Kani: a QoS-aware hypervisor-level scheduler for cloud computing environments

Cloud computing environments (CCEs) are expected to deliver their services with qualities in service level agreements. On the other hand, they typically employ virtualization technology to consolidate multiple workloads on the same physical machine, thereby enhancing the overall utilization of physical resources. Most existing virtualization technologies are, however, unaware of their delivered quality of services (QoS). For example, the Xen hypervisor merely focuses on fair sharing of processor resources. We believe that CCEs have got married with traditional virtualization technologies without many traits in common. To bridge the gap between these two technologies, we have designed and implemented Kani, a QoS-aware hypervisor-level scheduler. Kani dynamically monitors the quality of delivered services to quantify the deviation between desired and delivered levels of QoS. Using this information, Kani determines how to allocate processor resources among running VMs so as to meet the expected QoS. Our evaluations of Kani scheduler prototype in Xen show that Kani outperforms the default Xen scheduler namely the Credit scheduler. For example, Kani reduces the average response time to requests to an Apache web server by up to \(93.6\,\%\); improves its throughput by up to \(97.9\,\%\); and mitigates the call setup time of an Asterisk media server by up to \(96.6\,\%\).     

Improving load balancing for data-duplication in big data cloud computing networks

Data transmission and retrieval in a cloud computing environment are usually handled by storage device providers or physical storage units leased by third parties. Improving network performance considering power connectivity and resource stability while ensuring workload balance is a hot topic in cloud computing. In this research, we have addressed the data duplication problem by providing two dynamic models with two variant architectures to investigate the strengths and shortcomings of architectures in Big Data Cloud Computing Networks. The problems of the data duplication process will be discussed accurately in each model. Attempts have been made to improve the performance of the cloud network by taking into account and correcting the flaws of the previously proposed algorithms. The accuracy of the proposed models have been investigated by simulation. Achieved results indicate an increase in the workload balance of the network and a decrease in response time to user requests in the model with a grouped architecture for all the architectures. Also, the proposed duplicate data model with peer-to-peer network architecture has been able to increase the cloud network optimality compared to the models presented with the same architecture.

     

Design a cloud storage platform for pervasive computing environments

An increasing number of personal electronic handheld devices (e.g., SmartPhone, netbook, MID and etc.), which make up the personal pervasive computing environments, are playing an important role in our daily lives. Data storage and sharing is difficult for these devices due to the data inflation and the natural limitations of mobile devices, such as the limited storage space and the limited computing capability. Since the emerging cloud storage solutions can provide reliable and unlimited storage, they satisfy to the requirement of pervasive computing very well. Thus we designed a new cloud storage platform which includes a series of shadow storage services to address these new data management challenges in pervasive computing environments, which called as “SmartBox”. In SmartBox, each device is associated its shadow storage with a unique account, and the shadow storage acts as backup center as well as personal repository when the device is connected. To facilitate file navigation, all datasets in shadow storage are organized based on file attributes which support the users to seek files by semantic queries. We implemented a prototype of SmartBox focusing on pervasive environments being made up of Internet accessible devices. Experimental results with the deployments confirm the efficacy of shadow storage services in SmartBox.     

Traffic load monitoring and load balancing for the Internet

This paper proposes a class-based multipath routing algorithm to support Quality of Service (QoS). The algorithm is called Two-level Class-based Routing with Prediction (TCRP). Since frequently flooding routing information is very expensive for dynamic routing, the TCRP is designed to have the traffic load information monitored in one stable period as a guide to control traffic forwarding in the next stable period. The monitoring function is implemented by adopting the leaky bucket mechanism. In TCRP, the path selection function can utilize resources on multipath to achieve load balancing, increase network throughput and reduce the queuing delay. The extensive simulation is conducted to analyze the performance of the TCRP algorithm. The simulation results show that the TCRP can reduce packet drops and increase network throughput in any size network topology. This revised version was published online in July 2006 with corrections to the Cover Date.     

Searching for SNPs with cloud computing

As DNA sequencing outpaces improvements in computer speed, there is a critical need to accelerate tasks like alignment and SNP calling. Crossbow is a cloud-computing software tool that combines the aligner Bowtie and the SNP caller SOAPsnp. Executing in parallel using Hadoop, Crossbow analyzes data comprising 38-fold coverage of the human genome in three hours using a 320-CPU cluster rented from a cloud computing service for about $85. Crossbow is available from .     

Best-KFF: a multi-objective preemptive resource allocation policy for cloud computing systems

Cluster Computing - Resource provisioning is a key issue in large-scale distributed systems such as cloud computing systems. Several resource provider systems utilized preemptive resource...     

