Scheduling task systems with resources.

Thesis. 1980. Ph.D.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.Vita.Bibliography: leaves 144-145.Ph.D

MOON: MapReduce On Opportunistic eNvironments

Abstract—MapReduce offers a flexible programming model for processing and generating large data sets on dedicated resources, where only a small fraction of such resources are every unavailable at any given time. In contrast, when MapReduce is run on volunteer computing systems, which opportunistically harness idle desktop computers via frameworks like Condor, it results in poor performance due to the volatility of the resources, in particular, the high rate of node unavailability. Specifically, the data and task replication scheme adopted by existing MapReduce implementations is woefully inadequate for resources with high unavailability. To address this, we propose MOON, short for MapReduce On Opportunistic eNvironments. MOON extends Hadoop, an open-source implementation of MapReduce, with adaptive task and data scheduling algorithms in order to offer reliable MapReduce services on a hybrid resource architecture, where volunteer computing systems are supplemented by a small set of dedicated nodes. The adaptive task and data scheduling algorithms in MOON distinguish between (1) different types of MapReduce data and (2) different types of node outages in order to strategically place tasks and data on both volatile and dedicated nodes. Our tests demonstrate that MOON can deliver a 3-fold performance improvement to Hadoop in volatile, volunteer computing environments

Lightweight EDF scheduling with deadline inheritance

EDFI is a lightweight real-time scheduling protocol that combines EDF with deadline inheritance over shared resources. We will show that EDFI is flexible during a tasks admission control, efficient with scheduling and dispatching, and straightforward in feasibility analysis. The application programmer only needs to specify a tasks timing constraints (deadline, period, runtime) and resource needs, after which EDFI can execute admission control, scheduling, dispatching and resource synchronisation automatically. EDFI avoids gratuitous task switching and its programming overhead as well as runtime overhead is very low, which makes it ideal for lightweight and featherweight kernels. We will illustrate the elegance of the underlying theory and we will shortly discuss the implementation of EDFI in three different operating systems

Scheduling Fork-Join Task Graphs to Heterogeneous Processors

The scheduling of task graphs with communication delays has been extensively studied. Recently, new results for the common sub-case of fork-join shaped task graphs were published, including an EPTAS and polynomial algorithms for special cases. These new results modelled the target architecture to consist of homogeneous processors. However, forms of heterogeneity become more and more common in contemporary parallel systems, such as CPU--accelerator systems, with their two types of resources. In this work, we study the scheduling of fork-join task graphs with communication delays, which is representative of highly parallel workloads, onto heterogeneous systems of related processors. We present an EPAS, and some polynomial time algorithms for special cases, such as with equal processing costs or unlimited resources. Lastly, we briefly look at the above described case of two resource-types and its implications. It is interesting to note, that all results here also apply to scheduling independent tasks with release times and deadlines.Comment: 14 page

Parallel Asynchronous Particle Swarm Optimization For Job Scheduling In Grid Environment

Grid computing is a new, large and powerful self managing virtual computer out of large collection of connected heterogeneous systems sharing various combination of resources and it is the combination of computer resources from multiple administrative domains applied to achieve a goal, it is used to solve scientific, technical or business problem that requires a great number of processing cycles and needs large amounts of data. One primary issue associated with the efficient utilization of heterogeneous resources in a grid environment is task scheduling. Task Scheduling is an important issue of current implementation of grid computing. The demand for scheduling is to achieve high performance computing. If large number of tasks is computed on the geographically distributed resources, a reasonable scheduling algorithm must be adopted in order to get the minimum completion time. Typically, it is difficult to find an optimal resource allocation for specific job that minimizes the schedule length of jobs. So the scheduling problem is defined as NP-complete problem and it is not trivial. Heuristic algorithms are used to solve the task scheduling problem in the grid environment and may provide high performance or high throughput computing or both. In this paper, a parallel asynchronous particle swarm optimization algorithm is proposed for job scheduling. The proposed scheduler allocates the best suitable resources to each task with minimal makespan and execution time. The experimental results are compared which shows that the algorithm produces better results when compared with the existing ant colony algorithm

Airlift scheduling for the upgraded command and control system of military airlift command.

April 1984This report describes a conceptual design for automation of the scheduling of airlift activities as part of the current upgrade of the MAC C2 System. It defines the airlift scheduling problem in generic terms before reviewing the current procedures used by MAC; and then a new scheduling system aimed at handling a very busy and dynamic wartime scenario, is introduced. The new system proposes "Airlift Scheduling Workstations" where MAC Airlift Schedulers would be able to manipulate symbolic information on a computer display to create and quickly modify schedules for aircraft, crews, and stations. For certain sub-problems in generating schedules, automated decision support algorithms would be used interactively to speed the search for feasible and efficient solutions. Airlift Scheduling Workstations are proposed to exist at each "Scheduling Cell", a conceptual organizational unit which has been given sole and complete responsibility for developing the schedule of activities for a specific set of airlift resources-aircraft by tail number, aircrew by name, and stations by location. A Mission Scheduling Database is located at each cell to support the Airlift Scheduling Workstation, and requires information communicated by Airlift Task Planners, and, Airlift Operators at many other locations. These locations would have smaller workstations with local databases, and database management software to assist Task Planners and Operators in viewing current committed and planned schedule information of particular interest to them, and to allow them to send information to the Mission Scheduling Database. The Command and Control processes for Airlift have been structured into a three level hierarchy in this report: Task Planning, Mission Scheduling, and Schedule Execution. Task Planners deal with Airlift Users and Mission Schedulers, but not Airlift Operators. Task Planning has three sub-processes: Processing User Requests; Assigning Requirements and Resources; and Monitoring Task Status. Task planning does not create missions, schedule the missions, or route aircraft. Mission Schedulers deal with Task Planners and Airlift Operators, but not Airlift Users. Mission Scheduling combines several sub-processes to allow efficient schedules to be quickly generated at the ASW (Airlift Scheduling Workstation). These sub-processes are: Mission Generation, Schedule Map Generation (for each type of aircraft), Crew Mission Sequence Generation, Station Schedule Generation, Management of Schedule Status, and Monitoring Schedule Execution and Resource Status. It is important that all these processes be co-located and processed by the Airlift Scheduling Cell. Schedule Execution is performed by Airlift Operators assigned by the scheduling process. It has three sub-processes: Monitor Assigned Schedules, Report Resources Assigned to Schedule, Report Local Capability Status. The assignment of local resources such as aircraft by tail, and crew by name is actually another scheduling process, but has not been studied in this report. Airlift Operators do not deal with Task Planners, but may deal with Airlift Users to finalize details of the scheduled operations. This three level hierarchy is compatible with the current organizational structures of MAC Command and Control. However, it is clear that both the current organizational structures and procedures of MAC Command and Control for both tactical and strategic airlift will be significantly affected by the introduction of the automated scheduling systems envisioned here. These changes will occur in an evolutionary manner after the upgraded MAC C2 system is introduced.Prepared for the Electronic Systems Division, Air Force Systems Command, USAF, Hanscom Air Force Base, Bedford, M