Exploring coordinated software and hardware support for hardware resource allocation

Multithreaded processors are now common in the industry as they offer high performance at a low cost. Traditionally, in such processors, the assignation of hardware resources between the multiple threads is done implicitly, by the hardware policies. However, a new class of multithreaded hardware allows the explicit allocation of resources to be controlled or biased by the software. Currently, there is little or no coordination between the allocation of resources done by the hardware and the prioritization of tasks done by the software.This thesis targets to narrow the gap between the software and the hardware, with respect to the hardware resource allocation, by proposing a new explicit resource allocation hardware mechanism and novel schedulers that use the currently available hardware resource allocation mechanisms.It approaches the problem in two different types of computing systems: on the high performance computing domain, we characterize the first processor to present a mechanism that allows the software to bias the allocation hardware resources, the IBM POWER5. In addition, we propose the use of hardware resource allocation as a way to balance high performance computing applications. Finally, we propose two new scheduling mechanisms that are able to transparently and successfully balance applications in real systems using the hardware resource allocation. On the soft real-time domain, we propose a hardware extension to the existing explicit resource allocation hardware and, in addition, two software schedulers that use the explicit allocation hardware to improve the schedulability of tasks in a soft real-time system.In this thesis, we demonstrate that system performance improves by making the software aware of the mechanisms to control the amount of resources given to each running thread. In particular, for the high performance computing domain, we show that it is possible to decrease the execution time of MPI applications biasing the hardware resource assignation between threads. In addition, we show that it is possible to decrease the number of missed deadlines when scheduling tasks in a soft real-time SMT system.Postprint (published version

Options in Scan Processing for Shared-Disk Parallel Database Systems

Shared-disk database systems offer a high degree of freedom in the allocation of workload compared to shared-nothing architectures. This creates a great potential for load balancing but also introduces additional complexity into the process of query scheduling. This report surveys the problems and opportunities faced in scan processing in a shared-disk environment. We list the parameters to tune and the decisions to make, as well as some known solutions and commonsense considerations, in order to identify the most promising areas of future research

Space and time adaptation for parallel applications via data over-partitioning.

Adaptive resource allocation is a new feature to run parallel applications. It is used to obtain better space and time sharing according to current workload, to schedule around obstacles through reservation and to cope with lack of accurate predictability on heterogeneous resources. The implementation of resource adaptation is potentially very expensive if total remapping or partitioning from scratch has to be performed. The existing popular run-time systems include AMPI and Dome. AMPI, which uses huge numbers of threads in MPI process to implement resource adaptation, suffers from frequent thread switches and loss of cache locality; and Dome, an object-based migration environment, suffers from lack of general language supports. When resource adaptation occurs, load balancing techniques are used to allocate the workload fairly across processors, so that each processor takes roughly the same time to execute the processes assigned to it, and that every processor has the same workload to obtain the best performance and maximize resource utilization. This thesis proposes a novel approach---Adaptive Time/space sharing via Over-Partitioning (ATOP)---to implement resource adaptation with better performance in terms of time overhead. Total workload is represented by a data graph. ATOP performs over-partitioning on the graph to create a certain number of workload pieces, or partitions, while processing partitions per processor as one data collection in a single MPI process. Typically, the number of partitions is set equal to the number of processors potentially allocated. This approach is feasible for the applications using 2n processors. In the cases where our over-partitioning approach does not perform well, or non-fitting numbers of resources need to be chosen, ATOP still provides the alternative option to repartition from scratch. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .H36. Source: Masters Abstracts International, Volume: 43-03, page: 0876. Adviser: A. C. Sodan. Thesis (M.Sc.)--University of Windsor (Canada), 2004

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial

Exploring coordinated software and hardware support for hardware resource allocation

