2022
Zhiyuan Yao; Yoann Desmouceaux; Juan Antonio Cordero; Mark Townsley; Thomas Heide Clausen
Aquarius-Enable Fast, Scalable, Data-Driven Service Management in the Cloud Journal Article
In: IEEE Transactions on Network and Service Management, 2022, ISSN: 1932-4537.
Abstract | Links | BibTeX | Tags: Chaire Cisco, Infrastructure for Big Data, Machine Learning, Network Monitoring
@article{nokeyi,
title = {Aquarius-Enable Fast, Scalable, Data-Driven Service Management in the Cloud},
author = {Zhiyuan Yao and Yoann Desmouceaux and Juan Antonio Cordero and Mark Townsley and Thomas Heide Clausen},
url = {https://ieeexplore.ieee.org/abstract/document/9852806},
doi = {10.1109/TNSM.2022.3197130},
issn = {1932-4537},
year = {2022},
date = {2022-12-01},
urldate = {2022-12-01},
journal = {IEEE Transactions on Network and Service Management},
abstract = {In order to dynamically manage and update networking policies in cloud data centers, Virtual Network Functions (VNFs) use, and therefore actively collect, networking state information -and in the process, incur additional control signaling and management overhead, especially in larger data centers. In the meantime, VNFs in production prefer distributed and straightforward heuristics over advanced learning algorithms to avoid intractable additional processing latency under high-performance and low-latency networking constraints. This paper identifies the challenges of deploying learning algorithms in the context of cloud data centers, and proposes Aquarius to bridge the application of machine learning (ML) techniques on distributed systems and service management. Aquarius passively yet efficiently gathers reliable observations, and enables the use of ML techniques to collect, infer, and supply accurate networking state information -without incurring additional signaling and management overhead. It offers fine-grained and programmable visibility to distributed VNFs, and enables both open-and close-loop control over networking systems. This paper illustrates the use of Aquarius with a traffic classifier, an auto-scaling system, and a load balancer -and demonstrates the use of three different ML paradigms -unsupervised, supervised, and reinforcement learning, within Aquarius, for network state inference and service management. Testbed evaluations show that Aquarius suitably improves network state visibility and brings notable performance gains for various scenarios with low overhead.},
keywords = {Chaire Cisco, Infrastructure for Big Data, Machine Learning, Network Monitoring},
pubstate = {published},
tppubtype = {article}
}
Zhiyuan Yao; Yoann Desmouceaux; Juan Antonio Cordero; Mark Townsley; Thomas Heide Clausen
Efficient Data-Driven Network Functions Proceedings Article
In: 30th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2022), 2022.
Abstract | Links | BibTeX | Tags: Chaire Cisco, Infrastructure for Big Data, Machine Learning, Network Management
@inproceedings{nokeyg,
title = {Efficient Data-Driven Network Functions},
author = {Zhiyuan Yao and Yoann Desmouceaux and Juan Antonio Cordero and Mark Townsley and Thomas Heide Clausen},
url = {https://arxiv.org/pdf/2208.11385},
year = {2022},
date = {2022-10-18},
urldate = {2022-10-18},
booktitle = {30th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2022)},
abstract = {Cloud environments require dynamic and adaptive networking policies. It is preferred to use heuristics over advanced learning algorithms in Virtual Network Functions (VNFs) in production becuase of high-performance constraints. This paper proposes Aquarius to passively yet efficiently gather observations and enable the use of machine learning to collect, infer, and supply accurate networking state information-without incurring additional signalling and management overhead. This paper illustrates the use of Aquarius with a traffic classifier, an autoscaling system, and a load balancer-and demonstrates the use of three different machine learning paradigms-unsupervised, supervised, and reinforcement learning, within Aquarius, for inferring network state. Testbed evaluations show that Aquarius increases network state visibility and brings notable performance gains with low overhead.},
keywords = {Chaire Cisco, Infrastructure for Big Data, Machine Learning, Network Management},
pubstate = {published},
tppubtype = {inproceedings}
}
Zhiyuan Yao; Zihan Ding; Thomas Heide Clausen
Multi-agent reinforcement learning for network load balancing in data center Proceedings Article
In: 31st ACM International Conference on Information and Knowledge Management (CIKM'22), 2022.
