The Experts below are selected from a list of 2223 Experts worldwide ranked by ideXlab platform
Rajkumar Buyya - One of the best experts on this subject based on the ideXlab platform.
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colocateme aggregation based energy performance and cost aware VM Placement and consolidation in heterogeneous iaas clouds
2021Co-Authors: Muhammad Zakarya, Lee Gillam, Khaled Salah, Omer F Rana, Santosh Tirunagari, Rajkumar BuyyaAbstract:In many production clouds, with the notable exception of Google, aggregation-based VM Placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users' costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this paper, we demonstrate how aggregation and segregation-based VM Placement policies lead to variabilities in energy efficiency, workload performance, and users' costs. We, then, propose various approaches to aggregation-based Placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of Placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is ~9.61% more energy and ~20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users' costs.
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dynamic VM Placement method for minimizing energy and carbon cost in geographically distributed cloud data centers
IEEE Transactions on Sustainable Computing, 2017Co-Authors: Atefeh Khosravi, Lachlan L H Andrew, Rajkumar BuyyaAbstract:Cloud data centers consume a large amount of energy that leads to a high carbon footprint. Taking into account a carbon tax imposed on the emitted carbon makes energy and carbon cost play a major role in data centers’ operational costs. To address this challenge, we investigate parameters that have the biggest effect on energy and carbon footprint cost to propose more efficient VM Placement approaches. We formulate the total energy cost as a function of the energy consumed by servers plus overhead energy, which is computed through power usage effectiveness (PUE) metric as a function of IT load and outside temperature. Furthermore, we consider that data center sites have access to renewable energy sources. This helps to reduce their reliance on “brown” electricity delivered by off-site providers, which is typically drawn from polluting sources. We then propose multiple VM Placement approaches to evaluate their performance and identify the parameters with the greatest impact on the total renewable and brown energy consumption, carbon footprint, and cost. The results show that the approach which considers dynamic PUE, renewable energy sources, and changes in the total energy consumption outperforms the others while still meeting cloud users’ service level agreements.
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a survey on load balancing algorithms for VM Placement in cloud computing
arXiv: Distributed Parallel and Cluster Computing, 2016Co-Authors: Wenhong Tian, Rajkumar BuyyaAbstract:The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM Placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.
Wenzhong Guo - One of the best experts on this subject based on the ideXlab platform.
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energy efficient VM Placement algorithms for cloud data center
International Conference on Cloud Computing, 2015Co-Authors: Xiuyan Lin, Zhanghui Liu, Wenzhong GuoAbstract:Cloud is the computing paradigm which provides computing resource as a service through network. The client can use computing resource in a convenient and on-demand way, just like the water and the electricity we use daily. The mapping between virtual machine and physical machine is the key of the VM scheduling problem. Nowadays we advocate low-carbon life. It calls for the green cloud computing solutions whether protecting the environment or saving the cost of cloud suppliers. The proposed VM Placement algorithm is energy-efficient, and considers the multi-dimentional resource constrains, such as CPU, memory, network bandwidth, and so on. The experimental results show that the proposed algorithms not only contribute a lot to energy saving, but also try best to meet the quality of service QoS. Therefore, we make significant savings in operating cost and make full use of various resources in the cloud data center. The algorithm has promising prospect in application.
Alex Delis - One of the best experts on this subject based on the ideXlab platform.
