Babak Hamidzadeh

An optimal periodic scheduler for dual-arm robots in cluster tools with residency constraints

Discusses a scheduling technique, for cluster tools, that addresses postprocessing residency constraints and throughput requirements. The residency constraints impose a limit on the postprocessing time that a material unit spends in a processing module. The technique searches in the time and resource domains for a feasible schedule with a maximum throughput. It operates in two main phases; the initial one of which (and the lower complexity one) computes a simple periodic schedule. For a large number of problem instances, the simple periodic schedule feasibly solves the problem. If a feasible schedule cannot be found in the first phase, the scheduler enters phase two (the higher complexity one) to compute a feasible schedule. During this phase, the scheduler incrementally increases the period only if necessary, to keep the throughput at a maximum. Several heuristics are designed and added to reduce the complexity of the scheduling algorithm. The resulting schedules are deadlock free, since resources are scheduled according to the times that they are available. Analytical and experimental analyses demonstrate the correctness and efficiency of our proposed technique.

Dynamic scheduling techniques for heterogeneous computing systems

There has been a recent increase of interest in heterogeneous computing systems, due partly to the fact that a single parallel architecture may not be adequate for exploiting all of a programs available parallelism. In some cases, heterogeneous systems have been shown to produce higher performance for lower cost than a single large machine. However, there has been only limited work on developing techniques and frameworks for partitioning and scheduling applications across the components of a heterogeneous system. In this paper we propose a general model for describing and evaluating heterogeneous systems that considers the degree of uniformity in the processing elements and the communication channels as a measure of the heterogeneity in the system. We also propose a class of dynamic scheduling algorithms for a heterogeneous computing system interconnected with an arbitrary communication network. These algorithms execute a novel optimization technique to dynamically compute schedules based on the potentially non-uniform computation and communication costs on the processors of a heterogeneous system. A unique aspect of these algorithms is that they easily adapt to different task granularities, to dynamically varying processor and system loads, and to systems with varying degrees of heterogeneity. Our simulations are designed to facilitate the evaluation of different scheduling algorithms under varying degrees of heterogeneity. The results show improved performance for our algorithms compared to the performance resulting from existing scheduling techniques.

Dynamic scheduling strategies for shared-memory multiprocessors

Efficiently scheduling parallel tasks on to the processors of a shared-memory multiprocessor is critical to achieving high performance. Given perfect information at compile-time, a static scheduling strategy can produce an assignment of tasks to processors that ideally balances the load among the processors while minimizing the run-time scheduling overhead and the average memory referencing delay. Since perfect information is seldom available, however, dynamic scheduling strategies distribute the task assignment function to the processors by having idle processors allocate work to themselves from a shared queue. While this approach can improve the load balancing compared to static scheduling, the time required to access the shared work queue adds directly to the overall execution time. To overlap the time required to dynamically schedule tasks with the execution of the tasks, we examine a class of self-adjusting dynamic scheduling (SADS) algorithms that centralizes the assignment of tasks to processors. These algorithms dedicate a single processor of the multiprocessor to perform a novel on-line branch-and-bound technique that dynamically computes partial schedules based on the loads of the other processors and the memory locality (affinity) of the tasks and the processors. Our simulation results show that this centralized scheduling outperforms self-scheduling algorithms even when using only a small number of processors.

Self-Adjusting Scheduling: An On-Line Optimization Technique for Locality Management and Load Balancing

Techniques for scheduling parallel tasks on to the processors of a multiprocessor architecture must tradeoff three interrelated factors: 1) scheduling and synchronization costs, 2) load balancing, and 3) memory locality. Current scheduling techniques typically consider only one or two of these three factors at a time. We propose a novel Self- Adjusting Scheduling (SAS) algorithm that addresses all three factors simultaneously. This algorithm dedicates a single processor to execute an on-line branch-and-bound algorithm to search for partial schedules concurrent with the execution of tasks previously assigned to the remaining processors. This overlapped scheduling and execution, along with self-adjustment of duration of partial scheduling periods reduces scheduling and synchronization costs significantly. To satisfy the load-balancing and locality management, SAS introduces a unified cost model that accounts for both of these factors simultaneously. We compare the simulated performance of SAS with the Affinity Scheduling algorithm (AFS). The results of our experiments demonstrate that the potential loss of performance caused by dedicating a processor to scheduling is outweighed by the higher performance produced by SASs dynamically adjusted schedules, even in systems with a small number of processors. SAS is a general on-line optimization technique that can be applied to a variety of dynamic scheduling problems.

