Q*: Energy and delay-efficient dynamic queue management in TCP/IP virtualized data centers

Modern data centers must provide performance assurance for complex system software such as multi-tier web applications. In addition, the power consumption of data centers needs to be minimized to reduce operating costs and avoid system overheating. Various power-efficient performance management strategies have been proposed based on dynamic voltage and frequency scaling (DVFS). Virtualization technologies have also made it possible to consolidate multiple virtual machines (VMs) onto a smaller number of active physical servers for even greater power savings, but at the cost of a higher overhead. This paper proposes a performance-controlled power optimization solution for virtualized data centers with multi-tier applications. While existing work relies on either DVFS or server consolidation in a separate manner, our solution utilizes both strategies for maximized power savings by integrating feedback control with optimization strategies. At the application level, a multi-input-multi-output controller is designed to achieve the desired performance for applications spanning multiple VMs, on a short time scale, by reallocating the CPU resources and DVFS. At the data center level, a power optimizer is proposed to incrementally consolidate VMs onto the most power-efficient servers on a longer time scale. Empirical results on a hardware testbed demonstrate that our solution can effectively achieve performance-assured power savings. Extensive simulation results, based on a trace file of 5,415 real servers, demonstrate the efficacy of our solution in large-scale data centers.

In the presence of latency sensitive geo-distributed applications, users require fast service for their queries. Cloud computing provides physical servers from its data center in order to process user requests. The cloud data center consumes a huge amount of energy due to lack of management of the data center servers as the container-based service consolidation is a nontrivial task. Since the containers require less resource footprint, consolidating it in servers might make resource availability sparse. In order to reduce the energy consumption of the cloud data center, we have proposed a green container-based consolidation of the services so that the maximum number of servers can be put into idle mode without affecting the application quality of experience. The service consolidation problem has been formulated as an optimization problem considering minimization of total energy consumption of the data center as the objective, and an algorithm named Energy Aware Service consolidation using baYesian optimization (EASY) has been proposed to solve the optimization. We have evaluated the EASY algorithm in simulation using python. The experimental results have shown that EASY improves the total energy consumption of the data centers. This improvement comes at the cost of a small increase of service response time as there exists a trade-off between energy consumption and service response time.

Today's data centers offer IT services mostly hosted on dedicated physical servers. Server virtualization provides a technical means for server consolidation. Thus, multiple virtual servers can be hosted on a single server. Server consolidation describes the process of combining the workloads of several different servers on a set of target servers. We focus on server consolidation with dozens or hundreds of servers, which can be regularly found in enterprise data centers. Cost saving is among the key drivers for such projects. This paper presents decision models to optimally allocate source servers to physical target servers while considering real-world constraints. Our central model is proven to be an NP-hard problem. Therefore, besides an exact solution method, a heuristic is presented to address large-scale server consolidation projects. In addition, a preprocessing method for server load data is introduced allowing for the consideration of quality-of-service levels. Extensive experiments were conducted based on a large set of server load data from a data center provider focusing on managerial concerns over what types of problems can be solved. Results show that, on average, server savings of 31 percent can be achieved only by taking cycles in the server workload into account.

Cloud computing has been a cost-efficient paradigm for deploying various applications in datacenters in recent years. Therefore, efficient provisioning for virtual data center (VDC) requests from different service providers (SPs) over physical data centers plays a vital role in improving the quality of service (QoS) and reducing the operational cost of SPs. Therefore, a significant attention has been paid for the VDC provisioning problem. However, few approaches have been proposed for the problem of reliable VDC embedding across multiple data centers, as most of them only consider the problem of VDC mapping within a single data center. In this paper, we study the problem of QoS-aware VDC provisioning across multiple data centers, such that the total bandwidth consumption in the inter-data center backbone network is minimized while satisfying the reliability requirement of each VDC request. We formulate this problem as a mathematical optimization problem by using integer linear programming (ILP) and propose an efficient heuristic algorithm called reliable VDC embedding (RVDCE) algorithm to solve this NP-hard problem. The simulation results show that the proposed algorithm performs better in terms of blocking ratio, CPU resource consumption, and bandwidth consumption of backbone network than the existing solution. In addition, this paper has also incorporated integrated security to minimize security vulnerabilities seen in other similar approaches. Apart from demonstrating how to resolve security challenges in our VDC proposal, cost calculations have been implemented to demonstrate the robustness, resiliency, validity, and effectiveness of the VDC provisioning solution for cloud computing.

