Heterogeneous parallel and distributed computing systems frequently must operate in environments where there is uncertainty in system parameters. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. In such an environment, the execution time of any given task may fluctuate substantially due to factors such as the content of data to be processed. Determining a resource allocation that is robust against this uncertainty is an important area of research. In this study, we define a stochastic robustness measure to facilitate resource allocation decisions in a dynamic environment where tasks are subject to individual hard deadlines and each task requires some input data to start execution. In this environment, the tasks that cannot meet their deadlines are dropped (i.e., discarded). We define methods to determine the stochastic completion times of tasks in the presence of the task dropping. The stochastic task completion time is used in the definition of the stochastic robustness measure. Based on this stochastic robustness measure, we design novel resource allocation techniques that work in immediate and batch modes, with the goal of maximizing the number of tasks that meet their individual deadlines. We compare the performance of our technique against several well-known approaches taken from the literature and adapted to our environment. Simulation results of this study demonstrate the suitability of our new technique in a dynamic heterogeneous computing system.

We present a decentralized market-based approach to resource allocation in a heterogeneous overlay network. This resource allocation strategy dynamically assigns resources in an overlay network to requests for service based on current system utilization, thus enabling the system to accommodate fluctuating demand for its resources. Our approach is based on a mathematical model of this resource allocation environment that treats the allocation of system resources as a constrained optimization problem. From the solution to the dual of this optimization problem, we derive a simple decentralized algorithm that is extremely efficient. Our results show the near optimality of the proposed approach through extensive simulation of this overlay network environment. The simulation study utilizes components taken from a real-world middleware application environment and clearly demonstrates the practicality of the approach in a realistic setting.

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 define energy-robustness as the probability that the energy budget is not violated, and makespan-robustness as 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.

On the modern battlefield cordon and search missions (also known as village searches) are conducted daily. Creating resource allocations that link search teams (e.g. soldiers, robots, unmanned aerial vehicles, military working dogs) to target buildings is difficult and time consuming in the static planning environment and is even more challenging in a time-constrained dynamic environment. Conducting dynamic resource allocation during the execution of a military village search mission is beneficial especially when the time to develop a static plan is limited and hence the quality of the plan is relatively poor. Dynamic heuristics can help improve the static plan because they are able to incorporate current state information that is unavailable prior to mission execution and thus produce more accurate results than static heuristics alone can achieve. There are currently no automated means to create these dynamic resource allocations for military use. Using robustness concepts, this paper proposes and compares dynamic resource allocation heuristics that create mission plans that are resilient against uncertainty in the environment and that save valuable time for military planning staff.

In this study, we consider an environment composed of a heterogeneous cluster of multicore-based machines used to analyze satellite images. The workload involves large data sets, and is typically subject to deadline constraints. Multiple applications, each represented by a directed acyclic graph (DAG), are allocated to a dedicated heterogeneous distributed computing system. Each vertex in the DAG represents a task that needs to be executed and task execution times vary substantially across machines. The goal of this research is to assign applications to multicore-based parallel system in such a way that all applications complete before a common deadline, and their completion times are robust against uncertainties in execution times. We define a measure that quantifies robustness in this environment. We design, compare, and evaluate two resource allocation heuristics that attempt to maximize robustness.

This research investigates the problem of robust static resource allocation for distributed computing systems operating under imposed Quality of Service (QoS) constraints. Often, such systems are expected to function in an environment where uncertainty in system parameters is common. In such an environment, the amount of processing required to complete a task may fluctuate substantially. Determining a resource allocation that accounts for this uncertainty—in a way that can provide a probability that a given level of QoS is achieved—is an important area of research. We have designed novel techniques for maximizing the probability that a given level of QoS is achieved. These techniques feature a unique application of both path relinking and local search within a Genetic Algorithm. In addition, we define a new methodology for finding resource allocations that are guaranteed to have a non-zero probability of addressing the timing constraints of the system. We demonstrate the use of this methodology within two unique steady-state genetic algorithms designed to maximize the robustness of resource allocations. The performance results for our techniques are presented for a simulated environment that models a heterogeneous cluster-based radar data processing center.

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.

We present a decentralized market-based approach to resource allocation in a heterogeneous overlay network. This resource allocation strategy dynamically assigns resources in an overlay network to requests for service based on current system utilization, thus enabling the system to accommodate fluctuating demand for its resources. Our approach is based on a mathematical model of this resource allocation environment that treats the allocation of system resources as a constrained optimization problem. From the solution to the dual of this optimization problem, we derive a simple decentralized algorithm that is extremely efficient. Our results show the near optimality of the proposed approach through extensive simulation of this overlay network environment. The simulation study utilizes components taken from a real-world middleware application environment and clearly demonstrates the practicality of the approach in a realistic setting.

We study heterogeneous computing (HC) systems that consist of a set of different machines that have varying capabilities. These machines are used to execute a set of heterogeneous tasks that vary in their computational complexity. Finding the optimal mapping of tasks to machines in an HC system has been shown to be, in general, an NP-complete problem. Therefore, heuristics have been used to find near-optimal mappings. The performance of allocation heuristics can be affected significantly by factors such as task and machine heterogeneities. In this paper, we identify different statistical measures used to quantify the heterogeneity of HC systems, and show the correlation between the performance of the heuristics and these measures through simple mapping examples and synthetic data analysis. In addition, we illustrate how regression trees can be used to predict the most appropriate heuristic for an HC system based on its heterogeneity.