Abstract | Links | BibTeX | Tags: Chaire Cisco, Infrastructure for Big Data, load balancing
@inproceedings{nokeyh,
title = {Multi-agent reinforcement learning for network load balancing in data center},
author = {Zhiyuan Yao and Zihan Ding and Thomas Heide Clausen},
url = {https://www.researchgate.net/profile/Zhiyuan_Yao13/publication/358163217_Multi-Agent_Reinforcement_Learning_for_Network_Load_Balancing_in_Data_Center/links/62fe5fd3e3c7de4c34666311/Multi-Agent-Reinforcement-Learning-for-Network-Load-Balancing-in-Data-Center.pdf},
doi = {10.1145/3511808.3557133},
year = {2022},
date = {2022-10-17},
urldate = {2022-10-17},
booktitle = {31st ACM International Conference on Information and Knowledge Management (CIKM'22)},
abstract = {This paper presents the network load balancing problem, a challenging real-world task for multi-agent reinforcement learning (MARL) methods. Conventional heuristic solutions like Weighted-Cost Multi-Path (WCMP) and Local Shortest Queue (LSQ) are less flexible to the changing workload distributions and arrival rates, with a poor balance among multiple load balancers. The cooperative network load balancing task is formulated as a Dec-POMDP problem, which naturally induces the MARL methods. To bridge the reality gap for applying learning-based methods, all models are directly trained and evaluated on a real-world system from moderate- to large-scale setups. Experimental evaluations show that the independent and “selfish” load balancing strategies are not necessarily the globally optimal ones, while the proposed MARL solution has a superior performance over different realistic settings. Additionally, the potential difficulties of the application and deployment of MARL methods for network load balancing are analysed, which helps draw the attention of the learning and network communities to such challenges.},
keywords = {Chaire Cisco, Infrastructure for Big Data, load balancing},
pubstate = {published},
tppubtype = {inproceedings}
}
Zhiyuan Yao; Yoann Desmouceaux; Juan Antonio Cordero; Thomas Heide Clausen
HLB: Towards Load-Aware Load-Balancing Journal Article
In: IEEE/ACM Transactions on Networking, 2022, ISSN: 1558-2566.
Abstract | Links | BibTeX | Tags: Chaire Cisco, Infrastructure for Big Data, load balancing
@article{nokey,
title = {HLB: Towards Load-Aware Load-Balancing},
author = {Zhiyuan Yao and Yoann Desmouceaux and Juan Antonio Cordero and Thomas Heide Clausen},
doi = {10.1109/TNET.2022.3177163},
issn = {1558-2566},
year = {2022},
date = {2022-06-05},
urldate = {2022-06-05},
journal = {IEEE/ACM Transactions on Networking},
abstract = {The purpose of network load balancers is to optimize quality of service to the users of a set of servers - basically, to improve response times and to reducing computing resources - by properly distributing workloads. This paper proposes a distributed, application-agnostic, Hybrid Load Balancer (HLB) that - without explicit monitoring or signaling - infers server occupancies and processing speeds, which allows making optimised workload placement decisions. This approach is evaluated both through simulations and extensive experiments, including synthetic workloads and Wikipedia replays on a real-world testbed. Results show significant performance gains, in terms of both response time and system utilisation, when compared to existing load-balancing algorithms.},
keywords = {Chaire Cisco, Infrastructure for Big Data, load balancing},
pubstate = {published},
tppubtype = {article}
}
2021
Zhiyuan Yao; Yoann Desmouceaux; Mark Townsley; Thomas Heide Clausen
Towards Intelligent Load Balancing in Data Centers Proceedings Article
In: Machine Learning for Systems at 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Dec 2021, Sydney, Australia, 2021.
