Virtualized Data Centers Research Articles

The Effect of LRB Parameters on Structural Responses for Blast and Seismic Loads

In the present paper, passive control technique such as base isolation system is studied under earthquake ground motions and underground blast-induced vibrations. The performance of the lead rubber bearing (LRB), idealized as Bouc–Wen model in mitigating the structural responses of a five-storey building model, is investigated. The earthquake ground motions are selected from ground motion database available on the portal COSMOS Virtual Data Centre, whereas the underground blast is modeled as an exponential decaying function as prescribed by Carvalho and Battista (Proc Inst Civ Eng Struct Build 156(3):243–253, 2003). The aim of the study is to analyze the effect of isolation parameters such as damping ratio, yield strength, post-yield stiffness ratio and yield displacement on the structural responses of the base-isolated building. Newmark’s step-by-step integration method is adopted to evaluate the structural responses of the building. It is observed that the LRB is very effective in reducing the structural accelerations and storey drifts induced in the building due to ground-induced vibration. The comparison of results show that high value of yield strength harvests low bearing displacement and low percentage reduction in the top floor absolute acceleration. In addition, the study also evaluates the energy dissipated by the isolated structure. The energy dissipated by the base-isolated (LRB) building subjected to blast-induced vibrations, show that an optimum value of yield strength is found to be in the range of 10–20% of the total weight of the structure.

HeteroOS

Heterogeneous memory management combined with server virtualization in datacenters is expected to increase the software and OS management complexity. State-of-the-art solutions rely exclusively on the hypervisor (VMM) for expensive page hotness tracking and migrations, limiting the benefits from heterogeneity. To address this, we design HeteroOS, a novel application-transparent OS-level solution for managing memory heterogeneity in virtualized system. The HeteroOS design first makes the guest-OSes heterogeneity-aware and then extracts rich OS-level information about applications' memory usage to place data in the 'right' memory avoiding page migrations. When such pro-active placements are not possible, HeteroOS combines the power of the guest-OSes' information about applications with the VMM's hardware control to track for hotness and migrate only performance-critical pages. Finally, HeteroOS also designs an efficient heterogeneous memory sharing across multiple guest-VMs. Evaluation of HeteroOS with memory, storage, and network-intensive datacenter applications shows up to 2x performance improvement compared to the state-of-the-art VMM-exclusive approach.

Joint network embedding and server consolidation for energy–efficient dynamic data center virtualization

Cloud computing has emerged in recent years as a promising paradigm that facilitates such new service models as Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). As the number of cloud service provider increases, there exists a demand to dynamically provision virtual data centers (VDC) on top of the infrastructure provider’s physical data centers. This research addresses problems related to energy and resource efficiently embedding virtual data centers inside physical data centers under dynamic resource allocation conditions, in which VDCs continuously join and leave the system. Dynamic VDC embedding is challenging as it is an NP-hard problem that should meet multiple objectives. In this article, we propose heuristic joint VDC embedding – server consolidation approaches as one solution for that problem. Evaluation results show that the joint approach outperforms existing ones in terms of resource and energy efficiency and can keep system complexity acceptable.

BEAMDB and MolD – databases for atomic and molecular collisional and radiative processes: Belgrade nodes of VAMDC

We present two atomic and molecular (A&M) databases, MolD and BEAMDB, hosted by the SerVO – the Serbian virtual observatory ( http://servo.aob.rs ). These databases and web applications have been implemented in accordance to the standards developed by Virtual Atomic and Molecular Data Centre (VAMDC, http://www.vamdc.eu ). The MolD database contains photo-dissociation cross-sections for individual rovibrational states of the diatomic molecular ions and rate coefficients for the atom-Rydberg atom chemi-ionisation and inverse electron-ion-atom chemi-recombination processes. The Belgrade electron/atom(molecule) database (BEAMDB) provides collisional data for electron interactions with atoms and molecules. Differential cross sections (DCS) are presented for both elastic and inelastic (excitation) cross sections in tabulated data tables. These DCS data are integrated over a full range of scattering angles in order to achieve integral, momentum transfer and viscosity cross sections as functions of impact electron energy. Beside these tables, energy loss spectra are presented in the graphical form.

