“…Unlike the original Fat‐Tree routing algorithm that does not consider the vSwitches or VMs in the topology, vSwitchFatTree marks vSwitches and routes each VM independently of the other VMs connected to a vSwitch.To cater non‐uniform VM distribution among the vSwitches, each VM is assigned a weight that corresponds to the proportion of the link it is allocated on the vSwitch. The weight is used in maintaining port counters for balancing path distribution in the Fat‐Tree.The scheme also enables generalized weighted Fat‐Tree routing where each VM can be assigned a weight based on its traffic profile or role priority in the network …”
Section: Routing Strategies For Vswitch‐based Subnetsmentioning
confidence: 68%
“…41 Consider a virtualized Fat-Tree topology with four end nodes (vSwitches), as shown in Figure 2A. The scheme also enables generalized weighted Fat-Tree routing where each VM can be assigned a weight based on its traffic profile or role priority in the network.…”
Section: Weighted Fat-tree Routing Algorithm For Vswitchmentioning
confidence: 99%
“…The scheme also enables generalized weighted Fat-Tree routing where each VM can be assigned a weight based on its traffic profile or role priority in the network. 41 Consider a virtualized Fat-Tree topology with four end nodes (vSwitches), as shown in Figure 2A. Each of the vSwitches connected to the leaf switch L1, vSw1, and vSw2 have two running VMs shown with their identifier in the Figure. The second leaf switch, L2, has vSw3 with three VMs, while one VM is running on the host vSwitch vSw4.…”
Section: Weighted Fat-tree Routing Algorithm For Vswitchmentioning
To meet the demands of communication-intensive workloads in the cloud, virtual machines (VMs) should utilize low overhead network communication paradigms. In general, such paradigms enable VMs to directly communicate with the hardware by means of a passthrough technology like Single-Root I/O Virtualization (SR-IOV). However, when passthrough-based virtualization is coupled with lossless interconnection networks, live migrations introduce scalability challenges due to the substantial network reconfiguration overhead. With these challenges in mind, we proposed a virtual switch (vSwitch) SR-IOV architecture for InfiniBand in our previous work titled "Towards the InfiniBand SR-IOV vSwitch Architecture". In this paper, we first suggest solutions to rectify the space-domain scalability issues that are present in vSwitch-enabled subnets as a result of the VMs using dedicated layer-two addresses. Then, we discuss routing strategies for virtualized environments using vSwitches and present a routing algorithm for Fat-Trees. We also present a reconfiguration method that minimizes imposed reconfiguration overhead on Fat-Trees.We perform an extensive evaluation of our prototype algorithms, and as vSwitch-enabled hardware does not yet exist, we deduce from empirical observations by emulating vSwitches with existing hardware, as well as large-scale simulations. Our results show significant reduction in the reconfiguration times as route recalculations can be eliminated, and for certain scenarios, the number of reconfiguration subnet management packets sent to switches is reduced from several hundred thousand down to a single one without degrading the routing quality.
“…Unlike the original Fat‐Tree routing algorithm that does not consider the vSwitches or VMs in the topology, vSwitchFatTree marks vSwitches and routes each VM independently of the other VMs connected to a vSwitch.To cater non‐uniform VM distribution among the vSwitches, each VM is assigned a weight that corresponds to the proportion of the link it is allocated on the vSwitch. The weight is used in maintaining port counters for balancing path distribution in the Fat‐Tree.The scheme also enables generalized weighted Fat‐Tree routing where each VM can be assigned a weight based on its traffic profile or role priority in the network …”
Section: Routing Strategies For Vswitch‐based Subnetsmentioning
confidence: 68%
“…41 Consider a virtualized Fat-Tree topology with four end nodes (vSwitches), as shown in Figure 2A. The scheme also enables generalized weighted Fat-Tree routing where each VM can be assigned a weight based on its traffic profile or role priority in the network.…”
Section: Weighted Fat-tree Routing Algorithm For Vswitchmentioning
confidence: 99%
“…The scheme also enables generalized weighted Fat-Tree routing where each VM can be assigned a weight based on its traffic profile or role priority in the network. 41 Consider a virtualized Fat-Tree topology with four end nodes (vSwitches), as shown in Figure 2A. Each of the vSwitches connected to the leaf switch L1, vSw1, and vSw2 have two running VMs shown with their identifier in the Figure. The second leaf switch, L2, has vSw3 with three VMs, while one VM is running on the host vSwitch vSw4.…”
Section: Weighted Fat-tree Routing Algorithm For Vswitchmentioning
To meet the demands of communication-intensive workloads in the cloud, virtual machines (VMs) should utilize low overhead network communication paradigms. In general, such paradigms enable VMs to directly communicate with the hardware by means of a passthrough technology like Single-Root I/O Virtualization (SR-IOV). However, when passthrough-based virtualization is coupled with lossless interconnection networks, live migrations introduce scalability challenges due to the substantial network reconfiguration overhead. With these challenges in mind, we proposed a virtual switch (vSwitch) SR-IOV architecture for InfiniBand in our previous work titled "Towards the InfiniBand SR-IOV vSwitch Architecture". In this paper, we first suggest solutions to rectify the space-domain scalability issues that are present in vSwitch-enabled subnets as a result of the VMs using dedicated layer-two addresses. Then, we discuss routing strategies for virtualized environments using vSwitches and present a routing algorithm for Fat-Trees. We also present a reconfiguration method that minimizes imposed reconfiguration overhead on Fat-Trees.We perform an extensive evaluation of our prototype algorithms, and as vSwitch-enabled hardware does not yet exist, we deduce from empirical observations by emulating vSwitches with existing hardware, as well as large-scale simulations. Our results show significant reduction in the reconfiguration times as route recalculations can be eliminated, and for certain scenarios, the number of reconfiguration subnet management packets sent to switches is reduced from several hundred thousand down to a single one without degrading the routing quality.
