ACM Transactions on Modeling and Performance Evaluation of Computing Systems

Scope & Guideline

Advancing the Science of Computing System Performance.

Introduction

Delve into the academic richness of ACM Transactions on Modeling and Performance Evaluation of Computing Systems with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN2376-3639
PublisherASSOC COMPUTING MACHINERY
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2016 to 2024
AbbreviationACM TRANS MODELING P / ACM Trans. Modeling Perform. Eval. Comput. Syst.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address1601 Broadway, 10th Floor, NEW YORK, NY 10019-7434

Aims and Scopes

The ACM Transactions on Modeling and Performance Evaluation of Computing Systems focuses on the analysis, modeling, and evaluation of computing systems, emphasizing methodologies that enhance system performance and efficiency. The journal's research spans various domains of computing, with a consistent commitment to advancing theoretical frameworks and practical applications in performance evaluation.
  1. Modeling Techniques for Computing Systems:
    The journal emphasizes various modeling techniques, including queuing theory, stochastic processes, and agent-based modeling, to analyze and predict the behavior of computing systems under different workloads and configurations.
  2. Performance Evaluation Metrics:
    Research published in the journal often explores new metrics and methodologies for assessing system performance, reliability, and efficiency, including metrics related to throughput, latency, and resource utilization.
  3. Optimization Strategies:
    A significant focus is placed on optimization strategies for computing systems, including load balancing, resource allocation, and scheduling algorithms, aimed at improving overall system performance.
  4. Emerging Technologies and Applications:
    The journal encompasses research on emerging technologies such as cloud computing, edge computing, and machine learning, investigating their impact on system performance and offering novel performance evaluation techniques.
  5. Interdisciplinary Approaches:
    There is a consistent emphasis on interdisciplinary methodologies that integrate concepts from computer science, operations research, and engineering to address complex performance evaluation challenges.
The journal has seen an evolution in its focus, with several emerging themes gaining traction in recent publications. These trends highlight the journal's responsiveness to the evolving landscape of computing systems and their performance evaluation.
  1. Machine Learning for Performance Optimization:
    There is a significant increase in research that applies machine learning techniques to optimize performance in various computing systems, indicating a trend toward leveraging AI for system improvement.
  2. Cloud and Edge Computing Performance:
    The exploration of performance evaluation in cloud and edge computing environments has surged, reflecting the growing importance of these paradigms in modern computing architectures.
  3. Energy Efficiency and Sustainability:
    Research focusing on energy-efficient computing and sustainability practices has become more prominent, highlighting the industry's shift towards environmentally conscious computing solutions.
  4. Real-time and Adaptive Systems:
    An emerging theme is the study of real-time and adaptive systems that can respond dynamically to changing workloads and environmental conditions, which is critical for modern applications.
  5. Cyber-physical Systems and IoT:
    The integration of performance evaluation methodologies in cyber-physical systems and the Internet of Things (IoT) is gaining traction, emphasizing the need for robust evaluation frameworks in these interconnected environments.

Declining or Waning

While the journal maintains a strong focus on various core areas, certain themes have shown a decline in prominence over recent years, suggesting a shift in research interest and priorities within the field.
  1. Traditional Queuing Theory:
    Research specifically focused on classical queuing models has decreased, indicating a shift toward more complex and realistic modeling approaches that incorporate modern computing challenges.
  2. Static Performance Models:
    There is a noticeable reduction in the publication of static performance models, suggesting a transition towards dynamic and adaptive modeling techniques that reflect real-time system behaviors.
  3. Basic Resource Allocation Strategies:
    Basic resource allocation strategies appear to be waning, with researchers preferring to explore more intricate and context-aware approaches that align with contemporary computing environments.
  4. Isolated System Evaluations:
    The focus on evaluating isolated systems without considering interactions in networked and distributed environments has diminished, reflecting the need for more holistic approaches to performance evaluation.

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