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Real-Time Task Scheduling Algorithm for IoT-Based applications in the Cloud-Fog Environment

JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2022年第4期30卷 54-54页

作者： Abohamama, A. S. El-Ghamry, Amir Hamouda, Eslam Univ Mansoura Fac Comp & Informat Sci 60 El Gomhoreya St Mansoura 35516 Egypt Jouf Univ Fac Comp & Informat Sci Jouf 2014 Saudi Arabia

IoT applications have become a pillar for enhancing the quality of life. However, the increasing amount of data generated by IoT devices places pressure on the resources of traditional cloud data centers. This prevents cloud data centers from fulfilling the requirements of IoT applications, particularly delay-sensitive applications. Fog computing is a relatively recent computing paradigm that extends cloud resources to the edge of the network. However, task scheduling in this computing paradigm is still a challenge. In this study, a semidynamic real-time task scheduling algorithm is proposed for bag-of-tasks applications in the cloud-fog environment. The proposed scheduling algorithm formulates task scheduling as a permutation-based optimization problem. A modified version of the genetic algorithm is used to provide different permutations for arrived tasks at each scheduling round. Then, the tasks are assigned, in the order defined by the best permutation, to a virtual machine, which has sufficient resources and achieves the minimum expected execution time. A conducted optimality study reveals that the proposed algorithm has a comparative performance with respect to the optimal solution. Additionally, the proposed algorithm is compared with first fit, best fit, the genetic algorithm, and the bees life algorithm in terms of makespan, total execution time, failure rate, average delay time, and elapsed run time. The experimental results show the superiority of the proposed algorithm over the other algorithms. Moreover, the proposed algorithm achieves a good balance between the makespan and the total execution cost and minimizes the task failure rate compared to the other algorithms. [GRAPHICS] .

关键词： Scheduling optimization Fog computing Cloud-fog system IoT applications bag-of-tasks applications

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GA-based cloud resource estimation for agent-based execution of bag-of-tasks applications

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INFORMATION SYSTEMS FRONTIERS 2012年第4期14卷 925-951页

作者： Gutierrez-Garcia, J. Octavio Sim, Kwang Mong Gwangju Inst Sci & Technol Dept Informat & Commun Kwangju 500712 South Korea

Executing bag-of-tasks applications in multiple Cloud environments while satisfying both consumers' budgets and deadlines poses the following challenges: How many resources and how many hours should be allocated? What types of resources are required? How to coordinate the distributed execution of bag-of-tasks applications in resources composed from multiple Cloud providers?. This work proposes a genetic algorithm for estimating suboptimal sets of resources and an agent-based approach for executing bag-of-tasks applications simultaneously constrained by budgets and deadlines. Agents (endowed with distributed algorithms) compose resources and coordinate the execution of bag-of-tasks applications. Empirical results demonstrate that the genetic algorithm can autonomously estimate sets of resources to execute budget-constrained and deadline-constrained bag-of-tasks applications composed of more economical (but slower) resources in the presence of loose deadlines, and more powerful (but more expensive) resources in the presence of large budgets. Furthermore, agents can efficiently and successfully execute randomly generated bag-of-tasks applications in multi-Cloud environments.

关键词： Cloud resource estimation bag-of-tasks applications Cloud resource management Multiagent systems Genetic algorithms Cloud computing

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Simulation-Based Performance Evaluation of an Energy-Aware Heuristic for the Scheduling of HPC applications in Large-Scale Distributed Systems 8

Simulation-Based Performance Evaluation of an Energy-Aware H...

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8th ACM/SPEC International Conference on Performance Engineering (ICPE)

作者： Stavrinides, Georgios L. Karatza, Helen D. Aristotle Univ Thessaloniki Dept Informat Thessaloniki 54124 Greece

ISBN: (纸本)9781450348997

As the distributed resources required for the processing of High Performance Computing (HPC) applications are becoming larger in scale and computational capacity, their energy consumption has become a major concern. Therefore, there is a growing focus from both the academia and the industry on the minimization of the carbon footprint of the computational resources, especially through the efficient scheduling of the workload. In this paper, a technique is proposed for the energy-aware scheduling of bag-of-tasks applications with time constraints in a large-scale heterogeneous distributed system. Its performance is evaluated by simulation and compared with a baseline algorithm. The simulation results show that the proposed heuristic not only reduces the energy consumption of the system, but also improves its performance.

