Cloud computing has revolutionised the development and deployment of applications by running them cost-effectively in remote data centres. With the increasing need for mobility and micro-services, particularly with the emerging 5G mobile broadband networks, there is also a strong demand for mobile edge computing (MEC). It enables applications to run in small cloud systems in close proximity to the user in order to minimise latencies. Both cloud computing and MEC have their own advantages and disadvantages. Combining these two computing paradigms in a unified multi-cloud platform has the potential of obtaining the best of both worlds. However, a comprehensive study is needed to evaluate the performance gains and the overheads imposed by this combination to real-world cloud applications. In this paper, we introduce a baseline performance evaluation in order to identify the fallacies and pitfalls of combining multiple cloud systems and MEC into a unified MEC-multi-cloud platform. For this purpose, we analyze the basic, application-independent performance metrics of average round-trip time (RTT) and average application payload throughput in a setup consisting of two private and one public cloud systems. This baseline performance analysis confirms the feasibility of MEC-multi-cloud and provides guidelines for designing an autonomic resource provisioning solution in terms of an extension proposed to our existing Melodic middleware platform for multi-cloud applications.

Summary 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.

Clouds offer flexible and economically attractive compute and storage solutions for enterprises. However, the effectiveness of cloud computing for high-performance computing (HPC) systems still remains questionable. When clouds are deployed on lossless interconnection networks, like InfiniBand (IB), challenges related to load-balancing, low-overhead virtualization, and performance isolation hinder full potential utilization of the underlying interconnect. Moreover, cloud data centers incorporate a highly dynamic environment rendering static network reconfigurations, typically used in IB systems, infeasible. In this paper, we present a framework for a self-adaptive network architecture for HPC clouds based on lossless interconnection networks, demonstrated by means of our implemented IB prototype. Our solution, based on a feedback control and optimization loop, enables the lossless HPC network to dynamically adapt to the varying traffic patterns, current resource availability, workload distributions, and also in accordance with the service provider-defined policies. Furthermore, we present IBAdapt, a simplified ruled-based language for the service providers to specify adaptation strategies used by the framework. Our developed self-adaptive IB network prototype is demonstrated using state-of-the-art industry software. The results obtained on a test cluster demonstrate the feasibility and effectiveness of the framework when it comes to improving Quality-of-Service compliance in HPC clouds.

InfiniBand (IB) is a widely used network interconnect for modern high-performance computing systems. In large IB fabrics, isolation of nodes is provided through partitioning. The routing algorithm, however, is unaware of these partitions in the network, Traffic flows belonging to different partitions might share links inside the network fabric. This sharing of intermediate links creates interference, which is particularly critical to avoid in multi-tenant environments like a cloud. In such systems, each tenant should experience predictable network performance, unaffected by the workload of other tenants. In addition, using current routing schemes, routes crossing partition boundaries are considered when distributing routes onto links in the network, despite the fact that these routes will never be used. The result is degraded load-balancing. In this paper, we present a novel partition-aware fat-tree routing algorithm, pFTree. The pFTree algorithm utilizes several mechanisms to provide network-wide isolation of partitions belonging to different tenant groups. Given the available network resources, pFTree starts by isolating partitions at the physical link level, and then moves on to utilize virtual lanes, if needed. Our experiments and simulations show that pFTree is able to significantly reduce the affect of inter-partition interference without any additional functional overhead. Furthermore, pFTree also provides improved load-balancing over the de facto standard IB fat-tree routing algorithm.

As the size of high-performance computing systems grows, the number of events requiring a network reconfiguration, as well as the complexity of each reconfiguration, is likely to increase. In large systems, the probability of component failure is high. At the same time, with more network components, ensuring high utilization of network resources becomes challenging. Reconfiguration in interconnection networks, like InfiniBand (IB), typically involves computation and distribution of a new set of routes in order to maintain connectivity and performance. In general, current routing algorithms do not consider the existing routes in a network when calculating new ones. Such configuration-oblivious routing might result in substantial modifications to the existing paths, and the reconfiguration becomes costly as it potentially involves a large number of source-destination pairs. In this paper, we propose a novel routing algorithm for IB based fat-tree topologies, SlimUpdate. SlimUpdate employs techniques to preserve existing forwarding entries in switches to ensure a minimal routing update, without any performance penalty, and with minimal computational overhead. We present an implementation of SlimUpdate in OpenSM, and compare it with the current de facto fat-tree routing algorithm. Our experiments and simulations show a decrease of up to 80% in the number of total path modifications when using SlimUpdate routing, while achieving similar or even better performance than the fat-tree routing in most reconfiguration scenarios.

Infini Band (IB) has become a popular network interconnect for high performance computing (HPC) systems. Many of the large IB-based HPC systems use some variant of the fat-tree topology to take advantage of the useful properties fat-trees offer. The fat-tree routing algorithm is one of the most efficient deterministic routing algorithms for fat-tree topologies. The algorithm ensures that the number of routes assigned to each link are balanced across the fabric. However, one problem with its load-balancing technique is that it assumes uniform traffic distribution in the network. When routes towards nodes that mainly consume large amount of data are assigned to share links in the fabric while alternative links are underutilized, sub-optimal network throughput is obtained. Also, as the fat tree algorithm routes nodes according to the indexing order, the performance may differ for two systems cabled in the exact same way. In this paper, we propose wFatTree, a novel fat-tree routing algorithm, which considers node traffic characteristics to balance load across the network links more evenly, and with predictable network performance. Our experiments and simulations show an improvement of up to 60% in total network throughput on large fat-tree installations when using wFatTree routing. Furthermore, wFatTree can also be used to prioritize traffic flowing towards the critical nodes in the network.