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exploiting network topology awareness for VM Placement in iaas clouds
International Conference on Cloud and Green Computing, 2013Co-Authors: Stefanos Georgiou, Konstantinos Tsakalozos, Alex DelisAbstract:In contemporary IaaS configurations, resources are distributed to users primarily through the assignment of virtual machines (VMs) to physical nodes (PMs). This resource allocation is typically done in a way that does not consider user preferences and is unaware of the underlying network layout. The latter is of key significance as cost of the cloud's internal network does not grow linearly to the size of the physical infrastructure. In this paper, we focus on IaaS clouds built on the highly fault-tolerant and scalable PortLand networks. We examine how the performance of the could can benefit from VM Placement algorithms that exploit user-provided hints regarding the features of sought VM interconnections within a virtual infrastructure. We propose and evaluate two such VM Placement algorithms: the first seeks to rapidly place the required VMs as closely as possible on the PortLand network starting with the most demanding virtual link and by following a greedy approach. The second approach identifies promising neighborhoods of PMs for deploying the virtual infrastructure sought. Both methods try to reduce the network utilization of the physical layer while taking advantage of the PortLand layout. Moreover, we seek to minimize the time expended for the Placement decision regardless of the size of the infrastructure. Our experimentation shows that our methods outperform the traditional methods (first-fit) in respect to network usage. Our greedy approach reduces the network traffic routed through the top-level core-switches in the PortLand topology by up to 75%. The second approach attains an additional 20% improvement.
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VM Placement in non homogeneous iaas clouds
International Conference on Service Oriented Computing, 2011Co-Authors: Konstantinos Tsakalozos, Mema Roussopoulos, Alex DelisAbstract:Infrastructure-as-a-Service (IaaS) cloud providers often combine different hardware components in an attempt to form a single infrastructure. This single infrastructure hides any underlying heterogeneity and complexity of the physical layer. Given a non-homogeneous hardware infrastructure, assigning VMs to physical machines (PMs) becomes a particularly challenging task. VM Placement decisions have to take into account the operational conditions of the cloud (e.g., current PM load) and load balancing prospects through VM migrations. In this work, we propose a service realizing a two-phase VM-to-PM Placement scheme. In the first phase, we identify a promising group of PMs, termed cohort, among the many choices that might be available; such a cohort hosts the virtual infrastructure of the user request. In the second phase, we determine the final VM-to-PM mapping considering all low-level constraints arising from the particular user requests and special characteristics of the selected cohort. Our evaluation shows that in large non-homogeneous physical infrastructures, we significantly reduce the VM Placement plan production time and improve plan quality.
Maolin Tang - One of the best experts on this subject based on the ideXlab platform.
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a penalty based genetic algorithm for the migration cost aware virtual machine Placement problem in cloud data centers
International Conference on Neural Information Processing, 2015Co-Authors: T.k. Sarker, Maolin TangAbstract:In the past few years, the virtual machine VM Placement problem has been studied intensively and many algorithms for the VM Placement problem have been proposed. However, those proposed VM Placement algorithms have not been widely used ini?źtoday's cloud data centers as they do not consider the migration cost from current VM Placement to the new optimal VM Placement. As a result, the gain from optimizing VM Placement may be less than the loss of the migration cost from current VM Placement to the new VM Placement. To address this issue, this paper presents a penalty-based genetic algorithm GA for the VM Placement problem that considers the migration cost in addition to the energy-consumption of the new VM Placement and the total inter-VM traffic flow in the new VM Placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM Placement problem.
Sourav Kanti Addya - One of the best experts on this subject based on the ideXlab platform.
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a game theoretic approach to estimate fair cost of VM Placement in cloud data center
IEEE Systems Journal, 2018Co-Authors: Sourav Kanti Addya, Bibhudatta Sahoo, Ashok Kumar Turuk, Anurag Satpathy, Mahasweta SarkarAbstract:Pricing of virtual machines (VMs) with different dimensions is a challenging task. VM pricing involves both capital and operational expenditures. Capital expenditure is fixed in nature, while operational expenditure is variable. A fraction of capital expenditure is included for VM pricing. An individual pays the cloud service provider (CSP) for his requested VM. If the users cooperate among themselves they may end up paying less to CSP for their requested VMs vis-a-vis they would have paid had they requested individually. In this paper, an n -person cooperative game is adopted to determine the price that users would pay for their requested VMs under a cooperative environment. Shapley value is used to estimate the fraction of capital expenditure that would be included in the VM price. An integer linear programming is proposed for energy-efficient Placement. For evaluation, VM configurations and pricing of popular CSPs—Microsoft Azure and Amazon EC2—are considered. Results show that users would pay less for their requested VMs if they cooperate. The energy consumed by the proposed VM Placement technique is compared with first fit decreasing (FFD) and enhanced first fit decreasing (EFFD). It is observed that the proposed technique consumes lesser energy compared to FFD and EFFD.
