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1. 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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2. AggVNF: Aggregate VNF Allocation and Migration in Dynamic Cloud Data Centers

   https://doi.org/10.1109/NetSoft60951.2024.10588942  

   Gonzalez, Christopher ; Tang, Bin ( June 2024 , IEEE Conference on Network Softwarization (NetSoft 2024))

   Service function chaining (SFC), consisting of a sequence of virtual network functions (VNFs), is the de-facto service provisioning mechanism in VNF-enabled data centers (VDCs). However, for the SFC, the dynamic and diverse virtual machine (VM) traffic must traverse a sequence of VNFs possibly installed at different locations at VDCs, resulting in prolonged network delay, redundant network traffic, and large consumption of cloud resources (e.g., bandwidth and energy). Such adverse effects of the SFC, which we refer to as SFC traffic storm, significantly impede its efficiency and practical implementation.In this paper, we solve the SFC traffic storm problem by proposing AggVNF, a framework wherein the VNFs of an SFC are implemented into one aggregate VNF while multiple instances of aggregate VNFs are available in the VDC. AggVNF adaptively allocates and migrates aggregate VNFs to optimize cloud resources in dynamic VDCs while achieving the load balance of VNFs. At the core of the AggVNF are two graph-theoretical problems that have not been adequately studied. We solve both problems by proposing optimal, approximate, and heuristic algorithms. Using real traffic patterns in Facebook data centers, we show that a) our VNF allocation algorithms yield traffic costs 56.3% smaller than the latest research using the SFC design, b) our VNF migration algorithms yield 84.2% less traffic than the latest research using the SFC design, and c) VNF migration is an effective technique in mitigating dynamic traffic in VDCs, reducing the total traffic cost by up to 24.8%. 

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3. Traffic-Optimal Virtual Network Function Placement and Migration in Dynamic Cloud Data Centers

   https://doi.org/10.1109/IPDPS53621.2022.00094  

   Tran, Vincent ; Sun, Jingsong ; Tang, Bin ; Pan, Deng ( May 2022 , 36th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2022))

   We propose a new algorithmic framework for traffic-optimal virtual network function (VNF) placement and migration for policy-preserving data centers (PPDCs). As dy- namic virtual machine (VM) traffic must traverse a sequence of VNFs in PPDCs, it generates more network traffic, consumes higher bandwidth, and causes additional traffic delays than a traditional data center. We design optimal, approximation, and heuristic traffic-aware VNF placement and migration algorithms to minimize the total network traffic in the PPDC. In particular, we propose the first traffic-aware constant-factor approximation algorithm for VNF placement, a Pareto-optimal solution for VNF migration, and a suite of efficient dynamic-programming (DP)-based heuristics that further improves the approximation solution. At the core of our framework are two new graph- theoretical problems that have not been studied. Using flow characteristics found in production data centers and realistic traffic patterns, we show that a) our VNF migration techniques are effective in mitigating dynamic traffic in PPDCs, reducing the total traffic cost by up to 73%, b) our VNF placement algorithms yield traffic costs 56% to 64% smaller than those by existing techniques, and c) our VNF migration algorithms outperform the state-of-the-art VM migration algorithms by up to 63% in reducing dynamic network traffic. 

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4. Service Function Chain Placement in Cloud Data Center Networks: a Cooperative Multi-Agent Reinforcement Learning Approach

   Gao Lynn ; Chen Yutian ; Tang Bin ( January 2021 , 11th EAI International Conference on Game Theory for Networks (GameNets 2021))

   Service function chaining (SFC), consisting of a sequence of virtual network functions (VNFs) (i.e., firewalls and load balancers), is an effective service provision technique in modern data center networks. By requiring cloud user traffic to traverse the VNFs in order, SFC im- proves the security and performance of the cloud user applications. In this paper, we study how to place an SFC inside a data center to mini- mize the network traffic of the virtual machine (VM) communication. We take a cooperative multi-agent reinforcement learning approach, wherein multiple agents collaboratively figure out the traffic-efficient route for the VM communication. Underlying the SFC placement is a fundamental graph-theoretical prob- lem called the k-stroll problem. Given a weighted graph G(V, E), two nodes s, t ∈ V , and an integer k, the k-stroll problem is to find the shortest path from s to t that visits at least k other nodes in the graph. Our work is the first to take a multi-agent learning approach to solve k- stroll problem. We compare our learning algorithm with an optimal and exhaustive algorithm and an existing dynamic programming(DP)-based heuristic algorithm. We show that our learning algorithm, although lack- ing the complete knowledge of the network assumed by existing research, delivers comparable or even better VM communication time while taking two orders of magnitude of less execution time. 

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5. 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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