Abstract | Links | BibTeX | Tags: Chaire Cisco, Infrastructure for Big Data
@inproceedings{yao2021intelligent,
title = {Towards Intelligent Load Balancing in Data Centers},
author = {Zhiyuan Yao and Yoann Desmouceaux and Mark Townsley and Thomas Heide Clausen},
url = {https://www.thomasclausen.net/wp-content/uploads/2021/11/2110.15788.pdf},
year = {2021},
date = {2021-12-01},
urldate = {2021-12-01},
booktitle = {Machine Learning for Systems at 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Dec 2021, Sydney, Australia},
abstract = {Network load balancers are important components in data centers to provide scalable services. Workload distribution algorithms are based on heuristics, e.g., Equal-Cost Multi-Path (ECMP), Weighted-Cost Multi-Path (WCMP) or naive machine learning (ML) algorithms, e.g., ridge regression. Advanced ML-based approaches help achieve performance gain in different networking and system problems. However, it is challenging to apply ML algorithms on networking problems in real-life systems. It requires domain knowledge to collect features from low-latency, high-throughput, and scalable networking systems, which are dynamic and heterogenous. This paper proposes Aquarius to bridge the gap between ML and networking systems and demonstrates its usage in the context of network load balancers. This paper demonstrates its ability of conducting both offline data analysis and online model deployment in realistic systems. The results show that the ML model trained and deployed using Aquarius improves load balancing performance yet they also reveals more challenges to be resolved to apply ML for networking systems.},
keywords = {Chaire Cisco, Infrastructure for Big Data},
pubstate = {published},
tppubtype = {inproceedings}
}
Zhiyuan Yao; Zihan Ding; Thomas Heide Clausen
Reinforced Workload Distribution Fairness Proceedings Article
In: Machine Learning for Systems at 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021.
Abstract | Links | BibTeX | Tags: Chaire Cisco, Infrastructure for Big Data, load balancing
@inproceedings{yao2021reinforced,
title = {Reinforced Workload Distribution Fairness},
author = {Zhiyuan Yao and Zihan Ding and Thomas Heide Clausen},
url = {https://www.thomasclausen.net/wp-content/uploads/2021/11/2111.00008-1.pdf},
year = {2021},
date = {2021-12-01},
urldate = {2021-12-01},
booktitle = {Machine Learning for Systems at 35th Conference on Neural Information Processing Systems (NeurIPS 2021)},
abstract = {Network load balancers are central components in data centers, that distributes workloads across multiple servers and thereby contribute to offering scalable services. However, when load balancers operate in dynamic environments with limited monitoring of application server loads, they rely on heuristic algorithms that require manual configurations for fairness and performance. To alleviate that, this paper proposes a distributed asynchronous reinforcement learning mechanism to-with no active load balancer state monitoring and limited network observations-improve the fairness of the workload distribution achieved by a load balancer. The performance of proposed mechanism is evaluated and compared with stateof-the-art load balancing algorithms in a simulator, under configurations with progressively increasing complexities. Preliminary results show promise in RLbased load balancing algorithms, and identify additional challenges and future research directions, including reward function design and model scalability.},
keywords = {Chaire Cisco, Infrastructure for Big Data, load balancing},
pubstate = {published},
tppubtype = {inproceedings}
}
Carmine Rizzi; Zhiyuan Yao; Yoann Desmouceaux; Mark Townsley; Thomas Heide Clausen
Charon: Load-Aware Load-Balancing in P4 Proceedings Article
In: 1st Joint International Workshop on Network Programmability & Automation (NetPA) at 17th International Conference on Network and Service Management (CNSM 2021),, 2021.