Towards effective science cloud provisioning for a large-scale high-throughput computing

The science cloud paradigm has been actively developed and investigated, but still requires a suitable model for science cloud system in order to support increasing scientific computation needs with high performance. This paper presents an effective provisioning model of science cloud, particularly for large-scale high throughput computing applications. In this model, we utilize job traces where a statistical method is applied to pick the most influential features to improve application performance. With these features, a system determines where VM is deployed (allocation) and which instance type is proper (provisioning). An adaptive evaluation step which is subsequent to the job execution enables our model to adapt to dynamical computing environments. We show performance achievements by comparing the proposed model with other policies through experiments and expect noticeable improvements on performance as well as reduction of cost from resource consumption through our model.     

CloudDMSS: robust Hadoop-based multimedia streaming service architecture for a cloud computing environment

The delivery of scalable, rich multimedia applications and services on the Internet requires sophisticated technologies for transcoding, distributing, and streaming content. Cloud computing provides an infrastructure for such technologies, but specific challenges still remain in the areas of task management, load balancing, and fault tolerance. To address these issues, we propose a cloud-based distributed multimedia streaming service (CloudDMSS), which is designed to run on all major cloud computing services. CloudDMSS is highly adapted to the structure and policies of Hadoop, thus it has additional capacities for transcoding, task distribution, load balancing, and content replication and distribution. To satisfy the design requirements of our service architecture, we propose four important algorithms: content replication, system recovery for Hadoop distributed multimedia streaming, management for cloud multimedia management, and streaming resource-based connection (SRC) for streaming job distribution. To evaluate the proposed system, we conducted several different performance tests on a local testbed: transcoding, streaming job distribution using SRC, streaming service deployment and robustness to data node and task failures. In addition, we performed three different tests in an actual cloud computing environment, Cloudit 2.0: transcoding, streaming job distribution using SRC, and streaming service deployment.     

Adaptive Combiner for MapReduce on cloud computing

MapReduce is a programming model to process a massive amount of data on cloud computing. MapReduce processes data in two phases and needs to transfer intermediate data among computers between phases. MapReduce allows programmers to aggregate intermediate data with a function named combiner before transferring it. By leaving programmers the choice of using a combiner, MapReduce has a risk of performance degradation because aggregating intermediate data benefits some applications but harms others. Now, MapReduce can work with our proposal named the Adaptive Combiner for MapReduce (ACMR) to automatically, smartly, and trainer for getting a better performance without any interference of programmers. In experiments on seven applications, MapReduce can utilize ACMR to get the performance comparable to the system that is optimal for an application.     

Energy-aware routing considering load balancing for SDN: a minimum graph-based Ant Colony Optimization

Software-Defined Network (SDN) technology is a network management approach that facilitates a high level of programmability and centralized manageability. By leveraging the control and data plane separation, an energy-aware routing model could be easily implemented in the networks. In the present paper, we propose a two-phase SDN-based routing mechanism that aims at minimizing energy consumption while providing a certain level of QoS for the users’ flows and realizing the link load balancing. To reduce the network energy consumption, a minimum graph-based Ant Colony Optimization (ACO) approach is used in the first phase. It prunes and optimizes the network tree by turning unnecessary switches off and providing an energy-minimized sub-graph that is responsible for the network existing flows. In the second phase, an innovative weighted routing approach is developed that guarantees the QoS requirements of the incoming flows and routes them so that to balance the loads on the links. We validated our proposed approach by conducting extensive simulations on different traffic patterns and scenarios with different thresholds. The results indicate that the proposed routing method considerably minimizes the network energy consumption, especially for congested traffics with mice-type flows. It can provide effective link load balancing while satisfying the users’ QoS requirements.

     

Trust management on user behavioral patterns for a mobile cloud computing

With the development of ubiquitous computing technology, users are using mobile devices which are for producing and accessing information. Due to the limited computing capability and storage, however, mobile cloud computing technology are emerging research issues in the architecture, design, and implementation. This paper proposes the trust management approach by analyzing user behavioral patterns for reliable mobile cloud computing. For this, we suggest a method to quantify a one-dimensional trusting relation based on the analysis of telephone call data from mobile devices. After that, we integrate inter-user trust relationship in mobile cloud environment. As a result, trustworthiness of data in data production, management, overall application, is enhanced.     

The case for cloud computing in genome informatics

With DNA sequencing now getting cheaper more quickly than data storage or computation, the time may have come for genome informatics to migrate to the cloud.     

Introduction to cloud computing: platforms and solutions

This special issue of the cluster computing journal will feature articles that discuss tools and applications for cloud computing. Specifically, it aims at delivering the state-of-the-art research on current cloud computing tools topics, and at promoting the cloud applications discipline by bringing to the attention of the community novel problems that must be investigated.     

