In the context of time series data, a contextual anomaly is considered an event or action that causes a deviation in the data values from the norm. This deviation may appear normal if we do not consider the timestamp associated with it. Detecting contextual anomalies in real-world time series data poses a challenge because it often requires domain knowledge and an understanding of the surrounding context. In this paper, we propose UoCAD, an online contextual anomaly detection approach for multivariate time series data. UoCAD employs a sliding window method to (re)train a Bi-LSTM model in an online manner. UoCAD uses the model to predict the upcoming value for each variable/feature and calculates the model's prediction error value for each feature. To adapt to minor pattern changes, UoCAD employs a double-check approach without immediately triggering an anomaly notification. Two criteria, individual and majority, are explored for anomaly detection. The individual criterion identifies an anomaly if any feature is detected as anomalous, while the majority criterion triggers an anomaly when more than half of the features are identified as anomalous. We evaluate UoCAD using an air quality dataset containing a contextual anomaly. The results show UoCAD's effectiveness in detecting the contextual anomaly across different sliding window sizes but with varying false positives and detection time consumption.

Interconnection networks are the communication backbone of modern high-performance computing systems and an optimised interconnection network is crucial for the performance and utilisation of the system as a whole. One element of the interconnection network is the routing algorithm, which directly influences how we are able to utilise the physical network topology. InfiniBand is one of the most common network architectures used in high-performance computing and traditionally it only supported static routing. For multi-path networks such as Fat-trees, static routing is inefficient because it cannot balance traffic in real-time nor utilise multiple paths efficiently under adversarial traffic. This again potentially leads to unnecessary contention and an underutilised network, which has led to numerous proposals on how to avoid this by using adaptive routing. Adaptive routing has recently been introduced in InfiniBand and in this paper we evaluate to what extent the expected benefits of adaptive routing is true for InfiniBand. Through a set of experiments on HDR InfiniBand equipment we describe the basic behaviour of adaptive routing in InfiniBand, its benefits in Fat tree topologies and the unfortunate side effects related to unfairness that adaptive routing in general might introduce, including such phenomena as the reverse parking lot problem and congestion spreading.

Providing a lightweight self-adaptive approach that does not need offline training in advance and meanwhile is able to detect anomalies in real time could be highly beneficial. Such an approach could be immediately applied and deployed on any commodity machine to provide timely anomaly alerts. To facilitate such an approach, this paper introduces SALAD, which is a Self-Adaptive Lightweight Anomaly Detection approach based on a special type of recurrent neural networks called Long Short-Term Memory (LSTM). Instead of using offline training, SALAD converts a target time series into a series of average absolute relative error (AARE) values on the fly and predicts an AARE value for every upcoming data point based on short-term historical AARE values. If the difference between a calculated AARE value and its corresponding forecast AARE value is higher than a self-adaptive detection threshold, the corresponding data point is considered anomalous. Otherwise, the data point is considered normal. Experiments based on a real-world time series dataset demonstrates that SALAD outperforms five other state-of-the-art anomaly detection approaches in terms of detection accuracy. In addition, the results also show that SALAD is lightweight and can be deployed on a commodity machine.

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.

Summary Exascale computing systems are being built with thousands of nodes. The high number of components of these systems significantly increases the probability of failure. A key component for them is the interconnection network. If failures occur in the interconnection network, they may isolate a large fraction of the machine. For this reason, an efficient fault‐tolerant mechanism is needed to keep the system interconnected, even in the presence of faults. A recently proposed topology for these large systems is the hybridk‐aryn‐directs‐indirect family that provides optimal performance and connectivity at a reduced hardware cost. This paper presents a fault‐tolerant routing methodology for thek‐aryn‐directs‐indirect topology that degrades performance gracefully in presence of faults and tolerates a large number of faults without disabling any healthy computing node. In order to tolerate network failures, the methodology uses a simple mechanism. For any source‐destination pair, if necessary, packets are forwarded to the destination node through a set of intermediate nodes (without being ejected from the network) with the aim of circumventing faults. The evaluation results shows that the proposed methodology tolerates a large number of faults. For instance, it is able to tolerate more than 99.5% of fault combinations when there are 10 faults in a 3‐D network with 1000 nodes using only 1 intermediate node and more than 99.98% if 2 intermediate nodes are used. Furthermore, the methodology offers a gracious performance degradation. As an example, performance degrades only by 1% for a 2‐D network with 1024 nodes and 1% faulty links.

Reconfiguration of high performance lossless interconnection networks is a cumbersome and time-consuming task. For that reason reconfiguration in large networks are typically limited to situations where it is absolutely necessary, for instance when severe faults occur. On the contrary, due to the shared and dynamic nature of modern cloud infrastructures, performance-driven reconfigurations are necessary to ensure efficient utilization of resources. In this work we present a scheme that allows for fast reconfigurations by limiting the task to subparts of the network that can benefit from a local reconfiguration. Moreover, our method is able to use different routing algorithms for different sub-parts within the same subnet. We also present a Fat-Tree routing algorithm that reconfigures a network given a user-provided node ordering. Hardware experiments and large scale simulation results show that we are able to significantly reduce reconfiguration times from 50% to as much as 98.7% for very large topologies, while improving performance.