A Filter-Based Dynamic Resource Management Framework for Virtualized Data Centers

Data centers adapt their operation to changing run-time conditions using energy-aware and SLA-compliant resource management techniques. In this context, current paper presents a novel filter-based dynamic resource management framework for virtualized data centers. By choosing and combining properly software filters performing the scheduling and resource management operations, the framework may be used in what-if analysis. The framework is evaluated by simulation for deploying batch best-effort jobs with time-varying CPU requirements.

Energy-aware auto-scaling algorithms for Cassandra virtual data centers

Apache Cassandra is an highly scalable and available NoSql datastore, largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra virtual data centers (VDCs). This paper address the problem of energy efficient auto-scaling of Cassandra VDC in managed Cassandra data centers. We propose three energy-aware autoscaling algorithms: Opt, LocalOpt and LocalOpt-H. The first provides the optimal scaling decision orchestrating horizontal and vertical scaling and optimal placement. The other two are heuristics and provide sub-optimal solutions. Both orchestrate horizontal scaling and optimal placement. LocalOpt consider also vertical scaling. In this paper: we provide an analysis of the computational complexity of the optimal and of the heuristic auto-scaling algorithms; we discuss the issues in auto-scaling Cassandra VDC and we provide best practice for using auto-scaling algorithms; we evaluate the performance of the proposed algorithms under programmed SLA variation, surge of throughput (unexpected) and failures of physical nodes. We also compare the performance of energy-aware auto-scaling algorithms with the performance of two energy-blind auto-scaling algorithms, namely BestFit and BestFit-H. The main findings are: VDC allocation aiming at reducing the energy consumption or resource usage in general can heavily reduce the reliability of Cassandra in term of the consistency level offered. Horizontal scaling of Cassandra is very slow and make hard to manage surge of throughput. Vertical scaling is a valid alternative, but it is not supported by all the cloud infrastructures.

Dynamic Routing Algorithms and Methods for Controlling Traffic Flows of Cloud Applications and Services

Nowadays, we see a steady growth in the use of cloud computing in modern business. This enables to reduce the cost of IT infrastructure owning and operation; however, there are some issues related to the management of data processing centers. One of these issues is the effective use of companies’ computing and network resources. The goal of optimization is to manage the traffic in cloud applications and services within data centers. Taking into account the multitier architecture of modern data centers, we need to pay a special attention to this task. The advantage of modern infrastructure virtualization is the possibility to use software-defined networks and software-defined data storages. However, the existing optimization of algorithmic solutions does not take into account the specific features of the network traffic formation with multiple application types. The task of optimizing traffic distribution for cloud applications and services can be solved by using software-defined infrastructure of virtual data centers. We have developed a simulation model for the traffic in software-defined networks segments of data centers involved in processing user requests to cloud application and services within a network environment. Our model enables to implement the traffic management algorithm of cloud applications and to optimize the access to storage systems through the effective use of data transmission channels. During the experimental studies, we have found that the use of our algorithm enables to decrease the response time of cloud applications and services and, therefore, to increase the productivity of user requests processing and to reduce the number of refusals.

An Adaptive IO Prefetching Approach for Virtualized Data Centers

Cloud and data center applications often make heavy use of virtualized servers, where flash-based solid-state drives (SSDs) have become popular alternatives over hard drives for data-intensive applications. Traditional data prefetching focuses on applications running on bare metal systems using hard drives. In contrast, virtualized systems using SSDs present different challenges for data prefetching. Most existing prefetching techniques, if applied unchanged in such environments, are likely to either fail to fully utilize SSDs, interfere with virtual machine I/O requests, or cause too much overhead if run in every virtualized instance.In this work, we demonstrate that data prefetching, when run in a virtualization-friendly manner can provide significant performance benefits for a wide range of data-intensive applications. We have designed and developed VIO-prefetching , consisting of accurate prediction of application needs in runtime and adaptive feedback-directed prefetching that scales with application needs, while being considerate to underlying storage devices and host systems. We have implemented a real system in Linux and evaluated it on different storage devices with the virtualization layer.Our comprehensive study provides insights of VIO-prefetching’s behavior at various virtualization system configurations, e.g., the number of VMs, in-guest processes, application types, etc. The proposed method improves virtual I/O performance up to 43 percent with the average of 14 percent for 1 to 12 VMs while running various applications on a Xen virtualization system.