DYNORAII: A REAL-TIME PLANNING ALGORITHM

There has been a recent rise in research on real-time planning algorithms. Most of these algorithms address either the issue of response-time constraints or the issue of dynamic environments. We propose a new real-time planning algorithm, DYNORAII, to address both of these issues simultaneously. DYNORAII is structured as a sequence of “partial planning and execution” cycles to avoid obsolescence of planned solutions at the time of execution. DYNORAII uses a stopping criterion to balance planning cost and execution cost to achieve near optimal response times. DYNORAII was used for the routing problem to optimize total cost in both static and dynamic environments. It shows better average-case time complexity than traditional real-time algorithms.

Time controlled dynamic scheduling of aperiodic real-time tasks

We introduce a new set of dynamic scheduling algorithms for scheduling and guaranteeing the deadline compliance of a set of aperiodic real-time tasks on a uniprocessor architecture. The task model selected is that of non-preemptable tasks with arbitrary start times and deadlines. The proposed algorithms address a fundamental trade-off in dynamic scheduling between the cost of scheduling and the quality of the resulting schedules. The algorithms control the time allocated to scheduling explicitly, in order to obtain good-quality schedules in reasonable times. We show that taking into account the scheduling time is crucial for honoring the deadlines of scheduled real-time tasks. We provide an experimental evaluation of our algorithms via performance comparisons with existing landmark algorithms that were originally designed to address some similar issues. The results of our experiments show that our algorithms outperform the existing techniques in several parameter configurations.

Adaptive planning and scheduling in dynamic task domains

There are many application domains such as flexible manufacturing systems (FMS) in which the world changes during the problem solving process or about which the problem solver does not have complete information a priori. A problem solver in such environments is required to take advantage of up-to-date information that becomes available, on line, and to use this information in order to avoid producing solutions that are obsolete by the time they are to be executed. In this paper, we propose an algorithm which performs problem solving on line in order to obtain new information about the availability of resources in its local surroundings. The algorithm performs partial planning followed by partial execution, in order to take immediate advantage of resources which become available and remain available for a short period of time. As part of this paper, we introduce a model of dynamicity included in a graph representation of a task. The decision on the time points at which the resource availability information is probed and updated is automatically made according to the parameters of the model of dynamicity in the environment. We provide theoretical and empirical analyses of the proposed algorithm for a routing problem in the proposed dynamic model. A set of candidate algorithms were chosen for the performance-comparison experiments each of which is suitable for a particular condition (i.e. some produce good results in a static environment and some produce better results in a highly dynamic environment). Results of our performance-comparison experiments show that the proposed algorithm performs as well as the best of the candidate algorithms under a wide range of experiment parameters. The results also show that the proposed algorithm is capable of automatically adapting to the degree of dynamicity in the environment.

EVALUATION OF REAL-TIME PROBLEM SOLVERS IN DYNAMIC ENVIRONMENTS

There are many real-time application domains in which the world changes during the problem solving process. Several real-time search algorithms have been proposed for problem solving in dynamic environments. However, there has not been any systematic evaluation and comparison of these algorithms. This paper provides a classification of different dynamic worlds. It then provides a detailed model of a dynamic world where changes occur in edge costs around a zero mean. A formal analysis of the model suggests that the static rank ordering of solution paths is preserved in the proposed dynamic model. The paper provides analysis of two real-time search algorithms, namely DYNORAII and RTA*, for the real-time path planning problem. DYNORAII addresses response-time constraints and dynamic world issues simultaneously. We provide new results on the path planning problem in the proposed dynamic model of graphs. We also provide experimental evaluation of DYNORAII and RTA* in their ability to minimize response-times in...

Understanding Structure and Mapping Content to Semantic Models

In this paper, we describe an effort to preserve the semantics and relationships between Digital Objects content. As the system ingests the content, it has to understand and parse structures of files and packages that hold bundles of digital content. It also has to map the content to an ontology that captures domain knowledge, allowing for preservation of the relationships among incoming data while improving data access and retrieval at the later time.

An optimal scheduling technique for dual-arm cluster tools with buffer modules

In a cluster tool where process modules have residency constraints, an optimal schedule may need to buffer the partially processed wafers. In previous approaches, one of the active resources (i.e. the transport module) was used as a temporary buffer. In this approach we try to use a resource that is usually available in cluster tools and is only used for tool maintenance reasons. This passive module that we refer to as buffer module, can hold the partially processed wafers, and free the transport module (TM). Our experiment shows that in 57% of the cases, because the TM can perform other actions while the wafer is buffered in the buffer module, a schedule with better throughput was found.