In large InfiniBand subnets the Subnet Manager (SM) is a potential bottleneck. When an InfiniBand subnet grows in size, the number of paths between hosts increases polynomials and the SM may not be able to serve the network in a timely manner when many concurrent path resolution requests are received. This scalability challenge is further amplified in a dynamic virtualized cloud environment. When a Virtual Machine (VM) with InfiniBand interconnect live migrates, the VM addresses change. These address changes result in additional load to the SM as communicating peers send Subnet Administration (SA) path record queries to the SM to resolve new path characteristics. In this paper we benchmark OpenSM to empirically demonstrate the SM scalability problems. Then we show that our novel SA Path Record Query caching scheme significantly reduces the load towards the SM. In particular, we show by using the Reliable Datagram Socket protocol that only a single initial SA path query is needed per communicating peer, independent of any subsequent (re)connection attempts.

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.

To meet the demands of the Exascale era and facilitate Big Data analytics in the cloud while maintaining flexibility, cloud providers will have to offer efficient virtualized High Performance Computing clusters in a pay-as-you-go model. As a consequence, high performance network interconnect solutions, like InfiniBand (IB), will be beneficial. Currently, the only way to provide IB connectivity on Virtual Machines (VMs) is by utilizing direct device assignment. At the same time to be scalable, Single-Root I/O Virtualization (SR-IOV) is used. However, the current SR-IOV model employed by IB adapters is a Shared Port implementation with limited flexibility, as it does not allow transparent virtualization and live-migration of VMs. In this paper, we explore an alternative SR-IOV model for IB, the virtual switch (vSwitch), and propose and analyze two vSwitch implementations with different scalability characteristics. Furthermore, as network reconfiguration time is critical to make live-migration a practical option, we accompany our proposed architecture with a scalable and topology agnostic dynamic reconfiguration method, implemented and tested using OpenSM. Our results show that we are able to significantly reduce the reconfiguration time as route recalculations are no longer needed, and in large IB subnets, for certain scenarios, the number of reconfiguration subnet management packets (SMPs) sent is reduced from several hundred thousand down to a single one.

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.

Interconnection networks are key components in high-performance computing (HPC) systems, their performance having a strong influence on the overall system one. However, at high load, congestion and its negative effects (e.g., Head-of-line blocking) threaten the performance of the network, and so the one of the entire system. Congestion control (CC) is crucial to ensure an efficient utilization of the interconnection network during congestion situations. As one major trend is to reduce the effective wiring in interconnection networks to reduce cost and power consumption, the network will operate very close to its capacity. Thus, congestion control becomes essential. Existing CC techniques can be divided into two general approaches. One is to throttle traffic injection at the sources that contribute to congestion, and the other is to isolate the congested traffic in specially designated resources. However, both approaches have different, but non-overlapping weaknesses: injection throttling techniques have a slow reaction against congestion, while isolating traffic in special resources may lead the system to run out of those resources. In this paper we propose EcoCC, a new Efficient and Cost-Effective CC technique, that combines injection throttling and congested-flow isolation to minimize their respective drawbacks and maximize overall system performance. This new strategy is suitable for current commercial switch architectures, where it could be implemented without requiring significant complexity. Experimental results, using simulations under synthetic and real trace-based traffic patterns, show that this technique improves by up to 55 percent over some of the most successful congestion control techniques.

Over the last decade, the Internet has grown at a tremendous speed in both size and complexity. Nowadays, a large number of important services – for instance e-commerce, healthcare and many others – depend on the availability of the underlying network. Clearly, service interruptions due to network problems may have a severe impact. On the long way towards the Future Internet, the complexity will grow even further. Therefore, new ideas and concepts must be evaluated thoroughly, and particularly in realistic, real-world Internet scenarios, before they can be deployed for production networks. For this purpose, various testbeds – for instance PlanetLab, GpENI or G-Lab – have been established and are intensively used for research. However, all of these testbeds lack the support for so-called multi-homing. Multi-homing denotes the connection of a site to multiple Internet service providers, in order to achieve redundancy. Clearly, with the need for network availability, there is a steadily growing demand for multi-homing. The idea of the NorNet Core project is to establish a Future Internet research testbed with multi-homed sites, in order to allow researchers to perform experiments with multi-homed systems. Particular use cases for this testbed include realistic experiments in the areas of multi-path routing, load balancing, multi-path transport protocols, overlay networks and network resilience. In this paper, we introduce the NorNet Core testbed as well as its architecture.

