Virtual Cluster Research Articles

Predictive Trajectory‐Based Mobile Data Gathering Scheme for Wireless Sensor Networks

Tradition wireless sensor networks (WSNs) transmit data by single or multiple hops. However, some sensor nodes (SNs) close to a static base station forward data more frequently than others, which results in the problem of energy holes and makes networks fragile. One promising solution is to use a mobile node as a mobile sink (MS), which is especially useful in energy‐constrained networks. In these applications, the tour planning of MS is a key to guarantee the network performance. In this paper, a novel strategy is proposed to reduce the latency of mobile data gathering in a WSN while the routing strategies and tour planning of MS are jointly optimized. First, the issue of network coverage is discussed before the appropriate number of clusters being calculated. A dynamic clustering scheme is then developed where a virtual cluster center is defined as the MS sojourn for data collection. Afterwards, a tour planning of MS based on prediction is proposed subject to minimizing the traveling distance to collect data. The proposed method is simulated in a MATLAB platform to show the overall performance of the developed system. Furthermore, the physical tests on a test rig are also carried out where a small WSN based on an unmanned aerial vehicle (UAV) is developed in our laboratory. The test results validate the feasibility and effectiveness of the method proposed.

Experimental Setup for Investigating the Efficient Load Balancing Algorithms on Virtual Cloud

Cloud computing has emerged as the primary choice for developers in developing applications that require high-performance computing. Virtualization technology has helped in the distribution of resources to multiple users. Increased use of cloud infrastructure has led to the challenge of developing a load balancing mechanism to provide optimized use of resources and better performance. Round robin and least connections load balancing algorithms have been developed to allocate user requests across a cluster of servers in the cloud in a time-bound manner. In this paper, we have applied the round robin and least connections approach of load balancing to HAProxy, virtual machine clusters and web servers. The experimental results are visualized and summarized using Apache Jmeter and a further comparative study of round robin and least connections is also depicted. Experimental setup and results show that the round robin algorithm performs better as compared to the least connections algorithm in all measuring parameters of load balancer in this paper.

The Organization Model of Big Data Innovation Alliance: A Multi-Case Study from the Perspective of Virtual Clusters

The Big Data Innovation Alliance can be understood as a cross-industry and cross-region spatial agglomeration phenomenon based on Big Data. From the two aspects of constituent elements and organizational characteristics, the Big Data Innovation Alliance is found to be a learning organization with a flat management structure. A multi-case study method is used to empirically test the virtual cluster characteristics of the big data innovation alliance organization model using four typical cases, and to analyze its generality and specificity based on the research variables. Several policy implications have been obtained for the big data innovation alliance organization model.

Virtual cluster for HPC education

For many institutions, it is challenging to procure and maintain resources to teach parallel and distributed computing. While existing dedicated environments such as XSEDE are available, they often...

Adaptive variable-size virtual clustering for control channel assignment in dynamic access networks: Design and simulations

Cognitive Radio (CR) technology was introduced to effectively exploit the under-utilization portions of the licensed spectrum. A primary challenge in enabling CR networking is how to coordinate the CR communications, in which CR users need to exchange their control information before data exchange can take place. Providing a reliable coordination mechanism in CR networks (CRNs) is not an easy task because of the dynamic nature of primary radio (PR) activities and the heterogeneity of both spectrum availability and CR users’ distribution. In this paper, we propose a dynamic-size clustering with CSMA/CA-based algorithm to overcome the challenge of achieving reliable coordination in CR networks (CRNs). CR users in our scheme are organized into dynamic size clusters and cooperate in selecting common control channels (CCCs) and use them for cluster coordination. The proposed scheme called Dynamic-hop virtual clustering distributed coordination (DH-VCDC). This scheme provides a dynamic-hop virtual clustering based on the traffic condition of both PR and CR users while achieving a minimum number of common available data channels per cluster, in which a better trade-off between the number of common idle channels per cluster and the cluster size is achieved. DH-VCDC scheme exploits the advantages of using Multi-packet Multi-channel Discontinuous Orthogonal Division Multiplexing (DOFDM) to reduce neighbor discovery time, allow CR users to have several cluster memberships, select multiple CCCs and simplify the inter-cluster coordination. CR users in our scheme are grouped to form virtual clusters each contains at least 2 CCCs (primary and backup CCCs). Three modes of DH-VCDC are presented: one-, two- and three-hop modes. Each CR must continuously monitor both PR and CR traffic loads and accordingly chooses the appropriate DH-VCDC mode. The performance of DH-VCDC is compared with reference schemes, in which significant performance improvement is reported.