Abstract | Links | BibTeX | Tags: Chaire Cisco, Infrastructure for Big Data, load balancing
@inproceedings{rizzi2021charon,
title = {Charon: Load-Aware Load-Balancing in P4},
author = {Carmine Rizzi and Zhiyuan Yao and Yoann Desmouceaux and Mark Townsley and Thomas Heide Clausen},
url = {https://www.thomasclausen.net/wp-content/uploads/2021/11/2110.14389.pdf},
year = {2021},
date = {2021-10-01},
urldate = {2021-01-01},
booktitle = {1st Joint International Workshop on Network Programmability & Automation (NetPA) at 17th International Conference on Network and Service Management (CNSM 2021),},
abstract = {Load-Balancers play an important role in data centers as they distribute network flows across application servers and guarantee per-connection consistency. It is hard however to make fair load balancing decisions so that all resources are efficiently occupied yet not overloaded. Tracking connection states allows to infer server load states and make informed decisions, but at the cost of additional memory space consumption. This makes it hard to implement on programmable hardware, which has constrained memory but offers line-rate performance. This paper presents Charon, a stateless load-aware load balancer that has line-rate performance implemented in P4-NetFPGA. Charon passively collects load states from application servers and employs the power-of-2-choices scheme to make data-driven load balancing decisions and improve resource utilization. Perconnection consistency is preserved statelessly by encoding server ID in a covert channel. The prototype design and implementation details are described in this paper. Simulation results show performance gains in terms of load distribution fairness, quality of service, throughput and processing latency.},
keywords = {Chaire Cisco, Infrastructure for Big Data, load balancing},
pubstate = {published},
tppubtype = {inproceedings}
}
Yoann Desmouceaux; Marcel Enguehard; Thomas Clausen
Joint Monitorless Load-Balancing and Autoscaling for Zero-Wait-Time in Data Centers Journal Article
In: IEEE Transactions on Network and Service Management, vol. 18, no. 1, pp. 672-686, 2021, ISSN: 1932-4537.
Abstract | Links | BibTeX | Tags: Chaire Cisco, Infrastructure for Big Data
@article{Desmouceaux2021,
title = {Joint Monitorless Load-Balancing and Autoscaling for Zero-Wait-Time in Data Centers},
author = {Yoann Desmouceaux and Marcel Enguehard and Thomas Clausen},
url = {https://www.thomasclausen.net/wp-content/uploads/2021/03/Joint-Monitorless-Load-Balancing-and-Autoscaling-for-Zero-Wait-Time-in-Data-Centers.pdf},
doi = {10.1109/TNSM.2020.3045059},
issn = {1932-4537},
year = {2021},
date = {2021-03-01},
journal = {IEEE Transactions on Network and Service Management},
volume = {18},
number = {1},
pages = {672-686},
abstract = {Cloud architectures achieve scaling through two main functions: (i) load-balancers, which dispatch queries among replicated virtualized application instances, and (ii) autoscalers, which automatically adjust the number of replicated instances to accommodate variations in load patterns. These functions are often provided through centralized load monitoring, incurring operational complexity. This article introduces a unified and centralized-monitoring-free architecture achieving both autoscaling and load-balancing, reducing operational overhead while increasing response time performance. Application instances are virtually ordered in a chain, and new queries are forwarded along this chain until an instance, based on its local load, accepts the query. Autoscaling is triggered by the last application instance, which inspects its average load and infers if its chain is under- or over-provisioned. An analytical model of the system is derived, and proves that the proposed technique can achieve asymptotic zero-wait time with high (and controlable) probability. This result is confirmed by extensive simulations, which highlight close-to-ideal performance in terms of both response time and resource costs.},
keywords = {Chaire Cisco, Infrastructure for Big Data},
pubstate = {published},
tppubtype = {article}
}
Mohammed Hawari; Thomas Clausen
OP4T: Bringing Advanced Network Packet Timestamping into the Field Proceedings Article
In: 2021 International Conference on Information Networking (ICOIN), pp. 137-142, 2021.
Abstract | Links | BibTeX | Tags: Chaire Cisco, Infrastructure for Big Data
@inproceedings{Hawari2021,
title = {OP4T: Bringing Advanced Network Packet Timestamping into the Field},
author = {Mohammed Hawari and Thomas Clausen},
url = {https://www.thomasclausen.net/wp-content/uploads/2021/03/paper.pdf},
doi = {10.1109/ICOIN50884.2021.9333927},
year = {2021},
date = {2021-01-13},
booktitle = {2021 International Conference on Information Networking (ICOIN)},
pages = {137-142},
abstract = {Because it is very bursty, the microsecond-scale temporal behaviour of network traffic in data-centres is chal- lenging to measure and understand. To bring observability into data-centre networks, this paper introduces the Open Platform for Programmable Precise Packet Timestamping (OP4T), a hardware architecture, targeting Field-Programmable Gateway Arrays (FPGAs), integrated into data-centre servers as a Smart Network Interface Card (SmartNIC), and flexible enough to enable advanced latency diagnosis.
