B.I.E.R. Broadcast Chaire Cisco Constrained Networks dc-optimization DNCP Homenet Infrastructure for Big Data Internet Broadcast IoT Linux LLN load balancing LoRA LPWAN MESH Modeling Multicast Network Greedy Applications Performance Performance Evaluation Reliable Content Distribution Scalability segment routing Sensor Networks
2018 |
Desmouceaux, Yoann; Toubaline, Sonia; Clausen, Thomas Flow-Aware Workload Migration in Data Centers Journal Article 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}, 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} } 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. |
2017 |
Desmouceaux, Yoann; Pfister, Pierre; Tollet, Jerome; Townsley, Mark W; Clausen, Thomas SRLB: The Power of Choices in Load Balancing with Segment Routing Inproceedings 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 W. 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} } 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. |