A virtual data center (VDC) is a combination of interconnected virtual servers hosted on a physical data center that hosts multiple such VDCs. This enables efficient sharing of the data center's resources while handling dynamic resource requirements of the clients. The SecondNet architecture (Guo et al. in Proceedings of ACMSIGCOMM conference on data communication, Barcelona, pp 63---74, 2009) realizes this VDC concept and includes a centralized VDC resource-mapping (virtual to physical) algorithm. Fault tolerance is an important requirement in data center-based services, in order to increase reliability and availability. In this paper, we propose a fault tolerance mechanism to handle server failures by efficiently migrating the virtual machines (VMs) hosted on the failed server to a new location. Using our mechanism, it is shown that recovery from all the faults is possible, even for a server utilization of 90 %. In order to reduce the impact of server failures on the VDCs hosted in the data center, we then present a new load balancing scheme based on clustering that efficiently allocates the VDCs on the data center. Using this scheme, we were able to reduce the affected number of VMs per server failure by 63 %, in case of a BCube network of size 625 nodes, and by 86 %, in case of a BCube network of size 1,296 nodes.

High energy consumption in data center has become a key problem, and the Carbon (CO $$_2$$ ) emissions from data center have serious impacts on environment. To improve traditional performance-energy model and minimize energy consumption in cloud data center, an energy-efficient and QoS-aware (EEQoS) model is developed for virtual resources consolidation. The EEQoS model is designed to investigate the trade-off between energy and QoS. The user’s satisfaction degree and Euclidean distance of resources are also respectively applied to the QoS model and power model. In addition, this EEQoS model is combined with the particle swarm optimization algorithm by setting the power consumption per QoS value as the objective function to consolidate virtual resources in data center. Compared with traditional power model, the proposed model reduces response time by 27.2%, cost by 31.4%, SLA violations by 40.5% and improves throughput by 13.5% on average while increasing less than 3.8% energy consumption during the whole test period.

The growth of energy consumption in data centers is becoming one of the significant problems all over the world. The power usage of IT devices and air-conditioning (AC) units accounts for up to three-quarters of total energy consumption in typical data centers. This study discusses the effectiveness of using shutter-controlled air flow management to control cold air flow into the servers. It can impact the power consumption of both, the servers and the AC units. A raised floor data center has two types of aisles - cold and hot. In most cases, excessive volumes of cold air are pumped into the servers using cold aisles. We are proposing the utilization of a shutter to control the air flow and reduce wastage while preventing damage due to increased CPU temperature. The shutter is intended to control the air flow and maintain the temperature appropriately according to the workload of the servers. We evaluated the proposed system for controlling the flow of air into the servers. The results show that cold air volumes can be reduced by up to 92% while maintaining appropriate control of CPU temperature.

Using Model Predictive Control in Data Centers for Dynamic Server Provisioning

Constraint-based techniques can solve challenging problems arising in highly diverse applications. This paper considers the problem of virtual data center (VDC) allocation, an important, emerging challenge for modern data center operators. To address this problem, we introduce Netsolver, a system for VDC allocation that is based on constraint solving. Netsolver represents a major improvement over existing approaches: it is sound, complete, and scalable, providing support for end-to-end, multi-path bandwidth guarantees across all the layers of hosting infrastructure, from servers to top-of-rack switches to aggregation switches to access routers. Netsolver scales to realistic data center sizes and VDC topologies, typically requiring just seconds to allocate VDCs of 5–15 virtual machines to physical data centers with 1000+ servers, maintaining this efficiency even when the data center is nearly saturated. In many cases, Netsolver can allocate 150%−300% as many total VDCs to the same physical data center as previous methods. Finally, we show how Netsolver can be extended with additional optimization constraints, such as VM affinity and hotspot minimization, demonstrating the flexibility of our approach.The performance and flexibility of Netsolver are made possible by our formalization of the VDC allocation problem in terms of multi-commodity flows, and the corresponding efficient handling of network flow problems in the underlying constraint solvers. This shows the importance of supporting flow-based constraints, which are more mature in ILP- vs. SMT-based constraint solving.

