J. Y. Shi

Elastic resource provisioning for scientific workflow scheduling in cloud under budget and deadline constraints

With the popularization and development of cloud computing, lots of scientific computing applications are conducted in cloud environments. However, current application scenario of scientific computing is also becoming increasingly dynamic and complicated, such as unpredictable submission times of jobs, different priorities of jobs, deadlines and budget constraints of executing jobs. Thus, how to perform scientific computing efficiently in cloud has become an urgent problem. To address this problem, we design an elastic resource provisioning and task scheduling mechanism to perform scientific workflow jobs in cloud. The goal of this mechanism is to complete as many high-priority workflow jobs as possible under budget and deadline constraints. This mechanism consists of four steps: job preprocessing, job admission control, elastic resource provisioning and task scheduling. We perform the evaluation with four kinds of real scientific workflow jobs under different budget constraints. We also consider the uncertainties of task runtime estimations, provisioning delays, and failures in evaluation. The results show that in most cases our mechanism achieves a better performance than other mechanisms. In addition, the uncertainties of task runtime estimations, VM provisioning delays, and task failures do not have major impact on the mechanism’s performance.

Fast multi-resource allocation with patterns in large scale cloud data center ☆

Abstract How to achieve fast and efficient resource allocation is an important optimization problem of resource management in cloud data center. On one hand, in order to ensure the user experience of resource requesting, the system has to achieve fast resource allocation to timely process resource requests; on the other hand, in order to ensure the efficiency of resource allocation, how to allocate multi-dimensional resource requests to servers needs to be optimized, such that servers resource utilization can be improved. However, most of existing approaches focus on finding out the mapping of each specific resource request to each specific server. This makes the complexity of resource allocation problem increases with the size of data center. Thus, these approaches cannot achieve fast and efficient resource allocation for large-scale data center. To address this problem, we propose a pattern based resource allocation mechanism based on the following findings. In a real-world cloud environment, the resource requests are usually classified into limited types. Thus, the mechanism first utilizes this feature to generate pattern information, which indicates which types of resource requests are suitable to be allocated together to a server. Then, the mechanism uses the pattern information as guidelines to make fast resource allocation decision and fully utilize servers multidimensional resources. Simulation experiments based on real and synthetic traces have shown that our mechanism significantly improves systems resource utilization and reduces the overall number of used servers.

Two-Phase Online Virtual Machine Placement in Heterogeneous Cloud Data Center

With the rapid development and popularity of cloud computing technology, more and more Collaborative Virtual Environment (CVE) systems are migrated to cloud computing environment to improve the effectiveness of resource usage. Virtual Machine (VM) placement in cloud data center is a key issue of providing high-efficient cloud platform for CVE system. However, most existing VM placement algorithms ignore the following characteristics of actual cloud environment: (1) VMs deployment requests arrive and leave dynamically, (2) Cloud data center usually consists of many heterogeneous Physical Machines (PMs). Ignoring these two characteristics result in an inefficient and unbalanced use of multiple resources of PMs. Thus using these algorithms directly will lead to a poor resource utilization. In this article, we propose a two-phase online VM placement algorithm, which helps the cloud data center to minimize different resource usages and aims at a more efficient use of multiple resources. Our algorithm selects the most suitable PM type for VM based on Cosine Similarity, and adaptively maps VMs to PMs by using an approximation algorithm. The proposed algorithm is evaluated by simulations. Experimental results show our proposed algorithm ensures a more efficient use of multiple resources over the existing approaches.

AMS-02 Monte Carlo Production in Science Operation Centre at Southeast University

Southeast University (SEU) Science Operation Centre (SOC) is one of the computing centres of the Alpha Magnetic Spectrometer (AMS-02) experiment. It provides 2016 CPU cores for AMS Monte Carlo production and a dedicated ~1Gbps Long Fat Network (LFN) for AMS data transmission between SEU and CERN. In this paper, the development and deployment of SEU SOCs automated Monte Carlo production management system is discussed in detail. Data transmission optimizations are further introduced in order to speed up the data transfer in LFN between SEU SOC and CERN. In addition, monitoring tool for SEU SOCs Monte Carlo production is also presented.

Resource provisioning optimization for service hosting on cloud platform

With the popularity of cloud computing technology, service hosting is used as a typical model to deploy different kinds of services on cloud platform. In recent years, how to effectively provide resources for service hosting has attracted more and more attention. However, most of the existing works only focused on how to effectively provide virtual machines for service hosting. They ignored how to efficiently place these virtual machines into physical servers, when considering multidimensional resource requirements. This may result in unreasonable virtual machine placement in servers, thereby causing the underutilization of resource. To address this problem, we propose a novel resource provisioning method including virtual machine provisioning for hosting service and virtual machine placement in servers. The proposed method decides how many virtual machines should be provided for each service by utilizing queuing theory. Then based on the virtual machines to be provided, the proposed method models the virtual machine placement problem as a variant of cutting stock problem, and decides how many servers should be provided by solving this problem. The proposed method is evaluated by simulations. Experimental results show the proposed method achieves a better performance than these baseline methods.