CSE 9133

Autonomic Computing: Foundation, Principles and Applications

INSTRUCTOR: Prof. Ioana Banicescu.

HOURS:

CLASS: Mon/Wed 3:30pm - 4:45pm, Butler 102.

OFFICE: 320 Butler Hall, Mon 2:00pm - 3:00pm or by appointment. Email: ioana@cse.msstate.edu.

325-2756.

PREREQUISITES:

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

OBJECTIVES:

The goal of this course is to introduce the students to the the principles, foundations, and proposed methodology of autonomic computing, an emerging area that addresses grand challenges of our society in the information/knowledge revolution age: the rapidly growing complexity of integrating and managing computing systems To meet these challenges, the autonomic computing requires extensive use of AI techniques, such as automated real-time reasoning and decision making, machine learning, and planning, as well as biologically inspired algorithms. Today, cloud computing presents significant technology trends, and it is reshaping information technology processes and the IT marketplace. This course offers students the opportunity to study the methodology and to evaluate the impact of the autonomic computing 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 the autonomic computing. 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.

COURSE REQUIREMENTS AND GRADING:

There will be frequent reading assignments, presentations, discussions 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 here . Important announcements will also be posted there.

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 algorithms, combinatorial scientific computing, Monte-Carlo simulations, linear, nonlinear and discrete optimization, and others), and their use in autonomic computing.

Cloud computing fundamentals, technologies and applications

The Role of grid computing technologies in cloud computing

Scientific clouds and HPC on competitive cloud resources

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

Ongoing widely known projects - strengths and limitations.

ACADEMIC HONESTY AND ACADEMIC MISCONDUCT:

Students are expected to familiarize themselves and follow the MSU Honor Code Honor Code, as well as the CS department's policies on academic misconduct and on academic honesty with regard to homeworks, projects, exams, papers

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.

[INSTRUCTOR] [HOURS] [PREREQUISITES] [OBJECTIVES] [REQUIREMENTS] [GRADING] [ANNOUNCEMENTS] [OUTLINE]

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