Wenhong Tian

A dynamic and integrated load-balancing scheduling algorithm for Cloud datacenters

One of the challenging scheduling problems in Cloud datacenters is to take the allocation and migration of reconfigurable virtual machines into consideration as well as the integrated features of hosting physical machines. We introduce a dynamic and integrated resource scheduling algorithm (DAIRS) for Cloud datacenters. Unlike traditional load-balance scheduling algorithms which consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.

A Toolkit for Modeling and Simulation of Real-Time Virtual Machine Allocation in a Cloud Data Center

Resource scheduling in infrastructure as a service (IaaS) is one of the keys for large-scale Cloud applications. Extensive research on all issues in real environment is extremely difficult because it requires developers to consider network infrastructure and the environment, which may be beyond the control. In addition, the network conditions cannot be predicted or controlled. Therefore, performance evaluation of workload models and Cloud provisioning algorithms in a repeatable manner under different configurations and requirements is difficult. There is still lack of tools that enable developers to compare different resource scheduling algorithms in IaaS regarding both computing servers and user workloads. To fill this gap in tools for evaluation and modeling of Cloud environments and applications, we propose CloudSched. CloudSched can help developers identify and explore appropriate solutions considering different resource scheduling algorithms. Unlike traditional scheduling algorithms considering only one factor such as CPU, which can cause hotspots or bottlenecks in many cases, CloudSched treats multidimensional resource such as CPU, memory and network bandwidth integrated for both physical machines and virtual machines (VMs) for different scheduling objectives (algorithms). In this paper, two existing simulation systems at application level for Cloud computing are studied, a novel lightweight simulation system is proposed for real-time VM scheduling in Cloud data centers, and results by applying the proposed simulation system are analyzed and discussed.

PAIRWISE ALIGNMENT OF INTERACTION NETWORKS BY FAST IDENTIFICATION OF MAXIMAL CONSERVED PATTERNS

A number of tools for the alignment of protein-protein interaction (PPI) networks have laid the foundation for PPI network analysis. They typically find conserved interaction patterns by various local or global search algorithms, and then validate the results using genome annotation. The improvement of the speed, scalability and accuracy of network alignment is still the target of ongoing research. In view of this, we introduce a connected-components based algorithm, called HopeMap for pairwise network alignment with the focus on fast identification of maximal conserved patterns across species. Observing that the number of true homologs across species is relatively small compared to the total number of proteins in all species, we start with highly homologous groups across species, find maximal conserved interaction patterns globally with a generic scoring system, and validate the results across multiple known functional annotations. The results are evaluated in terms of statistical enrichment of gene ontology (GO) terms and KEGG ortholog groups (KO) within conserved interaction patters. HopeMap is fast, with linear computational cost, accurate in terms of KO groups and GO terms specificity and sensitivity, and extensible to multiple network alignment.

A Framework for Implementing and Managing Platform as a Service in a Virtual Cloud Computing Lab

With the rapid development of Internet and Cloud computing, there are more and more network resources. Sharing, management and on-demand allocation of network resources are particularly important in Cloud computing. Platform as a Service (PaaS) is one of the key services in Cloud computing. PaaS is very attractive for schools, research institutions and enterprises which need reducing IT costs, improving computing platform sharing and meeting license constraints. However, nearly all current available cloud computing platforms are either proprietary or their software infrastructure is invisible to the research community except for a few open-source platforms. For universities and research institutes, more open and testable experimental platforms are needed in a lab-level with PCs. In this paper, a framework for managing PaaS in a virtual Cloud computing lab is developed. The framework implements the user management, resource management and access management. The system has good expandability and can improve resource’s sharing and utilization.

Open-source simulators for Cloud computing: Comparative study and challenging issues

Abstract Resource scheduling in infrastructure as a service (IaaS) is one of the keys for large-scale Cloud applications. Extensive research on all issues in real environment is extremely difficult because it requires developers to consider network infrastructure and the environment, which may be beyond the control. In addition, the network conditions cannot be controlled or predicted. Performance evaluations of workload models and Cloud provisioning algorithms in a repeatable manner under different configurations are difficult. Therefore, simulators are developed. To understand and apply better the state-of-the-art of Cloud computing simulators, and to improve them, we study four known open-source simulators. They are compared in terms of architecture, modeling elements, simulation process, performance metrics and scalability in performance. Finally, a few challenging issues as future research trends are outlined.

Adaptive Dimensioning of Cloud Data Centers

Cloud data centers (CDCs) provide key infrastructure for Cloud computing. Allocating computing resources is very important for the CDCs to function efficiently. Current allocation of resources in CDCs is mostly dedicated and static.However, workloads for Cloud applications are highly variable which cause poor application performance, poor resource utilization or both. In this paper, adaptive dimensioning methods for CDCs are developed so that right amount of computing resources are allocated for variable workloads to meet quality of service requirements.

A Service Framework for Scientific Workflow Management in the Cloud

Cloud computing is an emerging computing paradigm that can offer unprecedented scalability and resources on demand, and is getting more and more adoption in the science community, while scientific workflow management systems provide essential support such as management of data and task dependencies, job scheduling and execution, provenance tracking, etc., to scientific computing. As we are entering into a “big data” era, it is imperative to migrate scientific workflow management systems into the cloud to manage the ever increasing data scale and analysis complexity. We propose a reference service framework for integrating scientific workflow management systems into various cloud platforms, which consists of eight major components, including Cloud Workflow Management Service, Cloud Resource Manager, etc., and six interfaces between them. We also present a reference framework for the implementation of Cloud Resource Manager, which is responsible for the provisioning and management of virtual resources in the cloud. We discuss our implementation of the framework by integrating the Swift scientific workflow management system with the OpenNebula and Eucalyptus cloud platforms, and demonstrate the capability of the solution using a NASA MODIS image processing workflow and a production deployment on the Science@Guoshi network with support for the Montage image mosaic workflow.

An online parallel scheduling method with application to energy-efficiency in cloud computing

This paper considers online energy-efficient scheduling of virtual machines (VMs) for Cloud data centers. Each request is associated with a start-time, an end-time, a processing time and a capacity demand from a Physical Machine (PM). The goal is to schedule all of the requests non-preemptively in their start-time-end-time windows, subjecting to PM capacity constraints, such that the total busy time of all used PMs is minimized (called MinTBT-ON for abbreviation). This problem is a fundamental scheduling problem for parallel jobs allocation on multiple machines; it has important applications in power-aware scheduling in cloud computing, optical network design, customer service systems, and other related areas. Offline scheduling to minimize busy time is NP-hard already in the special case where all jobs have the same processing time and can be scheduled in a fixed time interval. One best-known result for MinTBT-ON problem is a g-competitive algorithm for general instances and unit-size jobs using First-Fit algorithm where g is the total capacity of a machine. In this paper, a

Prepartition: A new paradigm for the load balance of virtual machine reservations in data centers

(1+\frac{g-2}{k}-\frac{g-1}{k^{2}})

FlexCloud: A Flexible and Extendible Simulator for Performance Evaluation of Virtual Machine Allocation

-competitive algorithm, Dynamic Bipartition-First-Fit (BFF) is proposed and proved for general case, where k is the ratio of the length of the longest interval over the length of the second longest interval for k>1 and g≥2. More results in general and special cases are obtained to improve the best-known bounds.