Semi-online task assignment policies for workload consolidation in cloud computing systems

Armant, Vincent; Cauwer, Milan De; Brown, Kenneth N.; O’Sullivan, Barry

doi:10.1016/j.future.2017.12.035

Cited by 23 publications

(8 citation statements)

References 12 publications

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“…Essentially, a container achieves the isolation through namespace to enable processes sharing the same system kernel and Linux Cgroups to set resource limits. Without hardware isolation, container-based sandboxing shows lower startup latency than coarse-grained consolidation strategies [11,147] in hypervisor-based VMs.…”

Section: Virtualization Layermentioning

confidence: 99%

The Serverless Computing Survey: A Technical Primer for Design Architecture

et al. 2022

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The development of cloud infrastructures inspires the emergence of cloud-native computing. As the most promising architecture for deploying microservices, serverless computing has recently attracted more and more attention in both industry and academia. Due to its inherent scalability and flexibility, serverless computing becomes attractive and more pervasive for ever-growing Internet services. Despite the momentum in the cloud-native community, the existing challenges and compromises still wait for more advanced research and solutions to further explore the potentials of the serverless computing model. As a contribution to this knowledge, this article surveys and elaborates the research domains in the serverless context by decoupling the architecture into four stack layers: Virtualization, Encapsule, System Orchestration, and System Coordination. Inspired by the security model, we highlight the key implications and limitations of these works in each layer, and make suggestions for potential challenges to the field of future serverless computing.

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Section: Virtualization Layermentioning

confidence: 99%

The Serverless Computing Survey: A Technical Primer for Design Architecture

et al. 2022

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“…Because of the variety of metaheuristic algorithms with similar principles but subtle details in their operation, it is difficult to determine the best one. In addition, population‐based metaheuristics can be combined with local search to improve the solutions in the population, which may lead to better results overall 38 …”

Section: Related Workmentioning

confidence: 99%

“…This optimization technique is well known in the evolutionary computation community 41 . In VM consolidation, most existing approaches using population‐based metaheuristics do not use local search, but some do 21,38 . As the application of these local searches could interfere with the applied metaheuristics, when a new simulation is initiated these can be configured to be inactive.…”

Section: Consolidation Techniquesmentioning

confidence: 99%

Comparison of workload consolidation algorithms for cloud data centers

Ponto

Kecskeméti

Mann

2021

Concurrency and Computation

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Workload consolidation is an important method for the efficient operation of cloud data centers, impacting important quality attributes such as resource utilization and power consumption. Many different approaches have been proposed for workload consolidation, but few comparative studies were executed to date. Therefore, it is unclear which of the proposed approaches work best in which situation. In this article, we present a comprehensive simulation‐based comparison of five workload consolidation techniques. We introduce a general framework for workload consolidation techniques to the DISSECT‐CF simulator to foster the development and comparison of efficient data center consolidation algorithms. We use this framework to evaluate the effectiveness of a first fit best fit decreasing heuristic, a custom heuristic, and three population‐based metaheuristics (genetic algorithm, artificial bee colony, and particle swarm optimization). The evaluation is based on a wide variety of real‐world workload traces. The five algorithms are compared in terms of total energy consumption, the duration of the simulation, and the number of migrations. Based on the results, there is no generally best consolidation technique. The results deliver insight into the pros and cons of the algorithms as well as the impact of different parameters. In particular, the results show that population‐based metaheuristics do not offer a significant gain in terms of solution quality to compensate for the increased simulation time.

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“…The variation of the method proposed in [66] differs in terms of the sorting order: it starts from the least demanding VM and allocates it to the server with the lowest power consumption. Similar strategies are described in [67], the most trivial being first fit (FF), in which the task is assigned to the first machine with sufficient capacity. Next fit (NF) tries to assign the task to the last allocated machine, as the easiest way of consolidation.…”

Section: Reactive Allocation Strategiesmentioning

confidence: 99%

“…Max rest on requirements (MRR) is also called the worst fit method, as it tends to allocate tasks to servers with the maximum remaining capacity. The idea of delayed allocation is the most important contribution presented in [67]. Task are allocated in groupssuch an approach utilizes server capacity much better than in the case of allocating single tasks.…”

Section: Reactive Allocation Strategiesmentioning

confidence: 99%

Energy Aware Data Centers and Networks: a Survey

Arabas

2018

JTIT

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The past years have brought about a great variety of clusters and clouds. This, combined with their increasing size and complexity, has resulted in an obvious need for power-saving control mechanisms. Upon presenting a basis on which such solutions - namely low-level power control interfaces, CPU governors and network topologies – are constructed, the paper summarizes network and cluster resources control algorithms. Finally, the need for integrated, hierarchical control is expressed, and speciﬁc examples are provided.

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Semi-online task assignment policies for workload consolidation in cloud computing systems

Cited by 23 publications

References 12 publications

The Serverless Computing Survey: A Technical Primer for Design Architecture

The Serverless Computing Survey: A Technical Primer for Design Architecture

Comparison of workload consolidation algorithms for cloud data centers

Energy Aware Data Centers and Networks: a Survey

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