A Two-Phase Energy-Aware Scheduling Approach for CPU-Intensive Jobs in Mobile Grids

Hirsch, Matías; Rodríguez, Juan Manuel; Mateos, Cristian; Zunino, Alejandro

doi:10.1007/s10723-016-9387-6

Cited by 29 publications

(37 citation statements)

References 39 publications

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“…Upon a job arrival, the proxy runs scheduling logic to select an SD based on its resources availability information. It is expected that resources information be periodically updated, eg, remaining battery, or informed by SDs when they register with a proxy as computing providers, eg, by indicating computing capability in Million of Float‐Point Operations per Second (MFLOPS) . Once a job is scheduled, ie, assigned to an SD for its execution, an input data transferring operation is triggered from the proxy to the selected SD.…”

Section: Modeling and Simulating Proxy‐based Sce For Executing Computmentioning

confidence: 99%

“…Assuming that a device joining an SCE announces the computing capabilities it offers to proxy, the default action consists in adding a new device to the computing provider candidates pool considered by a scheduler. This type of event might also be used by the newly proposed schedulers to execute some kind of job rebalancing technique to take advantage of an SD idle computing capabilities, which were not consider in a job assignments round performed before the occurrence of the device join event. Lastly, a device _ left event processing involves removing a device (an the resources it offers) from the pool of device candidates.…”

Section: Modeling and Simulating Proxy‐based Sce For Executing Computmentioning

confidence: 99%

“…The default behavior of SchedulerProxy class with respect to events can be, in turn, overwritten with a new implementation of the processEvent(Event e) method. Listing shows the code of a ranking‐based job scheduling logic that implements the E‐SEAS scheduler …”

Section: Usage Example and Simulation Inputmentioning

confidence: 99%

“…To show the user interaction effect, ie, CPU usage and energy shared among user processes and jobs, we simulated different SCE settings by varying the amount of dedicated devices (those without user activity). All SCE instances are composed by a hundred devices (20 Acer A100 devices, 41 ViewSonic Viewpad 10s devices, and 39 Samsung I5500 devices) and process the same jobs set . Job assignments are performed with a theoretical scheduling criterion that assigns as many consecutive arrived jobs to each device as it is able to execute.…”

Section: Verificationmentioning

confidence: 99%

“…In those situations where SDs share physical locality with data acquisition devices, scavenging the joint computing capabilities to execute jobs is aligned with the fog/edge computing principles, which advocate to bring computing resources closer to where computing needs are originated. The SDs' computing potential scavenging has also been proposed as a way of increasing the computing power of Grid distributed computing platforms …”

Section: Introductionmentioning

confidence: 99%

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DewSim: A trace‐driven toolkit for simulating mobile device clusters in Dew computing environments

et al. 2019

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Summary Dew computing is an emerging computing paradigm, which aims at minimizing the dependency over existing internetwork back‐haul, ie, being dependent on processing resources offered by remote servers. Smartphones and tablets ubiquity and powerful computing hardware motivated researchers to investigate the way of providing Dew computing services by exploiting the aggregated capabilities of devices in a vicinity, a smart device cluster. Consequently, research on resource management is necessary to learn how to scavenge resources from such a cluster, deal with devices heterogeneity, limitations, and dynamic resource availability. Simulation is commonly practiced for studying resource management in other distributed computing research fields, specially due to the complexity involved in the set up of experiments. However, a free‐to‐use purpose specific toolkit for studying smart device clusters do not exist or have been documented. Current simulation efforts do not allow researchers to faithfully represent key singularities of such environment, which are energy depletion and nondedicated nature of computing resources. We propose a trace‐based toolkit built on modular software artifacts to speed up research in resource management techniques in Dew environments. A trace‐driven methodology is adopted to assure practical value of simulated scenarios. The toolkit comprises a device profiler application for Android to capture generic battery and CPU traces from real devices, a profile mixer to create user interaction baseline traces through generic ones, and an extensible engine to simulate the execution of workloads configurable via text files. Verification and validation tests were run to show correctness and reliability of our simulation approach.

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Section: Modeling and Simulating Proxy‐based Sce For Executing Computmentioning

confidence: 99%

Section: Modeling and Simulating Proxy‐based Sce For Executing Computmentioning

confidence: 99%

Section: Usage Example and Simulation Inputmentioning

confidence: 99%

Section: Verificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DewSim: A trace‐driven toolkit for simulating mobile device clusters in Dew computing environments

et al. 2019

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Real‐time partitioned scheduling: Exploiting the inter‐resource affinity for task allocation on multiprocessors

Akram

Bai

et al. 2019

Concurrency and Computation

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Real-time edge computing is forging its place in cloud computing rapidly, and requirements for high-performance edge devices are becoming increasingly complex. Multiprocessor edge devices are an attractive choice to meet these higher performance requirements. However, multiprocessor devices encounter inherent challenges when handling on-chip shared resources. The concurrent access to these resources by the tasks requesting more than one shared resource and running on multiple processors may face huge blocking times, which can lead to missed hard real-time deadlines and cause a catastrophic system failure. To reduce the task blocking time, we propose a task allocation algorithm that takes advantage of inter-resource affinity and allocates all the tasks accessing multiple shared resources having inter-resource affinity on the same processor of the multiprocessor. The proposed algorithm reduces global resources and remote blocking, which subsequently increase schedulability of task sets and reduce the processor utilization. In our experiments, we compare the proposed task allocation algorithm SRTA with existing well-known task allocation strategies SPA, ROP, and blocking-agnostic FFD. The experimental results reveal that the SRTA, on the average, can allocate 1.80, 2.34, and 3.14 times more task sets as compared to ROP, SPA, and FFD, respectively and reduces the number of globally shared resources significantly.

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A Model for Hour-Wise Prediction of Mobile Device Energy Availability

Longo

Mateos

Zunino

2018

Advances in Intelligent Systems and Computing

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A Two-Phase Energy-Aware Scheduling Approach for CPU-Intensive Jobs in Mobile Grids

Cited by 29 publications

References 39 publications

DewSim: A trace‐driven toolkit for simulating mobile device clusters in Dew computing environments

DewSim: A trace‐driven toolkit for simulating mobile device clusters in Dew computing environments

Real‐time partitioned scheduling: Exploiting the inter‐resource affinity for task allocation on multiprocessors

A Model for Hour-Wise Prediction of Mobile Device Energy Availability

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