Task (computing)

About: Task (computing) is a research topic. Over the lifetime, 9718 publications have been published within this topic receiving 129364 citations.

Scalable Spatial Crowdsourcing: A Study of Distributed Algorithms

Abstract: Recently spatial crowd sourcing was introduced as a natural extension to traditional crowd sourcing allowing for tasks to have a geospatial component, i.e., A task can only be performed if a worker is physically present at the location of the task. The problem of assigning spatial tasks to workers in a spatial crowd sourcing system can be formulated as a weighted bipartite b-matching graph problem that can be solved optimally by existing methods for the minimum cost maximum flow problem. However, these methods are still too complex to run repeatedly for an online system, especially when the number of incoming workers and tasks increases. Hence, we propose a class of approaches that utilizes an online partitioning method to reduce the problem space across a set of cloud servers to construct independent bipartite graphs and solve the assignment problem in parallel. Our approaches solve the spatial task assignment approximately but competitive to the exact solution. We experimentally verify that our approximate approaches outperform the centralized and Map Reduce version of the exact approach with acceptable accuracy and thus suitable for online spatial crowd sourcing at scale.

Optimization for Speculative Execution in Big Data Processing Clusters

Abstract: A big parallel processing job can be delayed substantially as long as one of its many tasks is being assigned to an unreliable or congested machine. To tackle this so-called straggler problem, most parallel processing frameworks such as MapReduce have adopted various strategies under which the system may speculatively launch additional copies of the same task if its progress is abnormally slow when extra idling resource is available. In this paper, we focus on the design of speculative execution schemes for parallel processing clusters from an optimization perspective under different loading conditions. For the lightly loaded case, we analyze and propose one cloning scheme, namely, the Smart Cloning Algorithm (SCA) which is based on maximizing the overall system utility. We also derive the workload threshold under which SCA should be used for speculative execution. For the heavily loaded case, we propose the Enhanced Speculative Execution (ESE) algorithm which is an extension of the Microsoft Mantri scheme to mitigate stragglers. Our simulation results show SCA reduces the total job flowtime, i.e., the job delay/ response time by nearly $6$ percent comparing to the speculative execution strategy of Microsoft Mantri. In addition, we show that the ESE Algorithm outperforms the Mantri baseline scheme by $71$ percent in terms of the job flowtime while consuming the same amount of computation resource.

System and method for managing processes

Abstract: The process management system includes task information indicative of tasks that define process steps for a group of processes. The task information defines the steps as software events and as non-software events. The system further includes task relationship information indicative of a relationship between the tasks to define the processes, and application information defining the software events. A user interface displays the tasks and enables the selection of the tasks. The processes are completed by selecting the tasks associated with a particular process and executing the software events and the non-software events corresponding to the selected tasks.

Priority based dynamic resource allocation in Cloud computing with modified waiting queue

Abstract: Today Cloud computing is on demand as it offers dynamic flexible resource allocation, for reliable and guaranteed services in pay-as-you-use manner, to Cloud service users. So there must be a provision that all resources are made available to requesting users in efficient manner to satisfy their needs. This resource provision is done by considering the Service Level Agreements (SLA) and with the help of parallel processing. Recent work considers various strategies with single SLA parameter. Hence by considering multiple SLA parameter and resource allocation by preemption mechanism for high priority task execution can improve the resource utilization in Cloud. In this paper we propose an algorithm which considered Preemptable task execution and multiple SLA parameters such as memory, network bandwidth, and required CPU time. An obtained experimental results show that in a situation where resource contention is fierce our algorithm provides better utilization of resources.

Analysis of Or-parallel execution models

Abstract: We discuss fundamental limitations of or-parallel execution models of nondeterministic programming languages. Or-parallelism corresponds to the execution of different nondeterministic computational paths in parallel. A natural way to represent the state of (parallel) execution of a nondeterministic program is by means of an or-parallel tree. We identify three important criteria that underlie the design of or-parallel implementations based on the or-parallel tree: constant-time access to variables, constant-time task creation, and constant-time task switching, where the term constant-time means that the time for these operations is independent of the number of nodes in the or-parallel tree, as well as the size of each node. We prove that all three criteria cannot be simultaneously satisfied by any or-parallel execution model based on a finite number of processors but unbounded memory. We discuss in detail the application of our result to the class of logic programming languages and show how our result can serve as a useful way to categorize the various or-parallel methods proposed in this field. We also discuss the suitability of different or-parallel implemenation strategies for different parallel architectures.