HGS Schedulers for Digital Audio Workstation like Applications

Digital Audio Workstation (DAW) applications are real-time applications that have special timing constraints. Hierarchical Group Scheduling (HGS) is a real-time scheduling framework that allows developers implement custom schedulers based on any scheduling algorithm through a process of direct interaction between client threads and their schedulers. Such scheduling could extend well beyond the common priority model that currently exists and could be a representation of arbitrary application semantics that can be well understood and acted upon by its associated scheduler. We like to term it "need based scheduling". In this thesis we first study some DAW implementations and later create a few different HGS schedulers aimed at assisting DAW applications meet their needs

Nested, but Separate: Isolating Unrelated Critical Sections in Real-Time Nested Locking

Prior work has produced multiprocessor real-time locking protocols that ensure asymptotically optimal bounds on priority inversion, that support fine-grained nesting of critical sections, or that are independence-preserving under clustered scheduling. However, while several protocols manage to come with two out of these three desirable features, no protocol to date accomplishes all three. Motivated by this gap in capabilities, this paper introduces the Group Independence-Preserving Protocol (GIPP), the first protocol to support fine-grained nested locking, guarantee a notion of independence preservation for fine-grained nested locking, and ensure asymptotically optimal priority-inversion bounds. As a stepping stone, this paper further presents the Clustered k-Exclusion Independence-Preserving Protocol (CKIP), the first asymptotically optimal independence-preserving k-exclusion lock for clustered scheduling. The GIPP and the CKIP rely on allocation inheritance (a.k.a. migratory priority inheritance) as a key mechanism to accomplish independence preservation

Priority-grouping method for parallel multi-scheduling in Grid

With the advent in multicore computers, the scheduling of Grid jobs can be made more effective if scaled to fully utilize the underlying hardware, and parallelized to benefit from the exploitation of multicores. The fact that sequential algorithms do not scale with multicore systems nor benefit from parallelism remains a major obstacle to scheduling in the Grid. As multicore systems become ever more pervasive in our computing lives, over reliance on such systems for passive parallelism does not offer the best option in harnessing the benefits of their multiprocessors for Grid scheduling. An explicit means of exploiting parallelism for Grid scheduling is required. The Group-based Parallel Multi-scheduler, introduced in this paper, is aimed at effectively exploiting the benefits of multicore systems for Grid scheduling by splitting jobs and machines into paired groups and independently scheduling jobs in parallel from those groups. We implemented two job grouping methods, Execution Time Balanced (ETB) and Execution Time Sorted then Balanced (ETSB), and two machine grouping methods, Evenly Distributed (EvenDist) and Similar Together (SimTog). For each method, we varied the number of groups between 2, 4 and 8. We then executed the MinMin Grid scheduling algorithm independently within the groups. We demonstrated that by sharing jobs and machines into groups before scheduling, the computation time for the scheduling process drastically improved by magnitudes of 85% over the ordinary MinMin algorithm when implemented on a HPC system. We also found that our balanced group based approach achieved better results than our previous Priority based grouping approach

Bulk Scheduling with the DIANA Scheduler

Results from the research and development of a Data Intensive and Network Aware (DIANA) scheduling engine, to be used primarily for data intensive sciences such as physics analysis, are described. In Grid analyses, tasks can involve thousands of computing, data handling, and network resources. The central problem in the scheduling of these resources is the coordinated management of computation and data at multiple locations and not just data replication or movement. However, this can prove to be a rather costly operation and efficient sing can be a challenge if compute and data resources are mapped without considering network costs. We have implemented an adaptive algorithm within the so-called DIANA Scheduler which takes into account data location and size, network performance and computation capability in order to enable efficient global scheduling. DIANA is a performance-aware and economy-guided Meta Scheduler. It iteratively allocates each job to the site that is most likely to produce the best performance as well as optimizing the global queue for any remaining jobs. Therefore it is equally suitable whether a single job is being submitted or bulk scheduling is being performed. Results indicate that considerable performance improvements can be gained by adopting the DIANA scheduling approach.Comment: 12 pages, 11 figures. To be published in the IEEE Transactions in Nuclear Science, IEEE Press. 200