On the energy cost of robustness for green virtual network function placement in 5G virtualized infrastructures

Next generation 5G networks will rely on virtualized Data Centers (vDC) to host virtualized network functions on commodity servers. Such Network Function Virtualization (NFV) will lead to significant savings in terms of infrastructure cost and reduced management complexity. However, green strategies for networking and computing inside data centers, such as server consolidation or energy aware routing, should not negatively impact the quality and service level agreements expected from network operators. In this paper, we study how robust strategies that place virtual network functions (VNF) inside vDC impact the energy savings and the protection level against resource demand uncertainty. We propose novel optimization models that allow the minimization of the energy of the computing and network infrastructure which is hosting a set of service chains that implement the VNFs. The model explicitly provides for robustness to unknown or imprecisely formulated resource demand variations, powers down unused routers, switch ports and servers, and calculates the energy optimal VNF placement and network embedding also considering latency constraints on the service chains. We propose both exact and heuristic methods. Our experiments were carried out using the virtualized Evolved Packet Core (vEPC), which allows us to quantitatively assess the trade-off between energy cost, robustness and the protection level of the solutions against demand uncertainty. Our heuristic is able to converge to a good solution in a very short time, in comparison to the exact solver, which is not able to output better results in a longer run as demonstrated by our numerical evaluation. We also study the degree of robustness of a solution for a given protection level and the cost of additional energy needed because of the usage of more computing and network elements.

On the benefits of resource disaggregation for virtual data centre provisioning in optical data centres

Virtual Data Centre (VDC) allocation requires the provisioning of both computing and network resources. Their joint provisioning allows for an optimal utilization of the physical Data Centre (DC) infrastructure resources. However, traditional DCs can suffer from computing resource underutilization due to the rigid capacity configurations of the server units, resulting in high computing resource fragmentation across the DC servers. To overcome these limitations, the disaggregated DC paradigm has been recently introduced. Thanks to resource disaggregation, it is possible to allocate the exact amount of resources needed to provision a VDC instance. In this paper, we focus on the static planning of a shared optically interconnected disaggregated DC infrastructure to support a known set of VDC instances to be deployed on top. To this end, we provide optimal and sub-optimal techniques to determine the necessary capacity (both in terms of computing and network resources) required to support the expected set of VDC demands. Next, we quantitatively evaluate the benefits yielded by the disaggregated DC paradigm in front of traditional DC architectures, considering various VDC profiles and Data Centre Network (DCN) topologies.

Towards Robust Green Virtual Cloud Data Center Provisioning

Cloud data center (CDC) network virtualization is being regarded as a promising technology to provide performance guarantee for cloud computing applications. One critical issue in CDC network virtualization technology is virtual data center (VDC) embedding, which deals with the CDC physical resource allocation to virtual nodes (virtual switches and virtual servers) and virtual links of a VDC. When node and link constraints (including CPU, memory, network bandwidth, and network delay) are both taken into account, the VDC embedding problem is NP-hard, even in the offline case. Node heterogeneity and CDC network scale bring challenges to the VDC embedding. This paper aims to embed a VDC in a robust and green way. We propose two effective, computation-efficient and energy-efficient embedding algorithms. Extensive simulations under various network scales and topologies are carried out to compare the proposed algorithms with the existing VDC embedding algorithms in terms of the VDC acceptance ratio, the long-term revenue of the cloud service provider (CSP), the CDC’s long-term energy consumption in light-load CDCs, and in terms of CSP's long-term revenue in heavy-load CDCs.

Congestion-Aware Embedding of Heterogeneous Bandwidth Virtual Data Centers With Hose Model Abstraction

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.

VmBBProfiler: a black-box profiling approach to quantify sensitivity of virtual machines to shared cloud resources

Virtualized Data Centers are packed with numerous web and cloud services nowadays. In such large infrastructures, providing reliable service platforms depends heavily on efficient sharing of physical machines (PMs) by virtual machines (VMs). To achieve efficient consolidation, performance degradation of co-located VMs must be correctly understood, modeled, and predicted. This work is a major step toward understanding such baffling phenomena by not only identifying, but also quantifying sensitivity of general purpose VMs to their demanded resources. vmBBProfiler, our proposed system in this work, is able to systematically profile behavior of any general purpose VM and calculate its sensitivity to system provided resources such as CPU, Memory, and Disk. vmBBProfiler is evaluated using 12 well-known benchmarks, varying from pure CPU/Mem/Disk VMs to mixtures of them, on three different PMs in our VMware-vSphere based private cloud. Extensive empirical results conducted over 1200 h of profiling prove the efficiency of our proposed models and solutions; it also opens doors for further research in this area.