In this paper, OP4T is specified, and an open-source im- plementation of that architecture is proposed, targeting the NetFPGA SUME prototyping board. By leveraging the P4 programming language, and partial reconfiguration, that open- source implementation is experimentally shown to enable in-band, precise packet timestamping, without sacrificing the achievable throughput. As an illustration, OP4T is shown to be usable to measure fine-grained properties of a software packet forwarder, e.g., packet batching.},
keywords = {Chaire Cisco, Infrastructure for Big Data},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, OP4T is specified, and an open-source im- plementation of that architecture is proposed, targeting the NetFPGA SUME prototyping board. By leveraging the P4 programming language, and partial reconfiguration, that open- source implementation is experimentally shown to enable in-band, precise packet timestamping, without sacrificing the achievable throughput. As an illustration, OP4T is shown to be usable to measure fine-grained properties of a software packet forwarder, e.g., packet batching.
2020
Mohammed Hawari; Juan Antonio Cordero; Thomas Clausen
High-Accuracy Packet Pacing on Commodity Servers for Constant-Rate Flows Journal Article
In: IEEE/ACM Transactions on Networking, pp. 1-15, 2020, ISSN: 1558-2566.
Abstract | Links | BibTeX | Tags: Infrastructure for Big Data, Packet Pacing
@article{9130915,
title = {High-Accuracy Packet Pacing on Commodity Servers for Constant-Rate Flows},
author = {Mohammed Hawari and Juan Antonio Cordero and Thomas Clausen},
url = {https://www.thomasclausen.net/wp-content/uploads/2020/08/2020-IEEE-TNET-High-Accuracy-Packet-Pacing-on-Commodity-Servers-for-Constant-Rate-Flows.pdf},
doi = {10.1109/TNET.2020.3001672},
issn = {1558-2566},
year = {2020},
date = {2020-01-01},
journal = {IEEE/ACM Transactions on Networking},
pages = {1-15},
abstract = {This addresses the problem of high-quality packet pacing for constant-rate packet consumption systems, with strict buffering limitations. A mostly-software pacing architecture is developed, which has minimal hardware requirements, satisfied by commodity servers - rendering the proposed solution easily deployable in existing (data-centre) infrastructures. Two algorithms (free-running and frequency-controlled pacing, for explicitly and implicitly indicated target rates, respectively) are specified, and formally analysed. The proposed solution, including both algorithms, is implemented, and is tested on real hardware and under real conditions. The performance of these implementations is experimentally evaluated and compared to existing mechanisms, available in general-purpose hardware. Results of both exhaustive experiments, and of an analytical modeling, indicate that the proposed approach is able to perform low-jitter packet pacing on commodity hardware, being thus suitable for constant rate transmission and consumption in media production scenarios.},
keywords = {Infrastructure for Big Data, Packet Pacing},
pubstate = {published},
tppubtype = {article}
}
2018
Yoann Desmouceaux; Mark Townsley; Thomas Clausen
Zero-Loss Virtual Machine Migration with IPv6 Segment Routing Proceedings Article
In: Proceedings 1st SR+SFC Workshop at IEEE CNSM, 2018.
Abstract | Links | BibTeX | Tags: Chaire Cisco, Infrastructure for Big Data, segment routing
@inproceedings{Desmouceaux2018e,
title = {Zero-Loss Virtual Machine Migration with IPv6 Segment Routing},
author = {Yoann Desmouceaux and Mark Townsley and Thomas Clausen},
url = {https://www.thomasclausen.net/wp-content/uploads/2019/11/Zero-Loss-Virtual-Machine-Migration-with-Segment-Routing.pdf},
year = {2018},
date = {2018-11-01},
booktitle = {Proceedings 1st SR+SFC Workshop at IEEE CNSM},
abstract = {With the development of large-scale data centers, Virtual Machine (VM) migration is a key component for resource optimization, cost reduction, and maintenance. From a network perspective, traditional VM migration mechanisms rely on the hypervisor running at the destination host advertising the new location of the VM once migration is complete. However, this creates a period of time during which the VM is not reachable, yielding packet loss.
