While a lot of work has been published on clustering of data on storage medium, little has been done about automating this process. This is an important area because with data proltferation, human attention has become a precious and expensive resource. Our goal is to develop an automatic and dynamic database clustering technique that will dynamically re-cluster a database with little intervention of a database administrator (DBA) and maintain an acceptable quevy response time at all times. In this paper we describe the issues that need to be solved when developing such a technique. 1.

This work presents several approaches for designing the memory management component of self-stabilizing operating systems. We state the requirements a memory manager should satisfy. One requirement is eventual memory hierarchy consistency among different copies of data residing in different (level of) memory devices e.g., RAM and Disk. Another requirement is stabilization preservation a condition in which the memory manager ensures that every process that is proven to stabilize independently, stabilizes under the (self-stabilizing scheduler and) memory manager operation too. Three memory managers that satisfy the above requirements are presented. The first allocates the entire physical memory to a single process at every given point in time. The second one uses fixed partition of memory between processes, while the last one uses memory leases for dynamic memory allocations. The use of leases in the scope of memory allocation in the operating system level is a new and important aspect of our self-stabilizing memory management.

This position paper advocates the use of the well defined and provable self-stabilization property of a system, to achieve the goals of the self- * paradigms and autonomic computing. Several recent results starting from hardware concerns, continuing with the operating system, and ending in the applications, are integrated: the self-stabilizing microprocessor, with the self-stabilizing operating system, the self-stabilization preserving compiler, and the self-stabilizing autonomic recoverer for applications.

Today, self-adaptation and self-management approaches to software engineering are viewed as specialized techniques and reach a somewhat limited community. In this paper, I overview the current state and expectation of self-adaptation and self-management impact in industry and in premier publication venues and identify what we, as a community, may do to improve such impact. In particular, I find that common evaluation methodologies make it relatively simple for self-adaptation and selfmanagement research to be compared to other such research, but not to more-traditional software engineering research. I argue that extending the evaluation to include comparisons to traditional software engineering techniques may improve a reader’s ability to judge the contribution of the research and increase its impact. Finally, I propose a set of evaluation guidelines that may ease the promotion of self-adaptation and self-management as mainstream software engineering techniques. 1.