scholarly journals Worst-case optimal algorithm for XPath evaluation over XML streams

2009 ◽  
Vol 75 (8) ◽  
pp. 465-485 ◽  
Author(s):  
Prakash Ramanan
Keyword(s):  
Optimal Algorithm ◽  
Worst Case ◽  
Xml Streams

scholarly journals Efficiency Through Procrastination: Approximately Optimal Algorithm Configuration with Runtime Guarantees

2017 ◽  
Author(s):  
Robert Kleinberg ◽  
Kevin Leyton-Brown ◽  
Brendan Lucier
Keyword(s):  
High Probability ◽  
Optimal Algorithm ◽  
Optimal Performance ◽  
Worst Case ◽  
Running Time ◽  
Performance Guarantees ◽  
Algorithm Configuration ◽  
Time Requirements

Algorithm configuration methods have achieved much practical success, but to date have not been backed by meaningful performance guarantees. We address this gap with a new algorithm configuration framework, Structured Procrastination. With high probability and nearly as quickly as possible in the worst case, our framework finds an algorithm configuration that provably achieves near optimal performance. Moreover, its running time requirements asymptotically dominate those of existing methods.

New Optimal Preemptively Scheduling for Real-Time Reconfigurable Sporadic Tasks Based on Earliest Deadline First Algorithm

2012 ◽  
Vol 4 (2) ◽  
pp. 65-81 ◽  
Author(s):  
Hamza Gharsellaoui ◽  
Mohamed Khalgui ◽  
Samir Ben Ahmed
Keyword(s):  
Response Time ◽  
Real Time ◽  
Dynamic Scheduling ◽  
Optimal Algorithm ◽  
Real Life ◽  
Sporadic Tasks ◽  
Periodic Tasks ◽  
Worst Case ◽  
Utilization Factor ◽  
Earliest Deadline

This paper examines the problem of scheduling the mixed workload of both sporadic (on-line) and periodic (off-line) tasks on uniprocessor in a hard real-time environment. The authors introduce an optimal earliest deadline scheduling algorithm to optimize response time while ensuring that all periodic tasks meet their deadlines and to accept as many sporadic tasks. A necessary and sufficient schedulability test is presented, and an efficient O(n+m) guarantee algorithm is proposed. This optimal algorithm results in dynamic scheduling solutions. They are presented by a proposed intelligent agent-based architecture where a software agent is used to evaluate the response time, to calculate the processor utilization factor and also to verify the satisfaction of real-time deadlines. The agent dynamically provides technical solutions for users where the system becomes unfeasible by sending sporadic tasks to idle times, by modifying the deadlines of tasks, the worst case execution times (WCETs), the activation time, by tolerating some non critical tasks according to the (m, n) firm and a reasonable cost, or in the worst case by removing some non hard (soft) tasks according to predefined heuristic. The authors implement the agent to support these services which are applied to extensive experiments with real-life design examples in order to demonstrate the effectiveness and the excellent performance of the new optimal algorithm in normal and overload conditions.

Some Scheduling Problems on a Single Machine with General Job Effects of Position-Dependent Learning and Start-Time-Dependent Deterioration

2015 ◽  
Vol 32 (02) ◽  
pp. 1550002 ◽  
Author(s):  
Jan-Yee Kung ◽  
Ming-Hung Shu
Keyword(s):  
Single Machine ◽  
Completion Time ◽  
Optimal Algorithm ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Maximum Lateness ◽  
Total Weighted Completion Time ◽  
General Function ◽  
Worst Case ◽  
Weighted Completion Time

Job learning and deterioration coexist in many realistic machine-job scheduling situations. However, in literature, the constructed forms of the machine scheduling models with job learning and/or deteriorating effects were specific types of functions, which constrained their applicability in practice. This paper introduces a new single-machine scheduling model, where the actual processing time of a job is a general function of its starting time as well as scheduled position, which shows a broad generalization in contrast to that of certain existing models. For three objectives corresponding to the single-machine scheduling problem–total weighted completion time, discounted total weighted completion time, and maximum lateness — this paper presents their respective approximation result on the basis of the worst-case bound analysis from the optimal algorithm. The results demonstrate that under our proposed model, minimization of scheduling operations such as the makespan, sum of the kth power of completion times, and total lateness are polynomially solvable. Moreover, under some feasible conditions for the scheduling parameters, the minimum optimization problems of the total weighted completion time, discounted total weighted completion time, maximum lateness, and total tardiness are all recognized as polynomial forms and their solutions are provided.

scholarly journals Solving Multiagent Networks using Distributed Constraint Optimization

AI Magazine ◽  
2008 ◽  
Vol 29 (3) ◽  
pp. 47 ◽  
Author(s):  
Jonathan P. Pearce ◽  
Milind Tambe ◽  
Rajiv Maheswaran
Keyword(s):  
Optimal Algorithm ◽  
Local Optimum ◽  
Constraint Optimization ◽  
Solution Quality ◽  
Worst Case ◽  
Distributed Constraint Optimization ◽  
Constraint Graph ◽  
Distributed Constraint Optimization Problem ◽  
The Cost

In many cooperative multiagent domains, the effect of local interactions between agents can be compactly represented as a network structure. Given that agents are spread across such a network, agents directly interact only with a small group of neighbors. A distributed constraint optimization problem (DCOP) is a useful framework to reason about such networks of agents. Given agents’ inability to communicate and collaborate in large groups in such networks, we focus on an approach called k-optimality for solving DCOPs. In this approach, agents form groups of one or more agents until no group of k or fewer agents can possibly improve the DCOP solution; we define this type of local optimum, and any algorithm guaranteed to reach such a local optimum, as k-optimal. The article provides an overview of three key results related to koptimality. The first set of results gives worst-case guarantees on the solution quality of k-optima in a DCOP. These guarantees can help determine an appropriate k-optimal algorithm, or possibly an appropriate constraint graph structure, for agents to use in situations where the cost of coordination between agents must be weighed against the quality of the solution reached. The second set of results gives upper bounds on the number of k-optima that can exist in a DCOP. These results are useful in domains where a DCOP must generate a set of solutions rather than a single solution. Finally, we sketch algorithms for k-optimality and provide some experimental results for 1-, 2- and 3-optimal algorithms for several types of DCOPs.

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