This paper introduces a method to perform zero-loss VM migration by using IPv6 Segment Routing (SR). Rather than letting the hypervisor update a locator mapping after VM migration is complete, a logical path consisting of the source and destination hosts is pre-provisioned. Packets destined to the migrating VM are sent through this path using SR, shortly before, during, and shortly after migration – the virtual router on the source host being in charge of forwarding packets locally if the VM migration has not completed yet, or to the destination host otherwise. The proposed mechanism is implemented as a VPP plugin, and feasibility of zero-loss VM migration is demonstrated with various workloads. Evaluation shows that this yields benefits in terms of session opening latency and TCP throughput.},
keywords = {Chaire Cisco, Infrastructure for Big Data, segment routing},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper introduces a method to perform zero-loss VM migration by using IPv6 Segment Routing (SR). Rather than letting the hypervisor update a locator mapping after VM migration is complete, a logical path consisting of the source and destination hosts is pre-provisioned. Packets destined to the migrating VM are sent through this path using SR, shortly before, during, and shortly after migration – the virtual router on the source host being in charge of forwarding packets locally if the VM migration has not completed yet, or to the destination host otherwise. The proposed mechanism is implemented as a VPP plugin, and feasibility of zero-loss VM migration is demonstrated with various workloads. Evaluation shows that this yields benefits in terms of session opening latency and TCP throughput.
Benoit Pit--Claudel; Yoann Desmouceaux; Pierre Pfister; Mark Townsley; Thomas Clausen
Stateless Load-Aware Load Balancing in P4 Proceedings Article
In: 1st P4 European Workshop (P4EU), 2018.
Abstract | Links | BibTeX | Tags: Chaire Cisco, Infrastructure for Big Data, load balancing, NetFPGA, segment routing
@inproceedings{Pit--Claudel2018,
title = {Stateless Load-Aware Load Balancing in P4},
author = {Benoit Pit--Claudel and Yoann Desmouceaux and Pierre Pfister and Mark Townsley and Thomas Clausen},
url = {http://www.thomasclausen.net/en/p4eu-2018/},
year = {2018},
date = {2018-09-24},
publisher = {1st P4 European Workshop (P4EU)},
abstract = {Leveraging the performance opportunities offered by programmable hardware, stateless load-balancing architectures allowing line-rate processing are appealing. Moreover, it has been demonstrated that significantly fairer load-balancing can be achieved by an architecture that considers the actual load of application instances when dispatching connection requests. Architectures which maintain per-connection state for resiliency and/or track application load state for fairness are, however, at odds with hardware-imposed memory constraints. Thus, a desirable load-balancer for programmable hardware would be both stateless and able to dispatch queries to application instances according to their current load.
This paper presents SHELL, a stateless application-aware load-balancer combining (i) a power-of-choices scheme using IPv6 Segment Routing to dispatch new flows to a suitable application instance from among multiple candidates, and (ii) the use of a covert channel to record/report which flow was assigned to which candidate in a stateless fashion. In addition, consistent hashing versioning is used to ensure that connections are maintained to the correct application instance, using Segment Routing to “browse” through the history when needed. The stateless design of SHELL makes it suitable for hardware implementation, and this paper describes the implementation of a P4-NetFPGA prototype. A performance evaluation of this SHELL implementation demonstrates throughput and latency characteristics comparable to other stateless load-balancing implementations, while enabling application instance-load-aware dispatching and significantly increasing per-connection consistency resiliency.},
keywords = {Chaire Cisco, Infrastructure for Big Data, load balancing, NetFPGA, segment routing},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents SHELL, a stateless application-aware load-balancer combining (i) a power-of-choices scheme using IPv6 Segment Routing to dispatch new flows to a suitable application instance from among multiple candidates, and (ii) the use of a covert channel to record/report which flow was assigned to which candidate in a stateless fashion. In addition, consistent hashing versioning is used to ensure that connections are maintained to the correct application instance, using Segment Routing to “browse” through the history when needed. The stateless design of SHELL makes it suitable for hardware implementation, and this paper describes the implementation of a P4-NetFPGA prototype. A performance evaluation of this SHELL implementation demonstrates throughput and latency characteristics comparable to other stateless load-balancing implementations, while enabling application instance-load-aware dispatching and significantly increasing per-connection consistency resiliency.