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.

Energy-efficient resource allocation within clusters and data centers is important because of the growing cost of energy. We study the problem of energy-constrained dynamic allocation of tasks to a heterogeneous cluster computing environment. Our goal is to complete as many tasks by their individual deadlines and within the system energy constraint as possible given that task execution times are uncertain and the system is oversubscribed at times. We use Dynamic Voltage and Frequency Scaling (DVFS) to balance the energy consumption and execution time of each task. We design and evaluate (via simulation) a set of heuristics and filtering mechanisms for making allocations in our system. We show that the appropriate choice of filtering mechanisms improves performance more than the choice of heuristic (among the heuristics we tested).

In the current military environment, village searches are conducted daily by a variety of search team types. Staff officers planning these resource allocation problems currently rely on experience and simple data tables to develop the plans. The Robust People, Animals, and Robots Search (RoPARS) planning tool for village searches developed at Colorado State University can assist military planners with this tedious process. The tool consists of a graphical user interface and a resource allocation engine. Its output is a mission plan that is robust against uncertainty in the battlefield environment (e.g., unit speed, temperature, enemy contact). The contributions of this paper include the RoPARS tool and its robustness concepts, mathematical models, and resource allocation heuristics.

Recently there has been an increased demand for imaging systems in support of high-speed digital printing. The required increase in performance in support of such systems can be accomplished through an effective parallel execution of image processing applications in a distributed cluster computing environment. The output of the system must be presented to a raster based display at regular intervals, effectively establishing a hard deadline for the production of each image. Failure to complete a rasterization task before its deadline will result in an interruption of service that is unacceptable. The goal of this research was to derive a metric for measuring robustness in this environment and to design a resource allocation heuristic capable of completing each rasterization task before its assigned deadline, thus, preventing any service interruptions. We present a mathematical model of such a cluster based raster imaging system, derive a robustness metric for evaluating heuristics in this environment, and demonstrate using the metric to make resource allocation decisions. The heuristics are evaluated within a simulation of the studied raster imaging system. We clearly demonstrate the effectiveness of the heuristics by comparing their results with the results of a resource allocation heuristic commonly used in this type of system.

One type of heterogeneous computing (HC) systems consists of machines with diverse capabilities harnessed together to execute a set of tasks that vary in their computational complexity. An HC system can be characterized using an Estimated Time to Compute (ETC) matrix. Each value in this matrix represents the ETC of a specific task on a specific machine when executed exclusively. Heuristics use the values in the ETC matrix to allocate tasks to machines in the HC system. The performance of resource allocation heuristics can be affected significantly by factors such as task and machine heterogeneities. Therefore, quantifying heterogeneity will allow a system to select a heuristic appropriate for the given heterogeneous environment. In this paper, we identify different central moments used to quantify the heterogeneity of ETC matrices obtained from real world systems and benchmark data, and show the effect of these moments on the performance of heuristics both through simple examples and simulations.

This research investigates the problem of robust dynamic resource allocation for heterogeneous distributed computing systems operating under imposed constraints. Often, such systems are expected to function in an environment where uncertainty in system parameters is common. In such an environment, the amount of processing required to complete an application may fluctuate substantially. Determining a resource allocation that accounts for this uncertainty---in a way that can provide a probability that a given level of service is achieved---is an important area of research. We define a mathematical model of stochastic robustness appropriate for a dynamic environment that can be used during resource allocation to aid heuristic decision making. In addition, we design a novel technique for maximizing stochastic robustness in this environment. Our performance results for this technique are compared with several well known resource allocation techniques in a simulated environment that models a heterogeneous distributed computing system.

This research investigates the problem of robust static resource allocation for distributed computing systems operating under imposed Quality of Service (QoS) constraints. Often, such systems are expected to function in a physical environment replete with uncertainty, which causes the amount of processing required to fluctuate substantially over time. Determining a resource allocation that accounts for this uncertainty in a way that can provide a probabilistic guarantee that a given level of QoS is achieved is an important research problem. The stochastic robustness metric proposed in this research is based on a mathematical model where the relationship between uncertainty in system parameters and its impact on system performance are described stochastically. The utility of the established metric is then exploited in the design of optimization techniques based on greedy and iterative approaches that address the problem of resource allocation in a large class of distributed systems operating on periodically updated data sets. The performance results are presented for a simulated environment that replicates a heterogeneous cluster-based radar data processing center. A mathematical performance lower bound is presented for comparison analysis of the heuristic results. The lower bound is derived based on a relaxation of the Integer Linear Programming formulation for a given resource allocation problem.

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.

Heterogeneous parallel and distributed computing systems may operate in an environment where certain system performance features degrade due to unpredictable circumstances. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. This work develops a model for quantifying robustness in a dynamic heterogeneous computing environment where task execution time estimates are known to contain errors. This mathematical expression of robustness is then applied to two different problem environments. Several heuristic solutions to both problem variations are presented that utilize this expression of robustness to influence mapping decisions.

This is an overview of the robust resource allocation research efforts that have been and continue to be conducted by the CSU robustness in computer systems group. Parallel and distributed computing systems, consisting of a (usually heterogeneous) set of machines and networks, frequently operate in environments where delivered performance degrades due to unpredictable circumstances. Such unpredictability can be the result of sudden machine failures, increases in system load, or errors caused by inaccurate initial estimation. The research into developing models and heuristics for parallel and distributed computing systems that create robust resource allocations is presented.