Workflow scheduling applying adaptable and dynamic fragmentation (WSADF) based on runtime conditions in cloud computing

Momenzadeh, Zahra; Safi-Esfahani, Faramarz

doi:10.1016/j.future.2018.07.041

Cited by 17 publications

(4 citation statements)

References 26 publications

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“…A learning-based admission control solution was presented in [27] which use the user-experience to grant access to required resources. A standard-based solution is presented in [28] to provide adaptability-related solutions to applications which require priority in the execution process.…”

Section: Related Workmentioning

confidence: 99%

Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services

et al. 2019

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Cloud computing is a paradigm that ensures the flexible, convenient and on-demand provisioning of a shared pool of configurable network and computing resources. Its services can be offered by either private or public infrastructures, depending on who owns the operational infrastructure. Much research has been conducted to improve a cloud’s resource provisioning techniques. Unfortunately, sometimes an abrupt increase in the demand for cloud services results in resource shortages affecting both providers and consumers. This uncertainty of resource demands by users can lead to catastrophic failures of cloud systems, thus reducing the number of accepted service requests. In this paper, we present Bouncer—a workload admission control scheme for cloud services. Bouncer works by ensuring that cloud services do not exceed the cloud infrastructure’s threshold capacity. By adopting an application-aware approach, we implemented Bouncer on software-defined network (SDN) infrastructure. Furthermore, we conduct an extensive study to evaluate our framework’s performance. Our evaluation shows that Bouncer significantly outperforms the conventional service admission control schemes, which are still state of the art.

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Section: Related Workmentioning

confidence: 99%

Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services

et al. 2019

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“…However, this solution did not take into account the overhead time related to communication between the nodes. In work [9], the authors proposed a model for task scheduling and fragmentation in scientific workflows. They solved the problem studied in terms of bandwidth and memory costs by taking into account the runtime conditions and the number of VMs without considering the heterogeneity of resources.…”

Section: Related Workmentioning

confidence: 99%

“…While the second is used in medicine, meteorology, etc. Workflows differ based on their type (business or scientific) and type of dependencies among their tasks [9]. Indeed, cloud providers have often designed and hosted workflow platform services.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost

Belgacem

Beghdad-Bey

2021

Cluster Comput

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Recently, modern businesses have started to transform into cloud computing platforms to deploy their workflow applications. However, scheduling workflow under resource allocation is significantly challenging due to the computational intensity of the workflow, the dependency between tasks, and the heterogeneity of cloud resources. During resource allocation, the cloud computing environment may encounter considerable problems in terms of execution time and execution cost, which may lead to disruptions in service quality given to users. Therefore, there is a necessity to reduce the makespan and the cost at the same time. Often, this is modeled as a multi-objective optimization problem. In this respect, the fundamental research issue we address in this paper is the potential trade-off between the makespan and the cost of virtual machine usage. We propose a HEFT-ACO approach, which is based on the heterogeneous earliest end time (HEFT), and the ant colony algorithm (ACO) to minimize them. Experimental simulations are performed on three types of realworld science workflows and take into account the properties of the Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than basic ACO, PEFT-ACO, and FR-MOS.

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“…It is not possible to perform benchmarking experiments in a repeatable, dispensable, and scalable environment using the real-world cloud, and simulation would be an alternative approach. According to paper [3], the CloudSim simulator is used to simulate a distributed environment with Java language and Planet-lab 2 data presented in CloudSim applied in [3,[23][24][25][26][27][28][29][30][31][32][33][34][35]. First, the described algorithms in the baseline research works are established, and the primary output is taken.…”

Section: Evaluationsmentioning

confidence: 99%

BFPF-Cloud: Applying SVM for Byzantine Failure Prediction to Increase Availability and Failure Tolerance in Cloud Computing

Beheshti

Safi-Esfahani

2020

SN COMPUT. SCI.

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One of the most important aspects of distributed systems is automatic failure recovery. In general, systems must be able to confront any type of failure. One issue is commonly overlooked in the subject of confronting the failures in services. Byzantine failures are the worst kind of arbitrary failures, either. The client should be ready for the worst possible conditions, especially if the server gives an answer that should never give. Sometimes, several servers, hand in hand together, make false answers deliberately. On the other hand, systems have no plan to protect themselves against byzantine failures that happen when the whole processes are not committed to a subject. It is possible that a server responds, but should not, and there is no way to detect that this is incorrect. The complexity of such failures is the main reason in cloud computing systems. However, several algorithms have been presented to detect failures by inspecting responses and address the problem. To detect Byzantine failures, the requests should be executed; first, responses should be created and gathered, and then, the responses are compared altogether. The failures occur when all the processes are unable to reach a consensus on an issue. In other words, Byzantine failures must occur first, and then, a solution should be considered to solve the problem. Accordingly, in cloud computing as a distributed infrastructure, the system should not be involved in severe failures. BFT-Cloud (Byzantine fault-tolerance cloud) as the previous research guarantees the robustness of systems when up to f of totally 3f + 1 resource providers are faulty. It uses replication techniques for overcoming failures, since a broad pool of nodes are available in the cloud. The challenge of the model is that a request should be executed several times to create a correct response that increases the number and duration of executions, either. While it is expected that all responses to requests to be correct without repeating the requests and needing to re-execute them. In this study, a framework is presented called BFPF-Cloud that introduces several features to be applied in algorithms that are based on support vector machine (SVM) to predict Byzantine failures. The reactive policy, along with the proactive one, is applied together to handle Byzantine failures. The main goal is to maintain reliability besides system availability. The experiments show that selecting the characteristics such as latency, PesNumber, MIPS, and failure probability of the replicas are the best features that SVM uses to predict Byzantine failures. In comparison to BFT-Cloud, the number of request re-execution and the execution time are decreased 69.91% on average; the number of repeated requests are decreased 69.78% on average; and throughput is improved 69.90% on average.

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Workflow scheduling applying adaptable and dynamic fragmentation (WSADF) based on runtime conditions in cloud computing

Cited by 17 publications

References 26 publications

Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services

Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services

Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost

BFPF-Cloud: Applying SVM for Byzantine Failure Prediction to Increase Availability and Failure Tolerance in Cloud Computing

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