Group-based parallel multi-scheduler for grid computing

With the advent in multicore computers, the scheduling of Grid jobs can be made more effective if scaled to fully utilize the underlying hardware, and parallelized to benefit from the exploitation of multicores. The fact that sequential algorithms do not scale with multicore systems nor benefit from parallelism remains a major obstacle to scheduling in the Grid. As multicore systems become ever more pervasive in our computing lives, over reliance on such systems for passive parallelism does not offer the best option in harnessing the benefits of their multiprocessors for Grid scheduling. An explicit means of exploiting parallelism for Grid scheduling is required. The Group-based Parallel Multi-scheduler, introduced in this paper, is aimed at effectively exploiting the benefits of multicore systems for Grid scheduling by splitting jobs and machines into paired groups and independently scheduling jobs in parallel from those groups. We implemented two job grouping methods, Execution Time Balanced (ETB) and Execution Time Sorted then Balanced (ETSB), and two machine grouping methods, Evenly Distributed (EvenDist) and Similar Together (SimTog). For each method, we varied the number of groups between 2, 4 and 8. We then executed the MinMin Grid scheduling algorithm independently within the groups. We demonstrated that by sharing jobs and machines into groups before scheduling, the computation time for the scheduling process drastically improved by magnitudes of 85% over the ordinary MinMin algorithm when implemented on a HPC system. We also found that our balanced group based approach achieved better results than our previous Priority based grouping approach

An EPTAS for machine scheduling with bag-constraints

Machine scheduling is a fundamental optimization problem in computer science. The task of scheduling a set of jobs on a given number of machines and minimizing the makespan is well studied and among other results, we know that EPTAS's for machine scheduling on identical machines exist. Das and Wiese initiated the research on a generalization of makespan minimization, that includes so called bag-constraints. In this variation of machine scheduling the given set of jobs is partitioned into subsets, so called bags. Given this partition a schedule is only considered feasible when on any machine there is at most one job from each bag. Das and Wiese showed that this variant of machine scheduling admits a PTAS. We will improve on this result by giving the first EPTAS for the machine scheduling problem with bag-constraints. We achieve this result by using new insights on this problem and restrictions given by the bag-constraints. We show that, to gain an approximate solution, we can relax the bag-constraints and ignore some of the restrictions. Our EPTAS uses a new instance transformation that will allow us to schedule large and small jobs independently of each other for a majority of bags. We also show that it is sufficient to respect the bag-constraint only among a constant number of bags, when scheduling large jobs. With these observations our algorithm will allow for some conflicts when computing a schedule and we show how to repair the schedule in polynomial-time by swapping certain jobs around

Research on bulk-cargo-port berth assignment based on priority of resource allocation

Purpose: The purpose of this paper is to propose a Priority of Resource Allocation model about how to utilize the resources of the port efficiently, through the improvement of traditional ant colony algorithm, the ship-berth matching relation constraint matrix formed by ontology reasoning. Design/methodology/approach: Through questionnaires?Explore factor analysis (EFA) and principal component analysis, the authors extract the importance of the goods, the importance of customers, and type of trade as the main factors of the ship operating priority. Then the authors combine berth assignment problem with the improved ant colony algorithm, and use the model to improve ship scheduling quality. Finally, the authors verify the model with physical data in a bulk-cargo-port in China. Findings: Test by the real data of bulk cargo port, it show that ships’ resource using priority and the length of waiting time are consistent; it indicates that the priority of resource allocation play a prominent role in improving ship scheduling quality. Research limitations: The questionnaires is limited in only one port group, more related Influence factors should be considered to extend the conclusion. Practical implications: The Priority of Resource Allocation model in this paper can be used to improve the efficiency of the dynamic berth assignment. Originality: This paper makes the time of ship in port minimized as the optimization of key indicators and establishes a dynamic berth assignment model based on improved ant colony algorithm and the ontology reasoning model.Peer Reviewe