Bo Hu Li

Some focusing points in development of modern modeling and simulation technology

Today, M&S combined with High Performance Computing is becoming the third important means for recognizing and rebuilding the objective world besides the theory research and experiment research. At present, the tendency of system modeling and simulation technology is developing towards networkitization, virtualization, intelligentization, collaboratization and pervasivization. Based on the research fruits of paper1 and authors recent research projects, this paper further discusses the meanings of modern M&S and its architecture. Furthermore, some focusing points in recent research and application of M&S are also discussed and prospected, including networkitized M&S technology based on modern network techniques, M&S technology on synthetized nature environment, Intelligent system modeling and intelligent simulation system, M&S technology of complex system and open, complex, huge system, virtual prototyping engineering technology, high performance computer, pervasive simulation technology etc.. At last, some conclusions are given.

Temporal Task Scheduling With Constrained Service Delay for Profit Maximization in Hybrid Clouds

As cloud computing is becoming growingly popular, consumers’ tasks around the world arrive in cloud data centers. A private cloud provider aims to achieve profit maximization by intelligently scheduling tasks while guaranteeing the service delay bound of delay-tolerant tasks. However, the aperiodicity of arrival tasks brings a challenging problem of how to dynamically schedule all arrival tasks given the fact that the capacity of a private cloud provider is limited. Previous works usually provide an admission control to intelligently refuse some of arrival tasks. Nevertheless, this will decrease the throughput of a private cloud, and cause revenue loss. This paper studies the problem of how to maximize the profit of a private cloud in hybrid clouds while guaranteeing the service delay bound of delay-tolerant tasks. We propose a profit maximization algorithm (PMA) to discover the temporal variation of prices in hybrid clouds. The temporal task scheduling provided by PMA can dynamically schedule all arrival tasks to execute in private and public clouds. The sub problem in each iteration of PMA is solved by the proposed hybrid heuristic optimization algorithm, simulated annealing particle swarm optimization (SAPSO). Besides, SAPSO is compared with existing baseline algorithms. Extensive simulation experiments demonstrate that the proposed method can greatly increase the throughput and the profit of a private cloud while guaranteeing the service delay bound.

New Advances of the Research on Cloud Simulation

Based on the research fruits of Cloud Simulation Platform [1], this paper expounds the latest research results of our team in cloud simulation, including the further research on the technology content and features of cloud simulation, architecture and service patterns of the cloud simulation system, technology system and several improved key technologies ( individuation virtual desktop technology, multi-users oriented dynamic building technology of virtual simulation environment, fault-tolerant migration technology for simulation resources, high performance cloud simulation supported co-simulation platform technology ) of cloud simulation and typical application demonstration system. The primary research and practice show that the proposed latest research results can better support “cloud simulation” pattern, which users can access services of simulation resource and capability on demand anytime and anywhere through network and cloud simulation platform, to accomplish varied activities in the whole simulation life-circle. Finally, this paper gives the prospect of the future work of cloud simulation.

CAWSAC: Cost-Aware Workload Scheduling and Admission Control for Distributed Cloud Data Centers

Multiple heterogeneous applications concurrently run in distributed cloud data centers (CDCs) for better performance and lower cost. There is a highly challenging problem of how to minimize the total cost of a CDCs provider in a market where the bandwidth and energy cost show geographical diversity. To solve the problem, this paper first proposes a revenue-based workload admission control method to judiciously admit requests by considering factors including priority, revenue and the expected response time. Then, this paper presents a cost-aware workload scheduling method to jointly optimize the number of active servers in each CDC, and the selection of Internet service providers for the CDCs provider. Finally, trace-driven simulation results demonstrate that the proposed methods can greatly reduce the total cost and increase the throughput of the CDCs provider in comparison to existing methods. Note to Practitioners-A cloud provider deploys its applications in geographically distributed CDCs to improve stability and reliability. For cost and performance, each CDC provides services through multiple ISPs that deliver traffic between millions of users and the CDCs provider. The geographical diversity of the bandwidth and energy cost brings the CDCs provider a big challenge of how to minimize the bandwidth and energy cost of the CDCs provider. This paper first proposes a revenue-based workload admission control method to selectively admit requests. Then, this paper proposes a cost-aware workload scheduling method to allocate requests among multiple available Internet service providers connecting to distributed CDCs. The scheduling strategy can intelligently dispatch requests, and achieve lower cost and higher throughput for the CDCs provider.

TTSA: An Effective Scheduling Approach for Delay Bounded Tasks in Hybrid Clouds

The economy of scale provided by cloud attracts a growing number of organizations and industrial companies to deploy their applications in cloud data centers (CDCs) and to provide services to users around the world. The uncertainty of arriving tasks makes it a big challenge for private CDC to cost-effectively schedule delay bounded tasks without exceeding their delay bounds. Unlike previous studies, this paper takes into account the cost minimization problem for private CDC in hybrid clouds, where the energy price of private CDC and execution price of public clouds both show the temporal diversity. Then, this paper proposes a temporal task scheduling algorithm (TTSA) to effectively dispatch all arriving tasks to private CDC and public clouds. In each iteration of TTSA, the cost minimization problem is modeled as a mixed integer linear program and solved by a hybrid simulated-annealing particle-swarm-optimization. The experimental results demonstrate that compared with the existing methods, the optimal or suboptimal scheduling strategy produced by TTSA can efficiently increase the throughput and reduce the cost of private CDC while meeting the delay bounds of all the tasks.

