Cost efficient resource allocation for real-time tasks in embedded systems

A B S T R A C T Nowadays, the transformations of metropolises into smart cities is a crucial factor in improving the living conditions of the inhabitants. The goal of the smart city concept is modern urban management using technical tools that offer state-of-the-art technologies, considering the applicable ecological standards while saving resources and achieving the expected results. The purpose of this article is to identify the areas of research analysed in the international literature in the field of smart cities. The bibliometric analysis was carried out to achieve the purpose. The analysis covered publications on smart cities published in Scopus and Web of Science databases from January 2009 to May 2019. Based on the bibliometric analysis, a bibliometric map was developed using the mapping technique VOS -the visualisation of similarities. Original clusters were created using the VOSviewer software. The bibliometric map visualises the results of the analysis that targeted the word coexistence. K E Y W O R D S smart city, smart city subareas, bibliometric map

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“…• cloud computing (Azhdari et al, 2019;Huang et al, 2019;Min-Allah et al, 2019;Wang et al, 2019;Del Esposte et al, 2019);…”

Section: Smart Technologyunclassified

Smart city concept in the light of the literature review

Winkowska

Szpilko

Pejić

2019

Engineering Management in Production and Services

143

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“…In context to previous methods, here jobs are placed over idle slots where voltage and frequency are low and it does not violate dependency limitations and increasing the delay time for a job to complete within the due time for a batch of jobs. Furthermore, [39] discusses heterogeneous VMs resource allocation for real time embedded systems. It executes the gathered data within the deadline from various sources like heterogeneous, distributed and decentralized nodes in cost and time effective manner.…”

Section: Related Workmentioning

confidence: 99%

An Energy and Performance Aware Scheduler for Real-Time Tasks in Cloud Datacentres

Ali

Qureshi

et al. 2020

IEEE Access

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Datacentres provide the foundations for cloud computing, but require large amounts of electricity for their operation. Approaches that promise to reduce power use by minimizing execution time, for example using different scheduling and resource management techniques, are discussed in the literature. This paper summarizes some of the most important scheduling techniques in clouds focusing on power consumption, covering VM-level, host-level and task-level scheduling where the most promising approach is task level scheduling, with energy savings by means of load filtering, consolidation, adapted CPU throughput, or host power control. We explore use of the rate monotonic (RM) and backfilling algorithms for real-time task scheduling in cloud environment because RM is the simplest fixed priority scheduling technique, and thus the choice for modern real-time systems, and prior uses of RM in task scheduling have demonstrated power efficiency with optimal results. We specifically consider deadlinebased tasks scheduling for real-time clouds which, to the best of our knowledge, has not been employed previously. RM with backfilling is experimentally evaluated and results show that, compared to the classical algorithms, all tasks were scheduled with minimum power consumption (5.5%-29.3%), on minimum resources (3.9%-25.2% less) while majority were meeting their deadlines (93.21%-94.7%). The approach can guarantee deadline oriented Software as a Service (SaaS) in cloud if arrival rate i.e. network transfer time can be estimated in advance. We subsequently provided an extension of the proposed approach to task-based load balancing for almost balanced resource utilization and approximately 1.0% to 1.6% energy efficiency.

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“…The cloud compute and storage resources are selected and allocated on the basis of nature of user application. In addition, the cloud storage resources provide facilities such as accommodating data replication to satisfy data requirements of the data-intensive real-time systems that need to access, process and transfer data files stored in distributed data repositories [1,2]. Examples of such applications are self-driving vehicles, which depend on the data and computations under a complex network of interconnected devices such as GPS, surveillance cameras, radar, laser light, odometry, etc., to perceive the surroundings [3,4].…”

Section: Introductionmentioning

confidence: 99%

Time and Cost Efficient Cloud Resource Allocation for Real-Time Data-Intensive Smart Systems

Qureshi

Fayaz

et al. 2020

Energies

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Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts.

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Cost efficient resource allocation for real-time tasks in embedded systems

Cited by 27 publications

References 28 publications

Smart city concept in the light of the literature review

Smart city concept in the light of the literature review

An Energy and Performance Aware Scheduler for Real-Time Tasks in Cloud Datacentres

Time and Cost Efficient Cloud Resource Allocation for Real-Time Data-Intensive Smart Systems

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