基于效用函数的OFDM混合业务资源调度算法*

主要研究了QoS和BE两种混合业务场景下OFDM无线网络下行链路的资源调度问题,提出了一个基于效用函数的跨层资源调度模型,其能够自适应地对两种业务进行资源的联合优化分配。该模型被抽象为一个非线性整数规划问题,优化目标是系统总效用最大化,同时满足同信道干扰（CCI）约束以及QoS业务的质量要求。将该非线性整数规划问题转换为连续松弛凸规划问题进行求解,并结合最优松弛解,提出了一种简单的动态子载波分配算法,即MMU(mix-max-utility) 算法。仿真结果验证了该调度算法能使系统较好地支持混合业务,系统     

Static heuristics for robust resource allocation of continuously executing applications

We investigate two distinct issues related to resource allocation heuristics: robustness and failure rate. The target system consists of a number of sensors feeding a set of heterogeneous applications continuously executing on a set of heterogeneous machines connected together by high-speed heterogeneous links. There are two quality of service (QoS) constraints that must be satisfied: the maximum end-to-end latency and minimum throughput. A failure occurs if no allocation is found that allows the system to meet its QoS constraints. The system is expected to operate in an uncertain environment where the workload, i.e., the load presented by the set of sensors, is likely to change unpredictably, possibly resulting in a QoS violation. The focus of this paper is the design of a static heuristic that: (a) determines a robust resource allocation, i.e., a resource allocation that maximizes the allowable increase in workload until a run-time reallocation of resources is required to avoid a QoS violation, and (b) has a very low failure rate (i.e., the percentage of instances a heuristic fails). Two such heuristics proposed in this study are a genetic algorithm and a simulated annealing heuristic. Both were “seeded” by the best solution found by using a set of fast greedy heuristics.     

Utility-based RAT selection optimization in heterogeneous wireless networks

Recent technological advances in wireless networks will enable the realization of an integrated heterogeneous wireless environment consisting of multiple Radio Access Technologies (RATs) within a network provider. One of the most important benefits is that this will allow providers to balance their traffic among their subsystems without compromising on QoS issues. In this paper we focus on the Network Selection problem to allocate terminals to the most appropriate RATs by jointly examining both users’ and providers’ preferences. We introduce three utility-based optimization functions based on the type of application that users request. We then formulate the terminal assignment problem as an optimization problem, which is recognized as NP-hard. We examine both offline and online selection and develop an optimal Branch and Bound (BB) algorithm, a Greedy heuristic, as well as three Strip Packing variations. BB behaves efficiently in both offline and online environments reducing the search procedure, while the proposed heuristics produce results close to the values we get from BB but with very low computational cost.     

A dynamic and reliability-driven scheduling algorithm for parallel real-time jobs executing on heterogeneous clusters

In this paper, a heuristic dynamic scheduling scheme for parallel real-time jobs executing on a heterogeneous cluster is presented. In our system model, parallel real-time jobs, which are modeled by directed acyclic graphs, arrive at a heterogeneous cluster following a Poisson process. A job is said to be feasible if all its tasks meet their respective deadlines. The scheduling algorithm proposed in this paper takes reliability measures into account, thereby enhancing the reliability of heterogeneous clusters without any additional hardware cost. To make scheduling results more realistic and precise, we incorporate scheduling and dispatching times into the proposed scheduling approach. An admission control mechanism is in place so that parallel real-time jobs whose deadlines cannot be guaranteed are rejected by the system. For experimental performance study, we have considered a real world application as well as synthetic workloads. Simulation results show that compared with existing scheduling algorithms in the literature, our scheduling algorithm reduces reliability cost by up to 71.4% (with an average of 63.7%) while improving schedulability over a spectrum of workload and system parameters. Furthermore, results suggest that shortening scheduling times leads to a higher guarantee ratio. Hence, if parallel scheduling algorithms are applied to shorten scheduling times, the performance of heterogeneous clusters will be further enhanced.     

