A survey and taxonomy of resource optimisation for executing bag-of-task applications on public clouds

Thai, Long; Varghese, Blesson; Barker, Adam

doi:10.1016/j.future.2017.11.038

Cited by 41 publications

(26 citation statements)

References 47 publications

(188 reference statements)

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“…In [5], Facebook reports that the jobs running on their own internal data centers are mostly independent tasks. Many works propose then scheduling the execution of independent tasks both on homogeneous and heterogeneous cloud environments [6]. In the former, the performance and pricing of all available VMs are the same.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Bag-of-Tasks Scheduler Tolerant to Temporal Failures in Clouds

Teylo

Arantes²,

Sens³

et al. 2019

2019 31st International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

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Cloud platforms have emerged as a prominent environment to execute high performance computing (HPC) applications providing on-demand resources as well as scalability. They usually offer different classes of Virtual Machines (VMs) which ensure different guarantees in terms of availability and volatility, provisioning the same resource through multiple pricing models. For instance, in Amazon EC2 cloud, the user pays per hour for on-demand VMs while spot VMs are unused instances available for lower price. Despite the monetary advantages, a spot VM can be terminated, stopped, or hibernated by EC2 at any moment.Using both hibernation-prone spot VMs (for cost sake) and on-demand VMs, we propose in this paper a static scheduling for HPC applications which are composed by independent tasks (bag-of-task) with deadline constraints. However, if a spot VM hibernates and it does not resume within a time which guarantees the application's deadline, a temporal failure takes place. Our scheduling, thus, aims at minimizing monetary costs of bagof-tasks applications in EC2 cloud, respecting its deadline and avoiding temporal failures. To this end, our algorithm statically creates two scheduling maps: (i) the first one contains, for each task, its starting time and on which VM (i.e., an available spot or on-demand VM with the current lowest price) the task should execute; (ii) the second one contains, for each task allocated on a VM spot in the first map, its starting time and on which on-demand VM it should be executed to meet the application deadline in order to avoid temporal failures. The latter will be used whenever the hibernation period of a spot VM exceeds a time limit.Performance results from simulation with task execution traces, configuration of Amazon EC2 VM classes, and VMs market history confirm the effectiveness of our scheduling and that it tolerates temporal failures.

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Section: Related Workmentioning

confidence: 99%

“…For instance, in [9] the authors present a heuristic algorithm for executing a bag-oftasks applications taking into account either budget or deadline constraints. In [6], Thai et al present an extensive survey and taxonomy of existing research in scheduling of bag-of-task applications on clouds.…”

Section: Related Workmentioning

confidence: 99%

A Bag-of-Tasks Scheduler Tolerant to Temporal Failures in Clouds

Teylo

Arantes²,

Sens³

et al. 2019

2019 31st International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

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“…Much research has focused on algorithms for scheduling bag‐of‐tasks applications on cloud resources, aiming to optimize application performance or monetary cost . Similarly, such research does not provide a complete solution for building epidemic simulation applications.…”

Section: Related Workmentioning

confidence: 99%

A service‐based framework for building and executing epidemic simulation applications in the cloud

Parlavantzas

Pham

Morin

et al. 2019

Concurrency and Computation

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The cloud has emerged as an attractive platform for resource-intensive scientific applications, such as epidemic simulators. However, building and executing such applications in the cloud presents multiple challenges, including exploiting elasticity, handling failures, and simplifying multi-cloud deployment. To address these challenges, this paper proposes a novel service-based framework called DiFFuSE that enables simulation applications with a bag-of-tasks structure to fully exploit cloud platforms. This paper describes how the framework is applied to restructure two legacy applications, simulating the spread of bovine viral diarrhea virus and Mycobacterium avium subspecies paratuberculosis, into elastic cloud-native applications. Experimental results show that the framework enhances application performance and allows exploring different cost-performance trade-offs while supporting automatic failure handling and elastic resource acquisition from multiple clouds. KEYWORDScloud computing, epidemic simulation, high performance computing, simulation models INTRODUCTIONAs global transport networks continue to expand, the risk of infectious disease transmission grows, making research in pathogen spread on humans and animals increasingly important. An essential tool for studying pathogen spread is computer simulation based on mechanistic modeling. However, running epidemic simulations at large scale is time consuming for multiple reasons. First, the simulation models can capture fine-grained dynamics, considering different population scales (individual, population, and meta-population) and accounting for multiple layouts (eg, combining population and infection dynamics with farm management decisions). 1 Second, some processes can be data-driven and exploit large amounts of real-life observed data, mostly concerning the population dynamics of hosts. Third, understanding the underlying biological system requires performing and comparing many simulation scenarios, eg, through a model sensitivity analysis. Finally, producing reliable results demands large numbers of simulation runs when models are stochastic.Speeding up the execution of large-scale simulations requires parallelism. Parallelism can be implemented at the single-computer level using multiple cores and processors as well as the distributed level using multiple computers interconnected through a network. There are currently three main options for obtaining the necessary computing resources for running simulations in parallel. The first option is using dedicated high-end multicore systems or clusters. The limitation is that such infrastructures are typically shared among many local users, resulting in long waiting times. At the same time, extending such infrastructures to increase user satisfaction is time consuming and costly. The second option is grid computing, which allows sharing computers from multiple clusters in multiple sites. The main limitation of grids is the heterogeneity of the software stacks in each cluster, which makes application deployment di...

