Just In Time Architecture (JITA) for dynamically composable data centers

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.

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.

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.

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.

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.

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.

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.

This study examines data center characteristics, loads, control systems, and technologies to identify demand response (DR) and automated DR (Open Auto-DR) opportunities and challenges. The study was performed in collaboration with technology experts, industrial partners, and data center facility managers and existing research on commercial and industrial DR was collected and analyzed. The results suggest that data centers, with significant and rapidly growing energy use, have significant DR potential. Because data centers are highly automated, they are excellent candidates for Open Auto-DR. 'Non-mission-critical' data centers are the most likely candidates for early adoption of DR. Data center site infrastructure DR strategies have been well studied for other commercial buildings; however, DR strategies for information technology (IT) infrastructure have not been studied extensively. The largest opportunity for DR or load reduction in data centers is in the use of virtualization to reduce IT equipment energy use, which correspondingly reduces facility cooling loads. DR strategies could also be deployed for data center lighting, and heating, ventilation, and air conditioning. Additional studies and demonstrations are needed to quantify benefits to data centers of participating in DR and to address concerns about DR's possible impact on data center performance or quality of service and equipment life span.

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%.

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.

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.

Data centers are often under-utilized due to over-provisioning as well as time-varying resource demands of typical enterprise applications. One approach to increase resource utilization is to consolidate applications in a shared infrastructure using virtualization. Meeting application-level quality of service (QoS) goals becomes a challenge in a consolidated environment as application resource needs differ. Furthermore, for multi-tier applications, the amount of resources needed to achieve their QoS goals might be different at each tier and may also depend on availability of resources in other tiers. In this paper, we develop an adaptive resource control system that dynamically adjusts the resource shares to individual tiers in order to meet application-level QoS goals while achieving high resource utilization in the data center. Our control system is developed using classical control theory, and we used a black-box system modeling approach to overcome the absence of first principle models for complex enterprise applications and systems. To evaluate our controllers, we built a testbed simulating a virtual data center using Xen virtual machines. We experimented with two multi-tier applications in this virtual data center: a two-tier implementation of RUBiS, an online auction site, and a two-tier Java implementation of TPC-W. Our results indicate that the proposed control system is able to maintain high resource utilization and meets QoS goals in spite of varying resource demands from the applications.

Recent research trends exhibit a growing imbalance between the demands of tenants' software applications and the provisioning of hardware resources. Misalignment of demand and supply gradually hinders workloads from being efficiently mapped to fixed-sized server nodes in traditional data centers. The incurred resource holes not only lower infrastructure utilization but also cripple the capability of a data center for hosting large-sized workloads. This deficiency motivates the development of a new rack-wide architecture referred to as the composable system. The composable system transforms traditional server racks of static capacity into a dynamic compute platform. Specifically, this novel architecture aims to link up all compute components that are traditionally distributed on traditional server boards, such as central processing unit (CPU), random access memory (RAM), storage devices, and other application-specific processors. By doing so, a logically giant compute platform is created and this platform is more resistant against the variety of workload demands by breaking the resource boundaries among traditional server boards. In this paper, we introduce the concepts of this reconfigurable architecture and design a framework of the composable system for cloud data centers. We then develop mathematical models to describe the resource usage patterns on this platform and enumerate some types of workloads that commonly appear in data centers. From the simulations, we show that the composable system sustains nearly up to 1.6 times stronger workload intensity than that of traditional systems and it is insensitive to the distribution of workload demands. This demonstrates that this composable system is indeed an effective solution to support cloud data center services.

Cloud computing is the use of computing assets or resources (hardware and software) that are consigned as a service over the internet. If cloud computing environment is deployed in a data centre than users can store their data in the cloud centers (i.e. Cloud storage) using storage as a service. When data centre provides storage as a service, the capacity of data centre is biggest challenge. The management required to maintain the capacity at the data centre result in increase of overhead and cost of service on cloud. Therefore to provide scalable and elastic cloud storage to the users or consumers virtualization of data centers is required. In this work, to manage the capacity of these virtual data centers efficiently, it is proposed to do the scheduling of resources over the data centers. The results of the implementation of the proposed methodology not only provides efficient capacity management but also reduces communication overheads and minimize computational cost of the cloud.

Spatio-temporal thermal-aware job scheduling to minimize energy consumption in virtualized heterogeneous data centers