嵌入式分布实时系统自适应资源管理架构

分析了开放嵌入式分布实时(Distributed Real-time and Embedded,DRE)系统的自适应资源管理需求,针对传统静态资源管理方法的不足,提出了一种面向开放DRE系统的自适应资源管理架构;该架构采用动态任务管理、实时资源分配和自适应控制三种关键技术,能够更加适用于开放、动态的环境,有效应对资源约束和资源状态、应用程序QoS的变化;提高系统性能和稳定性;屏蔽底层硬件资源的异构性,为上层应用提供统一的服务接口。     

一种基于周期性特征的数据中心在线负载资源预测方法

以Web服务、流式计算为代表的在线负载是数据中心的主要负载之一。在线负载请求到达的波动性驱动其资源需求的动态变化。因此,快速、准确的在线负载资源预测是数据中心合理分配资源、保障负载执行效率的关键。然而,既有在线负载资源预测方法或无法进行长期准确的预测,或依赖于海量样本数据并具有较大的时间开销。为此,提出了一种基于请求周期性特征的在线负载资源预测方法PRP。PRP面向在线负载请求的周期性特征,采用自相关函数识别负载资源使用的变化周期;基于变化周期进行资源使用样本序列分割及资源使用子序列分类;最终基于分类子序列采用线性加权方法预测在线负载的资源需求。实验结果表明,PRP在预测准确度和时间开销方面有较大的提升。     

服务网格虚拟服务动态部署和自适应策略

以"服务"的形式包装网格资源已成为一种趋势,并得到网格界的一致认可.为了更加充分灵活的利用网格资源,提出了一个网格虚拟服务动态部署架构以及基于此架构的服务平衡调度算法,通过服务按需部署和实时监控,动态调整资源在不同任务间的分配,并在需要时进行任务迁移,保证应用的服务质量.实验结果表明此系统较其它系统在资源利用率、QoS命中率上都有一定的提高.     

A Hybrid Offline-Online Approach to Adaptive Downlink Resource Allocation Over LTE

Radio resource management mechanisms in current and future wireless networks is expected to face an enormous challenge due to the ever increasing demand for bandwidth and latency sensitive applications on mobile devices. This is because an optimal resource allocation scheme which attempts to multiplex the available bandwidth in order to maximize Quality of service (QoS), will pose an exponential computational burden at eNodeB. In order to minimize such computational overhead, this work proposes a hybrid offline-online resource allocation strategy which effectively allocates all the available resources among flows such that their QoS requirements are satisfied. The flows are firstly classified into priority buckets based on real-time criticality factors. During the offline phase, the scheduler attempts to maintain the system load within a pre-specified safe threshold value by selecting an appropriate number of buckets. This offline selection procedure makes use of supervisory control theory of discrete event systems to synthesize an offline scheduler. Next, we have devised an online resource allocation strategy which runs on top of the offline policy and attempts to minimize the impact of the inherent variability in wireless networks. Simulation results show that the proposed scheduling framework is able to provide satisfactory QoS to all end users in most practical scenarios.      

A game-theoretic method of fair resource allocation for cloud computing services

As cloud-based services become more numerous and dynamic, resource provisioning becomes more and more challenging. A QoS constrained resource allocation problem is considered in this paper, in which service demanders intend to solve sophisticated parallel computing problem by requesting the usage of resources across a cloud-based network, and a cost of each computational service depends on the amount of computation. Game theory is used to solve the problem of resource allocation. A practical approximated solution with the following two steps is proposed. First, each participant solves its optimal problem independently, without consideration of the multiplexing of resource assignments. A Binary Integer Programming method is proposed to solve the independent optimization. Second, an evolutionary mechanism is designed, which changes multiplexed strategies of the initial optimal solutions of different participants with minimizing their efficiency losses. The algorithms in the evolutionary mechanism take both optimization and fairness into account. It is demonstrated that Nash equilibrium always exists if the resource allocation game has feasible solutions.     