EXtended CASA Line Analysis Software Suite (XCLASS)

The eXtended CASA Line Analysis Software Suite (XCLASS) is a toolbox for the Common Astronomy Software Applications package (CASA) containing new functions for modeling interferometric and single dish data. Among the tools is the myXCLASS program which calculates synthetic spectra by solving the radiative transfer equation for an isothermal object in one dimension, whereas the finite source size and dust attenuation are considered as well. Molecular data required by the myXCLASS program are taken from an embedded SQLite3 database containing entries from the Cologne Database for Molecular Spectroscopy CDMS) and JPL using the Virtual Atomic and Molecular Data Center (VAMDC) portal. Additionally, the toolbox provides an interface for the model optimizer package Modeling and Analysis Generic Interface for eXternal numerical codes (MAGIX), which helps to find the best description of observational data using myXCLASS (or another external model program), i.e., finding the parameter set that most closely reproduces the data.

CALM: Survivable Virtual Data Center Allocation in Cloud Networks

Cloud data centers have become a popular infrastructure to host diversified application services for tenants. To provide agility and elasticity in resource usage for cloud services, the virtual data center (VDC) is proposed to allocate both virtual machines (VM) and network bandwidth. However, at cloud scale, hardware (e.g., link, server, and switch) failures are inevitable, which may lead to degradation in service performance. To address this challenge, we study the survivable virtual data center allocation problem (SVAP), which aims at allocating survivable virtual data center (SVDC) to each tenant to guarantee resource demands will always be satisfied even after failures. Our objective is to minimize the total bandwidth consumption in order to accommodate more SVDCs. We prove that SVAP is NP-hard and design the Collocation-Aware survivable VM placement and Link Mapping algorithm (CALM). CALM solves the problem in two stages, i.e., VM placement (VMP) and virtual link mapping (VLM). We further find that without an appropriate VMP strategy, VLM cannot lead to the minimum network resource usage. Therefore, we propose a polynomial-time algorithm called collocation-aware survivable placement (CASP) for VMP. For the VLM stage, we formulate a linear programming model to map flows onto the data center network in order to ensure survivability under switch failures. We evaluate the performance via simulations and show that CALM could save up to 42 percent network resource compared to the baseline algorithm. We further show that CALM uses only additional 13 percent network resource to guarantee survivability as compared to a typical VDC strategy.

Optimization of traffic distribution control in software-configurable infrastructure of virtual data center based on a simulation model

Currently, the proportion of use of cloud computing technology in today's business processes of companies is growing steadily. Despite the fact that it allows you to reduce the cost of ownership and operation of IT infrastructure, there are a number of problems related to the control of data centers. One such problem is the efficiency of the use of available companies compute and network resources. One of the directions of optimization is the process of traffic control of cloud applications and services in data centers. Given the multi-tier architecture of modern data center, this problem does not quite trivial. The advantage of modern virtual infrastructure is the ability to use software-configurable networks and software-configurable data storages. However, existing solutions with algorithmic optimization does not take into account a number of features forming network traffic with multiple classes of applications. Within the framework of the exploration solved the problem of optimizing the distribution of traffic cloud applications and services for the software-controlled virtual data center infrastructure. A simulation model describing the traffic in data center and software-configurable network segments involved in the processing of user requests for applications and services located network environment that includes a heterogeneous cloud platform and software-configurable data storages. The developed model has allowed to implement cloud applications traffic management algorithm and optimize access to the storage system through the effective use of the channel for data transmission. In experimental studies found that the application of the developed algorithm can reduce the response time of cloud applications and services, and as a result improve the performance of processing user requests and to reduce the number of failures.