Data structures for parallel resource management

The problem of resource management for many processor architectures can be viewed as the problem of simultaneously updating data structures that hold system state. An approach in which the possibility of using structures with weakened specifications is examined, is presented. Specifically, data structures that weaken the specification of a priority queue, permitting it to be updated simultaneously by multiple processes are introduced. Two structures, the concurrent heap and the software banyan are proposed, along with their associated algorithms for update. The algorithms are shown to possess attractive properties of simultaneous update and throughput. The results of simulation and actual implementations show that such data structures can improve the execution times of parallel algorithms quite significantly. These structures are proposed as possible basic building blocks for implementation of resource allocation in operating systems     

: A heterogeneous environment for HPC applications

Technological directions for innovative HPC software environments are discussed in this paper. We focus on industrial user requirements of heterogeneous multidisciplinary applications, performance portability, rapid prototyping and software reuse, integration and interoperability of standard tools. The various issues are demonstrated with reference to the PQE2000project and its programming environment Skeleton-based Integrated Environment ( ). includes a coordination language, , allowing the designers to express, in a primitive and structured way, efficient combinations of data parallelism and task parallelism. The goal is achieving fast development and good efficiency for applications in different areas. Modules developed with standard languages and tools are encapsulated into structures to form the global application. Performance models associated to the coordination language allow powerful optimizations to be introduced both at run time and at compile time without the direct intervention of the programmer. The paper also discusses the features of the environment related to debugging, performance analysis tools, visualization and graphical user interface. A discussion of the results achieved in some applications developed using the environment concludes the paper.     

GPU-aware resource management in heterogeneous cloud data centers

The Journal of Supercomputing - The power of rapid scalability and easy maintainability of cloud services is driving many high-performance computing applications from company server racks into...     

Radio resource management in emerging heterogeneous wireless networks

Deployment of heterogeneous wireless networks is spreading throughout the world as users want to be connected anytime, anywhere, and anyhow. Meanwhile, these users are increasingly interested in multimedia applications such as video streaming and Voice over IP (VoIP), which require strict Quality of Service (QoS) support. Provisioning network resources with such constraints is a challenging task. In fact, considering the availability of various access technologies (WiFi, WiMAX, or cellular networks), it is difficult for a network operator to find reliable criteria to select the best network that ensures user satisfaction while maximizing network utilization. Designing an efficient Radio Resource Management (RRM), in this type of environment, is mandatory for solving such problems. In order to provide a better understanding of RRM’s design, this paper presents a detailed investigation of key challenges that constitute an efficient RRM framework. More importantly, an overview with a classification of recent solutions, in terms of decision making, is provided along with the discussion.     

Distributed resource management with heterogeneous linear controls

Relative differentiation in distributed resource sharing can be implemented using heterogeneous linear controls with binary feedback and this method can provide efficient and weighted max–min fair resource allocations. We prove this using a discrete-time model of a single resource, shared among a number of users with heterogeneous Additive Increase Multiplicative Decrease (AIMD) controls. AIMD has been implemented in the congestion avoidance mechanism of Internet's Transmission Control Protocol (TCP) and beyond its simplicity it has been proved extremely efficient and robust. We show how AIMD can be parametrized in order to allow the scaling of user allocations according to a given set of weights. We also analyze the effects of different parameter choices on the performance and the oscillating behaviour of the system. Our analysis is supported by simulations and the results provide useful insights to the performance and the properties of distributed resource sharing.     

SkIE: A heterogeneous environment for HPC applications

Technological directions for innovative HPC software environments are discussed in this paper. We focus on industrial user requirements of heterogeneous multidisciplinary applications, performance portability, rapid prototyping and software reuse, integration and interoperability of standard tools. The various issues are demonstrated with reference to the PQE2000project and its programming environment Skeleton-based Integrated Environment (SkIE). SkIEincludes a coordination language, SkIECL, allowing the designers to express, in a primitive and structured way, efficient combinations of data parallelism and task parallelism. The goal is achieving fast development and good efficiency for applications in different areas. Modules developed with standard languages and tools are encapsulated into SkIECLstructures to form the global application. Performance models associated to the coordination language allow powerful optimizations to be introduced both at run time and at compile time without the direct intervention of the programmer. The paper also discusses the features of the SkIEenvironment related to debugging, performance analysis tools, visualization and graphical user interface. A discussion of the results achieved in some applications developed using the environment concludes the paper.     