Can Existing Improvements of Water, Sanitation, and Hygiene (WASH) in Urban Slums Reduce the Burden of Typhoid Fever in These Settings?

Sustained investments in water, sanitation, and hygiene (WASH) have lagged in resource-poor settings; incremental WASH improvements may, nonetheless, prevent diseases such as typhoid in disease-endemic populations. Using prospective data from a large cohort in urban Kolkata, India, we evaluated whether baseline WASH variables predicted typhoid risk in a training subpopulation (n = 28 470). We applied a machine learning algorithm to the training subset to create a composite, dichotomous (good, not good) WASH variable based on 4 variables, and evaluated sensitivity and specificity of this variable in a validation subset (n = 28 470). We evaluated in Cox regression models whether residents of "good" WASH households experienced a lower typhoid risk after controlling for potential confounders. We constructed virtual clusters (radius 50 m) surrounding each household to evaluate whether a prevalence of good WASH practices modified the typhoid risk in central household members. Good WASH practices were associated with protection in analyses of all households (hazard ratio [HR] = 0.57; 95% confidence interval [CI], .37-.90; P = .015). This protection was evident in persons ≥5 years old at baseline (HR = 0.47; 95% CI, .34-.93; P = .005) and was suggestive, though not statistically significant, in younger age groups (HR = 0.61; 95% CI, .27-1.38; P = .235). The level of surrounding household good WASH coverage was also associated with protection (HR = 0.988; 95% CI, .979-.996; P = .004, for each percent coverage increase). However, collinearity between household WASH and WASH coverage prevented an assessment of their independent predictive contributions. In this typhoid-endemic setting, natural variation in household WASH was associated with typhoid risk. If replicated elsewhere, these findings suggest that WASH improvements may enhance typhoid control, short of major infrastructural investments.

Performance Analysis of GFDM Modulation in Heterogeneous Network for 5G NR

Network densification becomes inevitable due to rapidly increasing mobile data traffic in the evolution of future cellular networks. Therefore, Heterogeneous Wireless Networks eliminate the challenges that are not covered in 4G-Long Term Evolution. The extensive contribution of the proposed work is to develop a cluster based algorithm that establishes the maximum node connections in the heterogeneous wireless network and also to implement effective Multi-User Generalized Frequency Division Multiplexing in the physical layer for future wireless communications. The presence of unconnected nodes (Base Stations) in the wireless network reduces the network efficiency and coverage capacity. Whenever a wireless device is connected to at least one isolated neighbor node (BS), it acts as a Virtual Clustered Head based on shortest path node and residual energy of the node. A Virtual Cluster (VC) based multipath routing is implemented for Heterogeneous Networks to maximize the Energy Efficiency (EE), Spectral Efficiency (SE), Channel Capacity (CC), and Network Capacity (NC) and minimize the Bit Error Rate and latency under different constraints in the network. The simulation results with GFDM modulation shows the improvement in the network performance and compared with existing OFDM results.