TRS: Temporal Request Scheduling with bounded delay assurance in a green cloud data center

Abstract The growing deployment of Internet services in cloud data centers significantly increases the grid energy cost of cloud providers. Considering the environmental effect, many of current cloud providers migrate to green cloud data centers (GCDCs), and seek to reduce the usage of brown energy by partially (or entirely) adopting renewable energy sources. However, the temporal diversity in the grid price, wind speed and solar irradiance makes it a big challenge to minimize the grid energy cost of a GCDC while meeting the performance of each delay bounded request. This work proposes a Temporal Request Scheduling algorithm (TRS) that jointly considers the temporal diversity. TRS considers the long tail in real-life requests’ delay, and can provide strict delay assurance to all arriving requests by scheduling them to execute within their delay bound. Besides, this work explicitly provides mathematical modeling of the relation between the service rate in a GCDC and the refusal of delay bounded requests. Specifically, TRS solves a constrained nonlinear optimization problem by a hybrid meta-heuristic in each of its iterations. Compared with some existing scheduling methods, TRS can achieve higher throughput and lower grid energy cost for a GCDC while meeting each request’s delay requirement.

Cost-aware request routing in multi-geography cloud data centres using software-defined networking

Current geographically distributed cloud data centres (CDCs) require gigantic energy and bandwidth costs to provide multiple cloud applications to users around the world. Previous studies only focus on energy cost minimisation in distributed CDCs. However, a CDC provider needs to deliver gigantic data between users and distributed CDCs through internet service providers (ISPs). Geographical diversity of bandwidth and energy costs brings a highly challenging problem of how to minimise the total cost of a CDC provider. With the recently emerging software-defined networking, we study the total cost minimisation problem for a CDC provider by exploiting geographical diversity of energy and bandwidth costs. We formulate the total cost minimisation problem as a mixed integer non-linear programming (MINLP). Then, we develop heuristic algorithms to solve the problem and to provide a cost-aware request routing for joint optimisation of the selection of ISPs and the number of servers in distributed CDCs. Besides, to tackle the dynamic workload in distributed CDCs, this article proposes a regression-based workload prediction method to obtain future incoming workload. Finally, this work evaluates the cost-aware request routing by trace-driven simulation and compares it with the existing approaches to demonstrate its effectiveness.

Workload-aware request routing in cloud data center using software-defined networking

Large latency of applications will bring revenue loss to cloud infrastructure providers in cloud data center. The ex- isting controllers of software-defined networking architecture can fetch and process traffic information in network. Therefore, the controllers can only optimize the network latency of applications. However, the serving latency of applications is also an important factor in delivered user-experience for arrival requests. Unintel- ligent request routing will cause large serving latency if arrival requests are allocated to overloaded virtual machines. To deal with the request routing problem, this work proposes the workload- aware software-defined networking controller architecture. Then, request routing algorithms are proposed to minimize the total round trip time for every type of requests by considering the congestion in network and the workload in VMs. This work finally provides the evaluation of the proposed algorithms on a simulated prototype. The simulation results show that the proposed methodology is ef- ficient using comparison with the existing approaches.

An Approach to Optimized Resource Allocation for Cloud Simulation Platform

Resource allocation for simulation applications in cloud simulation environment brings new challenges to infrastructure service providers. In order to meet the constraint of SLA and to allocate the available virtualized resources optimally, this paper first presents autonomic resource management architecture, and then proposes a resource allocation algorithm for infrastructure service providers who want to minimize infrastructure cost and SLA violations. Our proposed algorithm can maximize the overall profit of infrastructure service providers when SLA guarantees are satisfied or violated in a dynamic resource sharing cloud simulation platform. The experimental evaluation with a realistic workload in cloud simulation platform, and the comparison with the existing algorithm demonstrate the feasibility of the algorithm and allow a cost-effective usage of resources in cloud simulation platform.

Web-HLA and Service-Enabled RTI in the Simulation Grid

HLA-based simulations in a grid environment have now become a main research hotspot in the M&S community, but there are many shortcomings of the current HLA running in a grid environment. This paper analyzes the analogies between HLA and OGSA from the software architecture point of view, and points out the service-oriented method should be introduced into the three components of HLA to overcome its shortcomings. This paper proposes an expanded running architecture that can integrate the HLA with OGSA and realizes a service-enabled RTI (SE-RTI). In addition, in order to handle the bottleneck problem that is how to efficiently realize the HLA time management mechanism, this paper proposes a centralized way by which the CRC of the SE-RTI takes charge of the time management and the dispatching of TSO events of each federate. Benchmark experiments indicate that the running velocity of simulations in Internet or WAN is properly improved.