CSE 9133

INTRODUCTION TO AUTONOMIC COMPUTING

Prof. Ioana Banicescu.

HOURS:

Class: Tue/Thu 3:30 - 4:45 pm, Butler 104.
Office: 320 Butler Hall, Tue/Thu 2:30 - 3:30 pm; other times by appointment only.
E-mail: ioana@cse.msstate.edu.


325-2756

TEXTBOOK and READINGS from:

• Richard Murch, Autonomic Computing, IBM Press, 2004, Prentice Hall.
• Nancy Forbes, Imitation of Life - How Biology Is Inspiring Computing, MIT Press, 2004.
• Recommended research papers.

PREREQUISITES:

Graduate status and permission of the instructor. Graduate level courses in algorithms, parallel algorithms, high performance computing and artificial intelligence are desirable.

OBJECTIVES and REQUIREMENTS:

The goal of this course is to introduce the students to principles, concepts, and proposed methodology of autonomic computing, an emerging area and a grand challenge for our society in the information/knowledge revolution age. The course will investigate the origins, goals, and promises of autonomic computing. It will also offer students the opportunity to study the methodology and evaluate the impact of this new research area to information technology and to the life of the society at large.

Students will have the opportunity to study both theoretical and experimental aspects of this research area. Analysis of adaptive algorithms, biologically inpired algorithms, and various linear, nonlinear and discrete optimization techniques used will be included in the studies. One of the goals of this course is the development of the students' appreciation of the importance and necessity of acquiring in-depth knowledge from various areas of science and engineering fields, and of how that knowledge can harmoniously be integrated, resulting in solutions that would optimize the functionality of autonomic computing systems. The class requires engagement in active participation through presentations and many discussions. A variety of reading material will be given throughout the semester. Students inclined to be both theoretical and/or experimental work are expected to bring their active contribution to this class.

GRADING:

There will be frequent readings assignements and presentations, as well as some writing assignments. All of them will account for 60% of the grade. The final exam will consist of a research project and/or a research paper which will account for 40% of the grade.

ANNOUNCEMENTS:

Students are responsible for checking the announcements regularly click here.

COURSE OUTLINE:

The course will cover material related to, but not limited to the
 following wide spectrum of topics:

Autonomic attributes and the grand challenge.

Complexity in autonomic computing and its forms.

Architecture, open standards, implementation considerations,
    enabling technology and development tools.

Machine learning and multiagent systems in the development of
    autonomic computing.

Biologically inspired computing (evolutionary algorithms,
    cellular automata, DNA computation, amorphous computing, etc).

Algorithms and optimization (graph algorithmss, combinatorial
    scientific computing, Monte-Carlo simulations, linear, nonlinear
    and discrete optimization, and others).

Ongoing widely known projects - strengths and limitations.

State-of-the-art of present technology in autonomic computing.

ACADEMIC HONESTY, ETHICS IN RESEARCH AND SCHOLARLY ACTIVITIES:

Students are expected to follow MSU as well as the CS department's policies on academic misconduct and on academic honesty with regard to homeworks, projects, exams, papers. These policies can be found on the MSU ethics in research and other scholarly activities and Computer Science and Engineering Departmental policy regarding professional conduct pages respectively.

ATTENDANCE AND ADD/DROP POLICIES:

Attendance is required. Roll will be taken, and absences will be listed on midterm and final grade reports. Attendance may influence grades at border line. Makeup exams are allowed only in special circumstances where absences are certified by the Dean of Students office. At the beginning of the semester, students are encouraged to also review the Add/Drop policy.

Students who are auditing this course must attend at least 75% of the class lectures to avoid a grade of F.