Constructing Reliable Computing Environments on Top of Amazon EC2 Spot Instances

Cloud provider Amazon Elastic Compute Cloud (EC2) gives access to resources in the form of virtual servers, also known as instances. EC2 spot instances (SIs) offer spare computational capacity at steep discounts compared to reliable and fixed price on-demand instances. The drawback, however, is that the delay in acquiring spots can be incredible high. Moreover, SIs may not always be available as they can be reclaimed by EC2 at any given time, with a two-minute interruption notice. In this paper, we propose a multi-workflow scheduling algorithm, allied with a container migration-based mechanism, to dynamically construct and readjust virtual clusters on top of non-reserved EC2 pricing model instances. Our solution leverages recent findings on performance and behavior characteristics of EC2 spots. We conducted simulations by submitting real-life workflow applications, constrained by user-defined deadline and budget quality of service (QoS) parameters. The results indicate that our solution improves the rate of completed tasks by almost 20%, and the rate of completed workflows by at least 30%, compared with other state-of-the-art algorithms, for a worse-case scenario.

Relationship between the cluster theory of liquids and the Frenkel—Andrade viscosity model

Based on the reference data on the dynamic viscosity of the melts of halides of the elements of Groups 1 and 11, the approximating dependences in the form of cluster-associate and Frenkel’s models were constructed at various temperatures. In the framework of the cluster-associate viscosity model, which is being developed by the authors, entities in the form of virtual clusters and associates determine the melt viscosity and serve as the flow units to that the energy of fluid motion is applied. The Frenkel’s model allows us to estimate the activation energy of fluidity. Calculations show that there is a linear correlation between this energy and the cluster association degree obtained in the framework of the cluster-associate model, and the proportionality coefficient has the meaning of the activation energy per a cluster. This energy does not exceed the van der Waals energy of an unsaturated intermolecular bond characterizing the interaction of particles in a liquid.

Availability-Aware and Energy-Efficient Virtual Cluster Allocation Based on Multi-Objective Optimization in Cloud Datacenters

With greater numbers of cloud applications being deployed in virtual clusters (VCs) and running in datacenters, the energy consumption of datacenters is experiencing rapidly cumulative growth. Thus, it is a critical issue that how to allocate virtual machines (VMs) of the VC in physical machines (PMs) for energy savings as much as possible. Considering each PM/switch has a certain failure rate, VMs may not be executed when they meet with any PM/switch fault. So, a compact allocation scheme may benefit in low energy consumption but increase the risk of violating the availability of the VC. In this paper, we consider four optimization objectives about the VC and the datacenter, i.e., availability, energy consumption, average resource utilization, and resource load balance. Then we propose a multi-objective optimization model and raise an evolution algorithm to trade-off among these four optimization objectives. Finally, experimental results show the effectiveness and efficiency of our algorithm.

Joint Availability Enhancement and Traffic Optimization of Virtual Cluster Allocation in Cloud Datacenters

As more and more services are deployed in the cloud datacenter, network traffic is growing exponentially. Virtual machines (VMs) of a virtual cluster (VC) must be allocated on physical machines (PMs) in the datacenter with a certain topology. Each VM needs some resources to run various services. Apparently, allocating VMs in the datacenter as compactly as possible can reduce traffic consumption and avoid bandwidth-related bottlenecks. However, loose allocation scheme can reduce the loss expectation of VMs due to failure possibilities of PMs and switches, and the availability of the VC is increased thereby. To enhance availability and reduce network bandwidth usage, it is significant to determine the scheme of allocating VMs of the VC. In this paper, we first introduce four typical datacenter architectures with network topologies and corresponding cost matrices, and extend to generality. Then we propose a joint optimization function to measure the risk of VC and the core bandwidth usage with a global availability constraint. Subsequently, an evolutionary algorithm is raised to minimize the value of the constrained optimization function. Finally, the evaluation results show the effectiveness of the proposed approach and performance advancement over the existing approaches.

