Grid resource management policies for load-balancing and energy-saving by vacation queuing theory

The resource management is the central component of grid system. The analysis of the workload log file of LCG including the job arrival and the resource utilization daily cycle shows that the idle sites in the Grid are the source of load imbalance and energy waste. Here we focus on these two issues: balancing the workload by transferring jobs to idle sites at prime time to minimize the response time and maximize the resource utilization; power management by switch the idle sites to sleeping mode at non-prime time to minimize the energy consume. We form the M/G/1 queue model with server vacations, startup and closedown to analysis the performance metrics to instruct the design of load-balancing and energy-saving policies. We provide our Adaptive Receiver Initiated (ARI) load-balancing strategy and power-management policy for energy-saving. The simulation experiments prove the accuracy of our analysis and the comparisons results indicate our policies are largely suitable for large-scale heterogeneous grid environment.     

An overhead and resource contention aware analytical model for overloaded Web servers

A Web server, when overloaded, shows a severe degradation of goodput initially, with the eventual settling of goodput as load increases further. Traditional performance models have failed to capture this behavior. In this paper, we propose an analytical model, which is a two-stage and layered queuing model of the Web server, which is able to reproduce this behavior. We do this by explicitly modelling the overhead processing, the user abandonment and retry behavior, and the contention for resources, for the FIFO and LIFO queuing disciplines. We show that LIFO provides better goodput in most overload situations. We compare our model predictions with experimental results from a test bed and find that our results match well with measurements.     

Probabilistic model checking for the quantification of DoS security threats

Secure authentication features of communication and electronic commerce protocols involve computationally expensive and memory intensive cryptographic operations that have the potential to be turned into denial-of-service (DoS) exploits. Recent proposals attempt to improve DoS resistance by implementing a trade-off between the resources required for the potential victim(s) with the resources used by a prospective attacker. Such improvements have been proposed for the Internet Key Exchange (IKE), the Just Fast Keying (JFK) key agreement protocol and the Secure Sockets Layer (SSL/TLS) protocol. In present article, we introduce probabilistic model checking as an efficient tool-assisted approach for systematically quantifying DoS security threats. We model a security protocol with a fixed network topology using probabilistic specifications for the protocol participants. We attach into the protocol model, a probabilistic attacker model which performs DoS related actions with assigned cost values. The costs for the protocol participants and the attacker reflect the level of some resource expenditure (memory, processing capacity or communication bandwidth) for the associated actions. From the developed model we obtain a Discrete Time Markov Chain (DTMC) via property preserving discrete-time semantics. The DTMC model is verified using the PRISM model checker that produces probabilistic estimates for the analyzed DoS threat. In this way, it is possible to evaluate the level of resource expenditure for the attacker, beyond which the likelihood of widespread attack is reduced and subsequently to compare alternative design considerations for optimal resistance to the analyzed DoS threat. Our approach is validated through the analysis of the Host Identity Protocol (HIP). The HIP base-exchange is seen as a cryptographic key-exchange protocol with special features related to DoS protection. We analyze a serious DoS threat, for which we provide probabilistic estimates, as well as results for the associated attacker and participants' costs.     

Efficient integration of fine-grained access control and resource brokering in grid

In this paper, we present a novel resource brokering service for grid systems which considers authorization policies of the grid nodes in the process of selecting the resources to be assigned to a request. We argue such an integration is needed to avoid scheduling requests onto resources the policies of which do not authorize their execution. Our service, implemented in Globus as a part of Monitoring and Discovery Service (MDS), is based on the concept of fine-grained access control (FGAC) which enables participating grid nodes to specify fine-grained policies concerning the conditions under which grid clients can access their resources. Since the process of evaluating authorization policies, in addition to checking the resource requirements, can be a potential bottleneck for a large scale grid, we also analyze the problem of the efficient evaluation of FGAC policies. In this context, we present GroupByRule, a novel method for policy organization and compare its performance with other strategies.

Designing a fuzzy system for controlling the armament fire in dynamic siege

This paper presents a fuzzy system for controlling the fire command in a surface-to-surface engagement by assuming siege of friendly side by enemy forces. The system must be capable of interacting with a dynamic and uncertain battle world in a real-time manner. In siege situation, it is assumed that the enemy troops are advancing toward friendly side in different directions and the defence resources are limited and nonrenewable at a single platform. Thus, resource allocation problem in a real-time manner is an important and vital component of battle management until coming of auxiliary forces. This paper addresses the design of a fuzzy system as an efficient tool for real-time decision-making in order to optimize the defense resource allocation in engagement.     

基于群决策的多Agent资源管理模型

在大部分系统中,资源都是有限供给的.因此,为了提高系统的性能,保证资源使用的效率与公平,必须对资源进行有效的管理.而合理分配资源和解决资源使用冲突是系统资源管理的核心问题.基于群决策方法提出了一个多Agent资源管理模型,以动态优先级的方式灵活地解决了系统资源分配与使用冲突问题.     

一种强安全约束机制下的云计算任务调度方案

针对云计算技术中的资源调度时间长、开销大、不考虑调度安全的问题,本文在对常见的云环境下工作流任务的资源调度问题进行建模的基础上,提出了一种强安全约束机制下的云计算任务调度方案。该方案按照所用方法的安全强度,再加上相关软件的安全程度,赋予虚拟机不同的安全等级;另一方面,其按照用户的需求等诸多方面,能够赋予操作不同的安全需求等级;最后使用变近邻粒子群算法对该问题进行了求解。在CloudSim仿真平台上的实验结果证明,本文提出的算法与用最大－最小蚁群算法、遗传算法与该算法相比,该算法具有很好的可用性和寻优能力。     

基于非合作博弈的OFDMA-WLAN系统资源分配算法研究

为满足无线局域网中基于正交频分多址技术(OFDMA-WLAN)的多用户通信需求,提出了一种基于非合作博弈的资源分配策略,利用博弈论这一有效工具对无线网络服务质量的资源分配问题进行研究,同时将用户对信道质量的不同需求转化为多用户对不同信道资源分配的非合作博弈竞争问题。此外,还将纳什均衡问题(NEP)细分为数个变分不等式子问题,通过凸优化函数对这些子问题分别求解。数值分析结果表明,所提算法在资源分配的公平性和数据传输率方面达到了更好的折中。     

Design optimization of resource combination for collaborative logistics network under uncertainty

Collaborative logistics networks (CLNs) are considered to be an effective organizational form for business cooperation that provides high stability and low cost. One common key issue regarding CLN resource combination is the network design optimization problem under discrete uncertainty (DU-CLNDOP). Operational environment changes and information uncertainty in network designs, due to partner selection, resource constrains and network robustness, must be effectively controlled from the system perspective. Therefore, a general two-stage quantitative framework that enables decision makers to select the optimal network design scheme for CLNs under uncertainty is proposed in this paper. Phase 1 calculates the simulation result of each hypothetical scenario of CLN resource combination using the expected value model with robust constraints. Phase 2 selects the optimal network design scheme for DU-CLNDOP using the orthogonal experiment design method. The validity of the model and method are verified via an illustrative example.