This paper presents lottery scheduling, a novel randomized resource allocation mechanism. Lottery scheduling provides efficient, responsive control over the relative execution rates of computations. Such control is beyond the capabilities of conventional schedulers, and is desirable in systems that service requests of varying importance, such as databases, media-based applications, and networks. Lottery scheduling also supports modular resource management by enabling concurrent modules to insulate their resource allocation policies from one another. A currency abstraction is introduced to flexibly name, share, and protect resource rights. We also show that lottery scheduling can be generalized to manage many diverse resources, such as I/O bandwidth, memory, and access to locks. We have implemented a prototype lottery scheduler for the Mach 3.0 microkernel, and found that it provides flexible and responsive control over the relative execution rates of a wide range of applications. The overhead imposed by our unoptimized prototype is comparable to that of the standard Mach timesharing policy.

We propose a parameterized, randomized edge coloring algorithm for use in coordinating data transfers in fully connected distributed architectures such as parallel 1/0 subsystems and multimedia information systems. Our approach is to preschedule 1/0 requests to eliminate contention for 1/0 ports while maintaining an efficient use of bandwidth. Request scheduling is equivalent to edge coloring a bipartite graph representing pending 1/0 requests. Although efficient optimal algorithms exist for centralized edge coloring where the global request graph is known, in distributed architectures heuristics must be used. We propose such heuristics and use experimental analysis to determine their ability to approach the centralized optimal. The performance of our algorithms is also compared with the work of other researchers experimentally. Our results show that our algorithms produce schedules within 5 % of the optimal schedule, a substantial improvement over existing algorithms. The use of experimental analysis allows us to evaluate the appropriateness of each heuristic for a variety of different architectural models and applications.