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“…In the simulations presented in this section, the deadline factor is fixed at 4 and the arrival rate varies from Poisson(0.002) to Poisson (8). Figure 9 shows the results of the simulation with a Weibull-based workload with zero provisioning times (ideal virtual machine instances).…”

Section: Arrival Rate Analysismentioning

confidence: 99%

Online scheduling of deadline‐constrained bag‐of‐task workloads on hybrid clouds

Peláez¹,

Campos²,

García

et al. 2018

Concurrency and Computation

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The advent of hybrid cloud technologies and public Infrastructures as a Service (IaaS) allows service developers to offer services to their customers with little upfront investment and to adapt services to different workload sizes. The problem of minimizing the costs of the hired public infrastructure while providing the quality of service needed by the final customer arises when using hybrid clouds. Several scheduling strategies have been proposed to solve this problem for services dealing with deadline-constrained bag-of-tasks workloads. Most of these solutions do not consider the variable performance of the clouds, the provisioning delay of virtual machine instances that affects the elasticity, and the impracticality of having good processing time estimations in real systems. We propose a scheduler algorithm that overcomes previous limitations and can minimize the cost of the infrastructure while maximizing the number of deadlines met by the service. Our solution can work autonomously by using sampled observations of the processing times and considers the heterogeneity and the provisioning time of the virtual machine instances. An evaluation was conducted by simulating different scenarios and workload types. Simulation results show that our solution obtains better or similar results than previous techniques in most scenarios in terms of deadlines met. KEYWORDS bag-of-task workloads, cloud computing, hybrid cloud, scheduling 1 INTRODUCTION The adoption of the Cloud Computing Paradigm 1 has changed the provision of computational resources. 2 Platforms such as Amazon EC2, Microsoft Azure, or Google Compute Engine offer computational resources under the Infrastructure as a Service (IaaS) paradigm. The main characteristics of these services are 3 : on-demand and self-service resources, virtualized computational resources accessed over broad networks, resource pooling, rapid elasticity, and pay-per-use. IaaS allows service providers to have access to pools of resources under the financial model of pay-per-use. In this scenario, it is very important to optimize the use of the hired resources while meeting the quality of service expected by end-users. The main strategy for this resource optimization is the right management of hired resources and the execution of the application using a suitable scheduler. 4In the case of deadline-constrained applications and workloads, the quality of service is mainly determined by the processing deadline. Therefore, the problem for a service provider is to meet the processing deadline of the jobs submitted by end-users while minimizing the IaaS resources hired to run the service attending the workload (ie, the cost). This is similar to a deadline-constrained job scheduling problem with a dynamic number of heterogeneous machines for the execution of the jobs. The number of machine types is related to the number of virtual machine types available from IaaS providers.Workloads composed of sets of independent jobs or tasks are commonly known as Bag-of-Tasks (BoT) applications. 5 BoT work...

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A survey and taxonomy of resource optimisation for executing bag-of-task applications on public clouds

Cited by 41 publications

References 47 publications

A Bag-of-Tasks Scheduler Tolerant to Temporal Failures in Clouds

A Bag-of-Tasks Scheduler Tolerant to Temporal Failures in Clouds

A service‐based framework for building and executing epidemic simulation applications in the cloud

Online scheduling of deadline‐constrained bag‐of‐task workloads on hybrid clouds

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