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1. FT-VMP: Fault-Tolerant Virtual Machine Placement in Cloud Data Centers

   https://doi.org/10.1109/ICCCN49398.2020.9209676  

   Gonzalez, Christopher; Tang, Bin (August 2020, International Conference on Computer Communications and Networks (ICCCN 2020))

   Virtual machine (VM) replication is an effective technique in cloud data centers to achieve fault-tolerance, load-balance, and quick-responsiveness to user requests. In this paper we study a new fault-tolerant VM placement problem referred to as FT-VMP. Given that different VM has different fault-tolerance requirement (i.e., different VM requires different number of replica copies) and compatibility requirement (i.e., some VMs and their replicas cannot be placed into some physical machines (PMs) due to software or platform incompatibility), FT-VMP studies how to place VM replica copies inside cloud data centers in order to minimize the number of PMs storing VM replicas, under the constraints that i) for fault-tolerant purpose, replica copies of the same VM cannot be placed inside the same PM and ii) each PM has a limited amount of storage capacity. We first prove that FT-VMP is NP-hard. We then design an integer linear programming (ILP)-based algorithm to solve it optimally. As ILP takes time to compute thus is not suitable for large scale cloud data centers, we design a suite of efficient and scalable heuristic fault-tolerant VM placement algorithms. We show that a) ILP-based algorithm outperforms the state-of-the-art VM replica placement in a wide range of network dynamics and b) that all our fault-tolerant VM placement algorithms are able to turn off significant number of PMs to save energy in cloud data centers. In particular, we show that our algorithms can consolidate (i.e., turn off) around 100 PMs in a small data center of 256 PMs and 700 PMs in a large data center of 1028PMs. 

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2. On Non-Preemptive VM Scheduling in the Cloud

   https://doi.org/10.1145/3219617.3219644  

   Psychas, Konstantinos; Ghaderi, Javad (January 2018, SIGMETRICS '18 Abstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems)

   We study the problem of scheduling VMs (Virtual Machines) in a distributed server platform, motivated by cloud computing applications. The VMs arrive dynamically over time to the system, and require a certain amount of resources (e.g. memory, CPU, etc) for the duration of their service. To avoid costly preemptions, we consider non-preemptive scheduling: Each VM has to be assigned to a server which has enough residual capacity to accommodate it, and once a VM is assigned to a server, its service cannot be disrupted (preempted). Prior approaches to this problem either have high complexity, require synchronization among the servers, or yield queue sizes/delays which are excessively large. We propose a non-preemptive scheduling algorithm that resolves these issues. In general, given an approximation algorithm to Knapsack with approximation ratio r , our scheduling algorithm can provide rβ fraction of the throughput region for β < r. In the special case of a greedy approximation algorithm to Knapsack, we further show that this condition can be relaxed to β<1. The parameters β and r can be tuned to provide a tradeoff between achievable throughput, delay, and computational complexity of the scheduling algorithm. Finally extensive simulation results using both synthetic and real traffic traces are presented to verify the performance of our algorithm. 

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3. Performance Comparison of Fault-Tolerant Virtual Machine Placement Algorithms in Cloud Data Centers

   Gonzalez, Christopher; Tang, Bin (March 2021, the First Computer Science Conference for CSU Undergraduates (CSCSU 2021))

   null (Ed.)

   Fault-tolerant virtual machine (VM) placement refers to the process of placing multiple copies of the same VM cloud application inside cloud data centers. The challenge is how to place required number of VM replicas while minimizing the number of physical machines (PMs) that store them, in order to save energy consumption of cloud data centers. We refer to it as fault-tolerant VM placement problem. In our previous work, we have proposed a greedy algorithm to solve this problem. In this paper, we compare it with an existing research that is based on well-known Welsh Powell Graph-Coloring Algorithm to place items into bins while considering the conflicts between items and items and items and bins. Via extensive simulations, we show that our greedy algorithm can turn off 40-50% more PMs than existing work and can place upto four times as many VM replicas as existing work, achieving much stronger fault-tolerance with less energy consumption. We also compare both algorithms with the optimal integer lin- ear programming (ILP)-based algorithm, which serves as the benchmark of the comparison. 

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4. FMDV: Dynamic Flow Migration in Virtual Network Function-Enabled Cloud Data Centers

   Aguilera, P.; Gonzalez, C.; Tang, B. (May 2022, IEEE International Conference on Communications (ICC 2022).)

   Abstract—Virtual Network Functions (VNFs) are software implementation of middleboxes (MBs) (e.g., firewalls and proxy servers) that provide performance and security guarantees for virtual machine (VM) cloud applications. In this paper, we study a new VM flow migration problem for dynamic VNF-enabled cloud data centers (VDCs). The goal is to migrate the VM flows in the dynamic VDCs to minimize the total network traffic while load-balancing VNFs with limited processing capabilities. We refer to the problem as FMDV: flow migration in dynamic VDCs. We propose an optimal and efficient minimum cost flow-based flow migration algorithm and two benefit-based efficient heuristic algorithms to solve the FMDV. Via extensive simulations, we show that our algorithms are effective in mitigating dynamic cloud traffic while achieving load balance among VNFs. In particular, all our algorithms reduce dynamic network traffic in all cases and our optimal algorithm always achieves the best traffic-mitigation effect, reducing the network traffic by up to 28% compared to the case without flow migration. 

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5. E-VM: An Elastic Virtual Machine Scheduling Algorithm to Minimize the Total Cost of Ownership in a Hybrid Cloud

   Milan Shetti, Bingzhe Li (October 2021, 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking)

   IEEE (Ed.)

   A hybrid cloud that combines both public and private clouds is becoming more and more popular due to the advantages of improved security, scalability, and guaranteed SLA (Service-Level Agreement) at a lower cost than a separate private or public cloud. The existing studies rarely consider VM migrations in a hybrid cloud environment with dynamically changed VM workloads. From an enterprise’s perspective, these migrations are necessary to minimize the cost of utilizing public clouds and guarantee SLAs of VMs in a hybrid cloud environment. In this paper, we propose an elastic VM allocation and migration algorithm for a hybrid cloud, called E-VM, to fully utilize the resources in a private cloud and to minimize the cost of using a public cloud while guaranteeing the SLAs of all VMs. The E-VM considers the bi-direction migration between private and public clouds. Two components, VM-predictor and VM-selector, are designed and implemented in E-VM to determine if a migration has to be triggered between private and public clouds and which VMs will be migrated to the opposite cloud, respectively. Moreover, E-VM is designed based on the existing public cloud pricing models and can be easily adapted to any cloud service provider. According to simulator results based on a set of captured industrial VM traces/workloads and additional experiments directly on a real-world hybrid cloud, the proposed E-VM can significantly reduce the total cost of using the public cloud compared to the existing VM migration schemes. 

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