Optimal distributed task scheduling in volunteer clouds

Sebastio, Stefano; Gnecco, Giorgio; Bemporad, Alberto

doi:10.1016/j.cor.2016.11.004

Cited by 30 publications

(19 citation statements)

References 24 publications

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“…At the same time, the experimental results show that the proposed algorithm increases the percentage of workflow that meets the deadline with a factor of 75%. Sebastio et al [29] proposed a framework to allocate tasks according to different policies in volunteer cloud systems. To maximize the number of the completed tasks, they take into account several different aspects.…”

Section: Task Scheduling Algorithms In Volunteer Computing Platformsmentioning

confidence: 99%

Dynamic Task Scheduling Algorithm with Deadline Constraint in Heterogeneous Volunteer Computing Platforms

Qiao

Lin

et al. 2019

Future Internet

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Volunteer computing (VC) is a distributed computing paradigm, which provides unlimited computing resources in the form of donated idle resources for many large-scale scientific computing applications. Task scheduling is one of the most challenging problems in VC. Although, dynamic scheduling problem with deadline constraint has been extensively studied in prior studies in the heterogeneous system, such as cloud computing and clusters, these algorithms can’t be fully applied to VC. This is because volunteer nodes can get offline whenever they want without taking any responsibility, which is different from other distributed computing. For this situation, this paper proposes a dynamic task scheduling algorithm for heterogeneous VC with deadline constraint, called deadline preference dispatch scheduling (DPDS). The DPDS algorithm selects tasks with the nearest deadline each time and assigns them to volunteer nodes (VN), which solves the dynamic task scheduling problem with deadline constraint. To make full use of resources and maximize the number of completed tasks before the deadline constraint, on the basis of the DPDS algorithm, improved dispatch constraint scheduling (IDCS) is further proposed. To verify our algorithms, we conducted experiments, and the results show that the proposed algorithms can effectively solve the dynamic task assignment problem with deadline constraint in VC.

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Section: Task Scheduling Algorithms In Volunteer Computing Platformsmentioning

confidence: 99%

Dynamic Task Scheduling Algorithm with Deadline Constraint in Heterogeneous Volunteer Computing Platforms

Qiao

Lin

et al. 2019

Future Internet

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“…Dalam studi literatur, berbagai teknik dekomposisi berbasis Lagrangian yang ditanamkan dalam framework DMPC [11] berhasil dikembangkan untuk menyelesaikan masalah optimisasi yang bersifat nonlinear dan MIP, seperti teknik dekomposisi Parallel/Serial Augmented Lagrangian dan Alternating Direction Method of Multipliers (ADMM) [12], [13]. Lebih lanjut, diaplikasikan teknik relaksasi kontinu untuk variabel biner dan integer yang terdapat pada masalah optimisasi [14] Tamping dilakukan untuk mengatur perataan lintasan sehingga unjuk kerja lintasan ditingkatkan ke tingkat tertentu. Ketika tamping tidak lagi efektif, operasi renewal dilakukan untuk sepenuhnya menggantikan ballast yang lama.…”

Section: Pendahuluanunclassified

Penjadwalan Pemeliharaan Trek Kereta dengan Metode Distributed Model Predictive Control

Faris

Ircham

2019

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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This work addresses the development of Distributed Model Predictive Control (DMPC) approaches for the planning of maintenance operations of large-scale railway infrastructure formulated as a Mixed-Integer Linear Programming (MILP) problem. The proposed optimization problem is solved using two different decomposition schemes: Alternating Direction Method of Multipliers (ADMM) and Distributed Robust Safe But Knowledgeable (DRSBK). The original distributed algorithms are modified to handle the non-convex nature of the optimization problem, hence improving the solution quality. The results of large-scale test instances show that DRSBK can outperform the conventional centralized approach and ADMM, by providing the closest-to-optimum solution while requiring the least computation time. Intisari-Makalah ini mengembangkan metode Distributed Model Predictive Control (DMPC) untuk perencanaan operasi pemeliharaan infrastruktur kereta api skala besar yang diformulasikan sebagai masalah Mixed-Integer Linear Programming (MILP). Masalah optimasi yang diusulkan kemudian diselesaikan dengan menggunakan dua skema optimasi DMPC yang berbeda: Alternating Direction Method of Multipliers (ADMM) dan Distributed Robust Safe But Knowledgeable (DRSBK). Selain itu, algoritme standarnya dimodifikasi untuk menangani sifat non-convex dari masalah optimasi, sehingga dapat meningkatkan kualitas solusi. Hasil pengujian dalam masalah skala besar menunjukkan bahwa DRSBK dapat mengungguli metode centralized yang konvensional dan ADMM, dengan menghasilkan solusi mendekati ke global optimum tetapi membutuhkan waktu komputasi minimum. Kata Kunci-Penjadwalan, rekayasa perkeretaapian, optimisasi, MIP, kendali prediktif.