SLA-Driven Clustering of QoS-Aware Application Servers

In this paper, we discuss the design, implementation, and experimental evaluation of a middleware architecture for enabling service level agreement (SLA)-driven clustering of QoS-aware application servers. Our middleware architecture supports application server technologies with dynamic resource management: application servers can dynamically change the amount of clustered resources assigned to hosted applications on-demand so as to meet application-level quality of service (QoS) requirements. These requirements can include timeliness, availability, and high throughput and are specified in SLAs. A prototype of our architecture has been implemented using the open-source J2EE application server JBoss. The evaluation of this prototype shows that our approach makes possible JBoss' resource usage optimization and allows JBoss to effectively meet the QoS requirements of the applications it hosts, i.e., to honor the SLAs of those applications     

Advanced multicast class-based bandwidth over-provisioning

Dynamic bandwidth aggregate over-provisioning has been proposed to provide differentiated services with scalable Quality of Service (QoS) control. Previous research showed that efficient over-reservation control allows minimizing QoS control overhead with reduced waste of bandwidth and blocking probability. However, existing over-reservation centric solutions are still inefficient in the face of network dynamics, as they are not able to accommodate the dynamic service demands, thus failing to optimize the bandwidth usage. This paper proposes a new Advanced Class-based resource Over-Reservation (ACOR) solution for multicast sessions, which seeks increased network resources utilization while minimizing QoS control overhead. More specifically, ACOR is able to dynamically update the share of bandwidth between different classes of service, providing improved results with less complexity than current solutions. Our analytical and simulation results prove the cost-effective and scalable QoS control capabilities of ACOR in decentralized networks with multiple distributed ingress routers coordinating the QoS control.     

From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multi-grid computing environments

The exploitation of service oriented technologies, such as Grid computing, is being boosted by the current service oriented economy trend, leading to a growing need of Quality of Service (QoS) mechanisms. However, Grid computing was created to provide vast amounts of computational power but in a best effort way. Providing QoS guarantees is therefore a very difficult and complex task due to the distributed and heterogeneous nature of their resources, specially the volunteer computing resources (e.g., desktop resources).The scope of this paper is to empower an integrated multi QoS support suitable for Grid Computing environments made of either dedicated and volunteer resources, even taking advantage of that fact. The QoS is provided through SLAs by exploiting different available scheduling mechanisms in a coordinated way, and applying appropriate resource usage optimization techniques. It is based on the differentiated use of reservations and scheduling in advance techniques, enhanced with the integration of rescheduling techniques that improve the allocation decisions already made, achieving a higher resource utilization and still ensuring the agreed QoS. As a result, our proposal enhances best-effort Grid environments by providing QoS aware scheduling capabilities.This proposal has been validated by means of a set of experiments performed in a real Grid testbed. Results show how the proposed framework effectively harnesses the specific capabilities of the underlying resources to provide every user with the desired QoS level, while, at the same time, optimizing the resources’ usage.     

基于边缘计算的新型任务卸载与资源分配策略

针对移动边缘计算（MEC）中密集型任务卸载时，系统开销较大和延时抖动明显的问题，提出一种新型资源分配策略。首先在系统时延约束下，分析了系统任务执行开销与终端设备的资源分配机制；其次建立了基于计算卸载和任务分配的联合凸优化目标；最后采用拉格朗日乘子法进行迭代更新得到最优解。仿真结果表明，所提任务卸载与资源分配方案在保证用户服务质量的同时降低了任务执行开销，并有效提升了MEC系统性能。     

Resource reclaiming in multiprocessor real-time systems

Most real-time scheduling algorithms schedule tasks with regard to their worst case computation times. Resources reclaiming refers to the problem of utilizing the resources left unused by a task when it executes in less than its worst case computation time, or when a task is deleted from the current schedule. Dynamic resource reclaiming algorithms that are effective, avoid any run time anomalies, and have bounded overhead costs that are independent of the number of tasks in the schedule are presented. Each task is assumed to have a worst case computation time, a deadline, and a set of resource requirements. The algorithms utilize the information given in a multiprocessor task schedule and perform online local optimization. The effectiveness of the algorithms is demonstrated through simulation studies     

