绝对延迟保证在Web应用服务器数据库连接池中的实现

网络QoS机制不足以提供完全的端到端的性能保证,在由三层结构构成的电子商务网站中,基于反馈控制理论,提出并实现了在应用服务器的数据库连接池中的绝对延迟保证,对数据库连接池作出了改进,确保带有高优先级请求的平均排队延迟不超过设定的阈值。通过系统辨识建立了数据库连接池的近似线性时不变模型(LTI),并设计了绝对延迟保证控制器,为Tomcat Web应用服务器实现了数据库连接池中闭环系统所有部件。测试结果表明,即使在并发请求数量剧烈变化时,控制器的设计也是有效的。     

Flubber: Two-level disk scheduling in virtualized environment

While virtualization enables multiple virtual machines (VMs)—with multiple operating systems and applications—to run within a physical server, it also complicates resource allocations trying to guarantee Quality of Service (QoS) requirements of the diverse applications running within these VMs. As QoS is crucial in the cloud, considerable research efforts have been directed towards CPU, memory and network allocations to provide effective QoS to VMs, but little attention has been devoted to disk resource allocation.This paper presents the design and implementation of Flubber, a two-level scheduling framework that decouples throughput and latency allocation to provide QoS guarantees to VMs while maintaining high disk utilization. The high-level throughput control regulates the pending requests from the VMs with an adaptive credit-rate controller, in order to meet the throughput requirements of different VMs and ensure performance isolation. Meanwhile, the low-level latency control, by the virtue of the batch and delay earliest deadline first mechanism (BD-EDF), re-orders all pending requests from VMs based on their deadlines, and batches them to disk devices taking into account the locality of accesses across VMs. We have implemented Flubber and made extensive evaluations on a Xen-based host. The results show that Flubber can simultaneously meet the different service requirements of VMs while improving the efficiency of the physical disk. The results also show improvement of up to 25% in the VM performance over state-of-art approaches: for example, in contract to the default Xen disk I/O scheduler—Completely Fair Queueing (CFQ)—besides achieving the desired QoS of each VM, Flubber speeds up the sequential and random reads by 17% and 25%, respectively, due to the efficient physical disk utilization.     

Aperiodic servers in a deadline scheduling environment

A real-time system may have tasks with soft deadlines, as well as hard deadlines. While earliest-deadline-first scheduling is effective for hard-deadline tasks, applying it to soft-deadline tasks may waste schedulable processor capacity or sacrifice average response time. Better average response time may be obtained, while still guaranteeing hard deadlines, with an aperiodic server. Three scheduling algorithms for aperiodic servers are described, and schedulability tests are derived for them. A simulation provides performance data for these three algorithms on random aperiodic tasks. The performances of the deadline aperiodic servers are compared with those of several alternatives, including background service, a deadline polling server, and rate-monotonic servers, and with estimates based on the M/M/1 queueing model. This adds to the evidence in support of deadline scheduling,versus fixed priority scheduling.     

一种新颖的带模糊截止时限的磁盘调度算法

设计了一种新的基于截止时限的磁盘调度算法,该算法支持带多优先级的请求。对于某些实时要求,其截止时限是不确定的或者不精确的,该算法采用模糊集来描述这类不确定性,模糊截止时限的隶属度函数表示对请求完成时间的满意程度。调度的目的是最优的指定优先级,使得截止时限的满意程度最大化。根据请求截止时限的不同,把满意程度划分为若干连续的区间。在每个不同的区间内,每个请求都对应有修正的截止时限,把请求按照其修正的截止时限非减的顺序分配优先级,才能实现请求优先级的最优配置。仿真结果表明该算法能有效的分配请求的优先级,降低请求的丢失率,保证了更多的请求得到满足。     

An approximation-based load-balancing algorithm with admission control for cluster web servers with dynamic workloads

The growth of web-based applications in business and e-commerce is building up demands for high performance web servers for better throughputs and lower user-perceived latency. These demands are leading to a widespread substitution of powerful single servers by robust newcomers, cluster web servers, in many enterprise companies. In this respect the load-balancing algorithms play an important role in boosting the performance of cluster servers. The previous load-balancing algorithms which were designed for the handling of static contents in web services suffer from significant performance degradation under dynamic and database-driven workloads. Regarding this, we propose an approximation-based load-balancing algorithm with admission control for cluster-based web servers in this study. Since it is difficult to accurately determine the loads of web servers through feedbacks from distributed agents in web servers, we propose an analytical model of a web server to estimate the web servers’ loads. To achieve this, the algorithm classifies requests based on their service times and track numbers of outstanding requests for each class of each web server node and also based on their resource demands to dynamically estimate the loads of each node. For the error handling of the model a proportional integral (PI) controller from control theory is used. Then the estimated available capacity of each web server is used for load balancing and admission control decisions. The implementation results with a standard benchmark confirm the effectiveness of the proposed scheme, which improves both the mean response time and the throughput of the cluster compared to rival load-balancing algorithms, and also avoids situations in which the cluster is overloaded, even when the request rates are beyond the cluster capacity.     

