Multi-agent Negotiation of Virtual Machine Migration Using the Lightweight Coordination Calculus

Anderson, Paul; Bijani, Shahriar; Vichos, Alexandros

doi:10.1007/978-3-642-30947-2_16

Cited by 6 publications

(8 citation statements)

References 6 publications

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“…Given that Cloud data centers are commonly composed of heterogeneous commodity hardware, classifying hosts in three levels may result in situations where underloaded agents managing a host with limited computing resources can pass from underloaded to overloaded by accepting a VM with high resource usage. This is because, in [2], no prior sampling of the resource usage of hosts is conducted for selecting the best destination host according to a criterion. In this regard, agents, in the present work, sample hosts' resource usages to select the best destination host according to a set of heuristics and policies.…”

Section: Related Workmentioning

confidence: 99%

“…In [48], there is no agent interaction to guarantee whether migrating the VM based merely on the location of its consumers will be better to the overall load balance of the Cloud. The agent-based load balancing mechanism proposed by Anderson et al [2] makes use of VM live migration endowed with agent interaction protocols enhanced with balancing heuristics, which are specified using lightweight coordination calculus. In [2], the agents managing hosts are classified as idle, overloaded, and underloaded.…”

Section: Related Workmentioning

confidence: 99%

“…The agent-based load balancing mechanism proposed by Anderson et al [2] makes use of VM live migration endowed with agent interaction protocols enhanced with balancing heuristics, which are specified using lightweight coordination calculus. In [2], the agents managing hosts are classified as idle, overloaded, and underloaded. Whereas idle agents are assumed to be balanced and take no part in load balancing, underloaded and overloaded agents interact with each other to migrate VMs from overloaded to underloaded agents.…”

Section: Related Workmentioning

confidence: 99%

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Agent-based load balancing in Cloud data centers

Gutiérrez-García

Ramírez–Nafarrate

2015

Cluster Comput

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Cloud data centers are generally composed of heterogeneous commodity servers hosting multiple virtual machines (VMs) with potentially different specifications and fluctuating resource usages. This may cause a resource usage imbalance within servers that may result in performance degradation and violations to service level agreements. This work proposes a collaborative agent-based problem solving technique capable of balancing workloads across commodity, heterogeneous servers by making use of VM live migration. The agents are endowed with (i) migration heuristics to determine which VMs should be migrated and their destination hosts, (ii) migration policies to decide when VMs should be migrated, (iii) VM acceptance policies to determine which VMs should be hosted, and (iv) front-end load balancing heuristics. The results show that agents, through autonomous and dynamic collaboration, can efficiently balance loads in a distributed manner outperforming centralized approaches with a performance comparable to commercial solutions, namely Red Hat, while migrating fewer VMs.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Agent-based load balancing in Cloud data centers

Gutiérrez-García

Ramírez–Nafarrate

2015

Cluster Comput

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“…The feasibility of this model for a real live system has been validated using a prototype implementation based on a real physical cluster. This is described in more detail in [1]. In addition, we used a simple simulator to investigate the behaviour of more complex models with a larger number of machines and more controlled loading.…”

Section: Interaction Models For Vm Allocationmentioning

confidence: 99%

“…Section 2 provides some background on LCC and the OpenKnowledge framework, and section 3 presents LCC interaction models for various scenarios involving virtual machine allocation. These interaction models, together with a live prototype which implements them on a real datacentre, are described in more detail in [1]. Section 4 presents new work which describes an extension of the interaction models to manage the workflows which are necessary to deploy the allocations when services are migrated between datacentres and live migration is not possible.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent Virtual Machine Management Using the Lightweight Coordination Calculus

Anderson

Bijani

Herry

2013

Lecture Notes in Computer Science

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LCC is a Lightweight Coordination Calculus which can be used to provide an executable, declarative specification of an agent interaction model. In this paper, we describe an LCC-based system for specifying the migration behaviour of virtual machines within, and between datacentres. We present some example models, showing how they can be used to implement different policies for the machine allocation and migration. We then show how LCC models can be used to manage the workflows that involve creation and deletion of virtual machines when migrating services between different datacentres.

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Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

Govardhan

Srinivasan

2022

Int J Communication

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Summary The exponential rise in decentralized computing demands has made cloud computing an inevitable technical need; however, under exceedingly high load dynamism, ensuring service level agreement (SLA) has remained a challenge. Despite of the fact that heuristic‐driven VM migration or load‐balancing methods are effective towards quality of service (QoS) and reliable cloud services, the major existing solutions ignore the issues of local minima and convergence and hence undergo SLA violation (SLAV). Moreover, most of the existing methods are designed as a reactive scheduling concept that often results into delayed scheduling and hence SLAV or QoS compromise. To alleviate such issues, in this paper, a robust intrinsically modified evolutionary computing‐driven dynamic load‐balancing model (intrinsically modified ant colony system [IMACS]) is proposed for mega cloud infrastructures. To address aforesaid issues, IMACS employs stochastic prediction‐based overload detection, followed by SLA‐sensitive VM selection and IMACS for load balancing. In IMACS, the use of stochastic prediction‐based overload detection method enabled timely hotspot detection and hence improved proactive decision ability. Moreover, it applies minimum migration time‐sensitive VM selection concept that too helped in guaranteeing load balancing without undergoing downtime or SLAV. To improve VM migration, unlike classical heuristic methods, we designed IMACS with multitype population, adaptive pheromone update, adaptive pheromone diffusion, and coevolution concept, which not only helped in reducing the search space and allied computation but also enabled time‐efficient scheduling decision. The overall proposed model was realized in sync with two controllers named local controller and global controller. Here, the first controller collects the real‐time VM and VM manager (VMM) information which is used to detect overloaded host and the VM to be migrated. While the global controller is designed with IMACS to exploit the information from local controller to execute QoS‐sensitive VM migration. The proposed IMACS‐driven load‐balancing model was realized with CloudSim platform were simulating it over 800 number of independent VMs, each encompassing autonomous task, the performance was analyzed in terms of SLAV, number of migrations, SLA per active host, and energy consumption. Simulation results revealed that the proposed model exhibits 20% higher migration, while retaining almost 19% better SLA in comparison to the other state‐of‐art methods like genetic algorithm or classical ant colony system‐based load‐balancing methods. IMACS exhibited 30% higher host shutdown signifying better energy efficiency (15% lower power consumption).

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Multi-agent Negotiation of Virtual Machine Migration Using the Lightweight Coordination Calculus

Cited by 6 publications

References 6 publications

Agent-based load balancing in Cloud data centers

Agent-based load balancing in Cloud data centers

Multi-agent Virtual Machine Management Using the Lightweight Coordination Calculus

Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

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