Flexible Grid service management through resource partitioning

In this paper, a distributed and scalable Grid service management architecture is presented. The proposed architecture is capable of monitoring task submission behaviour and deriving Grid service class characteristics, for use in performing automated computational, storage and network resource-to-service partitioning. This partitioning of Grid resources amongst service classes (each service class is assigned exclusive usage of a distinct subset of the available Grid resources), along with the dynamic deployment of Grid management components dedicated and tuned to the requirements of a particular service class introduces the concept of Virtual Private Grids. We present two distinct algorithmic approaches for the resource partitioning problem, the first based on Divisible Load Theory (DLT) and the second built on Genetic Algorithms (GA). The advantages and drawbacks of each approach are discussed and their performance is evaluated on a sample Grid topology using NSGrid, an ns-2 based Grid simulator. Results show that the use of this Service Management Architecture in combination with the proposed algorithms improves computational and network resource efficiency, simplifies schedule making decisions, reduces the overall complexity of managing the Grid system, and at the same time improves Grid QoS support (with regard to job response times) by automatically assigning Grid resources to the different service classes prior to scheduling.     

A distributed multiple dimensional QoS constrained resource scheduling optimization policy in computational grid

This paper is to solve efficient QoS based resource scheduling in computational grid. It defines a set of QoS dimensions with utility function for each dimensions, uses a market model for distributed optimization to maximize the global utility. The user specifies its requirement by a utility function. A utility function can be specified for each QoS dimension. In the grid, grid task agent acted as consumer pay for the grid resource and resource providers get profits from task agents. The task agent' utility can then be defined as a weighted sum of single-dimensional QoS utility function. QoS based grid resource scheduling optimization is decomposed to two subproblems: joint optimization of resource user and resource provider in grid market. An iterative multiple QoS scheduling algorithm that is used to perform optimal multiple QoS based resource scheduling. The grid users propose payment for the resource providers, while the resource providers set a price for each resource. The experiments show that optimal QoS based resource scheduling involves less overhead and leads to more efficient resource allocation than no optimal resource allocation.     

Integrated QoS-aware resource management and scheduling with multi-resource constraints

In dynamic real-time systems such as sensor networks, mobile ad hoc networking and autonomous systems, the mapping between level of service and resource requirements is often not fixed. Instead, the mapping depends on a combination of level of service and outside environmental factors over which the application has no direct control. An example of an application where environmental factors play a significant role is radar tracking. In radar systems, resources must be shared by a set of radar tasks including tracking, searching and target confirmation tasks. Environmental factors such as noise, heating constraints of the radar and the speed, distance and maneuverability of tracked targets dynamically affect the mapping between the level of service and resource requirements. The QoS manager in a radar system must be adaptive, responding to dynamic changes in the environment by efficiently reallocating resource to maintain an acceptable level of service. In this paper, we present an integrated QoS optimization and dwell scheduling scheme for a radar tracking application. QoS optimization is performed using the Q-RAM (Baugh, 1973, ghosh-et al.,2004a approach. Heuristics are used to achieve a two order magnitude of reduction in optimization time over the basic Q-RAM approach allowing QoS optimization and scheduling of a 100 task radar problem to be performed in as little as 700 ms with only a 0.1% QoS penality over Q-RAM alone. Sourav Ghosh received the B.Tech degree in Electronics and Electrical Communications Engineering from Indian Institute of Technology, Kharagpur, India, in 1997, and the M.S. and Ph.D. degrees in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, PA, USA, in 1999 and 2004 respectively. He is currently working as a Technical Staff at Oracle in Cluster Database Group (RAC). His research interest includes OS resource management and scheduling, performance analysis, Quality of Service (QoS) and real-time systems.     