Cost- and performance-aware resource selection for parallel software on heterogeneous cloud

Cloud providers offer flexible infrastructures and on-demand services, including the capability to deploy low cost virtual resources of many different types. However, the diversity of cloud resources followed by the important trade-off between cost and performance makes the resource selection a challenging task for users in the case of parallel communication-intensive software. The paper presents cost- and performance-aware resource selection for parallel discrete element method (DEM) software as a service (SaaS) on heterogeneous OpenStack cloud. The developed resource selection uses preliminary application-specific benchmarks of size smaller than targeted problems and the performance prediction based on speedup of parallel computations to obtain Pareto optimal solutions and to select the best configuration of containers from user's perspective. Hybrid parallelization of DEM software is developed by using OpenCL for shared-memory multi-core architectures and MPI for internode communications on distributed-memory computer clusters. Round up and proportional pricing schemes are examined and compared from a user's perspective. Lower cost of computations obtained by using the proportional pricing scheme is always preferable for users. However, the difference approaches 1.0% of the cost calculated by using proportional pricing scheme, when long lasting computations are performed. The prediction tends to underestimate the execution time of DEM SaaS, but its accuracy is sufficient to obtain the same Pareto optimal solutions by using measured and predicted execution times. Pareto front and linear scalarization propose to select configurations of containers capable of exploiting higher memory bandwidth, which is specific to memory bandwidth bound DEM computations.     

Jcluster: an efficient Java parallel environment on a large‐scale heterogeneous cluster

In this paper, we present Jcluster, an efficient Java parallel environment that provides some critical services, in particular automatic load balancing and high‐performance communication, for developing parallel applications in Java on a large‐scale heterogeneous cluster. In the Jcluster environment, we implement a task scheduler based on a transitive random stealing (TRS) algorithm. Performance evaluations show that the scheduler based on TRS can make any idle node obtain a task from another node with much fewer stealing times than random stealing (RS), which is a well‐known dynamic load‐balancing algorithm, on a large‐scale cluster. In the performance aspects of communication, with the method of asynchronously multithreaded transmission, we implement a high‐performance PVM‐like and MPI‐like message‐passing interface in pure Java. The evaluation of the communication performance is conducted among the Jcluster environment, LAM‐MPI and mpiJava on LAM‐MPI based on the Java Grande Forum's pingpong benchmark. Copyright © 2005 John Wiley & Sons, Ltd.     

SMB协议在异构网络并行FDTD计算中的应用研究

在多系统异构局域网中,由于不同操作环境的消息传递接口（MPI）程序缺乏互操作性,使得并行时域有限差分运算(FDTD)难以充分利用局域网内的计算资源。对此,提出利用应用层服务消息块（SMB）协议实现异构FDTD计算,并通过内存文件存取、内存映射数组以及引入冗余计算等方法来缓解与克服SMB通信延迟对并行性能的影响。数值模拟实例验证了新方法的可行性与正确性,所得加速比、并行效率等性能指标参数与常规同构MPI消息传递方法基本相当。     

Methods of resource management in problem-oriented computing environment

One of the important classes of computational problems is problem-oriented workflow applications executed in distributed computing environment. A problem-oriented workflow application can be represented by a directed graph whose vertices are tasks and arcs are data flows. For a problem-oriented workflow application, we can get a priori estimates of the task execution time and the amount of data to be transferred between the tasks. A distributed computing environment designed for the execution of such tasks in a certain subject domain is called problem-oriented environment. To efficiently use resources of the distributed computing environment, special scheduling algorithms are applied. Nowadays, a great number of such algorithms have been proposed. Some of them (like the DSC algorithm) take into account specific features of problem-oriented workflow applications. Others (like Min–Min algorithm) take into account many-core structure of nodes of the computational network. However, none of them takes into account both factors. In this paper, a mathematical model of problem-oriented computing environment is constructed, and a new problem-oriented scheduling (POS) algorithm is proposed. The POS algorithm takes into account both specifics of the problem-oriented jobs and multi-core structure of the computing system nodes. Results of computational experiments comparing the POS algorithm with other known scheduling algorithms are presented.     

JEL: unified resource tracking for parallel and distributed applications

When parallel applications are run in large‐scale distributed environments, such as grids, peer‐to‐peer (P2P) systems, and clouds, the set of resources used can change dynamically as machines crash, reservations end, and new resources become available. It is vital for applications to respond to these changes. Therefore, it is necessary to keep track of the available resources—a problem which is known to be notoriously difficult. In this article we argue that resource tracking must be provided as the standard functionality in the lower parts of the software stack. We propose a general solution to resource tracking: the Join–Elect–Leave (JEL) model. JEL provides unified resource tracking for parallel and distributed applications across environments. JEL is a simple yet powerful model based on notifying when resources have Joined or Left the computation. We demonstrate that JEL is suitable for resource tracking in a wide variety of programming models, ranging from the fixed resource sets traditionally used in MPI‐1 to flexible grid‐oriented programming models. We compare several JEL implementations, and show these to perform and scale well in several real‐world scenarios involving grids, clouds and P2P systems applied concurrently, and wide‐area systems with failing resources. Using JEL, we have won the first prize in a number of international distributed computing competitions. Copyright © 2010 John Wiley & Sons, Ltd.     