关键词： Energy-aware scheduling bag-of-tasks applications time constraints large-scale distributed systems simulation performance evaluation

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Improving scalability of bag-of-tasks applications running on master-slave platforms

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PARALLEL COMPUTING 2009年第2期35卷 57-71页

作者： da Silva, Fabricio A. B. Senger, Hermes Univ Lisbon Dept Informat Fac Ciencias P-1200 Lisbon Portugal Univ Presbiteriana Mackenzie Fac Comp & Informat Sao Paulo Brazil Univ Catolica Santos Santos Brazil

bag-of-tasks applications are parallel applications composed of independent tasks. Examples of bag-of-tasks (BoT) applications include Monte Carlo simulations, massive searches (such as key breaking), image manipulation applications and data mining algorithms. This paper analyzes the scalability of bag-of-tasks applications running on master-slave platforms and proposes a scalability-related measure dubbed input file affinity. In this work, we also illustrate how the input file affinity, which is a characteristic of an application, can be used to improve the scalability of bag-of-tasks applications running on master-slave platforms. The input file affinity was considered in a new scheduling algorithm dubbed Dynamic Clustering, which is oblivious to task execution times. We compare the scalability of the Dynamic Clustering algorithm to several other algorithms, oblivious and non-oblivious to task execution times, proposed in the literature. We show in this paper that, in several situations, the oblivious algorithm Dynamic Clustering has scalability performance comparable to non-oblivious algorithms, which is remarkable considering that our oblivious algorithm uses much less information to schedule tasks. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Master-slave platforms Scalability analysis bag-of-tasks applications

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Randomized approximation scheme for resource allocation in hybrid-cloud environment

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JOURNAL OF SUPERCOMPUTING 2014年第2期69卷 576-592页

作者： HoseinyFarahabady, MohammadReza Lee, Young Choon Zomaya, Albert Y. Univ Sydney Sch Informat Technol Ctr Distributed & High Performance Comp Sydney NSW 2006 Australia Natl ICT Australia NICTA Sydney NSW 1430 Australia

Using the virtually unlimited resource capacity of public cloud, dynamic scaling out of large-scale applications is facilitated. A critical question arises practically here is how to run such applications effectively in terms of both cost and performance. In this paper, we explore how resources in the hybrid-cloud environment should be used to run bag-of-tasks applications. Having introduced a simple yet effective objective function, our algorithm helps the user to make a better decision for realization of his/her goal. Then, we cope with the problem in two different cases of "known" and "unknown" running time of available tasks. A solution to approximate the optimal value of user's objective function will be provided for each case. Specifically, a fully polynomial-time randomized approximation scheme based on a Monte Carlo sampling method will be presented in case of unknown running time. The experimental results confirm that our algorithm approximates the optimal solution with a little scheduling overhead.

关键词： Cloud resource allocation Randomized approximation scheme Monte Carlo sampling bag-of-tasks applications Divisible load theory (DLT) Optimality criterion

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A family of heuristics for agent-based elastic Cloud bag-of-tasks concurrent scheduling

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2013年第7期29卷 1682-1699页

作者： Gutierrez-Garcia, J. Octavio Sim, Kwang-Mong Gwangju Inst Sci & Technol Dept Informat & Commun Kwangju 500712 South Korea Univ Kent Sch Comp Chatham ME4 4AG Kent England

The scheduling and execution of bag-of-tasks applications (BoTs) in Clouds is performed on sets of virtualized Cloud resources that start being exhausted right after their allocation disregarding whether tasks are being executed. In addition, BoTs may be executed in potentially heterogeneous sets of Cloud resources, which may be either previously allocated for a different and fixed number of hours or dynamically reallocated as needed. In this paper, a family of 14 scheduling heuristics for concurrently executing BoTs in Cloud environments is proposed. The Cloud scheduling heuristics are adapted to the resource allocation settings (e.g., 1-hour time slots) of Clouds by focusing on maximizing Cloud resource utilization based on the remaining allocation times of Cloud resources. Cloud scheduling heuristics supported by information about ROT tasks (e.g., task size) and/or Cloud resource performances are proposed. Additionally, scheduling heuristics that require no information of either Cloud resources or tasks are also proposed. The Cloud scheduling heuristics support the dynamic inclusion of new Cloud resources while scheduling and executing a given BoT without rescheduling. Furthermore, an elastic Cloud resource allocation mechanism that autonomously and dynamically reallocates Cloud resources on demand to BoT executions is proposed. Moreover, an agent-based Cloud BoT scheduling approach that supports concurrent and parallel scheduling and execution of BoTs, and concurrent and parallel dynamic selection and composition of Cloud resources (by making use of the well-known contract net protocol) from multiple and distributed Cloud providers is designed and implemented. Empirical results show that BoTs can be (i) efficiently executed by attaining similar (in some cases shorter) makespans to commonly used benchmark heuristics (e.g., Max-min), (ii) effectively executed by achieving a 100% success execution rate even with high BoT execution request rates and executing BoTs i

关键词： Scheduling heuristics bag-of-tasks applications Cloud computing Multi-agent systems Agent-based Cloud computing Elastic Cloud resource allocation

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Handling Uncertainty: Pareto-Efficient BoT Scheduling on Hybrid Clouds

Handling Uncertainty: Pareto-Efficient BoT Scheduling on Hyb...