Availability-Aware Virtual Cluster Allocation in Bandwidth-Constrained Datacenters

As greater numbers of data-intensive applications are required to process big data in bandwidth-constrained datacenters with heterogeneous physical machines (PMs) and virtual machines (VMs), network core traffic is experiencing rapid growth. The VMs of a virtual cluster (VC) must be allocated as compactly as possible to avoid bandwidth-related bottlenecks. Since each PM/switch has a certain failure probability, a VC may not be executed when it meets with any PM/switch fault. Although the VMs of a VC can be spread out across different fault domains to minimize the risk of violating the availability requirement of the VC, this increases the network core traffic. Therefore, avoiding the decrease in availability caused by the heterogeneous PM/switch failure probabilities and bandwidth-related bottlenecks has been a constant challenge. In this paper, we first introduce a joint optimization function to measure the overall risk cost and overall bandwidth usage in the network core to allocate the same set of data-intensive applications. We then introduce an approach to maximize the value of the joint optimization function. Finally, we performed a side-by-side comparison with prior algorithms, and the experimental results show that our approach outperforms the other existing algorithms.

Improving the efficiency of HPC data movement on container-based virtual cluster

Today, lightweight virtualization technologies have been widely deployed on data centers and HPC clusters to provide highly efficient and elastic resource provisioning. Virtualization has also been extended to the I/O stack in operating system. For example, virtual switch has become the primary provider of I/O services for data movement among various light-weight virtual machines, such as Docker and Kubernetes. However, I/O stack virtualization introduces performance degradation and scalability bottleneck to the data movements of HPC computing framework, such as MPI based collective data movements and bursty asynchronous data movements. In order to study the bottleneck, we quantify and analyze the performance degradation involving with HPC data movements on virtual clusters. Then, we design a set of two-stage methods to proactively adapt the virtual network and data movement procedures. This can enhance the performance of HPC collective data movements by up to 3$$\times $$. Meanwhile, a cross-layer middleware is designed to improve the performance and scalability of bursty asynchronous data movements. Our evaluation shows that it can improve the performance of real scientific application by 34.6%.

Simulation of Energy Efficient Clustering Technique in a Gaussian Network

In Wireless sensor network (WSN) the sensor nodes are randomly distributed in the network. As the sensor nodes are adhoc in nature the geographical location must be assessed earlier and for data transmission. Maintenance of the energy level in each sensor node is very challengeable. In this paper a Gaussian network is deployed with four virtual grids and cluster head is obtained using the Gaussian integer. The traditional DSDV protocol is modified and simulated in Network Simulator. The energy efficiency of modified DSDV with regard to increase in number of nodes in compared with traditional AODV protocol and found a better performance of the modified DSDV protocol.

К вопросу развития туристической отрасли посредством реализации социально-экономического потенциала регионов России (на примере регионов зоны Арктики)

The tourism industry is one of the most dynamically developing and promising sectors of the global economy, but currently the tourism industry contributes less than 3.5% to Russia’s GDP, which cannot be recognized as corresponding to the huge tourist and recreational potential. The “Strategy for the Development of Tourism in the Russian Federation until 2035” formulates the tasks of increasing the efficiency of functioning. The importance of the tasks actualizes the study of all scientific and applied aspects of the development of gions of the Russian Federation, one of the key elements of which is the tourist and recreational potential. The article considers the management of tourist and recreational activities in the context of the socio-economic development of the subject of the Russian Federation. The results of studying world experience in the field of tourism are presented and the possibilities of using best practices in Russia are investigated. The mechanism of integration of tourist and recreational zones of the country into a single economic system is substantiated and a model of a virtual tourist and recreational cluster is proposed. The possibilities of implementing public-private partnership projects as a source of financing the tourism industry on the example of Arctic tourism are considered. The directions of development of the tourism industry through the implementation of the socio-economic potential of the regions of Russia are formulated.