Yoann Desmouceaux; Sonia Toubaline; Thomas Clausen
Flow-Aware Workload Migration in Data Centers Journal Article
In: Springer - Journal of Network and Systems Management (JONS), 2018.
Abstract | Links | BibTeX | Tags: Chaire Cisco, dc-optimization, Infrastructure for Big Data
@article{Desmouceaux2018a,
title = {Flow-Aware Workload Migration in Data Centers},
author = {Yoann Desmouceaux and Sonia Toubaline and Thomas Clausen},
url = {https://link.springer.com/epdf/10.1007/s10922-018-9452-5?author_access_token=qm_40d91CsNLlZ_vZ0tZFPe4RwlQNchNByi7wbcMAY4xSrvbLplDMLQ3AN9vWEoUIxtZAIdnOGAzJH5W3YOrbGteOLvaEXsEE1xFv66lVxTKlL40BAS25fsaLf8w1RJAvY69owHWqhJkTmAZpvdCkQ%3D%3D
http://www.thomasclausen.net/wp-content/uploads/2018/03/jons-2018.pdf},
doi = {10.1007/s10922-018-9452-5},
year = {2018},
date = {2018-03-10},
journal = {Springer - Journal of Network and Systems Management (JONS)},
abstract = {In data centers, subject to workloads with heterogeneous (and sometimes short) lifetimes, workload migration is a way of attaining a more efficient utilization of the underlying physical machines.
To not introduce performance degradation, such workload migration must take into account not only machine resources, and per-task resource requirements, but also application dependencies in terms of network communication.
This articleformat presents a workload migration model capturing all of these constraints.
A linear programming framework is developed allowing accurate representation of per-task resources requirements and inter-task network demands. Using this, a multi-objective problem is formulated to compute a re-allocation of tasks that (i) maximizes the total inter-task throughput, while (ii) minimizing the cost incurred by migration and (iii) allocating the maximum number of new tasks.
A baseline algorithm, solving this multi-objective problem using the $epsilon$-constraint method is proposed, in order to generate the set of Pareto-optimal solutions. As this algorithm is compute-intensive for large topologies, a heuristic, which computes an approximation of the Pareto front, is then developed, and evaluated on different topologies and with different machine load factors. These evaluations show that the heuristic can provide close-to-optimal solutions, while reducing the solving time by one to two order of magnitudes.},
keywords = {Chaire Cisco, dc-optimization, Infrastructure for Big Data},
pubstate = {published},
tppubtype = {article}
}
To not introduce performance degradation, such workload migration must take into account not only machine resources, and per-task resource requirements, but also application dependencies in terms of network communication.
This articleformat presents a workload migration model capturing all of these constraints.
A linear programming framework is developed allowing accurate representation of per-task resources requirements and inter-task network demands. Using this, a multi-objective problem is formulated to compute a re-allocation of tasks that (i) maximizes the total inter-task throughput, while (ii) minimizing the cost incurred by migration and (iii) allocating the maximum number of new tasks.
A baseline algorithm, solving this multi-objective problem using the $epsilon$-constraint method is proposed, in order to generate the set of Pareto-optimal solutions. As this algorithm is compute-intensive for large topologies, a heuristic, which computes an approximation of the Pareto front, is then developed, and evaluated on different topologies and with different machine load factors. These evaluations show that the heuristic can provide close-to-optimal solutions, while reducing the solving time by one to two order of magnitudes.
Yoann Desmouceaux; Thomas Clausen; Juan Antonio Cordero; Mark Townsley
Reliable Multicast with B.I.E.R. Journal Article
In: IEEE/KICS Journal of Communications and Networks (JCN), vol. 20, no. 2, pp. 182-197, 2018.
