Ryan Friese

Utility Functions and Resource Management in an Oversubscribed Heterogeneous Computing Environment

We model an oversubscribed heterogeneous computing system where tasks arrive dynamically and a scheduler maps the tasks to machines for execution. The environment and workloads are based on those being investigated by the Extreme Scale Systems Center at Oak Ridge National Laboratory. Utility functions that are designed based on specifications from the system owner and users are used to create a metric for the performance of resource allocation heuristics. Each task has a time-varying utility (importance) that the enterprise will earn based on when the task successfully completes execution. We design multiple heuristics, which include a technique to drop low utility-earning tasks, to maximize the total utility that can be earned by completing tasks. The heuristics are evaluated using simulation experiments with two levels of oversubscription. The results show the benefit of having fast heuristics that account for the importance of a task and the heterogeneity of the environment when making allocation decisions in an oversubscribed environment. The ability to drop low utility-earning tasks allow the heuristics to tolerate the high oversubscription as well as earn significant utility.

An Analysis Framework for Investigating the Trade-Offs between System Performance and Energy Consumption in a Heterogeneous Computing Environment

Rising costs of energy consumption and an ongoing effort for increases in computing performance are leading to a significant need for energy-efficient computing. Before systems such as supercomputers, servers, and datacenters can begin operating in an energy-efficient manner, the energy consumption and performance characteristics of the system must be analyzed. In this paper, we provide an analysis framework that will allow a system administrator to investigate the tradeoffs between system energy consumption and utility earned by a system (as a measure of system performance). We model these trade-offs as a bi-objective resource allocation problem. We use a popular multi-objective genetic algorithm to construct Pareto fronts to illustrate how different resource allocations can cause a system to consume significantly different amounts of energy and earn different amounts of utility. We demonstrate our analysis framework using real data collected from online benchmarks, and further provide a method to create larger data sets that exhibit similar heterogeneity characteristics to real data sets. This analysis framework can provide system administrators with insight to make intelligent scheduling decisions based on the energy and utility needs of their systems.

Energy and Makespan Tradeoffs in Heterogeneous Computing Systems using Efficient Linear Programming Techniques

Resource management for large-scale high performance computing systems pose difficult challenges to system administrators. The extreme scale of these modern systems require task scheduling algorithms that are capable of handling at least millions of tasks and thousands of machines. These large computing systems consume vast amounts of electricity leading to high operating costs. System administrators try to simultaneously reduce operating costs and offer state-of-the-art performance; however, these are often conflicting objectives. Highly scalable algorithms are necessary to schedule tasks efficiently and to help system administrators gain insight into energy/performance trade-offs of the system. System administrators can examine this trade-off space to quantify how much a difference in the performance level will cost in electricity, or analyze how much performance can be expected within an energy budget. In this study, we design a novel linear programming based resource allocation algorithm for a heterogeneous computing system to efficiently compute high quality solutions for simultaneously minimizing energy and makespan. These solutions are used to bound the Pareto front to easily trade-off energy and performance. The new algorithms are highly scalable in both solution quality and computation time compared to existing algorithms, especially as the problem size increases.

Scalable linear programming based resource allocation for makespan minimization in heterogeneous computing systems

Resource management for large-scale high performance computing systems poses difficult challenges to system administrators. The extreme scale of these modern systems require task scheduling algorithms that are capable of handling at least millions of tasks and thousands of machines. Highly scalable algorithms are necessary to efficiently schedule tasks to maintain the highest level of performance from the system. In this study, we design a novel linear programming based resource allocation algorithm for heterogeneous computing systems to efficiently compute high quality solutions for minimizing makespan. The novel algorithm tightly bounds the optimal makespan from below with an infeasible schedule and from above with a fully feasible schedule. The new algorithms are highly scalable in terms of solution quality and computation time as the problem size increases because they leverage similarity in tasks and machines. This novel algorithm is compared to existing algorithms via simulation on a few example systems. We present a novel scheduling algorithm for heterogeneous computing environments.Uses groupings of similar tasks and machines to reduce the computational complexity.Computes upper and lower bounds on the optimal makespan.Schedule approaches a lower bound on the makespan as the number of tasks increases.Scheduling algorithm run time scales linearly with the number of tasks.

Efficient and Scalable Pareto Front Generation for Energy and Makespan in Heterogeneous Computing Systems

The rising costs and demand of electricity for high-performancecomputing systems pose difficult challenges to system administrators that are trying to simultaneously reduce operating costs and offer state-of-the-art performance. However, system performance and energy consumption are often conflicting objectives. Algorithms are necessary to help system administrators gain insight into this energy/performance trade-off. Through the use of intelligent resource allocation techniques, system administrators can examine this trade-off space to quantify how much a given performance level will cost in electricity, or see what kind of performance can be expected when given an energy budget. A novel algorithm is presented that efficiently computes tight lower bounds and high quality solutions for energy and makespan. These solutions are used to bound the Pareto front to easily trade-off energy and performance. These new algorithms are shown to be highly scalable in terms of solution quality and computation time compared to existing algorithms.