Power optimization for dynamic configuration in heterogeneous web server clusters

To reduce the environmental impact, it is essential to make data centers green, by turning off servers and tuning their speeds for the instantaneous load offered, that is, determining the dynamic configuration in web server clusters. We model the problem of selecting the servers that will be on and finding their speeds through mixed integer programming; we also show how to combine such solutions with control theory. For proof of concept, we implemented this dynamic configuration scheme in a web server cluster running Linux, with soft real-time requirements and QoS control, in order to guarantee both energy-efficiency and good user experience. In this paper, we show the performance of our scheme compared to other schemes, a comparison of a centralized and a distributed approach for QoS control, and a comparison of schemes for choosing speeds of servers.     

On-Line Algorithms for the Dynamic Traveling Repair Problem

We consider the dynamic traveling repair problem in which requests with deadlines arrive through time on points in a metric space. Servers move from point to point at constant speed. The goal is to plan the motion of servers so that the maximum number of requests are met by their deadline. We consider a restricted version of the problem in which there is a single server and the length of time between the arrival of a request and its deadline is constant. We give upper bounds for the competitive ratio of two very natural algorithms as well as several lower bounds for any deterministic algorithm. Most of the results in this paper are expressed as a function of β, the diameter of the metric space. In particular, we prove that the upper bound given for one of the two algorithms is within a constant factor of the best possible competitive ratio.     

Differentiated Real-Time Data Services for E-Commerce Applications

The demand for real-time e-commerce data services has been increasing recently. In many e-commerce applications, it is essential to process user requests within their deadlines, i.e., before the market status changes, using fresh data reflecting the current market status. However, current data services are poor at processing user requests in a timely manner using fresh data. To address this problem, we present a differentiated real-time data service framework for e-commerce applications. User requests are classified into several service classes according to their importance, and they receive differentiated real-time performance guarantees in terms of deadline miss ratio. At the same time, a certain data freshness is guaranteed for all transactions that commit within their deadlines. A feedback-based approach is applied to differentiate the deadline miss ratio among service classes. Admission control and adaptable update schemes are applied to manage potential overload. A simulation study, which reflects the e-commerce data semantics, shows that our approach can achieve a significant performance improvement compared to baseline approaches. Our approach can support the specified per-class deadline miss ratios maintaining the required data freshness even in the presence of unpredictable workloads and data access patterns, whereas baseline approaches fail.     

一种Web集群系统下的QoS控制策略

本文提出了一种在Web集群环境下的QoS控制策略。集群前端分配器以会话单位来分配访问请求,保证后端服务器间负载的合理均衡分配,同时后端服务器采用基于会话的QoS控制策略,实现对集群系统的服务质量控制。     

Resource Allocation for Session-Based Two-Dimensional Service Differentiation on e-Commerce Servers

A scalable e-commerce server should be able to provide different levels of quality of service (QoS) to different types of requests based on clients' navigation patterns and the server capacity. E-Commerce workloads are composed of sessions. In this paper, we propose a session-based two-dimensional (2D) service differentiation model for online transactions: intersession and intrasession. The intersession model aims to provide different levels of QoS to sessions from different customer classes, and the intrasession model aims to provide different levels of QoS to requests in different states of a session. A primary performance metric of online transactions is slowdown. It measures the waiting time of a request relative to its service time. We present a processing rate allocation scheme for 2D proportional slowdown differentiation. We then introduce service slowdown as a systemwide QoS metric of an e-commerce server. It is defined as the weighted sum of request slowdown in different sessions and in different session states. We formulate the problem of 2D service differentiation as an optimization of processing rate allocation with the objective of minimizing the service slowdown of the server. We prove that the derived rate allocation scheme based on the optimization guarantees client requests' slowdown to be square-root proportional to their prespecified differentiation weights in both intersession and intrasession dimensions. We evaluate this square-root proportional rate allocation scheme and a proportional rate allocation scheme via extensive simulations. Results validate that both schemes can achieve predictable, controllable, and fair 2D service differentiation on e-commerce servers. The square-root proportional rate allocation scheme provides 2D service differentiation at a minimum cost of service slowdown.     