Water-energy tradeoffs in data centers: A case study in hot-arid climates

Performance-controlled server consolidation for virtualized data centers with multi-tier applications

With the rapid grow of the volume of data and internet application, as an efficient and promising infrastructure, data center has been widely deployed .data center provide a variety of perform for network services, applications such as video stream, cloud compute and so on. All this services and applications call for volume, compute, bandwidth, and latency. Existing data centers lacks enough flexible so they provide poor support in QOS, deployability, manageability, and defense when facing attacks. Virtualized data centers are a good solution to these problems. Compared to existing data centers, virtualized data centers do better in resource utilization, scalability, and flexibility.

With ubiquitous presence and perpetual growth of software applications and computing devices connected to the Internet, organizations and businesses are faced with increasing challenges in operating and maintaining technologies that support their IT infrastructure. Among the goals of technology management are cost-effectiveness, energy efficiency, and secure management of information technologies, which are expected to produce maximum utility value. Virtualization has been proposed as a promising solution to the challenges facing today's IT managers. From virtualized servers to virtualized networks to virtual data centers, various virtualized systems have been explored as means of easing IT operation challenges. One of the major benefits of virtualization is the possibility of building a network of virtual machines and storage infrastructure between the virtual data center and external physical networks. A data center involves a large number of networking devices such as switches, routers, and firewalls. With traditional networking, managing networks in data centers is a complex task due to the closed architecture and vertical integration among the networking devices. Software-defined networking (SDN) is a new architectural paradigm, enabling flexible and dynamic management of networks and associated services. This paper presents the benefits of virtualization in general, the advantages of adopting virtualization in data centers, a practical implementation of virtual data centers on OpenStack, and implementing SDN to manage networks in the virtual data center.

Maximizing the number of virtual infrastructures spawned out of a data center is a prime concern of cloud service providers to improve their revenue and the customers' quality-of-experience. Optimal placement of topology sensitive virtual data centers on its physical counterparts increases the resource utilization and thereby the revenue. However, provisioning, scaling, and de-provisioning of virtual data centers, over the time, leaves the cloud data center fragmented. Embedding new virtual data centers on a fragmented data center would require migration of virtual machines and virtual network, already mapped to it. However, these migrations are costly in terms of the resource usage and possible violations of the terms of service level agreement. The costs of these migrations associated with a new embedding should be controlled to increase the profit. In this paper, the problem of finding virtual machine migrations with minimum cost is formally defined and two meta-heuristic solutions are proposed for it. We consider the memory size, the rate of page dirtying, the class of application running on the VM, and the bandwidth of the migration path for modeling the cost of migration. The objective is to find a pattern of virtual machine migrations so that the total cost for embedding a new virtual data center is minimized. Experimental results show that the proposed technique can reduce the average migration time upto 30% and the penalty for corresponding service level agreement violation by 200%.

Data centers have been gaining increasing interest due to their capacity to store a large amount of information; as a result they have become the main support for the deployment of cloud computing. Currently, data centers face performance problems, such as: lack of guaranteed quality of service, security risks, management complexity and inflexibility. Virtualized data centers are an emerging tool to address these issues. However, their implementation brings a major challenge: How to optimally allocate the resource demands of virtual data centers on top of the physical infrastructure, so that the data center operating costs are reduced, improving the revenue for infrastructure providers and fulfilling the agreements of quality of service with the end users? This challenge is known as the Virtual Data Center Embedding problem. This paper presents survey of current research in this problem, and also proposes a taxonomic classification of the main approaches to solve it. Finally, opportunities for future research in resource allocation for virtualized data centers are discussed.