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crow search based virtual machine Placement strategy in cloud data centers with live migration
Computers & Electrical Engineering, 2017Co-Authors: Anurag Satpathy, Sourav Kanti Addya, Ashok Kumar Turuk, Banshidhar Majhi, Gadadhar SahooAbstract:Abstract Cloud computing has emerged as the most revolutionary technology in the field of computing. The cloud service providers (CSPs) have high computational facilities called data centers (DCs) at their disposal. CSPs provide services to the users through virtual machines (VMs). VM Placement is the mapping of VMs onto physical machine called hosts. In this paper, we propose a two-tier virtual machine Placement algorithm. Firstly, we propose a queueing structure to manage and schedule a large set of VMs. Secondly, a multi-objective VM Placement algorithm called crow search based VM Placement (CSAVMP) is proposed to reduce the resources wastage and power consumption at the data centers. VM migration is an indispensable part of any cloud platform for activities like maintenance, load balancing, fault tolerance etc. Three different migration strategies namely serial, parallel, improved serial have been tested and a comparative result has been produced.
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Simulated annealing based VM Placement strategy to maximize the profit for Cloud Service Providers
Engineering Science and Technology an International Journal, 2017Co-Authors: Sourav Kanti Addya, Mahasweta Sarkar, Bibhudatta Sahoo, Ashok Kumar Turuk, Sanjay Kumar BiswashAbstract:Virtual machine (VM) Placement strategies reported in the literature focuses mainly on minimization of power consumption and maximization of placed VMs. The revenue earned by a cloud service provider (CSP) depends on the number of VMs placed. Increasing the number of VMs placed by a CSP not only increases the power consumption but also decreases the profit margin of the CSP. In this paper, we propose a technique called maximum VM Placement with minimum power consumption (MVMP) to maximize the profit earned by a CSP. The proposed technique attempts to maximize the revenue and minimize the power budget. It is formulated as a bi-objective optimization problem, and is solved using simulated annealing (SA) technique. To reach a sub-optimal solution more randomness is applied to SA. Our MVMP algorithm is compared to five state of the art algorithms in the realm of strategic VM Placement, namely Marotta and Avallone (MA) approach, Hybrid genetic algorithm (HGA), Modified Best-Fit decreasing (MBFD), First-Fit decreasing (FFD) and Random deployment. We observe that MVMP performs better than Marotta and Avallone (MA) approach, HGA, MBFD, FFD and Random Placement in terms of number of servers used, energy consumption, profit and execution time. Scalability of MVMP is verified using two different scenarios: (i) fixed number of VMs and, (ii) fixed number of servers. It is observed that MVMP is scalable too.
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A resource aware VM Placement strategy in cloud data centers based on crow search algorithm
2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), 2017Co-Authors: Anurag Satpathy, Sourav Kanti Addya, Ashok Kumar Turuk, Banshidhar Majhi, Gadadhar SahooAbstract:Virtual machine (VM) Placement in cloud data centers is a challenging task. With the increasing popularity of cloud computing across the globe, a large number of VMs are to be consolidated on a minimum number of data centers (DCs) to optimize the energy consumption and data center utilization. In this paper, we propose a resource aware approach based on a metaheuristic crow search algorithm (CSA) to consolidate a large number of VMs on minimal DCs to meet the Service level agreement (SLA) and desired quality of service (QoS) with maximum data center utilization. We propose two independent techniques, (i) greedy crow search (GCS), (ii) travelling salesman problem based hybrid crow search (TSPCS), to meet the desired objectives. A comparative study has been made from the obtained results. To evaluate the performance of proposed methods we compare them with the classical First Fit (FF) approach and the proposed methods significantly outperform the classical method.
