A Deadline Constrained Preemptive Scheduler Using Queuing Systems for Multi-Tenancy Clouds

Ru, Jia; Yang, Yun; Grundy, John; Keung, Jacky; Li, Hao

doi:10.1109/cloud.2019.00022

Cited by 11 publications

(9 citation statements)

References 8 publications

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“…Next, based on these efficiency metrics, the performance and efficiency of the proposed hybrid algorithm is compared against the modified np‐EDF & p‐EDF and couple of latest cloud computing algorithms as benchmark algorithms. We have considered benchmark‐1 as Earliest Maximal Waiting Time First algorithm which also focus on guaranteed task completion within deadline along with preemption 29 and benchmark‐2 as Learning automata based algorithm which focuses on deadline of the tasks 30 . We ran a random set of tasks in a sequence of 10, 20, 30, 40, and 50 task count, using the proposed hybrid algorithm and existing benchmark algorithms.…”

Section: Resultsmentioning

confidence: 99%

Efficient task scheduling in cloud environment

Rana

Gupta

2022

Int J Communication

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Cloud computing is the provision of on-demand computing resources over the internet and on a pay-as-you-go basis, ranging from software to computation power. Task scheduling and its execution is a fundamental requirement of cloud environment. However, dynamic scheduling of tasks on basis of priority is a challenging area such that the tasks finish before their deadline. Earliest Deadline First (EDF) has been considered in literature for task scheduling to meet the deadlines. However, basic EDF (i.e., which schedules tasks on basis of deadline only)is not suitable for cloud environment. Therefore, this work proposes modified Preemptive EDF (p-EDF) and Non-Preemptive EDF (np-EDF) algorithms considering task priority and cloud provider cost. As both algorithms have their own merits and de-merits, a hybrid EDF is further proposed which makes decision dynamically whether to cause preemption or not, using a Determiner function. The objective of the work is to avoid unnecessary wastage of CPU power and time due to unnecessary preemptions, along with avoiding unnecessary deadline misses such that the high priority task does not wait for the low priority task to end. Simulation results show that the proposed algorithm outperforms other considered benchmark scheme for different performance parameters such as Deadline Miss Count, Preemption Count and average waiting time.

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Section: Resultsmentioning

confidence: 99%

Efficient task scheduling in cloud environment

Rana

Gupta

2022

Int J Communication

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“…On the contrary, the naïve DRF was proposed in work Reference 35 and was only compared with slot‐based and CPU‐based fair scheduling algorithm. Similarly, more experiments about DLAforBT presented in Section 6 were described in our work 64 and more experiments about PDSonQueue showed in Section 7 were described in our work 65 …”

Section: Methodsmentioning

confidence: 93%

“…The overall resource allocation strategy used in our scheduling framework was proposed in our earlier work, 24 and the described deadline constrained scheduler and data locality aware scheduler in 3DSF have been extended from our earlier work 64,65 . A major new contribution of this work was to integrate all these three schedulers together, with highly efficient performance.…”

Section: A Deadline‐constrained and Data Locality‐aware Dynamic Schedmentioning

confidence: 99%

“…For real‐time applications, deadline is a major criterion in judging QoS. We present a real‐time, preemptive, deadline constrained scheduler using queuing theory—PDSonQueue—which enables better QoS, such as reducing deadline‐based SLA violation, shortening jobs' completion time and improving system's throughput and performance 65 . PDSonQueue, as a dynamic priority real‐time greedy job scheduler builds an M/M/n mathematical queuing model to accurately predict jobs' execution time and waiting time, where jobs arrive by following a stochastic process and request resources.…”

Section: A Multitenant Deadline Constrained Job Schedulermentioning

confidence: 99%

“…When jobs are submitted, the profiles of the jobs and the cloud system are initialized ( Line:1 ). If the jobs that have been submitted to process have deadlines, 3DSF invokes deadline constrained scheduler – PDSonQueue 65 to schedule the jobs according to their deadlines ( Lines:2‐4 ). After partition, the job j is divided into tasks ( Lines:7‐8 ).…”

Section: Integration Of Individual Schedulersmentioning

confidence: 99%

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An efficient deadline constrained and data locality aware dynamic scheduling framework for multitenancy clouds

Yang

Grundy

et al. 2020

Concurrency and Computation

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Summary Scheduling and resource allocation in clouds is used to harness the power of the underlying resource pool. Service providers can meet quality of service (QoS) requirements of tenants specified in Service Level Agreements. Improving resource allocation ensures that all tenants will receive fairer access to system resources, which improves overall utilization and throughput. Real‐time applications and services require critical deadlines in order to guarantee QoS. A growing number of data‐intensive applications drive the optimization of scheduling through utilizing data locality in which the scheduler locates a task and ensures the task's relevant data to be on the same server. Choosing suitable scheduling mechanisms for running applications that support multitenancy has consistently been a major challenge. This work proposes a new adaptive Deadline constrained and Data locality aware Dynamic Scheduling Framework “ 3DSF“ that orchestrates different schedulers based on varied requirements. This framework considers tenants' deadline‐based QoS requirements, cloud system's performance and a method of resource allocation to improve resource utilization, system throughput and reduce jobs' completion time. 3DSF contains: (a) a real‐time, preemptive, deadline constrained job scheduler, (b) an optimized data locality aware scheduler, (c) an improved Dominant Resource Fairness greedy resource allocation approach, and (d) an adaptive suite to integrate above‐mentioned schedulers together.

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