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.

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).

It's sad that it took four weeks to reach an agreement. Things wouldn't have been so bad if trash service had gone uninterrupted, but that wasn't the case. What worries us is that leaving such large amounts of trash uncol- lected is a health concern and truly unsanitary. Part of the new contract mentions that when the agreement expires, there will be no strikes or lockouts. We're glad that both par- ties had the intelligence to avoid this kind of situation in the future, but why wasn't this idea put into use this time around? Garbage collection is a service that should never be stopped or interrupted no matter what is hap- pening behind the scenes. Workers could have continued to work while negotiations were happening and it was a childish move on the part of Waste Management to lock out