“…Arithmetic routing is commonly used in SW to fill the routing tables of the switches of table-based technologies (see, e.g., [22] which generates routes arithmetically and then embed them in the routing tables of an Infiniband IN). There also exist more advanced strategies (also for Infiniband) that take into consideration the congestion of the links by storing this information in the routing tables together with the destination address to perform routing decisions [23]. More recently, the Bull EXascale Interconnect (BXI) [10] has followed a similar approach.…”
Power consumption is the main hurdle in the race for designing Exascale-capable computing systems which would require deploying millions of computing elements. While this problem is being addressed by designing increasingly more power-efficient processing subsystems, little effort has been put on reducing the power consumption of the interconnection network. This is precisely the objective of this work, in which we study the benefits, in terms of both area and power, of avoiding costly and power-hungry CAM-based routing tables deep-rooted in all current networking technologies. We present our custom-made, FPGAbased router based on a simple, arithmetic routing engine which is shown to be much more power-and area-efficient than even a relatively small 2K-entry routing table which requires as much area and one order of magnitude more power than our router.
“…Another valid reason to trigger a network reconfiguration is in the case where a routing algorithm is traffic-aware, i.e. the routing algorithm is taking into account the current traffic characteristics to improve performance [109]. If the traffic pattern changes, it is desired to reconfigure the network.…”
The advent of the Internet of Things, sensor and social networks, to mention just a few examples, all contribute towards the solid establishment of the Big Data era. High Performance Computing (HPC) becomes necessary for the efficient processing of the massive amounts of data our society generates, and cloud computing is a critical component to deliver this processing power to a broader audience that cannot afford to acquire and maintain such complex computing systems themselves. However, HPC specific technology and performance is not yet apt to be delivered efficiently over highly flexible and dynamic environments, as typically are the virtualized cloud infrastructures.In this thesis, we address challenges that arise in high performance dynamic cloud environments, that are equipped with HPC specific technology, in the context of networking and virtualization. We use InfiniBand, a high performance lossless interconnection network as the basis of our research, and first show that lossless networks pose prime challenges when the nature of the infrastructure is very dynamic, i.e. exhibits continuous changes. Then we propose a network I/O virtualization architecture, the InfiniBand vSwitch architecture, that can make lossless network technologies more favorable in the cloud. Moreover, we propose different network reconfiguration methods to enable performance-driven reconfigurations in very large network topologies that are commonly found in data centers. Performance-driven reconfigurations are frequently needed to adapt to unpredictable workload changes resulting from the shared and on-demand nature of a cloud platform, or when cloud providers employ live migration of virtual machines to optimize the resource usage of their infrastructure. Last but not least, we propose a new Quality-of-Service metric, called delay, to capture the directly observable service degradation in consolidated cloud environments. We suggest that the delay can be used as a direct service level agreement metric between cloud providers and cloud tenants.
AcknowledgementsFirst, I would like to express my gratitude to my three supervisors; Ernst Gunnar Gran, Tor Skeie and Kyrre Begnum. Without their positiveness, supervision, suggestions and encouragement, my PhD journey would not have been so fruitful and enjoyable during both the good and hard times. Then I would like to thank Bjørn Dag Johnsen from Oracle Norway, our closest collaborate in the ERAC project, the project that mainly funded this thesis, for his enthusiastic attitude and participation in the important discussions that shaped the direction of my work and introduced industrial relevance. My appreciation goes as well to Feroz Zahid for being the most easygoing colleague whom I could imagine sharing office with, and a brilliant and ambitious associate, and Sven-Arne Reinemo for his supervision during my early days.A special thank you must be directed to Hårek Haugerud, Anis Yazidi, Hugo Lewi Hammer, Laurence Marie Anna Habib and the whole NETSYS group at the Oslo and Akershus...
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