ICCAC/CallPapers.html at master · dan7800/ICCAC · GitHub

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    <meta name="description" content="2016 IEEE International Conference on Cloud and Autonomic Computing (ICCAC)">

    <title>ICCAC 2016 Conference</title>

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<h1>Call For Papers</h1>


           <h2>Overview</h2>
           <p>
           Enterprise-scale cloud platforms and services systems present common and cross-cutting challenges in maximizing power efficiency and performance while maintaining predictable and reliable behavior, and at the same time responding appropriately to environmental and system changes such as hardware failures and varying workloads. Autonomic computing systems address the challenges in managing these environments by integrating monitoring, decision-processing and actuation capabilities to autonomously manage resources and applications based on high-level policies.</p>

          <p>
          Research in cloud and autonomic computing spans a variety of areas, from distributed systems, computer architecture, middleware services, databases and data-stores, networks, machine learning, and control theory. The purpose of the Fourth International Conference on Cloud and Autonomic Computing (ICCAC) is to bring together researchers and practitioners across these disciplines to address the multiple facets of cloud and autonomic computing.</p>

          <p>
          Papers are solicited on a broad array of topics of relevance to cloud and autonomic computing and their intersections, including those that bear on connections and relationships among different research areas or report on prototype systems or experiences. The goal is to continue our international forum focused on the latest research, applications, and technologies aimed at making cloud and autonomic computing systems and services easy to design, to deploy, and to implement, while also being self-manageable, self-regulating and scalable with little involvement of humans or system administrators.

          </p>


          <h2>Topics of interest include, but are not limited to:</h2>

          <h3>Autonomic Cloud Computing</h3>
          <ul>


              <li>Self-managing cloud services</li>
              <li>Autonomic resource and energy management in cloud computing</li>
              <li>Autonomic cloud applications and services</li>
              <li>Autonomic virtual cloud resources and services</li>
              <li>Cloud workload characterization and prediction</li>
              <li>Monitoring, modeling and analysis of cloud resources and services</li>
              <li>Anomaly behavior analysis of autonomic systems and services</li>

          </ul>


           <h3>Autonomics for Extreme Scales</h3>
          <ul>
            <li>Large scale autonomic systems</li>
            <li>Self-optimizing and self-healing at peta-computing scale</li>
            <li>Self-managing middleware and tools for extreme scales</li>
            <li>Experiences in autonomic systems and applications at extreme scales (peta/exa-computing)</li>

          </ul>


           <h3>Autonomic Computing Foundations and Design Methods</h3>
          <ul>
            <li>Evaluation, validation and quality and correctness assessment of autonomic loops</li>
            <li>Theoretical frameworks for modeling and analyzing autonomic computing systems, control and decision theory</li>
            <li>Model-based design, software engineering, formal methods, testing, programming languages and environments support</li>
            <li>Knowledge representation and visualization of behavior of autonomic systems and services</li>

            </ul>


           <h3>Autonomic Computing Systems, Tools and Applications</h3>
          <ul>
            <li>Self-protection techniques of computing systems, networks and applications</li>
            <li>Stochastic analysis and prediction of autonomic systems and applications</li>
            <li>Benchmarks and tools to evaluate and compare different architectures to implement autonomic cloud systems</li>
            <li>High performance autonomic applications</li>
            <a name="Poster"></a> <!-- Had to put anchor tag here due to heading issues. -->
            <li>Self-* applications in science and engineering</li>
            <li>Self-* Human Machine Interface</li>

          </ul>

          <h2>Paper/Poster Submission and Publication</h2>
          <p>

       Full papers (a maximum of 12 pages in length), industrial experience reports (a maximum of 8 pages) and posters (a maximum of 4 pages) are invited on a wide variety of topics relating to cloud and autonomic computing as indicated above. All papers must follow the IEEE proceedings format. All manuscripts will be reviewed and judged on merits including originality, significance, interest, correctness, clarity, and relevance to the broader community. Papers are strongly encouraged to report experiences, measurements, and user studies, and to provide an appropriate quantitative evaluation.</p>

       <p>
        Submitted papers must include original work, and may not be under consideration for another conference or journal. They should also not be under review or be submitted to another {workshop, conference, or journal} during the ICCAC 2016 review process. Authors should submit full papers or posters electronically following the instructions from the ICCAC 2016 conference web site. Accepted papers and posters will appear in proceedings distributed at the conference and available electronically. Authors of accepted papers/poster are expected to register for and present their work at the conference.

          </p>

          <b>Authors are also encouraged to submit a poster or demo that summarizes and highlights the main points of their paper.</b>


          <h2>Submission Instructions</h2>

          Please register and submit your ICCAC paper using the submission link: <a href="https://iccac16.hotcrp.com/" target="_blank">https://iccac16.hotcrp.com/</a>


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