Abstract not found

In this paper, we present surplus fair scheduling (SFS), a proportional-share CPU scheduler designed for symmetric multiprocessors. We first show that the infeasibility of certain weight assignments in multiprocessor environments results in unfairness or starvation in many existing proportional-share schedulers. We present a novel weight readjustment algorithm to translate infeasible weight assignments to a set of feasible weights. We show that weight readjustment enables existing proportional-share schedulers to significantly reduce, but not eliminate, the unfairness in their allocations. We then present surplus fair scheduling, a proportional-share scheduler that is designed explicitly for multiprocessor environments. We implement our scheduler in the Linux kernel and demonstrate its efficacy through an experimental evaluation. Our results show that SFS can achieve proportionate allocation, application isolation and good interactive performance, albeit at a slight increase in scheduling overhead. We conclude from our results that a proportionalshare scheduler such as SFS is not only practical but also desirable for server operating systems.

In this paper, we present Deadline Fair Scheduling (DFS), a proportionate-fair CPU scheduling algorithm for multiprocessor servers. A particular focus of our work is to investigate practical issues in instantiating proportionatefair (P-fair) schedulers into conventional operating systems. We show via a simulation study that characteristics of conventional operating systems such as the asynchrony in scheduling multiple processors, frequent arrivals and departures of tasks, and variable quantum durations can cause proportionate-fair schedulers to become nonwork-conserving. To overcome this drawback, we combine DFS with an auxiliary work-conserving scheduler to ensure work-conserving behavior at all times. We then propose techniques to account for processor affinities while scheduling tasks in multiprocessor environments. We implement the resulting scheduler in the Linux kernel and evaluate its performance using various applications and benchmarks. Our experimental results show that DFS can achieve proportionate allocation, performance isolation and work-conserving behavior at the expense of a small increase in the scheduling overhead. We conclude that practical considerations such as work-conserving behavior and processor affinities when incorporated into a P-fair scheduler such as DFS can result in a practical approach for scheduling tasks in a multiprocessor operating system.

In this paper, we argue that conventional operating systems need to be enhanced with predictable resource management mechanisms to meet the diverse performance requirements of emerging multimedia and web applications. We present QLinux---a multimedia operating system based on the Linux kernel that meets this requirement. QLinux employs hierarchical schedulers for fair, predictable allocation of processor, disk and network bandwidth, and accounting mechanisms for appropriate charging of resource usage. We experimentally evaluate the efficacy of these mechanisms using benchmarks and real-world applications. Our experimental results show that (i) emerging applications can indeed benefit from predictable allocation of resources, and (ii) the overheads imposed by the resource allocation mechanisms in QLinux are small. For instance, we show that the QLinux CPU scheduler can provide predictable performance guarantees to applications such as web servers and MPEG players, albeit at the expense of increasing the scheduling overhead from 1 s to 4 s. We conclude from our experiments that the benefits due to the resource management mechanisms in QLinux outweigh their increased overheads, making them a practical choice for conventional operating systems.

Desktop operating systems such as Windows and Linux base scheduling decisions on CPU consumption — processes that consume fewer CPU cycles are prioritized, assuming that interactive processes gain from this as they spend most of their time waiting for user input. However, this doesn’t work for modern multimedia applications, which require significant CPU resources. We therefore suggest a new metric to identify interactive processes, by explicitly measuring interactions with the user, and use it to design and implement a process scheduler. Measurements using a variety of applications indicate that this scheduler is very effective in distinguishing between competing interactive and non-interactive processes.

Cloud computing platforms enable users to rent computing and storage resources on-demand to run their networked applications and employ virtualization to multiplex virtual servers belonging to different customers on a shared set of servers. In this paper, we empirically evaluate the efficacy of cloud platforms for running latency-sensitive multimedia applications. Since multiple virtual machines running disparate applications from independent users may share a physical server, our study focuses on whether dynamically varying background load from such applications can interfere with the performance seen by latency-sensitive tasks. We first conduct a series of experiments on Amazon’s EC2 system to quantify the CPU, disk, and network jitter and throughput fluctuations seen over a period of several days. We then turn to a laboratory-based cloud and systematically introduce different levels of background load and study the ability to isolate applications under different settings of the underlying resource control mechanisms. We use a combination of micro-benchmarks and two realworld applications—the Doom 3 game server and Apple’s Darwin Streaming Server—for our experimental evaluation. Our results reveal that the jitter and the throughput seen by a latency-sensitive application can indeed degrade due to background load from other virtual machines. The degree of interference varies from resource to resource and is the most pronounced for disk-bound latencysensitive tasks, which can degrade by nearly 75 % under sustained background load. We also find that careful configuration of the resource control mechanisms within the virtualization layer can mitigate, but not eliminate, this interference.

