Temporal Workload Research Articles

The Workload Curve: Subjective Mental Workload.

In this paper I begin looking for evidence of a subjective workload curve. Results from subjective mental workload assessments are often interpreted linearly. However, I hypothesized that ratings of subjective mental workload increase nonlinearly with unitary increases in working memory load. Two studies were conducted. In the first, the participant provided ratings of the mental difficulty of a series of digit span recall tasks. In the second study, participants provided ratings of mental difficulty associated with recall of visual patterns. The results of the second study were then examined using a mathematical model of working memory. An S curve, predicted a priori, was found in the results of both the digit span and visual pattern studies. A mathematical model showed a tight fit between workload ratings and levels of working memory activation. This effort provides good initial evidence for the existence of a workload curve. The results support further study in applied settings and other facets of workload (e.g., temporal workload). Measures of subjective workload are used across a wide variety of domains and applications. These results bear on their interpretation, particularly as they relate to workload thresholds.

Optimal Resource Provisioning and the Impact of Energy-Aware Load Aggregation for Dynamic Temporal Workloads in Data Centers

An important goal of data center providers is to minimize their operational cost, which reflected through the wear-and-tear cost and the energy consumption cost. In this paper, we present optimization formulations to minimize the cost of ownership in terms of server energy consumption and server- wear-and-tear cost under three different data center server setups (homogeneous, heterogeneous, and hybrid hetero-homogeneous clusters) for dynamic temporal workloads. Our studies show that the homogeneous model takes significantly less computational time than the heterogeneous model (by an order of magnitude). To com- pute optimal configurations in near real time for large-scale data centers, we propose two modes for using our models: aggregation by maximum (preserves workload deadline) and aggregation by mean (relaxes workload deadline). In addition, we propose two aggregation methods for use in each of the two modes: static (peri- odic) aggregation and dynamic (aperiodic)aggregation. We found that in the aggregation by maximum mode, dynamic aggregation resulted in cost savings of up to approximately 18% over the static aggregation. In the aggregation by mean mode, dynamic aggrega- tion saved up to approximately a 50% workload rearrangement compared with the static aggregationby mean mode.

Temporal Workload Analysis and its Application to Power-Aware Scheduling

Power-aware scheduling reduces CPU energy consumption in hard real-time systems through dynamic voltage scaling(DVS). The basic idea of power-aware scheduling is to find slacks available to tasks and reduce CPU‟s frequency or lower its voltage using the found slacks. In this paper, we introduce temporal workload of a system which specifies how much busy its CPU is to complete the tasks at current time. Analyzing temporal workload provides a sufficient condition of schedulability of preemptive early-deadline first scheduling and an effective method to identify and distribute slacks generated by early completed tasks. The simulation results show that proposed algorithm reduces the energy consumption by 10-70% over the existing algorithm and its algorithm complexity is O(n). So, practical on-line scheduler could be devised using the proposed algorithm.

Thermal and Energy Management of High-Performance Multicores: Distributed and Self-Calibrating Model-Predictive Controller

As result of technology scaling, single-chip multicore power density increases and its spatial and temporal workload variation leads to temperature hot-spots, which may cause nonuniform ageing and accelerated chip failure. These critical issues can be tackled by closed-loop thermal and reliability management policies. Model predictive controllers (MPC) outperform classic feedback controllers since they are capable of minimizing performance loss while enforcing safe working temperature. Unfortunately, MPC controllers rely on a priori knowledge of thermal models and their complexity exponentially grows with the number of controlled cores. In this paper, we present a scalable, fully distributed, energy-aware thermal management solution for single-chip multicore platforms. The model-predictive controller complexity is drastically reduced by splitting it in a set of simpler interacting controllers, each one allocated to a core in the system. Locally, each node selects the optimal frequency to meet temperature constraints while minimizing the performance penalty and system energy. Comparable performance with state-of-the-art MPC controllers is achieved by letting controllers exchange a limited amount of information at runtime on a neighborhood basis. In addition, we address model uncertainty by supporting learning of the thermal model with a novel distributed self-calibration approach that matches well the controller architecture.

Cognitive work analysis to evaluate the problem of patient falls in an inpatient setting

To identify factors in the nursing work domain that contribute to the problem of inpatient falls, aside from patient risk, using cognitive work analysis. A mix of qualitative and quantitative methods were used to identify work constraints imposed on nurses, which may underlie patient falls. Data collection was done on a neurology unit staffed by 27 registered nurses and utilized field observations, focus groups, time-motion studies and written surveys (AHRQ Hospital Survey on Patient Culture, NASA-TLX, and custom Nursing Knowledge of Fall Prevention Subscale). Four major constraints were identified that inhibit nurses' ability to prevent patient falls. All constraints relate to work processes and the physical work environment, opposed to safety culture or nursing knowledge, as currently emphasized. The constraints were: cognitive 'head data', temporal workload, inconsistencies in written and verbal transfer of patient data, and limitations in the physical environment. To deal with these constraints, the nurses tend to employ four workarounds: written and mental chunking schemas, bed alarms, informal querying of the previous care nurse, and informal video and audio surveillance. These workarounds reflect systemic design flaws and may only be minimally effective in decreasing risk to patients. Cognitive engineering techniques helped identify seemingly hidden constraints in the work domain that impact the problem of patient falls. System redesign strategies aimed at improving work processes and environmental limitations hold promise for decreasing the incidence of falls in inpatient nursing units.