Divide the Task, Multiply the Outcome: Cooperative VM Consolidation

Sedaghat, Mina; Hernández-Rodríguez, Francisco; Elmroth, Erik; Girdzijauskas, Šarūnas

doi:10.1109/cloudcom.2014.81

Cited by 8 publications

(18 citation statements)

References 5 publications

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“…Two cooperating nodes determine whether to migrate a VM based on the defined objectives. While [43] did not take into account the cost or duration of the conflict before applying the migration, [64] incorporated migration cost into the decision making. In contrast to other proposals, the authors in [44] incorporate the power consumption of the decision engine.…”

Section: Adaptation Techniquementioning

confidence: 99%

“…VMs are redistributed by moving a VM from a lower loaded to a higher loaded node if it can be hosted. In [64], the authors take into account power consumption in the decision making. The authors in [31] aim to reduce the number of nodes used in migration as well as reduce VM migration times at the same time, by using a multi-objective Genetic Algorithm based on hybrid group encoding.…”

Section: Adaptation Techniquesmentioning

confidence: 99%

“…For a distributed and decentralised approach to managing the data centre, the authors in [43,64] proposed a peer-to-peer protocol that enables nodes to communicate directly without a centralised controller. A periodic node discovery service enables nodes to find new neighbouring nodes to communicate with.…”

Section: Decision Engine Architecturementioning

confidence: 99%

“…A disadvantage of this approach is it migrates VMs that are already at risk of SLA violation, due to the CPU utilisation, and further increases the risk by adding the cost of live migration. In [43,64], the authors use a heuristic implemented as a peer-to-peer protocol, enabling nodes to communicate directly without a centralised controller. Two cooperating nodes determine whether to migrate a VM based on the defined objectives.…”

Section: Adaptation Techniquementioning

confidence: 99%

“…On each round of the protocol, two cooperating nodes determine to migrate a VM based on defined objectives. The distributed approaches in [43,64] are used to redistribute the load across the cluster as well consolidate VMs. Using simulation, the authors claim their approaches can manage more than 100,000 nodes.…”

Section: Decision Engine Architecturementioning

confidence: 99%

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Adaptation in cloud resource configuration: a survey

2016

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With increased demand for computing resources at a lower cost by end-users, cloud infrastructure providers need to find ways to protect their revenue. To achieve this, infrastructure providers aim to increase revenue and lower operational costs. A promising approach to addressing these challenges is to modify the assignment of resources to workloads. This can be used, for example, to consolidate existing workloads; the new capability can be used to serve new requests or alternatively unused resources may be turned off to reduce power consumption. The goal of this paper is to highlight features, approaches and findings in the literature, in order to identify open challenges and facilitate future developments. We present a definition of cloud systems adaptation, a classification of the key features and a survey of adapting compute and storage configuration. Based on our analysis, we identify three open research challenges: characterising the workload type, accurate online profiling of workloads, and building highly scalable adaptation mechanisms.

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Section: Adaptation Techniquementioning

confidence: 99%

Section: Adaptation Techniquesmentioning

confidence: 99%

Section: Decision Engine Architecturementioning

confidence: 99%

Section: Adaptation Techniquementioning

confidence: 99%

Section: Decision Engine Architecturementioning

confidence: 99%

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Adaptation in cloud resource configuration: a survey

2016

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A hierarchical decentralized architecture to enable adaptive scalable virtual machine migration

Hummaida

Paton

Sakellariou

2022

Concurrency and Computation

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Cloud computing is an established paradigm for end users to access resources. Cloud infrastructure providers seek to maximize accepted requests, meet Service Level Agreements (SLAs), and reduce operational costs by dynamically allocating Virtual Machines (VMs) to physical nodes. Many solutions have been presented to manage cloud infrastructure, however, these tend to be centralized and suffer in their ability to maintain Quality of Service (QOS) and support data centers with thousands of nodes.Decentralized approaches, with no central management, can manage large data centers. However, these tend to reduce the ability to obtain an optimal resource allocation across the data center. To address this, we propose a hybrid hierarchical decentralized architecture that achieves lower SLA violations and lowers network traffic. We used simulation to evaluate our proposal in practice with a variety of existing VM placement policies.

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Stable Marriage Matching for Homogenizing Load Distribution in Cloud Data Center

Sangar

Upreti

Haugerud

et al. 2020

Lecture Notes in Computer Science

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Running a sheer virtualized data center with the help of Virtual Machines (VM) is the de facto-standard in modern data centers. Live migration offers immense flexibility opportunities as it endows the system administrators with tools to seamlessly move VMs across physical machines. Several studies have shown that the resource utilization within a data center is not homogeneous across the physical servers. Load imbalance situations are observed where a significant portion of servers are either in overloaded or underloaded states. Apart from leading to inefficient usage of energy by underloaded servers, this might lead to serious QoS degradation issues in the overloaded servers. In this paper, we propose a lightweight decentralized solution for homogenizing the load across different machines in a data center by mapping the problem to a Stable Marriage matching problem. The algorithm judiciously chooses pairs of overloaded and underloaded servers for matching and subsequently VM migrations are performed to reduce load imbalance. For the purpose of comparisons, three different greedy matching algorithms are also introduced. In order to verify the feasibility of our approach in real-life scenarios, we implement our solution on a small test-bed. For the larger scale scenarios, we provide simulation results that demonstrate the efficiency of the algorithm and its ability to yield a near-optimal solution compared to other algorithms. The results are promising, given the low computational footprint of the algorithm.

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Divide the Task, Multiply the Outcome: Cooperative VM Consolidation

Cited by 8 publications

References 5 publications

Adaptation in cloud resource configuration: a survey

Adaptation in cloud resource configuration: a survey

A hierarchical decentralized architecture to enable adaptive scalable virtual machine migration

Stable Marriage Matching for Homogenizing Load Distribution in Cloud Data Center

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