Study of an Iterative Technique to Minimize Completion Times of Non-Makespan Machines

Briceño, Luis; Oltikar, M.; Siegel, Howard Jay; Maciejewski, Anthony A.

doi:10.1109/ipdps.2007.370325

Cited by 9 publications

(7 citation statements)

References 16 publications

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“…The K-percent Best algorithm [1], [6], [7], [13], [23], [30], [31] combines both the MET and the Maximum Utility heuristics by choosing machines based on both these heuristics. When a new task arrives, the K-percent Best algorithm picks a machine in two stages.…”

Section: ) K-percent Bestmentioning

confidence: 99%

“…The Min-Min heuristic [6], [7], [11], [13], [16], [18], [21], [23], [24], [30], [35], [36], [38], [41] is a two phase greedy heuristic. In the first phase, it independently finds the minimum completion time machine for every task.…”

Section: Batch Mode Heuristics 1) Min-min Completion Timementioning

confidence: 99%

“…The Sufferage heuristic [4], [6], [8], [11], [14], [18], [21], [23], [24], [30], [32], [34], [35], [40] attempts to maximize the utility earned by assigning every task to the machine that would give the highest utility, but when multiple tasks want the same machine it gives preference to the task that would "suffer" the most if it were not assigned to that machine. If max utility (h) is the best obtainable utility of task h, and smax utility (h) is the second highest utility obtainable for task h, then the sufferage value (suff ) is suff = max utility (h) − smax utility (h).…”

Section: ) Sufferagementioning

confidence: 99%

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Time Utility Functions for Modeling and Evaluating Resource Allocations in a Heterogeneous Computing System

Briceño

Khemka

Siegel³

et al. 2011

2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PHD Forum

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Abstract-This study considers a heterogeneous computing system and corresponding workload being investigated by the Extreme Scale Systems Center (ESSC) at Oak Ridge National Laboratory (ORNL). The ESSC is part of a collaborative effort between the Department of Energy (DOE) and the Department of Defense (DoD) to deliver research, tools, software, and technologies that can be integrated, deployed, and used in both DOE and DoD environments. The heterogeneous system and workload described here are representative of a prototypical computing environment being studied as part of this collaboration. Each task can exhibit a time-varying importance or utility to the overall enterprise. In this system, an arriving task has an associated priority and precedence. The priority is used to describe the importance of a task, and precedence is used to describe how soon the task must be executed. These two metrics are combined to create a utility function curve that indicates how valuable it is for the system to complete a task at any given moment. This research focuses on using time-utility functions to generate a metric that can be used to compare the performance of different resource schedulers in a heterogeneous computing system. The contributions of this paper are: (a) a mathematical model of a heterogeneous computing system where tasks arrive dynamically and need to be assigned based on their priority, precedence, utility characteristic class, and task execution type, (b) the use of priority and precedence to generate time-utility functions that describe the value a task has at any given time, (c) the derivation of a metric based on the total utility gained from completing tasks to measure the performance of the computing environment, and (d) a comparison of the performance of resource allocation heuristics in this environment.

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Section: ) K-percent Bestmentioning

confidence: 99%

Section: Batch Mode Heuristics 1) Min-min Completion Timementioning

confidence: 99%

Section: ) Sufferagementioning

confidence: 99%

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Time Utility Functions for Modeling and Evaluating Resource Allocations in a Heterogeneous Computing System

Briceño

Khemka

Siegel³

et al. 2011

2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PHD Forum

Self Cite

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“…The Min-Min heuristic is widely used in the area of resource allocation [7,10,11,13,15,16,18,25]. It takes a given set of potential SSs and assigns remaining users to a potential SSs or to the M S. Recall that it possible for a potential SS to not have any users connected to it.…”

Section: Min-min Rtmentioning

confidence: 99%

“…The Min-Min heuristic is widely used in the area of resource allocation [7,10,11,14,16,17,19,27]. It takes a given set of DCUs and assigns remaining users to a DCU or to the M S. The procedure to implement the Min-Min RT is shown in Figure 8.…”

Section: Min-min Rtmentioning

confidence: 99%

Resource allocation in a client/server hybrid network for virtual world environments

Briceño

Siegel

Maciejewski

et al. 2008

2008 IEEE International Symposium on Parallel and Distributed Processing

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The creation of a virtual world environment (VWE) has significant costs, such as maintenance of server rooms, server administration, and customer service. The initial development cost is not the only factor that needs to be considered; factors such as the popularity of a VWE and unexpected technical problems during and after the launch can affect the final cost and success of a VWE. The capacity of servers in a client/server VWE is hard to scale and cannot adjust quickly to peaks in demand while maintaining the required response time. To handle these peaks in demand, we propose to employ users' computers as secondary servers. The introduction of users' computers as secondary servers allows the performance of the VWE to support an increase in users. In this study, we develop and implement five static heuristics to implement a secondary server scheme that reduces the time taken to compute the state of the VWE. The number of heterogeneous secondary servers, conversion of a player to a secondary server, and assignment of players to secondary servers are determined by the heuristics implemented in this study. A lower bound of the performance is derived to evaluate the results of the heuristics.

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A Genetic-Based Scheduling Algorithm to Minimize the Makespan of the Grid Applications

Entezari‐Maleki

Movaghar

2010

Communications in Computer and Information Science

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Study of an Iterative Technique to Minimize Completion Times of Non-Makespan Machines

Abstract: Heterogeneous computing (HC) is the coordinated use of different types of machines, networks, and interfaces to

Cited by 9 publications

References 16 publications

Time Utility Functions for Modeling and Evaluating Resource Allocations in a Heterogeneous Computing System

Time Utility Functions for Modeling and Evaluating Resource Allocations in a Heterogeneous Computing System

Resource allocation in a client/server hybrid network for virtual world environments

A Genetic-Based Scheduling Algorithm to Minimize the Makespan of the Grid Applications

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