Number Of Outstanding Requests Research Articles

Scheduling Data Broadcast: An Efficient Cut-Off Point Between Periodic and On-Demand Data

Collecting statistics from client requests, the broadcast server partitions data items into hot and cold-item sets to perform a hybrid broadcast scheduling. Hot items are broadcast periodically with periods based on their access probabilities. Cold items are scheduled based on the waiting time and the number of outstanding requests. We investigate the optimal cut-off point between hot and cold items by analyzing the average response time as a function of the cut-off point. The effectiveness of our scheme is shown by simulations using web query log data.

데이터 방송 스케줄링에서 핫아이템과 콜드아이템의 분리를 위한 효율적인 컷오프 포인트에 관한 연구

Collecting statistics from client requests, the broadcast server partitions data items into hot and cold-item sets with the optimal cut-off point. Hot items are broadcast periodically with periods based on their access probabilities. In a time slot with no hot items scheduled, the server broadcasts a proper cold item considering the waiting time and the number of outstanding requests. We analyze the optimal the cut-off point by calculating average response time as a function of the cut-off point. Simulation results show that our proposed algorithm outperforms existing methods in various circumstances.

Estimation-Based Load-Balancing with Admission Control for Cluster Web Servers

The growth of the World Wide Web and web-based applications is creating demand for high performance web servers to offer better throughput and shorter user-perceived latency. This demand leads to widely used cluster-based web servers in the Internet infrastructure. Load balancing algorithms play an important role in boosting the performance of cluster web servers. Previous load balancing algorithms suffer a significant performance drop under dynamic and database-driven workloads. We propose an estimation-based load balancing algorithm with admission control for cluster-based web servers. Because it is difficult to accurately determine the load of web servers, we propose an approximate policy. The algorithm classifies requests based on their service times and tracks the number of outstanding requests from each class in each web server node to dynamically estimate each web server load state. The available capacity of each web server is then computed and used for the load balancing and admission control decisions. The implementation results confirm that the proposed scheme improves both the mean response time and the throughput of clusters compared to rival load balancing algorithms and prevents clusters being overloaded even when request rates are beyond the cluster capacity.

Time-critical on-demand data broadcast: algorithms, analysis, and performance evaluation

On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and deal with dynamic user access patterns. With the rapid growth of time-critical information services in emerging applications, there is an increasing need for the system to support timely data dissemination. This paper investigates online scheduling algorithms for time-critical on-demand data broadcast. We propose a novel scheduling algorithm called SIN-/spl alpha/ that takes the urgency and number of outstanding requests into consideration. An efficient implementation of SIN-/spl alpha/ is presented. We also analyze the theoretical bound of request drop rate when the request arrival rate rises toward infinity. Trace-driven experiments show that SIN-/spl alpha/ significantly outperforms existing algorithms over a wide range of workloads and approaches the analytical bound at high request rates.

Evaluating the impact of locality on the performance of large-scale SCI multiprocessors

Hierarchical ring-based multiprocessor systems are attractive and enjoy several advantages over other type of systems. They ensure unique paths between nodes, simple node interfaces and simple cross-ring connections. Furthermore, employing point-to-point links allows the system to run at high clock rate which increases bandwidth and decreases latency. This paper investigates the performance of hierarchical ring-based shared-memory multiprocessors. Rings in the hierarchy are composed of point-to-point, unidirectional links and apply the Scalable Coherent Interface (SCI) protocol. We pay special emphasis on the impact of locality on processor and interconnection design issues such as number of outstanding requests, and ring topology. We find that in order to exploit the power of hierarchical multiprocessors an accurate and appropriate model of locality must be used. Hierarchical multiprocessors that are well balanced (uniform) tend to provide lower latency and higher system throughput. For non-uniform systems, high degree of locality is required for the hierarchies to perform well. However, restricting the number of outstanding transactions per processor is important in decreasing packets latency and avoiding network contention.

A Scalable Shared Queue on a Distributed Memory Machine

The emergence of low latency, high throughput routers means that network locality issues no longer dominate the performance of parallel algorithms. One of the key performance issues is now the even distribution of work across the machine, as the problem size and number of processors increase. This paper describes the implementation of a highly scalable shared queue, supporting the concurrent insertion and deletion of elements. The main characteristics of the queue are that there is no fixed limit on the number of outstanding requests and the performance scales linearly with the number of processors (subject to increasing network latencies). The queue is implemented using a general-purpose computational model, called the WPRAM. The model includes a shared address space which uses weak coherency semantics. The implementation makes extensive use of pairwise synchronization and concurrent atomic operations to achieve scalable performance. The WPRAM is targeted at the class of distributed memory machines which use a scalable interconnection network.

Simulations with SCI as a data carrier in data acquisition systems

Detailed simulations of processor networks based on the Scalable Coherent Interface (SCI) show that SCI is suitable as a data carrier in data acquisition systems where the total bandwidth need is in the multi GBytes/s range and a low latency is required. The objective of these simulations was to find topologies with low latency and high bandwidth, but also with the cost of implementation in mind. A ring-to-ring bridge has been used as the building element for the networks. The simulations have been performed on regular k-ary n-cube type topologies from a few tens of nodes and up to about 500 nodes under different load conditions. Among the parameters which have been manipulated in the simulations are the number of nodes, topology structure, number of outstanding requests and load in the system.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Maximizing performance in a striped disk array

Improvements in disk speeds have not kept up with improvements in processor and memory speeds. One way to correct the resulting speed mismatch is to stripe data across many disks. In this paper, we address how to stripe data to get maximum performance from the disks. Specifically, we examine how to choose the striping unit, i.e. the amount of logically contiguous data on each disk. We synthesize rules for determining the best striping unit for a given range of workloads. We show how the choice of striping unit depends on only two parameters: 1) the number of outstanding requests in the disk system at any given time, and 2) the average positioning time × data transfer rate of the disks. We derive an equation for the optimal striping unit as a function of these two parameters; we also show how to choose the striping unit without prior knowledge about the workload.