Stratus

Chung, Andrew; Park, Jun Woo; Ganger, Gregory R.

doi:10.1145/3267809.3267819

Cited by 64 publications

(9 citation statements)

References 33 publications

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“…To overcome such limitations, some solutions jointly process batches of tasks. For instance, Stratus [29] proposes an algorithm that targets the IaaS (Infrastructure as a Service) scenario; specifically, it aims to maximize the use of the purchased resources by co-allocating tasks onto the same VMs. Quincy [30] introduces the concept of flow scheduling, where the problem of job scheduling is converted to an equivalent min-cost max-flow problem.…”

Section: Related Workmentioning

confidence: 99%

Scheduling Multi-Component Applications Across Federated Edge Clusters With Phare

Castellano,

Galantino,

Risso

et al. 2024

IEEE Open J. Commun. Soc.

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The shift towards agile microservice architecture has enabled significant benefits for IT companies but has also resulted in increased complexity for Cloud orchestration tools. Traditional tools were designed for centralized data centers and are ineffective for locating microservices in geographically-distributed edge-like infrastructures. This paper presents Phare, a decentralized scheduling algorithm designed to optimize the placement of microservices by satisfying their computing and communication demands while minimizing deployment costs. Phare employs a heuristic-based approach to solve the NP-Hard scheduling problem, prioritizing the microservices with the more stringent requirements and placing them on the most convenient computing facilities, based on the concept of affinity, contributing to the field by providing a more holistic approach to resource scheduling in edge computing. We validate our approach against Firmament, the state-of-the-art workload scheduling algorithm for component-based applications, on simulated edge infrastructures with hundreds of clusters. Phare achieves up to a 10× reduction in terms of deployment costs compared to Firmament while providing a much lower scheduling latency.

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Section: Related Workmentioning

confidence: 99%

Scheduling Multi-Component Applications Across Federated Edge Clusters With Phare

Castellano,

Galantino,

Risso

et al. 2024

IEEE Open J. Commun. Soc.

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“…To reduce the service cost, Chung et al [22] introduced Stratus, which was used to pack the tasks on the machines using the estimated runtime for the incoming jobs. The JVuPredict [23] algorithm was used to predict the estimated runtime of the tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

Containerized Microservices Orchestration and Provisioning in Cloud Computing: A Conceptual Framework and Future Perspectives

et al. 2022

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Cloud computing is a rapidly growing paradigm which has evolved from having a monolithic to microservices architecture. The importance of cloud data centers has expanded dramatically in the previous decade, and they are now regarded as the backbone of the modern economy. Cloud-based microservices architecture is incorporated by firms such as Netflix, Twitter, eBay, Amazon, Hailo, Groupon, and Zalando. Such cloud computing arrangements deal with the parallel deployment of data-intensive workloads in real time. Moreover, commonly utilized cloud services such as the web and email require continuous operation without interruption. For that purpose, cloud service providers must optimize resource management, efficient energy usage, and carbon footprint reduction. This study presents a conceptual framework to manage the high amount of microservice execution while reducing response time, energy consumption, and execution costs. The proposed framework suggests four key agent services: (1) intelligent partitioning: responsible for microservice classification; (2) dynamic allocation: used for pre-execution distribution of microservices among containers and then makes decisions for dynamic allocation of microservices at runtime; (3) resource optimization: in charge of shifting workloads and ensuring optimal resource use; (4) mutation actions: these are based on procedures that will mutate the microservices based on cloud data center workloads. The suggested framework was partially evaluated using a custom-built simulation environment, which demonstrated its efficiency and potential for implementation in a cloud computing context. The findings show that the engrossment of suggested services can lead to a reduced number of network calls, lower energy consumption, and relatively reduced carbon dioxide emissions.

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“…The key design is to leverage lightweight virtualization, a.k.a, containers to make tasks preemptable in cluster scheduling. Stratus 47 proposes a cost‐aware container scheduler, which orchestrates batch job execution on virtual clusters, dynamically allocated collections of virtual machine instances. To our best knowledge, the preemption performance in current preemptive schedulers such as References 42,46,48,49 is limited and most cloud deadline schedulers rarely consider preemption.…”

Section: Related Workmentioning

confidence: 99%

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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Stratus

Cited by 64 publications

References 33 publications

Scheduling Multi-Component Applications Across Federated Edge Clusters With Phare

Scheduling Multi-Component Applications Across Federated Edge Clusters With Phare

Containerized Microservices Orchestration and Provisioning in Cloud Computing: A Conceptual Framework and Future Perspectives

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

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