The Shortest-Remaining-Processing-Time (SRPT) scheduling policy has long been known to be optimal for minimizing mean response time (sojourn time). Despite this fact, SRPT scheduling is rarely used in practice. It is believed that the performance improvements of SRPT over other scheduling policies stem from the fact that SRPT unfairly penalizes the large jobs in order to help the small jobs. This belief has led people to instead adopt “fair ” scheduling policies such as Processor-Sharing (PS), which produces the same expected slowdown for jobs of all sizes. This paper investigates formally the problem of unfairness in SRPT scheduling as compared with PS scheduling. The analysis assumes an M/G/1 model, and emphasizes job size distributions with a heavy-tailed property, as are characteristic of empirical workloads. The analysis shows that the degree of unfairness under SRPT is surprisingly small. The M/G/1/SRPT and M/G/1/PS queues are also analyzed under overload and closed-form expressions for mean response time as a function of job size are proved in this setting.

We consider two fundamental problems in dynamic scheduling: scheduling to meet deadlines in a preemptive multiprocessor setting, and scheduling to provide good response time in a number of scheduling environments. When viewed from the perspective of traditional worst-case analysis, no good on-line algorithms exist for these problems, and for some variants no good off-line algorithms exist unless P = NP. We study these problems using a relaxed notion of competitive analysis, introduced by Kalyanasundaram and Pruhs, in which the on-line algorithm is allowed more resources than the optimal off-line algorithm to which it is compared. Using this approach, we establish that several well-known on-line algorithms, that have poor performance from an absolute worst-case perspective, are optimal for the problems in question when allowed moderately more resources. For the optimization of average flow time, these are the first results of any sort, for any NP-hard version of the problem, that indicate that...

Under high loads, a Web server may be servicing manyhundreds of connections concurrently. In traditional

Is it possible to reduce the expected response time ofevery request at a web server, simply by changing the order in which we schedule the requests? That is the question we ask in this paper. This paper proposes a method for improving the performance of web servers servicing static HTTP requests. The idea is to give preference to those requests which are short, or have small remaining processing requirements, in accordance with the SRPT (Shortest Remaining Processing Time) scheduling policy. The implementation is at the kernel level and in-volves controlling the order in which socket buffers are drained into the network.Experiments are executed both in a LAN and a WAN environment. We use the Linux operating system and the Apache and Flash web servers. Results indicate that SRPT-based scheduling of connections yields significant reductions in delay at the web server. These result in a substantial reduction inmean response time, mean slowdown, and variance in response time for both the LAN and WAN environments. Significantly, and counter to intuition, the large requests are only negligibly penalized or not at all penalized as a result of SRPT-based scheduling.

As satellite, wireless and Cable TV-based networks spread their reach, there is an increased infrastructure of high-bandwidth links into the home and on the road. Much of this enhanced infrastructure inherently relies on broadcast technology to deliver data to large user populations. This increase in broadcast capacity has been complemented by the growth of large-scale information-centric applications. Many of these applications such as wireless internets and traffic information systems are pull-based, that is, they respond to on-demand user requests. In this paper, we study the scheduling problems arising in such on-demand broadcast environments for applications with data requests of varying sizes, and the novel issues that arise therein. We study the problem in its generality while much of the previous work has focused on one special case or the other, such as, assuming identical-sized data requests, or static client access profiles known by the server a priori, etc. Traditionally,...

It is common to classify scheduling policies based on their mean response times. Another important, but sometimes opposing, performance metric is a scheduling policy’s fairness. For example, a policy that biases towards short jobs so as to minimize mean response time, may end up being unfair to long jobs. In this paper we define three types of unfairness and demonstrate large classes of scheduling policies that fall into each type. We end with a discussion on which jobs are the ones being treated unfairly. 1

Most well-managed web servers perform well most of the time. Occasionally, however, every popular web server experiences transient overload. An overloaded web server typically displays signs of its affliction within a few seconds. Work enters the web server at a greater rate than the web server can complete it, causing the number of connections at the server to build up. This implies large delays for clients accessing the server. This paper provides a systematic performance study of exactly what happens when a web server is run under transient overload, both from the perspective of the server and from the perspective of the client. Second, this paper proposes and evaluates a particular kernel-level solution for improving the performance of web servers under overload. The solution is based on SRPT connection scheduling. We show that SRPT-based scheduling improves overload performance across a variety of client and server-oriented metrics.

Service differentiation that provides prioritized service qualities to multiple classes of client requests can effectively utilize available server resources. This paper studies how demand-driven service differentiation in terms of end-user performance can be supported in cluster-based network servers. Our objective is to deliver better services to high priority request classes without over-sacrificing low priority classes. To achieve this objective, we propose a dynamic scheduling scheme, called DDSD, that adapts to fluctuating request resource demands by periodically repartitioning servers. This scheme also employs priority-based admission control to drop excessive user requests and achieve soft performance guarantees. For each scheduling period, our scheme monitors the system status and uses a queuing model to approximate server behaviors and guide resource allocation. Our experiments show that the proposed technique achieves demand-driven service differentiation while maximizing resource utilization and that it can substantially outperform static server partitioning.

This note briey summarizes some results from two papers: [4] and [23]. These papers pose the following question: Is it possible to reduce the expected response time of every request at a web server, simply by changing the order in which we schedule the requests? In [4] we approach this question analytically via an M/G/1 queue. In [23] we approach the same question via implementation involving an Apache web server running on Linux.

We consider the classical problem of online job scheduling on uniprocessor and multiprocessor machines. For a given job, we measure the quality of service provided by an algorithm by the stretch of the job, which is defined as the ratio of the amount of time that the job spends in the system to the processing time of the job. For a given sequence of jobs, we measure the performance of an algorithm by the average stretch achieved by the algorithm over all the jobs in the sequence. The average stretch metric has been used to evaluate the performance of scheduling algorithms in many applications arising in databases, networks and systems; however, no formal analysis of scheduling algorithms is known for the average stretch metric. The main contribution of this paper is to show that the shortest remaining processing time algorithm (SRPT) is O(1)-competitive with respect to average stretch for both uniprocessors as well as multiprocessors. For uniprocessors, we prove that SRPT is 2-competi...