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42nd Annual International Conference on Parallel Processing (ICPP)

作者： HoseinyFarahabady, M. Reza Samani, Hamid R. D. Leslie, Luke M. Lee, Young Choon Zomaya, Albert Y. Univ Sydney Ctr Distributed & High Performance Comp Sch Informat Technol Sydney NSW 2006 Australia

ISBN: (纸本)9780769551173

Coping with uncertainty is a challenging and complex problem particularly in hybrid cloud environments-private cloud plus public cloud. Conflicting goals of minimizing the cost and performance, unknown prior knowledge about task running times, and a lack of estimation tools are just a few of the challenges that resource management systems in those environments encounter. The aim in this paper is to find Pareto-optimal schedules for large-scale bag-of-tasks (BoT) applications that meet user-defined constraints, such as deadline or budget or some tradeoff between them. BoT applications are common in science and engineering and consist of many independent tasks. To achieve the user's chosen Pareto-optimal schedule, we develop a dynamic resource allocation process for hybrid clouds. We also present a hybrid approach to estimating task running times that incorporates several estimators with a feedback control system to cope with the inherent uncertainty in such estimation. Through extensive experiments on a testbed hybrid cloud, using Amazon EC2 as a public cloud, we show that the proposed approach can achieve near-optimality with little overhead, and consistently achieves a solution within 2% of the user's chosen Pareto-optimal schedule. Further, we demonstrate that our approach performs better than an extended List scheduling approach by reducing both the total cost and time needed to run the application by almost 20% and 5% on average, respectively.

关键词： Uncertainty Resource allocation Cloud bursting Cost efficiency Pareto-frontier bag-of-tasks applications

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Practical scheduling of bag-of-tasks applications on grids with dynamic resilience

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IEEE TRANSACTIONS ON COMPUTERS 2007年第6期56卷 815-825页

作者： Lee, Young Choon Zomaya, Albert Y. Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia

Over the past decade, the grid has emerged as an attractive platform to tackle various large-scale problems, especially in science and engineering. One primary issue associated with the efficient and effective utilization of heterogeneous resources in a grid is scheduling. Grid scheduling involves a number of challenging issues, mainly due to the dynamic nature of the grid. There are only a handful of scheduling schemes for grid environments that realistically deal with this dynamic nature that have been proposed in the literature. In this paper, two novel scheduling algorithms, called the Shared-Input-data-based Listing (SIL) algorithm and the Multiple Queues with Duplication (MQD) algorithm for bag-of-tasks (BoT) applications in grid environments are proposed. The SIL algorithm targets scheduling data-intensive BoT (DBoT) applications, whereas the MOD algorithm deals with scheduling computationally intensive BoT (CBoT) applications. Their common and primary forte is that they make scheduling decisions without fully accurate performance prediction information. Another point to note is that both scheduling algorithms adopt task duplication as an attempt to reduce serious schedule increases. Our evaluation study employs a number of experiments with various simulation settings. The results show the practicability and competitiveness of our algorithms when compared to existing methods.

关键词： scheduling grids grid computing parallel computing bag-of-tasks applications

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The ShareGrid Peer-to-Peer Desktop Grid: Infrastructure, applications, and Performance Evaluation

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JOURNAL OF GRID COMPUTING 2010年第4期8卷 543-570页

作者： Anglano, Cosimo Canonico, Massimo Guazzone, Marco Univ Piemonte Orientale Dipartimento Informat I-15121 Alessandria Italy

Peer-to-Peer (P2P) Desktop Grids are computing infrastructures that aggregate a set of desktop-class machines in which all the participating entities have the same roles, responsibilities, and rights. In this paper, we present ShareGrid, a P2P Desktop Grid infrastructure based on the OurGrid middleware, that federates the resources provided by a set of small research laboratories to easily share and use their computing resources. We discuss the techniques and tools we employed to ensure scalability, efficiency, and usability, and describe the various applications used on it. We also demonstrate the ability of ShareGrid of providing good performance and scalability by reporting the results of experimental evaluations carried out by running various applications with different resource requirements. Our experience with ShareGrid indicates that P2P Desktop Grids can represent an effective answer to the computing needs of small research laboratories, as long as they provide both ease of management and use, and good scalability and performance.

关键词： Desktop Grids Volunteer computing bag-of-tasks applications Peer-to-Peer systems

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Bridging the high performance computing gap:: the OurGrid experience 07

Bridging the high performance computing gap:: the OurGrid ex...

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7th IEEE International Symposium on Cluster Computing and the Grid

作者： Brasileiro, Francisco Araujo, Eliane Voorsluys, William Oliveira, Milena Figueiredo, Flavio Univ Fed Campina Grande Dept Sistemas & Comp Lab Sistemas Distribuidos Av Aprigio Veloso S-N Bloco CO BR-58109970 Campina Grande PB Brazil

ISBN: (纸本)9780769528335

High performance computing is currently not affordable for those users that cannot rely on having a highly qualified computing support team. To cater for these users' needs we have proposed, implemented and deployed OurGrid. OurGrid is a peer-to-peer grid middleware that supports the automatic creation of large computational grids for the execution of embarrassingly parallel applications. It has been used to support the OurGrid Community - a public free-to-join grid that is in production since December 2004. In this paper we show how the OurGrid Community has been used to support the execution of a number of applications. Further we discuss the main benefits brought up by the system and the difficulties that have been faced by the system developers and the users and managers of the OurGrid Community.

关键词： grid computing peer-to-peer free-to-join public grid bag-of-tasks applications

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