Dynamic bandwidth allocation for 3G wireless systems—A fuzzy approach

3G Wireless systems are to support multiple classes of traffic with widely different characteristics and quality of service (QoS) requirements. A major challenge in this system is to guarantee the promised QoS for the admitted users, while maximizing the resource allocation through dynamic resource sharing. In the case of multimedia call, each of the services has its own distinct QoS requirements concerning probability of blocking (P_B), service access delay (SAD), and access delay variation (ADV). The 3G wireless system attempts to deliver the required QoS by allocating appropriate resources (e.g. bandwidth, buffers), and bandwidth allocation is a key in achieving this. Dynamic bandwidth allocation policies reported so far in the literature deal with audio source only. They do not consider QoS requirements. In this work, a fuzzy logic (FL)-based dynamic bandwidth allocation algorithm for multimedia services with multiple QoS (P_B, SAD, ADV, and the arrival rate) requirements are presented and analyzed. Here, each service can declare a range of acceptable QoS levels (e.g. high, medium, and low). As QoS demand varies, the proposed algorithm allocates the best possible bandwidth to each of the services. This maximizes the utilization and fair distribution of resources. The proposed allocation method is validated in a variety of scenarios. The results show that the required QoS can be obtained by appropriately tuning the fuzzy logic controller (FLC).     

Adaptive resource management for dynamic distributed real-time applications

The dynamic distributed real-time applications run on clusters with varying execution time, so re-allocation of resources is critical to meet the applications’s deadline. In this paper we present two adaptive recourse management techniques for dynamic real-time applications by employing the prediction of responses of real-time tasks that operate in time sharing environment and run-time analysis of scheduling policies. Prediction of response time for resource reallocation is accomplished by historical profiling of applications’ resource usage to estimate resource requirements on the target machine and a probabilistic approach is applied for calculating the queuing delay that a process will experience on distributed hosts. Results show that as compared to statistical and worst-case approaches, our technique uses system resource more efficiently.     

An opportunistic resource allocation approach for mixed QoS and non-QoS connections in OFDMA wireless networks

An opportunistic resource allocation approach is proposed to guarantee both fair resource allocation and high system throughput under combinations of QoS and non-QoS connections in OFDMA networks. This approach features dynamic connection classification and packet prioritization based on real-time network conditions and QoS constraints. A classifier is first employed to prioritize QoS connections by observing the channel state of each subscriber station and the utilization of network resources. It performs a finite-horizon Markov decision process with dynamic rules affected by system load. The transmission order of packets is then determined by an opportunistic multiservice scheduler according to the QoS requirements of connections and the output of the classifier. Having the scheduling result, an allocator assigns slots to the scheduled packets, and its output is linked back to the connection classifier through a resource usage observer for all subscriber stations. The sub-channel allocation problem is also solved by cooperation between the slot allocator and the packet scheduler. Results of numerical analysis and NS2 simulation confirm the advantages claimed above. The same conclusion can also be drawn from the comparison with several existing approaches in terms of system throughput, service successful ratio, average spectral efficiency, and system revenue.     

Task complexity analysis and QoS management for mapping dynamic video-processing tasks on a multi-core platform

This paper addresses efficient mapping and reconfiguration of advanced video applications onto a general purpose multi-core platform. By accurately modeling the resource usage for an application, allocation of processing resources on the platform can be based on the actually needed resources instead of a worst-case approach, thereby improving Quality-of-Service (QoS). Here, we exploit a new and strongly upcoming class of dynamic video applications based on image and content analysis for resource management and control. Such applications are characterized by irregular computing behavior and memory usage. It is shown that with linear models and statistical techniques based on the Markov modeling, a rather good accuracy (94?C97%) for predicting the resource usage can be obtained. This prediction accuracy is so good that it allows resource prediction at runtime, thereby leading to an actively controlled system management.     