A Methodology for Co-Location Aware Application Performance Modeling in Multicore Computing

As multicore processor architectures are now prevalent in server nodes of parallel and distributed computing systems, it has become important to characterize the performance of applications run on these architectures. This study investigates the performance degradation an application experiences from memory interference due to other applications colocated on cores of the same multicore processor. We propose a methodology for designing models that are capable of utilizing varying amounts of information relating to an application and its co-located applications to predict the applications execution time performance degradation due to co-location. We evaluate the models sing several application co-location scenarios based on real world test data from two scientific benchmark suites on two server class Intel Xeon multicore processors.

Utility Driven Dynamic Resource Management in an Oversubscribed Energy-Constrained Heterogeneous System

In this paper, we address the problem of scheduling dynamically-arriving tasks to machines in an oversubscribed heterogeneous computing environment. Each task has a monotonically decreasing utility function associated with it that represents the utility (or value) based on the tasks completion time. Our system model is designed based on the environments of interest to the Extreme Scale Systems Center at Oak Ridge National Laboratory. The goal of our scheduler is to maximize the total utility earned from task completions while satisfying an energy constraint. We design an energy-aware heuristic and compare its performance to heuristics from the literature. We also design an energy filtering technique for this environment that is used in conjunction with the heuristics. The filtering technique adapts to the energy remaining in the system and estimates a fair-share of energy that a tasks execution can consume. The filtering technique improves the performance of all the heuristics and distributes the consumption of energy throughout the day. Based on our analysis, we recommend the level of filtering to maximize the performance of scheduling techniques in an oversubscribed environment.

HPC node performance and energy modeling with the co-location of applications

Multicore processors have become an integral part of modern large-scale and high-performance parallel and distributed computing systems. Unfortunately, applications co-located on multicore processors can suffer from decreased performance and increased dynamic energy use as a result of interference in shared resources, such as memory. As this interference is difficult to characterize, assumptions about application execution time and energy usage can be misleading in the presence of co-location. Consequently, it is important to accurately characterize the performance and energy usage of applications that execute in a co-located manner on these architectures. This work investigates some of the disadvantages of co-location, and presents a methodology for building models capable of utilizing varying amounts of information about a target application and its co-located applications to make predictions about the target application’s execution time and the system’s energy use under arbitrary co-locations of a wide range of application types. The proposed methodology is validated on three different server class Intel Xeon multicore processors using eleven applications from two scientific benchmark suites. The model’s utility for scheduling is also demonstrated in a simulated large-scale high-performance computing environment through the creation of a co-location aware scheduling heuristic. This heuristic demonstrates that scheduling using information generated with the proposed modeling methodology is capable of making significant improvements over a scheduling heuristic that is oblivious to co-location interference.

Energy-aware resource management for computing systems

This corresponds to the material in the invited keynote presentation by H. J. Siegel, summarizing the research in [1], [2]. We address the problem of assigning dynamically-arriving tasks to machines in a heterogeneous computing environment. These machines execute a workload composed of different tasks, where the tasks have diverse computational requirements. Each task has a utility function associated with it that represents the value of completing that task, and this utility decreases the longer it takes a task to complete. The goal of our resource manager is to maximize the sum of the utilities earned by all tasks arriving in the system over a given interval of time, while satisfying an energy constraint. We describe example energy-aware resource management methods to accomplish this goal, and compare their performance. We also study the bi-objective problem of maximizing system utility and minimizing the system energy consumption. This analysis technique allows system administrators to investigate the trade-offs between these conflicting goals.

Robust static planning tool for military village search missions: model and heuristics

In the contemporary military environment, making decisions on how to best utilize resources to accomplish a mission with a set of specified constraints is difficult. A Cordon and Search of a village (a.k.a. village search) is an example of such a mission. Leaders must plan the mission, assigning assets (e.g. soldiers, robots, unmanned aerial vehicles, military working dogs) to accomplish the given task in accordance with orders from higher headquarters. Computer tools can assist these leaders in making decisions, and do so in a manner that will ensure the chosen solution is within mission constraints and is robust against uncertainty in environmental parameters. Currently, no such tools exist at the tactical or operational level to assist decision makers in their planning process and, as a result, individual experience and simplistic data tables are the only tools available. Using robustness concepts, this paper proposes a methodology, a mathematical model, and resource allocation heuristics for static planning of village searches that result in a decision-making tool for military leaders.