Fault tolerance mechanisms for virtual data center architectures

Virtual Data Center (VDC) embedding has drawn significant attention recently because of the growing need for efficient and flexible means of Data Center (DC) resource allocation. Existing studies on VDC embedding mainly focus on improving DCs' resource utilization. However, an important problem that has not been considered in VDC embedding solutions is the creation of hot spots by excessive heat dissipation and hot air generation from racks in DCs, which have significant adverse effects on energy consumption of the cooling system and IT equipment lifespan. To address this issue, we propose a temperature-aware VDC embedding scheme to avoid hot spots by minimizing the maximum temperature of hot air drawn to each rack. Meanwhile, we also aim to reduce the total power consumption of IT equipment in this scheme. A Mixed Integer Linear Programming (MILP) model and a heuristic algorithm are developed to implement the proposed VDC embedding scheme. Numerical results show that the proposed temperature-aware embedding scheme can significantly outperform a load-balanced embedding scheme in terms of maximum rack temperature, total power consumption of IT equipment, and VDC rejection ratio. Compared with a scheme that focuses only on maximizing IT resource utilization, which we refer to as IT-only energy-efficient scheme, our proposed scheme can significantly reduce maximum rack temperature and VDC rejection ratio while keeping the total power consumption of IT equipment close to that of an IT-only energy-efficient scheme.

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.

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%.

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.

Improved water shower model for greener data centers

Predictable network performance is critical for cloud applications and can be achieved by providing tenants a dedicated virtual data center (VDC) with bandwidth guarantee. Recently, the extended Hose model was applied to the VDC abstraction to characterize the tradeoff between cost and network performance. The acceptability determination problem of a VDC with heterogeneous bandwidth demand was proved to be NP-complete, even in the simple tree topology. In this paper, we investigate the embedding problem for heterogeneous bandwidth VDC in substrate networks of general topology. The embedding problem involves two coupled sub-problems: virtual machine (VM) placement and multipath route assignment. First, we formulate the route assignment problem with linear programming to minimize the maximum link utilization, and provide $K$ -widest path load-balanced routing with controllable splitting paths. Next, we propose a polynomial-time heuristic algorithm, referred to as the perturbation algorithm, for the VM placement. The perturbation algorithm is congestion-aware as it detects the bandwidth bottlenecks in the placement process and then selectively relocates some assigned VMs to eliminate congestion. Simulation results show that our algorithm performs better in comparison with the existing well-known algorithms: first-fit, next-fit, and greedy, and very close to the exponential-time complexity backtracking algorithm in typical data center network architectures. For the tree substrate network, the perturbation algorithm performs better than the allocation-range algorithm. For the homogeneous bandwidth VDC requests, the perturbation algorithm produces a higher success rate than the recently proposed HVC-ACE algorithm. Therefore, it provides a compromised solution between time complexity and network performance.

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.

The lack of performance isolation in multi-tenant datacenters at appliances like middleboxes and storage servers results in volatile application performance. To insulate tenants, we propose giving them the abstraction of a dedicated virtual datacenter (VDC). VDCs encapsulate end-to-end throughput guarantees--specified in a new metric based on virtual request cost--that hold across distributed appliances and the intervening network.We present Pulsar, a system that offers tenants their own VDCs. Pulsar comprises a logically centralized controller that uses new mechanisms to estimate tenants' demands and appliance capacities, and allocates datacenter resources based on flexible policies. These allocations are enforced at end-host hypervisors through multi-resource token buckets that ensure tenants with changing workloads cannot affect others. Pulsar's design does not require changes to applications, guest OSes, or appliances. Through a prototype deployed across 113 VMs, three appliances, and a 40 Gbps network, we show that Pulsar enforces tenants' VDCs while imposing overheads of less than 2% at the data and control plane.

The virtual data center (VDC) is a truly evolutionary computing model, presenting the data center as a service view to a single computer, which virtualizes all hardware and software behind it. Recent variations of the VDC model include grid, autonomic, modular, fabric, and utility center, each with a focus on total cost of ownership benefits. A VDC, which satisfies quality of service (QoS) at guaranteed resource costs, can be extended to manage energy as a first class resource and guarantees energy at predictable costs. This article describes how virtual data centers differ from traditional data centers for energy management and surveys the ecological benefits of this emerging model. Emphasis is on dynamic extensions to VDC virtualization that leverage real-time migration of live, stateful applications.

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.

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.

By employing the virtual machines (VMs) consolidation technique at a virtualized data center, optimal mapping of VMs to physical machines (PMs) can be performed. The type of optimization approach and the policy of detecting the appropriate time to implement the consolidation process are influential in the performance of the consolidation technique. In a majority of researches, the consolidation approach merely focuses on the management of underloaded or overloaded PMs, while a number of VMs could also be in an underload or overload state. Managing an abnormal state of VM results in the postponement of PM getting into an abnormal state as well and affects the implementation time of the consolidation process. For the aim of optimal VM consolidation in this research, a self-adaptive architecture is presented to detect and manage underloaded and overloaded VMs /PMs in reaction to workload changes in the data center. The goal of consolidation process is employing the minimum number of active VMs and PMs, while guaranteeing the quality of service (QoS). Assessment criteria of QoS are two parameters including average number of requests in the PM buffer and average waiting time in the VM. To evaluate these two parameters, a probabilistic model of the data center is proposed by applying the queuing theory. The assessment results of the probabilistic model form a basis for decision-making in the modules of the proposed architecture. Numerical results obtained from the assessment of the probabilistic model via discrete-event simulator under various parameter settings confirm the efficiency of the proposed architecture in achieving the aims of the consolidation process.

More than bin packing: Dynamic resource allocation strategies in cloud data centers

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.

Nowadays cloud computing provides an effective way of implementing infrastructure as a service (IaaS). However virtualized data centers still face many challenges, such as low resource utilization of physical machines (PMs) and imbalanced server loads. Virtual machine (VM) consolidation based on live migration allows administrator to dynamically redeploy VMs into PMs for better resource utilization. Common VM consolidation methods usually focus on one challenge, and pay little attention to others or just ignore them, while effective VM redeployment should make tradeoffs between these challenges, and more importantly, should not let other challenges become worse. On the other hand, since VM live migration leads to performance degradation of applications, consolidation work should control migration cost. In this paper, we provide a manner to comprehensively consider power consumption, load balancing, communication delay and migration cost during VM redeployment. And we formalize VM consolidation as a multiobjective optimization problem, then solve this problem with an improved genetic algorithm. Simulation experiments based on real world workload trace show that compared with single objective optimization approaches our method effectively make tradeoffs between optimized objectives and has better overall performance, which is more practical in real data centers.