In a loss less interconnection network, network congestion needs to be detected and resolved to ensure high performance and good utilization of network resources at high network load. If no countermeasure is taken, congestion at a node in the network will stimulate the growth of a congestion tree that not only affects contributors to congestion, but also other traffic flows in the network. Left untouched, the congestion tree will block traffic flows, lead to underutilization of network resources and result in a severe drop in network performance. The InfiniBand standard specifies a congestion control (CC) mechanism to detect and resolve congestion before a congestion tree is able to grow and, by that, hamper the network performance. The InfiniBand CC mechanism includes a rich set of parameters that can be tuned in order to achieve effective CC. Even though it has been shown that the CC mechanism, properly tuned, is able to improve both throughput and fairness in an interconnection network, it has been questioned whether the mechanism is fast enough to keep up with dynamic network traffic, and if a given set of parameter values for a topology is robust when it comes to different traffic patterns, or if the parameters need to be tuned depending on the applications in use. In this paper we address both these questions. Using the three-stage fat-tree topology from the Sun Data center InfiniBand Switch 648 as a basis, and a simulator tuned against CC capable InfiniBand hardware, we conduct a systematic study of the efficiency of the InfiniBand CC mechanism as the network traffic becomes increasingly more dynamic. Our studies show that the InfiniBand CC, even when using a single set of parameter values, performs very well as the traffic patterns becomes increasingly more dynamic, outperforming a network without CC in all cases. Our results show throughput increases varying from a few percent, to a seventeen-fold increase.

Existing fat-tree routing algorithms fully exploit the path diversity of a fat-tree topology in the context of compute node traffic, but they lack support for deadlock-free and fully connected switch-to-switch communication. Such support is crucial for efficient system management, for example, in InfiniBand (IB) systems. With the general increase in system management capabilities found in modern InfiniBand switches, the lack of deadlock-free switch-to-switch communication is a problem for fat-tree-based IB installations because management traffic might cause routing deadlocks that bring the whole system down. This lack of deadlock-free communication affects all system management and diagnostic tools using LID routing. In this paper, we propose the sFtree routing algorithm that guarantees deadlock-free and fully connected switch-to-switch communication in fat-trees while maintaining the properties of the current fat-tree algorithm. We prove that the algorithm is deadlock free and we implement it in OpenSM for evaluation. We evaluate the performance of the sFtree algorithm experimentally on a small cluster and we do a large-scale evaluation through simulations. The results confirm that the sFtree routing algorithm is deadlock-free and show that the impact of switch-to-switch management traffic on the end-node traffic is negligible.

In loss less interconnection networks such as InfiniBand, congestion control (CC) can be an effective mechanism to achieve high performance and good utilization of network resources. The InfiniBand standard describes CC functionality for detecting and resolving congestion, but the design decisions on how to implement this functionallity is left to the hardware designer. One must be cautious when making these design decisions not to introduce fairness problems, as our study shows. In this paper we study the relationship between congestion control, switch arbitration, and fairness. Specifically, we look at fairness among different traffic flows arriving at a hot spot switch on different input ports, as CC is turned on. In addition we study the fairness among traffic flows at a switch where some flows are exclusive users of their input ports while other flows are sharing an input port (the parking lot problem). Our results show that the implementation of congestion control in a switch is vulnerable to unfairness if care is not taken. In detail, we found that a threshold hysteresis of more than one MTU is needed to resolve arbitration unfairness. Furthermore, to fully solve the parking lot problem, proper configuration of the CC parameters are required.

Existing congestion control mechanisms in interconnects can be divided into two general approaches. One is to throttle traffic injection at the sources that contribute to congestion, and the other is to isolate the congested traffic in specially designated resources. These two approaches have different, but non-overlapping weaknesses. In this paper we present in detail a method that combines injection throttling and congested-flow isolation. Through simulation studies we first demonstrate the respective flaws of the injection throttling and of flow isolation. Thereafter we show that our combined method extracts the best of both approaches in the sense that it gives fast reaction to congestion, it is scalable and it has good fairness properties with respect to the congested flows.

In lossless interconnection networks congestion control (CC) can be an effective mechanism to achieve high performance and good utilization of network resources. Without CC, congestion in one node may grow into a congestion tree that can degrade the performance severely. This degradation can affect not only contributors to the congestion, but also throttles innocent traffic flows in the network. The InfiniBand standard describes CC functionality for detecting and resolving congestion. The InfiniBand CC concept is rich in the way that it specifies a set of parameters that can be tuned in order to achieve effective CC. There is, however, limited experience with the InfiniBand CC mechanism. To the best of our knowledge, only a few simulation studies exist. Recently, InfiniBand CC has been implemented in hardware, and in this paper we present the first experiences with such equipment. We show that the implemented InfiniBand CC mechanism effectively resolves congestion and improves fairness by solving the parking lot problem, if the CC parameters are appropriately set. By conducting extensive testing on a selection of the CC parameters, we have explored the parameter space and found a subset of parameter values that leads to efficient CC for our test scenarios. Furthermore, we show that the InfiniBand CC increases the performance of the well known HPC Challenge benchmark in a congested network.