Application-aware virtual machine migration in data centers

Shrivastava, Vivek; Zerfos, Petros; Lee, Kang‐Won; Jamjoom, Hani; Liu, Yew-Huey; Banerjee, Suman

doi:10.1109/infcom.2011.5935247

Cited by 212 publications

(101 citation statements)

References 12 publications

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“…In the following proposals [53,55] for VM migration by considering network trac among virtual machines, but they only consider the total sent data between virtual machine and the distance between physical machines during doing migration. Jun et al…”

Section: Virtual Machine Migrationmentioning

confidence: 99%

On Service Level Agreement Assurance in Cloud Computing Data Centers

Zainelabden

Ibrahim

Kliazovich

et al. 2016

2016 IEEE 9th International Conference on Cloud Computing (CLOUD)

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SaaS is providing cloud applications like all the normal classes of applications of normal computing like the web applications, le applications, email applications, real-time applications, highly interactive applications, massive data analysis applications, high performance computing applications and mobile cloud applications. Cloud computing uses the internet data centers to host the applications and data storage and also the processing power in the addition to the virtualization. Clouds are a huge stack of easily and usefully virtualized services and resources (Like software, platform and hardware).These resources are used by people all over the world and dynamically congured to accommodate more and more load and scale to a huge number of users. This stack of resources and services is delivered to the customers by a pay-per-use model which veries that the services provided by the cloud providers are provided by means of service level agreements.In this master thesis, we present an assurance to the service level agreement between the cloud users and the cloud services providers by trying to assess and evaluate the cloud computing data centers services and applications provided to the cloud users. Also we introduce mathematical test models for the web, le, real time and distributed applications. We can use these test models to see the behavior of the applications over a long time.In this master thesis, we classify the cloud applications into 8 dierent classes of applications and then identify the failures and problems which aect these cloud applications.We classify also all failures types which may occur in cloud data centers like network failures, physical server failures, VM unavailability, individual failures racks and individual component failures. We got the important metrics for each class of applications and developed real scenarios for four classes. Then, we introduce dierent failures to these scenarios to see what is the impact in the most important metrics of applications. Then, we simulate these scenarios in Network Simulator 2. Finally, we solved and mitigated the failures in simulation by using two failure mitigation techniques. The rst one is the virtual machine migration and redundancy. The second technique is the forward error correction.Experimental results acquired over the course of this master's thesis give an assessment and verication of the cloud applications services provided by the cloud provider data centers. And also verify that the cloud data center provider gives their users the best services performance and high QoS, we use this verication to assure SLA response times metric and a good quality and performance of services as mentioned inside the SLA document. This verication will be given to the cloud customers.iii iv Acknowledgment "Today don't beg, don't ask, Just thank God in silence; for all the blessings in your life. " Paulo CoelhoThe road to want something is always guided by unseen strong currents of support from people who believe that you will do it what you have set...

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Section: Virtual Machine Migrationmentioning

confidence: 99%

On Service Level Agreement Assurance in Cloud Computing Data Centers

Zainelabden

Ibrahim

Kliazovich

et al. 2016

2016 IEEE 9th International Conference on Cloud Computing (CLOUD)

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“…Recently, several studies leverage VM migration or placement to optimize the network metrics like traffic cost and endto-end latency [24], [25]. In [24], Shrivastava et al proposed to rebalance workloads across physical machines by shifting the VMs away from overloaded physical machines.…”

Section: B Vm Live Migration and Applicationmentioning

confidence: 99%

“…In [24], Shrivastava et al proposed to rebalance workloads across physical machines by shifting the VMs away from overloaded physical machines. The goal was to offload the overloaded physical machines while minimize the congestion caused by the migration traffic.…”

Section: B Vm Live Migration and Applicationmentioning

confidence: 99%

VirtualKnotter: Online virtual machine shuffling for congestion resolving in virtualized datacenter

et al. 2014

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Abstract-Our measurements on production datacenter traffic together with recently-reported results [1] suggest that datacenter networks suffer from long-lived congestion caused by core network oversubscription and unbalanced workload placement. In contrast to traditional traffic engineering approaches that optimize flow routing, in this paper, we explore the opportunity to address the continuous congestion via optimizing VM placement in virtualized datacenters. To this end, we present VirtualKnotter, an efficient online VM placement algorithm to reduce congestion with controllable VM migration traffic as well as low time complexity. Our evaluation with both real and synthetic traffic patterns shows that VirtualKnotter performs close to the baseline algorithm in terms of link unitization, with only 5%-10% migration traffic of the baseline algorithm. Furthermore, VirtualKnotter decreases link congestion time by 53% for the production datacenter traffic.

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“…The solution supports a broad range of management objectives with a single algorithm that performs adaptive placement. The solution is fully decentralized and highly Most works in the literature, for instance [19][20][21][22][23][24][25][26][27], address only a single management objective. The result in [28] supports multiple management objectives, but each objective with a different algorithm.…”

Section: Related Workmentioning

confidence: 99%

Allocating Compute and Network Resources Under Management Objectives in Large-Scale Clouds

2013

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We consider the problem of jointly allocating compute and network resources in a large Infrastructure-as-a-Service (IaaS) cloud. We formulate the problem of optimally allocating resources to virtual data centers (VDCs) for four well-known management objectives: balanced load, energy efficiency, fair allocation, and service differentiation. Then, we outline an architecture for resource allocation, which centers around a set of cooperating controllers, each solving a problem related to the chosen management objective. We illustrate how a global management objective is mapped onto objectives that govern the execution of these controllers. For a key controller, the Dynamic Placement Controller, we give a detailed distributed design, which is based on a gossip protocol that can switch between management objectives. The design is applicable to a broad class of management objectives, which we characterize through a property of the objective function. The property ensures the applicability of an iterative descent method that the gossip protocol implements. We evaluate, through simulation, the dynamic placement of VDCs for a large cloud under changing load and VDC churn. Simulation results show that this controller is effective and highly scalable, up to 100'000 nodes, for the management objectives considered.

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Application-aware virtual machine migration in data centers

Cited by 212 publications

References 12 publications

On Service Level Agreement Assurance in Cloud Computing Data Centers

On Service Level Agreement Assurance in Cloud Computing Data Centers

VirtualKnotter: Online virtual machine shuffling for congestion resolving in virtualized datacenter

Allocating Compute and Network Resources Under Management Objectives in Large-Scale Clouds

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