New comprehensive model based on virtual clusters and absorbing Markov chains for energy-efficient virtual machine management in cloud computing

Utilizing from energy-aware solutions along with maintaining service-level agreements is one of the most important research issues in cloud computing. In the proposed model, monitoring the status of resources and analysing the obtained data have led to proper placement and consolidation of virtual machines through targeted migrations at the right time. In the virtual machine placement policy, the definition of absorption mode has been used in simulated annealing algorithm in addition to the formation of virtual clusters to prevent from unlimited increase in the length of created Markov chain in any temperature while maintaining the convergence. The results of simulations obtained from various scenarios in CloudSim indicated the proposed model has led to energy savings up to 14.3%, 19% and 21% on low load, average load and high load, respectively, compared to the best understudy algorithm, while the SLA violation has also led to a decrease in all three modes.

Fine-grained data-locality aware MapReduce job scheduler in a virtualized environment

Big data overwhelmed industries and research sectors. Reliable decision making is always a challenging task, which requires cost-effective big data processing tools. Hadoop MapReduce is being used to store and process huge volume of data in a distributed environment. However, due to huge capital investment and lack of expertise to set up an on-premise Hadoop cluster, big data users seek cloud-based MapReduce service over the Internet. Mostly, MapReduce on a cluster of virtual machines is offered as a service for a pay-per-use basis. Virtual machines in MapReduce virtual cluster reside in different physical machines and co-locate with other non-MapReduce VMs. This causes to share IO resources such as disk and network bandwidth, leading to congestion as most of the MapReduce jobs are disk and network intensive. Especially, the shuffle phase in MapReduce execution sequence consumes huge network bandwidth in a multi-tenant environment. This results in increased job latency and bandwidth consumption cost. Therefore, it is essential to minimize the amount of intermediate data in the shuffle phase rather than supplying more network bandwidth that results in increased service cost. Considering this objective, we extended multi-level per node combiner for a batch of MapReduce jobs to improve makespan. We observed that makespan is improved up to 32.4 $$\%$$ by minimizing the number of intermediate data in shuffle phase when compared to classical schedulers with default combiners.

Virtual Cluster Deployment Model for Large-Scale Data Processing Jobs

The virtual cluster needs to deploy virtual nodes with different computing modes for Large-scale data computing jobs including many tasks with different computing modes. There are resource unbalance among the virtual cluster subsets, which is consisted of virtual nodes with the same computing mode. So the resource utilization of virtual cluster is poor due to the time-varying and complexity of workloads. To solve these issues, a novel dynamic deployment model based on Docker was proposed to optimize resource configuration of virtual cluster. Hybrid deployment scheme includes coarse-grained deployment and fine-grained deployment in order to absorb the mutation of resource demand when processing the jobs encapsulated with complex computing logic. This deployment model can reduce job waiting time and improve performance by providing more resource for running jobs. The coarse-grained deployment mechanism has the ability of decreasing deploying overhead and improving stability from global optimal perspective. And the fine-grained deployment mechanism can increases the deploying accuracy from local optimal perspective. The experiments show that this model can improve the execution efficiency of job by 2.3 percent compared with static deploying scheme and improve the virtual CPU utilization about 7.9 to 25.9 percent compared with other approaches.

Виртуальный кластер как проект

Виртуальный кластер как проект

Methodology of Management of Innovative Development of Complex Socio-Economic Systems in the Information Society

The article presents methodological recommendations for managing the innovative development of complex socio-economic systems in the context of movement towards the information society, the formation and development of problem-oriented information and communication social spaces, the formation of specialized virtual innovation clusters, the organization of effective interaction between government bodies and local self-government, economic entities, their associations, other organizations and citizens having common interests and a common goal. The proposed methodological recommendations and model of innovative development can, with account of specific features, be effectively applied in various sectors of the national economy. Using the research results in practice will contribute to solving the problem of innovative modernization of the country, adapting authorities, organizations and citizens to the conditions of the global information society, increasing the functioning and competitiveness of complex socio-economic systems, improving quality and increasing the volume of socially significant products. An intelligent control system is proposed and a technology for managing the innovative development of complex socio-economic systems is developed.