Computer Resource Usability Modelling from Virtual-Machine

Workload Traces: A Cognitive HCI Perspective

Fadi Farha1,* Tony Salloom2

1 The Faculty of Informatics Engineering, Aleppo University, Syria

2 Head of Computer Vision Department, Synthoid AI, Shanghai, China

Received: December 19, 2025 Revised: February 06, 2026 Accepted: March 05, 2026 ⋆ Corresponding author

ABSTRACT

Computer usability is often discussed through screen layout, navigation, and task flow, although the experience of

using a computer also depends on whether processor, memory, storage, and network resources remain available

when the user needs them. This paper develops a Computer Resource Usability Index (CRUI) for interpreting

virtual-machine resource traces as indicators of user-facing usability risk. The proposed index converts CPU, memory,

disk, and network measurements into a bounded resource-friction score and then maps this score into four actionable

usability states: comfortable, watch, constrained, and strained. The analysis uses a processed extract following the

public GWA-T-12 Bitbrains trace structure, which records VM-level resource metrics for enterprise applications.

The results show that resource usability is not explained by CPU usage alone; imbalance across resource channels,

I/O pressure, and variability also contribute to predicted friction. The findings provide a practical bridge between

infrastructure monitoring and cognitive HCI by translating low-level resource traces into interface-relevant decisions

such as when to defer background tasks, warn the user, or allocate additional headroom.

Keywords: Computer usability Resource utilization Virtual machines Human-computer interaction Resource friction

1. INTRODUCTION

Usability in human-computer interaction is usually evaluated

through visible aspects of an interface: whether the user understands

the available actions, completes a task with few

errors, and receives feedback at the right time. Yet a user’s

experience of a computer also depends on the condition of

the resources behind the interface. When CPU cycles, memory,

disk access, or network capacity become constrained,

the interface can appear slow, unresponsive, or inconsistent

even if its visual design is correct. This makes computerresource

usability a legitimate HCI concern rather than only

an infrastructure-management issue.

The problem is increasingly relevant because many userfacing

systems are delivered through virtual machines, containers,

remote desktops, learning platforms, enterprise applications,

and cloud-hosted workspaces. In such environments,

a visible delay may originate from a resource bottleneck

rather than from an interface design fault. Adaptive HCI

work has therefore begun to argue that interaction quality

should be understood as a product of both human factors and

system conditions [1,2]. For resource-intensive applications,

the user’s cognitive effort may rise not because the task is difficult

but because the system interrupts the expected rhythm

of action and feedback.

Public workload traces provide an opportunity to study this

issue empirically. The GWA-T-12 Bitbrains trace is a public

VM-level resource dataset containing performance metrics

such as CPU usage, memory usage, disk throughput, and

network throughput for enterprise workloads [3,4]. Although