NPSCS: Non-Preemptive Stochastic Coflow Scheduling With Time-Indexed LP Relaxation

Mao, Ruijiu; Aggarwal, Vaneet

doi:10.1109/tnsm.2021.3051657

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…The researchers provide a broad range of various task scheduling models, each with its unique set of internal working components. For instance, research from [5][6] suggests using geo-distributed data analytics and a selfadaptive task scheduling model to estimate high-density data patterns while mapping workloads to various cloud architectures. This strategy, however, cannot be used to a variety of tasks since it is not scalable.…”

Section: Litrature Reviewmentioning

confidence: 99%

NASLMRP: Design of a Negotiation Aware Service Level Agreement Model for Resource Provisioning in Cloud Environments

Shelke,

Shahapurkar

2023

IJRITCC

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Cloud resource provisioning requires examining tasks, dependencies, deadlines, and capacity distribution. Scalability is hindered by incomplete or complex models. Comprehensive models with low-to-moderate QoS are unsuitable for real-time scenarios. This research proposes a Negotiation Aware SLA Model for Resource Provisioning in cloud deployments to address these challenges. In the proposed model, a task-level SLA maximizes resource allocation fairness and incorporates task dependency for correlated task types. This process's new tasks are processed by an efficient hierarchical task clustering process. Priority is assigned to each task. For efficient provisioning, an Elephant Herding Optimization (EHO) model allocates resources to these clusters based on task deadline and make-span levels. The EHO Model suggests a fitness function that shortens the make-span and raises deadline awareness. Q-Learning is used in the VM-aware negotiation framework for capacity tuning and task-shifting to post-process allocated tasks for faster task execution with minimal overhead. Because of these operations, the proposed model outperforms state-of-the-art models in heterogeneous cloud configurations and across multiple task types. The proposed model outperformed existing models in terms of make-span, deadline hit ratio, 9.2% lower computational cycles, 4.9% lower energy consumption, and 5.4% lower computational complexity, making it suitable for large-scale, real-time task scheduling.

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Section: Litrature Reviewmentioning

confidence: 99%

NASLMRP: Design of a Negotiation Aware Service Level Agreement Model for Resource Provisioning in Cloud Environments

Shelke,

Shahapurkar

2023

IJRITCC

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show abstract

“…Varys [8] is one of the initial works to solve coflow scheduling in data centers by proposing the Smallest-Effective-Bottleneck-First (SEBF) heuristic to decide the order among coflows and Minimum-Allocation-for-Desired-Duration (MADD) for bandwidth allocation. Later, several works have proposed approximation algorithms for coflow scheduling [19] [23] [24] [25] [10] [26]. The problem of scheduling dependent coflows has also been studied in [27] [28] [29].…”

Section: Related Workmentioning

confidence: 99%

Multihop Offloading of Multiple DAG Tasks in Collaborative Edge Computing

Sahni

Cao

Yang

et al. 2021

IEEE Internet Things J.

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Collaborative edge computing has become a popular paradigm where edge devices collaborate by sharing resources. Data dissemination is a fundamental problem in CEC to decide what data is transmitted from which device and how. Existing works on data dissemination have not focused on coflow scheduling in CEC, which involves deciding the order of flows within and across coflows at network links. Coflow implies a set of parallel flows with a shared objective. The existing works on coflow scheduling in data centers usually assume a non-blocking switch and do not consider congestion at different links in the multihop path in CEC, leading to increased coflow completion time (CCT). Furthermore, existing works do not consider multiple flow sources that cannot be ignored, as data can have duplicate copies at different edge devices. This work formulates the multisource coflow scheduling problem in CEC, which includes jointly deciding the source and flow ordering for multiple coflows to minimize the sum of CCT. This problem is shown to be NPhard and challenging as each flow can have multiple dependent conflicts at multiple links. We propose a source and coflowaware search and adjust (SCASA) heuristic that first provides an initial solution considering the coflow characteristics. SCASA further improves the initial solution using the source search and adjust heuristic by leveraging the knowledge of both coflows and network congestion at links. Evaluation done using simulation experiments shows that SCASA leads to up to 83% reduction in the sum of CCT compared to benchmarks without a joint solution.

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BMTVDS2: a novel hybrid bioinspired model for task-and-VM-dependency and deadline aware scheduling via dual service level agreements

2023

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This paper discusses the design of a novel hybrid bioinspired model for task-and-VM-dependency and deadline aware scheduling via dual service level agreements. The model uses a combination of grey wolf optimization with the league championship algorithm, to perform efficient scheduling operations. These optimization techniques model a fitness function that incorporates task make-span, task deadline, mutual dependencies with other tasks, the capacity of VMs, and energy needed for scheduling operations. This assists in improving its scheduling performance for multiple use cases. To perform these tasks, the model initially deploys a task-based service level agreement (SLA) method, which assists in enhancing task and requesting-user diversity. This is followed by the design of a VM-based SLA model, which reconfigures the VM's internal characteristics to incorporate multiple task types. The model also integrates deadline awareness along with task-level and VM-level dependency awareness, which assists in improving its scheduling performance under real-time task and cloud scenarios. The proposed model is able to improve cloud utilization by 8.5%, increase task diversity by 8.3%, reduce the delay needed for resource provisioning by 16.5%, and reduce energy consumption by 9.1%, making for a wide variety of real-time cloud deployments.

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NPSCS: Non-Preemptive Stochastic Coflow Scheduling With Time-Indexed LP Relaxation

Cited by 6 publications

References 33 publications

NASLMRP: Design of a Negotiation Aware Service Level Agreement Model for Resource Provisioning in Cloud Environments

NASLMRP: Design of a Negotiation Aware Service Level Agreement Model for Resource Provisioning in Cloud Environments

Multihop Offloading of Multiple DAG Tasks in Collaborative Edge Computing

BMTVDS2: a novel hybrid bioinspired model for task-and-VM-dependency and deadline aware scheduling via dual service level agreements

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