As more business applications have become web enabled, the web server architecture has evolved to provide performance isolation, service differentiation, and QoS guarantees. Various server mechanisms that provide QoS extensions, however, rely on external administrators to set the right parameter values for their desirable performance. Due to the complexity of handling varying workloads and bursty traffic, configuring such parameters optimally becomes a challenge. In this paper we describe an observation-based approach for self-managing web servers that can adapt to changing workloads while maintaining the QoS requirements of different classes. In this approach, the system state is monitored continuously and parameter values of various system resources---primarily the accept queue and the CPU---are adjusted to maintain the system-wide QoS goals. We implement our techniques using the Apache web server and the Linux operating system. We first demonstrate the need to manage different resources in the system depending on the workload characteristics. We then experimentally demonstrate that our observation-based system monitors such workload changes and adjusts the resource parameters of the accept queue and CPU schedulers in order to maintain the QoS requirements of the different classes.

As more business applications have become web enabled, the web server architecture has evolved to provide performance isolation, service differentiation, and QoS guarantees. Various server mechanisms that provide QoS extensions, however, rely on external administrators to set the right parameter values for their desirable performance. Due to the complexity of handling varying workloads and bursty traffic, configuring such parameters optimally becomes a challenge. In this paper, we describe an observation-based approach for selfmanaging web servers that can adapt to changing workloads while maintaining the QoS requirements of different classes. In this approach, the system state is monitored continuously and parameter values of various system resources—primarily the accept queue and the CPU—are adjusted to maintain the system-wide QoS goals. We implement our techniques using the Apache web server and the Linux operating system. We first demonstrate the need to manage different resources in the system depending on the workload characteristics. We then experimentally demonstrate that our observation-based system monitors such as workload changes and adjusts the resource parameters of the accept queue and CPU schedulers in order to maintain the QoS requirements of the different classes. q 2005 Elsevier B.V. All rights reserved. Keywords: Web server; Self-managing; Dynamic resource allocation

Hierarchical scheduling has been proposed as a scheduling technique to achieve aggregate resource partitioning among related groups of threads and applications in uniprocessor and packet scheduling environments. Existing hierarchical schedulers are not easily extensible to multiprocessor environments because (i) they do not incorporate the inherent parallelism of a multiprocessor system while resource partitioning, and (ii) they can result in unbounded unfairness or starvation if applied to a multiprocessor system in a naive manner. In this paper, we present hierarchical multiprocessor scheduling (H-SMP): a novel hierarchical CPU scheduling algorithm designed for a symmetric multiprocessor (SMP) platform. The novelty of this algorithm lies in its combination of space- and time-multiplexing to achieve desired bandwidth partition among the nodes of the hierarchical scheduling tree. This algorithm is also characterized by its ability to incorporate existing proportional-share algorithms as auxiliary schedulers to achieve efficient hierarchical CPU partitioning. In addition, we present a generalized weight feasibility constraint that specifies the limit on the achievable CPU bandwidth partitioning in a multiprocessor hierarchical framework, and propose a hierarchical weight readjustment algorithm designed to transparently satisfy this feasibility constraint. We evaluate the properties of H-SMP using hierarchical surplus fair scheduling (H-SFS): an instantiation of H-SMP that employs surplus fair scheduling (SFS) as an auxiliary algorithm. This evaluation is carried out through a simulation study that shows that H-SFS provides better fairness properties in multiprocessor environments as compared to existing algorithms and their naive extensions.

Resource management is a vital task of all operating systems. It is the responsibility of operating system to ensure that all programs requesting resources are getting resources in a timely manner. Various recourse allocation strategies are there which provide guidance for operating systems to make resource allocation decisions. This article studies about the resource management using a fare share scheduler. The fair share scheduler ensures that resources are allocated to programs in an efficient manner and this ensures fairness in resource allocation. Keywords: Fairness, Fair Share Scheduling. 1.