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.

Today’s data centers face the issue of balancing electricity use and completion times of their workloads. Rising electricity costs are forcing data center operators to either operate within an electricity budget or to reduce electricity use as much as possible while still maintaining service agreements. Energy-aware resource allocation is one technique a system administrator can employ to address both problems: optimizing the workload completion time (makespan) when given an energy budget, or to minimize energy consumption subject to service guarantees (such as adhering to deadlines). In this paper, we study the problem of energy-aware static resource allocation in an environment where a collection of independent (non-communicating) tasks (“bag-of-tasks”) is assigned to a heterogeneous computing system. Computing systems often operate in environments where task execution times vary (e.g., due to cache misses or data dependent execution times). We model these execution times stochastically, using probability density functions. We want our resource allocations to be robust against these variations, where we defineenergy-robustnessas the probability that the energy budget is not violated, andmakespan-robustnessas the probability a makespan deadline is not violated. We develop and analyze several heuristics for energy-aware resource allocation for both energy-constrained and deadline-constrained problems.

The creation of a Massive Multi-Player On-line Game (MMOG) has significant costs, such as maintenance of server rooms, server administration, and customer service. The capacity of servers in a client/server MMOG is hard to scale and cannot adjust quickly to peaks in demand while maintaining the required response time. To handle these peaks in demand, we propose to employ users’ computers as secondary servers. The introduction of users’ computers as secondary servers allows the performance of the MMOG to support an increase in users. Here, we consider two cases: first, for the minimization of the response times from the server, we develop and implement five static heuristics to implement a secondary server scheme that reduces the time taken to compute the state of the MMOG. Second, for our study on fairness, the goal of the heuristics is to provide a “fair” environment for all the users (in terms of similar response times), and to be “robust” against the uncertainty of the number of new players that may join a given system configuration. The number of heterogeneous secondary servers, conversion of a player to a secondary server, and assignment of players to secondary servers are determined by the heuristics implemented in this study.

In a heterogeneous environment, uncertainty in system parameters may cause performance features to degrade considerably. It then becomes necessary to design a system that is robust. Robustness can be defined as the degree to which a system can function in the presence of inputs different from those assumed. In this research, we focus on the design of robust static resource allocation heuristics suitable for a heterogeneous compute cluster that minimize the energy required to complete a given workload. In this study, we mathematically model and simulate a heterogeneous computing system that is assumed part of a larger warehouse scale computing environment. Task execution times/energy consumption may vary significantly across different data sets in our heterogeneous cluster; therefore, the execution time of each task on each node is modeled as a random variable. A resource allocation is considered robust if the probability that all tasks complete by a system deadline is at least 90%. To minimize the energy consumption of a specific resource allocation, dynamic voltage frequency scaling (DVFS) is employed. However, other factors, such as system overhead (spent on fans, disks, memory, etc.) must also be mathematically modeled when considering minimization of energy consumption. In this research, we propose three different heuristics that employ DVFS to minimize energy consumed by a set of tasks in our heterogeneous computing system. Finally, a lower bound on energy consumption is provided to gauge the performance of our heuristics.

This work considers the satellite data processing portion of a space-based weather monitoring system. It uses a heterogeneous distributed processing platform. There is uncertainty in the arrival time of new data sets to be processed, and resource allocation must be robust with respect to this uncertainty. The tasks to be executed by the platform are classified into two broad categories: high priority (e.g., telemetry, tracking, and control), and revenue generating (e.g., data processing and data research). In this environment, the resource allocation of the high-priority tasks must be done before the resource allocation of the revenue generating tasks. A two-part allocation scheme is presented in this research. The goal of first part is to find a resource allocation that minimizes makespan of the high-priority tasks. The robustness for the first part of the mapping is defined as the difference between this time and the expected arrival of the next data set. For the second part, the robustness of the mapping is the difference between the expected arrival time and the time at which the revenue earned is equal to the operating cost. Thus, the heuristics for the second part find a mapping that minimizes the time for the revenue (gained by completing revenue generating tasks) to be equal to the cost. Different resource allocation heuristics are designed and evaluated using simulations, and their performance is compared to a mathematical bound.

The environment considered in this research is a massive multiplayer online gaming (MMOG) environment. Each user controls an avatar (an image that represents and is manipulated by a user) in a virtual world and interacts with other users. An important aspect of MMOG is maintaining a fair environment among users (i.e., not give an unfair advantage to users with faster connections or more powerful computers). The experience (either positive or negative) the user has with the MMOG environment is dependent on how quickly the game world responds to the user's actions. This study focuses on scaling the system based on demand, while maintaining an environment that guarantees fairness. Consider an environment where there is a main server (MS) that controls the state of the virtual world. If the performance falls below acceptable standards, the MS can off-load calculations to secondary servers (SSs). An SS is a user's computer that is converted into a server. Four heuristics are proposed for determining the number of SSs, which users are converted to SSs, and how users are assigned to the SSs and the MS. The goal of the heuristics is to provide a "fair" environment for all the users, and to be "robust" against the uncertainty of the number of new players that may join a given system configuration. The heuristics are evaluated and compared by simulation.

The creation of a virtual world environment (VWE) has significant costs, such as maintenance of server rooms, server administration, and customer service. The initial development cost is not the only factor that needs to be considered; factors such as the popularity of a VWE and unexpected technical problems during and after the launch can affect the final cost and success of a VWE. The capacity of servers in a client/server VWE is hard to scale and cannot adjust quickly to peaks in demand while maintaining the required response time. To handle these peaks in demand, we propose to employ users' computers as secondary servers. The introduction of users' computers as secondary servers allows the performance of the VWE to support an increase in users. In this study, we develop and implement five static heuristics to implement a secondary server scheme that reduces the time taken to compute the state of the VWE. The number of heterogeneous secondary servers, conversion of a player to a secondary server, and assignment of players to secondary servers are determined by the heuristics implemented in this study. A lower bound of the performance is derived to evaluate the results of the heuristics.

Heterogeneous computing (HC) is the coordinated use of different types of machines, networks, and interfaces to maximize the combined performance and/or cost effectiveness of the system. Heuristics for allocating resources in an HC system have different optimization criteria. A common optimization criterion is to minimize the completion time of the last to finish machine (makespan). In some environments, it is useful to minimize the finishing times of the other machines in the system, i.e., those machines that are not the last to finish. Consider a production environment where a set of known tasks are to be mapped to resources off-line before execution begins. Minimizing the finishing times of all the machines will provide the earliest available ready time for these machines to execute tasks that were not initially considered. In this study, we examine an iterative approach that decreases machine finishing times by repeatedly running a resource allocation heuristic. The goal of this study is to investigate whether this iterative procedure can reduce the finishing time of some machines compared to the mapping initially generated by the heuristic. We show that the effectiveness of the iterative approach is heuristic dependent and study the behavior of the iterative approach for each of the chosen heuristics. This work which identifies heuristics can and cannot attain improvements in the completion time of non-make span machines using this iterative approach.

Heterogeneous distributed computing systems often must operate in an environment where system parameters are subject to uncertainty. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. We present a methodology for quantifying the robustness of resource allocations in a dynamic environment where task execution times are stochastic. The methodology is evaluated through measuring the robustness of three different resource allocation heuristics within the context of a stochastic dynamic environment. A Bayesian regression model is fit to the combined results of the three heuristics to demonstrate the correlation between the stochastic robustness metric and the presented performance metric. The correlation results demonstrated the significant potential of the stochastic robustness metric to predict the relative performance of the three heuristics given a common objective function.