RHAS: robust hybrid auto-scaling for web applications in cloud computing

Cluster Computing - The elasticity characteristic of cloud services attracts application providers to deploy applications in a cloud environment. The scalability feature of cloud computing gives...     

Load monitoring and system-traffic-aware live VM migration-based load balancing in cloud data center using graph theoretic solutions

This paper proposes solutions to monitor the load and to balance the load of cloud data center. The proposed solutions work in two phases and graph theoretical concepts are applied in both phases. In the first phase, cloud data center is modeled as a network graph. This network graph is augmented with minimum dominating set concept of graph theory for monitoring its load. For constructing minimum dominating set, this paper proposes a new variant of minimum dominating set (V-MDS) algorithm and is compared with existing construction algorithms proposed by Rooji and Fomin. The V-MDS approach of querying cloud data center load information is compared with Central monitor approach. The second phase focuses on system and network-aware live virtual machine migration for load balancing cloud data center. For this, a new system and traffic-aware live VM migration for load balancing (ST-LVM-LB) algorithm is proposed and is compared with existing benchmarked algorithms dynamic management algorithm (DMA) and Sandpiper. To study the performance of the proposed algorithms, CloudSim3.0.3 simulator is used. The experimental results show that, V-MDS algorithm takes quadratic time complexity, whereas Rooji and Fomin algorithms take exponential time complexity. Then the V-MDS approach for querying Cloud Data Center load information is compared with the Central monitor approach and the experimental result shows that the proposed approach reduces the number of message updates by half than the Central monitor approach. The experimental results show on load balancing that the developed ST-LVM-LB algorithm triggers lesser Virtual Machine migrations, takes lesser time and migration cost to migrate with minimum network overhead. Thus the proposed algorithms improve the service delivery performance of cloud data center by incorporating graph theoretical solutions in monitoring and balancing the load.     

Fast protocol for radiochromic film dosimetry using a cloud computing web application

PurposeTo investigate the feasibility of a fast protocol for radiochromic film dosimetry to verify intensity-modulated radiotherapy (IMRT) plans.Method and materialsEBT3 film dosimetry was conducted in this study using the triple-channel method implemented in the cloud computing application (Radiochromic.com). We described a fast protocol for radiochromic film dosimetry to obtain measurement results within 1 h.Ten IMRT plans were delivered to evaluate the feasibility of the fast protocol. The dose distribution of the verification film was derived at 15, 30, 45 min using the fast protocol and also at 24 h after completing the irradiation. The four dose maps obtained per plan were compared using global and local gamma index (5%/3 mm) with the calculated one by the treatment planning system. Gamma passing rates obtained for 15, 30 and 45 min post-exposure were compared with those obtained after 24 h.ResultsSmall differences respect to the 24 h protocol were found in the gamma passing rates obtained for films digitized at 15 min (global: 99.6% ± 0.9% vs. 99.7% ± 0.5%; local: 96.3% ± 3.4% vs. 96.3% ± 3.8%), at 30 min (global: 99.5% ± 0.9% vs. 99.7% ± 0.5%; local: 96.5% ± 3.2% vs. 96.3 ± 3.8%) and at 45 min (global: 99.2% ± 1.5% vs. 99.7% ± 0.5%; local: 96.1% ± 3.8% vs. 96.3 ± 3.8%).ConclusionsThe fast protocol permits dosimetric results within 1 h when IMRT plans are verified, with similar results as those reported by the standard 24 h protocol.     

DDOps: dual-direction operations for load balancing on non-dedicated heterogeneous distributed systems

Given the increasing prevalence of compute/data intensive applications, the explosive growth in data, and the emergence of cloud computing, there is an urgent need for effective approaches to support such applications in non-dedicated heterogeneous distributed environments. This paper proposes an efficient technique for handling parallel tasks, while dynamically maintaining load balancing. Such tasks include concurrently downloading files from replicated sources; simultaneously using multiple network interfaces for message transfers; and executing parallel computations on independent distributed processors. This technique, DDOps, (Dual Direction Operations) enables efficient utilization of available resources in a parallel/distributed environment without imposing any significant control overhead. The idea is based on the notion of producer pairs that perform tasks in parallel from opposite directions and the consumers that distribute and control the work and receive and combine the results. Most dynamic load balancing approaches require prior knowledge and/or constant monitoring at run time. In DDOps, load balancing does not require prior knowledge or run-time monitoring. Rather, load balancing is automatically inherent as the tasks are handled from the opposite directions, allowing the processing to continue until the producers meet indicating the completion of all tasks at the same time. Thus DDOps is most suitable for heterogeneous environments where resources vary in specifications, locations, and operating conditions. In addition, since DDOps does not require producers to communicate at all, the network effect is minimized.