An utility-based job scheduling algorithm for Cloud computing considering reliability factor

Yang, Bo; Xu, Xiaofei; Tan, Feng; Park, Dong Ho

doi:10.1109/csc.2011.6138559

Cited by 32 publications

(21 citation statements)

References 10 publications

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“…To address this issue, they first present a heuristics for resource provisioning under a given maintenance schedule and, then, build on the heuristics to solve the joint resource provisioning and maintenance scheduling problem. Yang et al [38] propose an algorithm that uses Markov chain models to schedule tasks so that they get the best value of utility. A job being executed on the Cloud possesses the following factors: deadline, data, and reward factor for completing it on time.…”

Section: Other Schemes For Runtime Virtual Machines Allocation Adaptionmentioning

confidence: 99%

Dependability-Oriented Resource Management Schemes for Cloud Computing Data Centers

Jhawar

Piuri

2015

Handbook on Data Centers

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Section: Other Schemes For Runtime Virtual Machines Allocation Adaptionmentioning

confidence: 99%

Dependability-Oriented Resource Management Schemes for Cloud Computing Data Centers

Jhawar

Piuri

2015

Handbook on Data Centers

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“…Retry is redundancy in time where a try again process starts after a failure is detected [15]. However, most current task scheduling and resource allocation algorithms [16][17][18] did not consider the prediction of resource availability or the connectivity among mobile nodes in the future, or the channel contention, which affects the performance of submitted applications.…”

Section: Related Workmentioning

confidence: 99%

Resilient Hybrid Mobile Ad-hoc Cloud Over Collaborating Heterogeneous Nodes

Khalifa

Azab

Eltoweissy

2014

Proceedings of the 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

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Abstract-The emergence of Mobile Ad-hoc Clouds (MACs) promises more effective and collaborative elastic resource-infinite computing.However, the highly dynamic, mobile, heterogeneous, fractionized, and scattered nature of computing resources coupled with the isolated non-cooperative nature of current resource management systems make it impossible for current virtualization and resource management techniques to guarantee resilient cloud service delivery. In this paper, we present PlanetCloud, our MAC management platform with an intrinsic support for resilient, highly mobile, cooperative, and dynamically-configurable MACs. We use PlanetCloud for the construction and management of resilient hybrid MACs (HMACs) over mobile and stationary computing resources. PlanetCloud comprises a trustworthy fine-grained virtualization layer and a task management layer. PlanetCloud employs the concepts of application virtualization and fractionation using intrinsically-resilient and aware micro virtual machines, or Cells in our terminology, to encapsulate executable applicationfractions. Such employment isolates the running application from the underlying physical resource enabling seamless execution over heterogeneous resources, lightweight load migration, and low cost of failure. Integral to PlanetCloud is resource forecasting and selection mechanism, which provide a MAC with future appropriate resource availability in space and time. Further, these features enable a large set of mobile, heterogeneous, and scattered resources to collaborate through PlanetCloud smart management platforms that seamlessly consolidates such resources into a resilient HMAC. Using analysis and simulation, we evaluate a PlanetCloud-managed resilient HMAC. Results show that PlanetCloud can provision high level of resource availability transparently maintaining the applications' QoS while preventing service disruption even in highly dynamic environments. Additionally, results showed that our approach to minimizing the cost of failure and facilitating easy load migration elevates the resilience of the HMAC to a great extent.

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“…Also, it improves the computation / communication ratio. Yang et al [15] have highlighted the issue of job scheduling in the Cloud Computing with considering hardware/software failure and recovery. They have proposed a Reinforcement Learning (RL( based algorithm that helps the scheduler to define scheduling decision with fault tolerable while maximizing utilities attained in the long term.…”

Section: Related Workmentioning

confidence: 99%

Job Scheduling based on Harmonization between the Requested and Available Processing Power in The Cloud Computing Environment

Ibrahim

El‐Bahnasawy²,

Omara

2015

IJCA

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The Cloud Computing is a most recent computing paradigm where IT services are provided and delivered over the Internet on demand and pay as you go. On the other hands, the task scheduling problem is considered one of the main challenges in the Cloud Computing environment, where a good mapping between the available resources and the users's tasks is needed to reduce the execution time of the users' tasks (i.e., reduce make-span), in the same time, increase the degree of capitalization from resources (i.e., increase resource utilization).In this paper, a new task scheduling algorithm has been proposed and implemented to reduce the make-span, as well as, increase the resources utilization by considering independent tasks. The proposed algorithm is based on calculating the total processing power of the available resources (i.e., VMs) and the total requested processing power by the users' tasks, then allocating a group of users' tasks to each VM according to the ratio of its needed power corresponding to the total processing power of all VMs.To evaluate the performance of the proposed algorithm, a comparative study has been done among the proposed algorithm, and the existed GA, and PSO algorithms. The experimental results show that the proposed algorithm outperforms other algorithms by reducing make-span and increasing the resources utilization.

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An utility-based job scheduling algorithm for Cloud computing considering reliability factor

Cited by 32 publications

References 10 publications

Dependability-Oriented Resource Management Schemes for Cloud Computing Data Centers

Dependability-Oriented Resource Management Schemes for Cloud Computing Data Centers

Resilient Hybrid Mobile Ad-hoc Cloud Over Collaborating Heterogeneous Nodes

Job Scheduling based on Harmonization between the Requested and Available Processing Power in The Cloud Computing Environment

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