Abstract | Links | BibTeX | Tags: B.I.E.R., Chaire Cisco, Infrastructure for Big Data, Performance Evaluation, Reliable Content Distribution
@article{Desmouceaux2018b,
title = {Reliable Multicast with B.I.E.R.},
author = {Yoann Desmouceaux and Thomas Clausen and Juan Antonio Cordero and Mark Townsley},
url = {http://www.thomasclausen.net/wp-content/uploads/2018/03/jcn-2018.pdf},
year = {2018},
date = {2018-03-01},
journal = {IEEE/KICS Journal of Communications and Networks (JCN)},
volume = {20},
number = {2},
pages = {182-197},
abstract = {Inter-network multicast protocols, which build and maintain multicast trees, incur both explicit protocol signalling, and maintenance of state in intermediate routers in the network. B.I.E.R. (Bit-Indexed Explicit Replication) is a technique which can provide a multicast service yet removes such complexities: in- termediate routers are unencumbered by group management, and no per-group state is to be maintained.
This paper explores the use of B.I.E.R. as a basis for develop- ing an efficient and reliable multicast mechanism, where redun- dant traffic is avoided, essential traffic is forwarded along shortest paths, and no per-flow state is required in intermediate routers. Evaluated by way of both an analytical model and network sim- ulation both in generic and in real network topologies with vary- ing background traffic loads, the proposed B.I.E.R.-based reliable multicast mechanism exhibits attractive performance attributes: it attains delivery success rates as high as any other reliable multicast service, but with significantly better link utilisation and no per-flow or per-group state in intermediate routers of the network.},
keywords = {B.I.E.R., Chaire Cisco, Infrastructure for Big Data, Performance Evaluation, Reliable Content Distribution},
pubstate = {published},
tppubtype = {article}
}
This paper explores the use of B.I.E.R. as a basis for develop- ing an efficient and reliable multicast mechanism, where redun- dant traffic is avoided, essential traffic is forwarded along shortest paths, and no per-flow state is required in intermediate routers. Evaluated by way of both an analytical model and network sim- ulation both in generic and in real network topologies with vary- ing background traffic loads, the proposed B.I.E.R.-based reliable multicast mechanism exhibits attractive performance attributes: it attains delivery success rates as high as any other reliable multicast service, but with significantly better link utilisation and no per-flow or per-group state in intermediate routers of the network.
2017
Yoann Desmouceaux; Pierre Pfister; Jerome Tollet; Mark Townsley; Thomas Clausen
SRLB: The Power of Choices in Load Balancing with Segment Routing Proceedings Article
In: In Proceedings of the 37th IEEE International Conference on Distributed Computing Systems (ICDCS), 2017.
Abstract | Links | BibTeX | Tags: Chaire Cisco, dc-optimization, Infrastructure for Big Data, load balancing, segment routing
@inproceedings{Desmouceaux2017b,
title = {SRLB: The Power of Choices in Load Balancing with Segment Routing},
author = {Yoann Desmouceaux and Pierre Pfister and Jerome Tollet and Mark Townsley and Thomas Clausen},
url = {http://www.thomasclausen.net/wp-content/uploads/2017/05/camera-ready-ieeepdfexpress.pdf},
year = {2017},
date = {2017-06-05},
booktitle = {In Proceedings of the 37th IEEE International Conference on Distributed Computing Systems (ICDCS)},
abstract = {Network load-balancers generally either do not take application state into account, or do so at the cost of a central- ized monitoring system. This paper introduces a load-balancer running exclusively within the IP forwarding plane, i.e. in an application protocol agnostic fashion – yet which still provides application-awareness and makes real-time, decentralized deci- sions. To that end, IPv6 Segment Routing is used to direct data packets from a new flow through a chain of candidate servers, until one decides to accept the connection, based on its local state. This way, applications themselves naturally decide on how to share incoming connections, while incurring minimal network overhead, and no out-of-band signaling.
Tests on different workloads – including realistic workloads such as replaying actual Wikipedia access traffic towards a set of replica Wikipedia instances – show significant performance benefits, in terms of shorter response times, when compared to a traditional random load-balancer.},
keywords = {Chaire Cisco, dc-optimization, Infrastructure for Big Data, load balancing, segment routing},
pubstate = {published},
tppubtype = {inproceedings}
}
Tests on different workloads – including realistic workloads such as replaying actual Wikipedia access traffic towards a set of replica Wikipedia instances – show significant performance benefits, in terms of shorter response times, when compared to a traditional random load-balancer.