A model of cloud application assignments in software-defined storages

The aim of this study is to analyze the structure and mechanisms of interaction of typical cloud applications and to suggest the approaches to optimize their placement in storage systems. In this paper, we describe a generalized model of cloud applications including the three basic layers: a model of application, a model of service, and a model of resource. The distinctive feature of the model suggested implies analyzing cloud resources from the user point of view and from the point of view of a software-defined infrastructure of the virtual data center (DC). The innovation character of this model is in describing at the same time the application data placements, as well as the state of the virtual environment, taking into account the network topology. The model of software-defined storage has been developed as a submodel within the resource model. This model allows implementing the algorithm for control of cloud application assignments in software-defined storages. Experimental researches returned this algorithm decreases in cloud application response time and performance growth in user request processes. The use of software-defined data storages allows the decrease in the number of physical store devices, which demonstrates the efficiency of our algorithm.

The efficient framework and algorithm for provisioning evolving VDC in federated data centers

Data center has been working as a cost-efficient infrastructure to store a large amount of data and host service applications. With the virtualization technology, a resource request submitted to data center can be abstracted as a virtual data center (VDC) request which consist of virtual machines (VMs) connected through virtual switches, routers and links with guaranteed bandwidth. As one of the challenges, VDC embedding/provisioning focuses on mapping VDC components onto physical nodes and links in data center. In this paper we study the problem of provisioning/embedding for evolving/dynamic VDC request across federated data centers, such that the total operation cost is minimized. We use the VM migration to reconfigure evolving/dynamic VDC for reducing the total operation cost, as well to consolidate the VDCs on as few servers as possible for reducing the number of active servers and thus lowering energy consumption. We design an efficient framework and algorithm for solving the studied NP-Hard problem. Finally, we evaluate and compare the performance of our proposed approach through extensive simulation experiments. The simulation results show that the proposed approach performs better in terms of lowering total operation cost and energy consumption than existing solution does.

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

The emerging utilization of Software-as-a-Service (SaaS) Fog computing centers as an Internet virtual computing commodity is raising concerns over the energy consumptions of networked data centers for the support of delay-sensitive applications. In addition to the energy consumed by the servers, the energy wasted by the network devices that support TCP/IP reliable inter-Virtual Machines (VMs) connections is becoming a significant challenge. In this paper, we propose and develop a framework for the joint characterization and optimization of TCP/IP SaaS Fog data centers that utilize a bank of queues for increasing the fraction of the admitted workload. Our goal is two-fold: (i) we maximize the average workload admitted by the data center; and, (ii) we minimize the resulting networking-plus-computing average energy consumption. For this purpose, we exploit the Lyapunov stochastic optimization approach, in order to design and analyze an optimal (yet practical) online joint resource management framework, which dynamically performs: (i) admission control; (ii) dispatching of the admitted workload; (iii) flow control of the inter-VM TCP/IP connections; (iv) queue control; (v) up/down scaling of the processing frequencies of the instantiated VMs; and, (vi) adaptive joint consolidation of both physical servers and TCP/IP connections. The salient features of the resulting scheduler (e.g., the Q* scheduler) are that: (i) it admits distributed and scalable implementation; (ii) it provides deterministic bounds on the instantaneous queue backlogs; (iii) it avoids queue overflow phenomena; and, (iv) it effectively tracks the (possibly unpredictable) time-fluctuations of the input workload, in order to perform joint resource consolidation without requiring any a prioriinformation and/or forecast of the input workload. Actual energy and delay performances of the proposed scheduler are numerically evaluated and compared against the corresponding ones of some competing and state-of-the-art schedulers, under: (i) Fast - Giga - 10Giga Ethernet switching technologies; (ii) various settings of the reconfiguration-consolidation costs; and, (iii) synthetic, as well as real-world workloads. The experimental results support the conclusion that the proposed scheduler can achieve over 30% energy savings.

Datacenter Virtualization with Optimization and Customization

The paper provides a way to provide virtualization administration as a service and also ways to customize, optimize and improvise the performance of latest available virtualization techniques. Till now virtualization provides utilization from 60-70% for hypervisor but there is no technique available to optimize the server and client associated with it. The paper delivers a solution which is oriented for VM administrators and VM providers. The solution once implemented will provide a secure, economical, interoperable, storage platform independent solution for virtualization setup for administrators. The implementation can be done in a few minutes but it will provide a boost to management of virtual machines from the base level. The security techniques discussed in paper can be improvised i.e. VPN and encryption can be implemented in place of port forwarding although it might affect performance.