The programming model of ASSIST, an environment for parallel and distributed portable applications

A software development system based upon integrated skeleton technology (ASSIST) is a proposal of a new programming environment oriented to the development of parallel and distributed high-performance applications according to a unified approach. The main goals are: high-level programmability and software productivity for complex multidisciplinary applications, including data-intensive and interactive software; performance portability across different platforms, in particular large-scale platforms and grids; effective reuse of parallel software; efficient evolution of applications through versions that scale according to the underlying technologies.

The purpose of this paper is to show the principles of the proposed approach in terms of the programming model (successive papers will deal with the environment implementation and with performance evaluation). The features and the characteristics of the ASSIST programming model are described according to an operational semantics style and using examples to drive the presentation, to show the expressive power and to discuss the research issues.

According to our previous experience in structured parallel programming, in ASSIST we wish to overcome some limitations of the classical skeletons approach to improve generality and flexibility, expressive power and efficiency for irregular, dynamic and interactive applications, as well as for complex combinations of task and data parallelism. A new paradigm, called “parallel module” (parmod), is defined which, in addition to expressing the semantics of several skeletons as particular cases, is able to express more general parallel and distributed program structures, including both data-flow and nondeterministic reactive computations. ASSIST allows the programmer to design the applications in the form of generic graphs of parallel components. Another distinguishing feature is that ASSIST modules are able to utilize external objects, including shared data structures and abstract objects (e.g. CORBA), with standard interfacing mechanisms. In turn, an ASSIST application can be reused and exported as a component for other applications, possibly expressed in different formalisms.     

Deadline and energy constrained dynamic resource allocation in a heterogeneous computing environment

Energy-efficient resource allocation within clusters and data centers is important because of the growing cost of energy. We study the problem of energy-constrained dynamic allocation of tasks to a heterogeneous cluster computing environment. Our goal is to complete as many tasks by their individual deadlines and within the system energy constraint as possible given that task execution times are uncertain and the system is oversubscribed at times. We use Dynamic Voltage and Frequency Scaling (DVFS) to balance the energy consumption and execution time of each task. We design and evaluate (via simulation) a set of heuristics and filtering mechanisms for making allocations in our system. We show that the appropriate choice of filtering mechanisms improves performance more than the choice of heuristic (among the heuristics we tested).     

Distributed object management integrating distributed information in heterogeneous environment

Organizations today need an optimized environment that builds and integrates diverse operating systems, hardware and applications. Distributed Computing provides us with a facility to transparently access all data, resources in the network. Current Research, projects that distributed object management concept along with the distributed computing environment can provide a single interface to manage the complexities of a heterogeneous environment. Using a uniform framework based on the DCE standard, we attempt to create an Object Oriented Distributed Computing Environment that will help us create open distributed applications with location independence for client applications in a multivendor network.

A better model for Open Distributed Computing aided by Object Oriented concepts is offered. Some of the research issues in realizing such a model are contemplated. We integrate an Object Manager with the OSF' DCE and discuss its benefits.     

A high performance parallel DCT with OpenCL on heterogeneous computing environment

A noteworthy thing in desktop PCs is that they can provide a great opportunity to increase the performance of processing multimedia data by exploiting task- and data-parallelism with multi-core CPU and many-core GPU. This paper presents a high performance parallel implementation of 2D DCT on this heterogeneous computing environment. For this purpose, Intel TBB (threading building blocks) and OpenCL (Open Compute Language) are utilized for task- and data-parallelism, respectively. The simulation result shows that the parallel DCT implementations far the serial ones in processing speed. Especially, OpenCL implementation shows a linear speedup, a typical SIMD characteristic as the increase of 2D data sets.     

Tuning parallel applications in parallel

In this paper, we present and evaluate a parallel algorithm for parameter tuning of parallel applications. We discuss the impact of performance variability on the accuracy and efficiency of the optimization algorithm and propose a strategy to minimize the impact of this variability. We evaluate our algorithm within the Active Harmony system, an automated online/offline tuning framework. We study its performance on three benchmark codes: PSTSWM, HPL and POP. Compared to the Nelder–Mead algorithm, our algorithm finds better configurations up to seven times faster. For POP, we were able to improve the performance of a production sized run by 59%.