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“…In our recent research e orts, we tackled the problem of collaborative task execution in the volunteer cloud [44] from several perspectives. In [48], we proposed a framework to allocate task requests according to di erent policies de ned by suitable integer programming problems. ose problems are solved in a distributed fashion, relying on the Alternating Direction Method of Multipliers.…”

Section: Related Workmentioning

confidence: 99%

A Holistic Approach for Collaborative Workload Execution in Volunteer Clouds

Sebastio

Amoretti

Lafuente

et al. 2018

ACM Trans. Model. Comput. Simul.

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e demand for provisioning, using and maintaining distributed computational resources is growing hand in hand with the quest for ubiquitous services. Centralized infrastructures such as cloud computing systems provide suitable solutions for many applications, but their scalability could be limited in some scenarios, e.g., in case of latency-dependent applications. e volunteer cloud paradigm aims at overcoming this limitation by encouraging clients to o er their own spare, perhaps unused, computational resources. Volunteer clouds are thus complex, large-scale, dynamic systems which demand for self-adaptive capabilities to o er e ective services, as well as modeling and analysis techniques to predict their behavior. In this paper, we propose a novel holistic approach for volunteer clouds supporting collaborative task execution services able to improve the QoS of compute-intensive workloads. We instantiate our approach by extending a recently proposed ant colony optimization algorithm for distributed task execution with a workload-based partitioning of the overlay network of the volunteer cloud. Finally, we evaluate our approach using simulation-based statistical analysis techniques on a workload benchmark provided by Google. Our results show that the proposed approach outperforms some traditional distributed task scheduling algorithms in the presence of compute-intensive workloads. e increasing role of so ware and the pervasiveness of computational devices have fostered the interest and demand for storage and computing services. Cloud computing is a recent but already well-established paradigm envisioning the use of computing as utility. anks to the cloud, users do not need to buy or maintain powerful computational devices, since they can use services remotely. Such services are customized according to the users' needs and requirements, and are provided transparently from where the resources are physically hosted. Another bene t of cloud systems is their ability to dynamically scale whenever application requirements change. In order to properly manage their resources, cloud providers o er contract-based services whose terms are speci ed in a Service Level Agreement (SLA), containing the required ality of Service (QoS) and the associated penalty fees in case of their violation.One of the greatest challenges in the cloud computing domain is the e cient use of resources, while guaranteeing the ful llment of SLAs. To this aim, particular a ention is given to autonomic computing techniques, which support the development of clouds as self-managing systems. Cloud features should automatically adapt to their internal status and the dynamically changing environment, without human intervention. Self-management may involve system maintenance, awareness, evolution, con guration, healing, tuning and optimization.Along with the latest advancements in virtualization technologies, the last decade has also witnessed a great leap forward in processing power, thanks to the advent of the multicore era. In turn, this scenario has brough...

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Optimal distributed task scheduling in volunteer clouds

Cited by 30 publications

References 24 publications

Dynamic Task Scheduling Algorithm with Deadline Constraint in Heterogeneous Volunteer Computing Platforms

Dynamic Task Scheduling Algorithm with Deadline Constraint in Heterogeneous Volunteer Computing Platforms

Penjadwalan Pemeliharaan Trek Kereta dengan Metode Distributed Model Predictive Control

A Holistic Approach for Collaborative Workload Execution in Volunteer Clouds

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