An Energy-Aware Host Resource Management Framework for Two-Tier Virtualized Cloud Data Centers

Summary Cloud services can be expanded and offered in a flexible manner depending on the needs of the users, and cloud computing has become more popular. The energy consumption to provide users with service quality is the biggest concern in virtualization technology. Therefore, reducing energy consumption is a key step in reducing the operating costs of various organizations' data centers. In this article, we propose a dynamic VM relocation (DVMR) system used inside data centers to study virtualization technologies for reducing energy consumption in cloud computing environments. The DVMR system provides a method for predicting PM load by using and selecting loaded and unloaded PM, thereby effectively managing virtualization and energy saving. Experimental evaluation is done by implementing load threshold‐based relocation. The results show that the proposed system can efficiently balance the load between different PMs and efficiently decrease the energy consumption of the entire system.

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“…12.5% per year. Therefore, it is essential to effectively reduce energy consumption and increase the energy efficiency of data centers 3–5 …”

Section: Introductionmentioning

confidence: 99%

Dynamic virtual machine relocation system for energy‐efficient resource management in the cloud

Madireddy

Ravindranath

2022

Concurrency and Computation

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“…Despite of the efficacy, the aforesaid VM migration or load‐balancing process undergoes uncertainty such as iterative hotspot creation, 10,11 higher SLAV, and more downtime that imposes service reliability 11 . In sync with the exceedingly rising demands and aforesaid challenges, there is the need of a robust and highly efficient load‐management strategies 10–16 where the multiple nodes or VMs could be provided sufficient resource without imposing any SLAV and/or downtime. Moreover, scheduling VM migration while guaranteeing QoS/SLA sensitive and energy efficiency can be of great significance 13–16 …”

Section: Introductionmentioning

confidence: 99%

“…In sync with the exceedingly rising demands and aforesaid challenges, there is the need of a robust and highly efficient load‐management strategies 10–16 where the multiple nodes or VMs could be provided sufficient resource without imposing any SLAV and/or downtime. Moreover, scheduling VM migration while guaranteeing QoS/SLA sensitive and energy efficiency can be of great significance 13–16 …”

Section: Introductionmentioning

confidence: 99%

“…Unlike classical bin packing‐oriented VM consolidation concepts, to ensure QoS demands, the migration controller requires considering other performance aspects as well, especially under dynamic cloud condition 19–24 . Migrating a VM while guaranteeing QoS or SLA constructs (no SLAV, higher availability or no or negligible SLAV per active host, 3,7,8,10,16 low power consumption, 14,15,25 high resource utilization, 26 etc.) turns out to be a NP‐hard problem 27 .…”

Section: Introductionmentioning

confidence: 99%

“…Majority of the classical load‐balancing methods consider VM migration as a bin‐packing problem 32 that often results into increased SLAV and iterative hotspot creation across cloud infrastructure. It can be a severe problem in case of mega cloud infrastructure 3,5,11,14,15 . To cope up with SLA‐sensitive load balancing, there is the need of a network‐adaptive (say, proactive) scheduling system which could ensure SLA‐sensitive resource allocation while guaranteeing that it does not impose iterative hotspot in the network and energy exhaustion 24 .…”

Section: Introductionmentioning

confidence: 99%

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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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FPHO: Fractional Pelican Hawks optimization based container consolidation in CaaS cloud

Patra,

Sahoo,

Turuk

2024

Concurrency and Computation

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Containers in cloud computing provide a logical packaging technique for applications to be isolated from the computing environment in which they actually execute, allowing for efficient sharing of memory, processor, storage, and network resources at the Operating System (OS) level. Since they are so compact, container‐based clouds have recently gained significant popularity. In order to maximize resource usage and minimize energy consumption, the container consolidation technique is widely employed in the cloud environment. This work introduces container consolidation in cloud computing that exploits Fractional Pelican Hawks Optimization (FPHO). In Container as a Service (CaaS) model, containers are placed in the Virtual Machines (VMs), and virtual machines are hosted in Physical Machines (PMs) or servers. The proposed method for container consolidation consists of two modules, namely, the host status module and the consolidation module. In the host status module, the PM's load is predicted using Long Short Term Memory (LSTM) and checked whether the PM is overloaded or underloaded using a threshold. If it is overloaded, the container selection algorithm is performed, and the migration list is also generated. In the consolidation module, the created migration list which is employed for the destination list to be created by an overloaded destination selector. In the same way, the underloaded list is also generated by the underloaded destination selector. Finally, the container and VM migration is carried out by considering the multi‐objectives such as predicted load, migration cost, resource utilization, energy consumption, network, and bandwidth which are optimally selected by the proposed FHPO. Here, FHPO is the combination of Fractional Pelican Optimization (FPO) and Fire Hawk Optimizer (FHO). The designed model achieved the measures with minimum energy consumption, resource utilization, Service Level Agreement (SLA), and Makespan as 0.066, 0.019, 0.054, and 0.066, respectively for setup one.

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An Energy-Aware Host Resource Management Framework for Two-Tier Virtualized Cloud Data Centers

Cited by 12 publications

References 37 publications

Dynamic virtual machine relocation system for energy‐efficient resource management in the cloud

Dynamic virtual machine relocation system for energy‐efficient resource management in the cloud

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

FPHO: Fractional Pelican Hawks optimization based container consolidation in CaaS cloud

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