Integrated dynamic scheduling of hard and QoS degradable real-time tasks in multiprocessor systems

Many time-critical applications require predictable performance and tasks in these applications have deadlines to be met. For tasks with hard deadlines, a deadline miss can be catastrophic while for Quality of Service (QoS) degradable tasks (soft real-time tasks) timely approximate results of poorer quality or occasional deadline misses are acceptable. Imprecise computation and (m,k)-firm guarantee are two workload models that quantify the trade-off between schedulability and result quality. In this paper, we propose dynamic scheduling algorithms for integrated scheduling of real-time tasks, represented by these workload models, in multiprocessor systems. The algorithms aim at improving the schedulability of tasks by exploiting the properties of these models in QoS degradation. We also show how the proposed algorithms can be adapted for integrated scheduling of multimedia streams and hard real-time tasks, and demonstrate their effectiveness in quantifying QoS degradation. Through simulation, we evaluate the performance of these algorithms using the metrics – success ratio (measure of schedulability) and quality. Our simulation results show that one of the proposed algorithms, multilevel degradation algorithm, outperforms the others in terms of both the performance metrics.     

Disk scheduling in video editing systems

Modern video servers support both video-on-demand and nonlinear editing applications. Video-on-demand servers enable the user to view video clips or movies from a video database, while nonlinear editing systems enable the user to manipulate the content of the video database. Applications such as video and news editing systems require that the underlying storage server be able to concurrently record live broadcast information, modify prerecorded data, and broadcast an authored presentation. A multimedia storage server that efficiently supports such a diverse group of activities constitutes the focus of this study. A novel real-time disk scheduling algorithm is presented that treats both read and write requests in a homogeneous manner in order to ensure that their deadlines are met. Due to real-time demands of movie viewing, read requests have to be fulfilled within certain deadlines; otherwise, they are considered lost. Since the data to be written into disk is stored in main memory buffers, write requests can be postponed until critical read requests are processed. However, write requests still have to be processed within reasonable delays and without the possibility of indefinite postponement. This is due to the physical constraint of the limited size of the main memory write buffers. The new algorithm schedules both read and write requests appropriately, to minimize the amount of disk reads that do not meet their presentation deadlines, and to avoid indefinite postponement and large buffer sizes in the case of disk writes. Simulation results demonstrate that the proposed algorithm offers low violations of read deadlines, reduces waiting time for lower priority disk requests, and improves the throughput of the storage server by enhancing the utilization of available disk bandwidth     

一种严格按比例派发服务的混合实时调度算法

在混合实时系统中,调度器必须既保证所有硬实时任务严格按照其时间约束在截止期内完成,又要尽可能地提高软实时任务和非实时任务的服务质量.提出了一种严格按比例派发服务器算法(RPDS),并以此为基础构建了一种层次式调度框架.RPDS将处理器时间流分成连续的小段,并在每一小段中强制为非硬实时任务分配一个时间片.实验结果表明,RPDS可以合理地为各种类型应用分配处理器时间,并且降低了实时任务的截止期错失率.     

基于区分Web QoS的负载均衡集群模型

随着电子商务的应用逐步深入,用户访问量的激增且服务请求多样．如何实现对所有请求的快速响应是当前解决的问题．针对此问题,本文提出采用基于区分WebQoS的负载均衡技术．建立了基于区分WebQoS的负载均衡的集群模型．根据请求类型和用户权限划分服务等级,高服务的请求具有高优先调度权,在集群当中通过动态反馈技术均衡调度到某个节点,从而达到区分WebQoS服务的目的,同时也保证集群服务器的负载均衡．该模型在网络环境及硬件环境相同的条件下,与常用的负载均衡技术进行了比较,实验结果证明本文提出的方法效果显著．     

ReDAL: An Efficient and Practical Request Distribution Technique for Application Server Clusters

Modern Web-based application infrastructures are based on clustered multitiered architectures, where request distribution occurs in two sequential stages: over a cluster of Web servers and over a cluster of application servers. Much work has focused on strategies for distributing requests across a Web server cluster in order to improve the overall throughput across the cluster. The strategies applied at the application layer are the same as those at the Web server layer because it is assumed that they transfer directly. In this paper, we argue that the problem of distributing requests across an application server cluster is fundamentally different from the Web server request distribution problem due to core differences in request processing in Web and application servers. We devise an approach for distributing requests across a cluster of application servers such that the overall system throughput is enhanced, and load across the application servers is balanced.     