Formal QoS Policy Based Grid Resource Provisioning Framework

Grid resource provisioning is a complex task that is often compromised due to non-availability of the desired resources. The heterogeneous and dynamic nature of the Grid resources depends on the Quality of Service (QoS) based resource provisioning for allocation of appropriate resources to Grid applications. Until resource provisioning is considered a fundamental capability, predictable QoS cannot be delivered to the Grid consumers. A resource provisioning policy based on QoS parameters is required for efficient Grid resource provisioning. In this paper a resource provisioning framework has been proposed. This framework offers resource provisioning policy that caters to provisioned resource allocation and resource scheduling. The policy rules have been specified in XML schema. The policy has been validated by Z Formal specification language and implementation has been demonstrated through a case study. The experimental results demonstrate that QoS based provisioned approach is effective in minimizing cost and submission burst time of applications in comparison to the existing approaches.     

User satisfaction aware routing and energy modeling of polymorphic network on chip architecture

In mobile devices, multiple applications contend for limited resources in the underlying embedded system framework. Application resource requirements in mobile systems vary by computation needs, energy consumption and user interaction frequency. Quality of service (QoS) is the predominant metric of choice to manage resources among contending applications. Resource allocation policies to support static QoS for applications do not reflect the changing demands of the user in contemporary network on chip (NoC) based embedded architectures. User satisfaction with the user interactions and user interface design ought to be the primary design driver. Some recent research has integrated a saturating, non-linear user satisfaction function in the application thread scheduler. The application and operating system level user satisfaction research assumes that the throughput of inter-thread edges is limited only by the computational constraints of the nodes. With NoC, however, NoC resource allocation policies play an important role in determining the inter-thread communication flow’s throughput and the resulting application level user satisfaction. In this paper, we filter down the user satisfaction from an application layer attribute to a router level attribute to improve the resource and energy utilization for routing in order to leverage the user satisfaction at the application and system level. We demonstrate that this technique improves the user satisfaction of audio (MP3) application by 10% while maintaining the user satisfaction of video (MPEG-2) application. Experiments also show that a fixed energy source can be extended for an average of 18% of the time using the NoC user satisfaction based energy optimization proposed in this research.     

QoS-Aware Admission Control and Dynamic Resource Provisioning Framework in Ubiquitous Multimedia Computing Environments

A ubiquitous service deployment is emerging in the multimedia, networking, and wireless mobile computing area. Therefore, there has been an increasing demand for ubiquitous computing environments to support a certain degree of quality of service (QoS) to meet various service requirements from different computing and networking applications, and to better utilize the computing resources. However, supporting QoS in the ubiquitous computing environments has also raised great concerns regarding the applicability of any QoS solution. Management of such ubiquitous multimedia applications requires new mechanisms, i.e., Soft-QoS framework, to be developed for admission control, negotiation, allocation, and scheduling. In this paper, we present a novel negotiated admission control algorithm that exploits the degradability property of applications to improve the performance of the system. The algorithm is based on setting aside a portion of the resources as reserves and managing it intelligently, so that the total utility of the system can be maximized. The mixed greedy and predictive strategy leads to an efficient protocol that also improves the system performance. We use the constructs of application benefit functions and resource demand functions in the integrated admission control and negotiation protocol. We applied our Soft-QoS framework to the admission controlling and resource scheduling for ubiquitous multimedia devices such as Continuous Media (CM) or Video-On-Demand (VOD) servers, where multimedia applications can generally tolerate certain variations on QoS parameters by providing multiple classes with consistently proportional rather than absolute QoS. Extensive simulation experiments are presented in the paper to evaluate the performance of the novel mechanisms and compare it against some other methods used in the past.This work was in part funded by DARPA through the SPAWARSYSCEN under Contract Number N66001-97-C-8525 and SK Telecom, Korea, under Contract Number (KU-R0405721).