Multithreaded processors are now common in the industry as they offer high performance at a low cost. Traditionally, in such processors, the assignation of hardware resources between the multiple threads is done implicitly, by the hardware policies. However, a new class of multithreaded hardware allows the explicit allocation of resources to be controlled or biased by the software. Currently, there is little or no coordination between the allocation of resources done by the hardware and the prioritization of tasks done by the software.This thesis targets to narrow the gap between the software and the hardware, with respect to the hardware resource allocation, by proposing a new explicit resource allocation hardware mechanism and novel schedulers that use the currently available hardware resource allocation mechanisms.It approaches the problem in two different types of computing systems: on the high performance computing domain, we characterize the first processor to present a mechanism that allows the software to bias the allocation hardware resources, the IBM POWER5. In addition, we propose the use of hardware resource allocation as a way to balance high performance computing applications. Finally, we propose two new scheduling mechanisms that are able to transparently and successfully balance applications in real systems using the hardware resource allocation. On the soft real-time domain, we propose a hardware extension to the existing explicit resource allocation hardware and, in addition, two software schedulers that use the explicit allocation hardware to improve the schedulability of tasks in a soft real-time system.In this thesis, we demonstrate that system performance improves by making the software aware of the mechanisms to control the amount of resources given to each running thread. In particular, for the high performance computing domain, we show that it is possible to decrease the execution time of MPI applications biasing the hardware resource assignation between threads. In addition, we show that it is possible to decrease the number of missed deadlines when scheduling tasks in a soft real-time SMT system

Enabling the “Easy Button” for Broad, Parallel Optimization of Functions Evaluated by Simulation

Java Optimization by Simulation (JOBS) is presented: an open-source, object-oriented Java library designed to enable the study, research, and use of optimization for models evaluated by simulation. JOBS includes several novel design features that make it easy for a simulation modeler, without extensive expertise in optimization or parallel computation, to define an optimization model with deterministic and/or stochastic constraints, choose one or more metaheuristics to solve it and run, using massively parallel function evaluation to reduce wall-clock times. JOBS is supported by a new language independent, application programming interface (API) for remote simulation model evaluation and a serverless computing environment to provide massively parallel function evaluation, on demand. Dynamic loop scheduling methods are evaluated in the serverless environment with the opportunity for significant resource contention for master node computing power and network bandwidth. JOBS implements several population-based and single-solution improvement metaheuristics (solvers) for real, discrete, and mixed problems. The object-oriented design is extendible with classes that drastically reduce the amount of code required to implement a new solver and encourage re-use of solvers as building blocks for creating new multi-stage solvers or memetic algorithms

Effective Resource and Workload Management in Data Centers

The increasing demand for storage, computation, and business continuity has driven the growth of data centers. Managing data centers efficiently is a difficult task because of the wide variety of datacenter applications, their ever-changing intensities, and the fact that application performance targets may differ widely. Server virtualization has been a game-changing technology for IT, providing the possibility to support multiple virtual machines (VMs) simultaneously. This dissertation focuses on how virtualization technologies can be utilized to develop new tools for maintaining high resource utilization, for achieving high application performance, and for reducing the cost of data center management.;For multi-tiered applications, bursty workload traffic can significantly deteriorate performance. This dissertation proposes an admission control algorithm AWAIT, for handling overloading conditions in multi-tier web services. AWAIT places on hold requests of accepted sessions and refuses to admit new sessions when the system is in a sudden workload surge. to meet the service-level objective, AWAIT serves the requests in the blocking queue with high priority. The size of the queue is dynamically determined according to the workload burstiness.;Many admission control policies are triggered by instantaneous measurements of system resource usage, e.g., CPU utilization. This dissertation first demonstrates that directly measuring virtual machine resource utilizations with standard tools cannot always lead to accurate estimates. A directed factor graph (DFG) model is defined to model the dependencies among multiple types of resources across physical and virtual layers.;Virtualized data centers always enable sharing of resources among hosted applications for achieving high resource utilization. However, it is difficult to satisfy application SLOs on a shared infrastructure, as application workloads patterns change over time. AppRM, an automated management system not only allocates right amount of resources to applications for their performance target but also adjusts to dynamic workloads using an adaptive model.;Server consolidation is one of the key applications of server virtualization. This dissertation proposes a VM consolidation mechanism, first by extending the fair load balancing scheme for multi-dimensional vector scheduling, and then by using a queueing network model to capture the service contentions for a particular virtual machine placement