On optimal lateness and tardiness scheduling in real-time systems

We address lateness and tardiness scheduling policies for real-time systems. It is well-known that preemptive Earliest Deadline First (EDF) minimizes the worst lateness and tardiness of a finite set of tasks with known arrival times, service times and deadlines to the finishing time, on a uniprocessor. We extend this result significantly, to include an arbitrary (possibly infinite) number of tasks with arbitrary arrival and service times, and deadlines, and to show thatEDF

1. minimizes the lateness and tardiness of the tasks that are in the system at an arbitrary time.
2. minimizes lateness within a busy interval, for an arbitrary, possibly infinite number of tasks.
3. maximizes the time to the first missed deadline, and
4. minimizes the length of time during which there is at least one missed deadline in the system.

We also show that a combination ofEDF and Shortest Remaining Processing Time First (SRPTF) policy minimizes maximum latenesses in a vector sense (as defined tin the paper) and minimizes the number of tasks that miss their deadline at the time the first missed deadline occurs. For non-preemptive non-idling polices, we establish new, similar results in a stochastic sense. We attempt extending our findings to multiprocessor systems. We demonstrate that under the assumptions of arbitrary distributions of arrival times, service times and deadlines, our results no longer hold true. When a further assumption of unit-length service times and integer-valued arrival times is introduced, we are able to re-establish the results in the multiprocessor case.     

Keta的Web QoS机制与实现

随着Web应用的急剧增长,为了提高Web服务器的处理能力,人们提出和实现了多种Web Server结构。在此基础上,对Web应用提供性能保证和区分服务,实现QoS的控制成为目前Web发展所迫切需要解决的问题。本文针对一种非对称多线程流水线服务器结构--Keta,研究并实现了请求的分级调度控制、高负载的内容自适应降级服务等内容。实验结果表 表明,这些策略机制是有效的。     

Energy-Efficient Redundant Execution of Processes in a Fault-Tolerant Cluster of Servers

This paper investigates into fault tolerance of cluster of servers and their energy efficiency to realize a reliable and energy aware server cluster system. A client issues a request to one server in a server cluster and the server sends a reply to the client in information systems. Once the server stops by fault, the client does not receive a reply of the request. Even if the request is performed on another server on detection of fault of the server, some QoS requirements like response time may not be satisfied. Hence, each request has to be redundantly performed on multiple servers to be tolerant of server faults. The redundant power consumption laxity-based (RPCLB) algorithm is discussed where multiple servers are selected to redundantly and energy-efficiently perform a request process in our previous studies. Since each application process is redundantly performed on more than one server, the larger amount of electric power is consumed. In this paper, we propose a novel and improved RPCLB (IRPCLB) algorithm to reduce the power consumption of servers, where once a process successfully terminates on one server, meaningless redundant processes are forced to terminate on the other servers. In the evaluation, we show the total power consumption of servers and total execution time of processes are reduced in homogeneous and heterogeneous types of clusters by the IRPCLB algorithm than the RPCLB and RR algorithms.     

High-Performance Resource Allocation and Request Redirection Algorithms for Web Clusters

With increasing richness in features such as personalization of content, Web applications are becoming increasingly complex and hence compute intensive. Traditional approaches for improving performance of static content Web sites have been based on the assumption that static content such as images are network intensive. However, these methods are not applicable to the dynamic content applications which are more compute intensive than static content. This paper proposes a suite of algorithms which jointly optimize the performance of dynamic content applications by reducing the client access times while also minimizing the resource utilization. A server migration algorithm allocates servers on-demand within a cluster such that the client access times are not affected even under sudden overload conditions. Further, a server selection mechanism enables statistical multiplexing of resources across clusters by redirecting requests away from overloaded clusters. We also propose a cluster decision algorithm which decides whether to migrate in additional servers at the local cluster or redirect requests remotely under different workload conditions. Through a combination of analytical modeling, trace-driven simulation over traces from large e-commerce sites and testbed implementation, we explore the performance savings achieved by the proposed algorithms.     

A multi-tier semantics for Hop

Hop is a multi-tier programming language where the behavior of interacting servers and clients are expressed by a single program. Hop adheres to the standard web programming style where servers elaborate HTML pages containing JavaScript code. This JavaScript code responds locally to user’s interactions but also (following the so-called Ajax style) requests services from remote servers. These services bring back new HTML fragments containing additional JavaScript code replacing or modifying the state of the client. This paper presents a continuation-based denotational semantics for a sequential subset of Hop. Though restricted to a single server and a single client, this semantics takes into account the key feature of Hop namely that the server elaborates client code to be run in the client’s browser. This new client-code dynamically requests services from the server which, again, elaborate new client code to be run in the client’s browser. This semantics details the programming model advocated by Hop and provides a sound basis for future studies such as security of web applications and web continuations.