based resource allocation provide more flexible and saves more cost than virtual service deployment with fixed virtual machines and demands. In addition, we study the content distribution problem with joint optimization goal and varied size of contents in cloud storage. Previous research on content distribution mainly focuses on reducing latency experienced by content customers. A few recent studies address the issue of bandwidth usage in CDNs, as the bandwidth consumption is an important issue due to its relevance to the cost of content providers. However, few researches consider both bandwidth consumption and delay performance for the content providers that use cloud storages with limited budgets, which is the focus of this study. We develop an efficient light-weight approximation algorithm toward the joint optimization problem of content placement. We also conduct the analysis of its theoretical complexities. The performance bound of the proposed approximation algorithm exhibits a much better worst case than those in previous studies. We further extend the approximate algorithm into a distributed version that allows it to promptly react to dynamic changes in users’ interests. The extensive results from both simulations and Planetlab experiments exhibit that the performance is near optimal for most of the practical conditions.Introduction -- Related work -- Energy efficient virtual network embedding for green data centers using data center topology and future migration -- Ant colony optimization based energy efficient virtual network embedding -- Energy aware container based resource allocation for virtual services in green data centers -- Achieving optimal content delivery using cloud storage -- Conclusions and future wor