Pasi Liljeberg

Online Reconfigurable Self-Timed Links for Fault Tolerant NoC

We propose link structures for NoC that have properties for tolerating efficiently transient, intermittent, and permanent errors. This is a necessary step to be taken in order to implement reliable systems in future nanoscale technologies. The protection against transient errors is realized using Hamming coding and interleaving for error detection and retransmission as the recovery method. We introduce two approaches for tackling the intermittent and permanent errors. In the first approach, spare wires are introduced together with reconfiguration circuitry. The other approach uses time redundancy, the transmission is split into two parts, where the data is doubled. In both structures the presence of permanent or intermittent errors is monitored by analyzing previous error syndromes. The links are based on self-timed signaling in which the handshake signals are protected using triple modular redundancy. We present the structures, operation, and designs for the different components of the links. The fault tolerance properties are analyzed using a fault model containing temporary, intermittent, and permanent faults that occur both as bursts and as single faults. The results show a considerable enhancement in the fault tolerance at the cost of performance and area, and with only a slight increase in power consumption.

Using Ant Colony System to Consolidate VMs for Green Cloud Computing

High energy consumption of cloud data centers is a matter of great concern. Dynamic consolidation of Virtual Machines (VMs) presents a significant opportunity to save energy in data centers. A VM consolidation approach uses live migration of VMs so that some of the under-loaded Physical Machines (PMs) can be switched-off or put into a low-power mode. On the other hand, achieving the desired level of Quality of Service (QoS) between cloud providers and their users is critical. Therefore, the main challenge is to reduce energy consumption of data centers while satisfying QoS requirements. In this paper, we present a distributed system architecture to perform dynamic VM consolidation to reduce energy consumption of cloud data centers while maintaining the desired QoS. Since the VM consolidation problem is strictly NP-hard, we use an online optimization metaheuristic algorithm called Ant Colony System (ACS). The proposed ACS-based VM Consolidation (ACS-VMC) approach finds a near-optimal solution based on a specified objective function. Experimental results on real workload traces show that ACS-VMC reduces energy consumption while maintaining the required performance levels in a cloud data center. It outperforms existing VM consolidation approaches in terms of energy consumption, number of VM migrations, and QoS requirements concerning performance.

Self-Adaptive System for Addressing Permanent Errors in On-Chip Interconnects

We present a self-contained adaptive system for detecting and bypassing permanent errors in on-chip interconnects. The proposed system reroutes data on erroneous links to a set of spare wires without interrupting the data flow. To detect permanent errors at runtime, a novel in-line test (ILT) method using spare wires and a test pattern generator is proposed. In addition, an improved syndrome storing-based detection (SSD) method is presented and compared to the ILT method. Each detection method (ILT and SSD) is integrated individually into the noninterrupting adaptive system, and a case study is performed to compare them with Hamming and Bose-Chaudhuri-Hocquenghem (BCH) code implementations. In the presence of permanent errors, the probability of correct transmission in the proposed systems is improved by up to 140% over the standalone Hamming code. Furthermore, our methods achieve up to 38% area, 64% energy, and 61% latency improvements over the BCH implementation at comparable error performance.

Smart hill climbing for agile dynamic mapping in many-core systems

Stochastic hill climbing algorithm is adapted to rapidly find the appropriate start node in the application mapping of network-based many-core systems. Due to highly dynamic and unpredictable workload of such systems, an agile run-time task allocation scheme is required. The scheme is desired to map the tasks of an incoming application at run-time onto an optimum contiguous area of the available nodes. Contiguous and unfragmented area mapping is to settle the communicating tasks in close proximity. Hence, the power dissipation, the congestion between different applications, and the latency of the system will be significantly reduced. To find an optimum region, we first propose an approximate model that quickly estimates the available area around a given node. Then the stochastic hill climbing algorithm is used as a search heuristic to find a node that has the required number of available nodes around it. Presented agile climber takes the steps using an adapted version of hill climbing algorithm named Smart Hill Climbing, SHiC, which takes the runtime status of the system into account. Finally, the application mapping is performed starting from the selected first node. Experiments show significant gain in the mapping contiguousness which results in better network latency and power dissipation, compared to state-of-the-art works.

Smart e-Health Gateway: Bringing intelligence to Internet-of-Things based ubiquitous healthcare systems

There have been significant advances in the field of Internet of Things (IoT) recently. At the same time there exists an ever-growing demand for ubiquitous healthcare systems to improve human health and well-being. In most of IoT-based patient monitoring systems, especially at smart homes or hospitals, there exists a bridging point (i.e., gateway) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks. These gateways have beneficial knowledge and constructive control over both the sensor network and the data to be transmitted through the Internet. In this paper, we exploit the strategic position of such gateways to offer several higher-level services such as local storage, real-time local data processing, embedded data mining, etc., proposing thus a Smart e-Health Gateway. By taking responsibility for handling some burdens of the sensor network and a remote healthcare center, a Smart e-Health Gateway can cope with many challenges in ubiquitous healthcare systems such as energy efficiency, scalability, and reliability issues. A successful implementation of Smart e-Health Gateways enables massive deployment of ubiquitous health monitoring systems especially in clinical environments. We also present a case study of a Smart e-Health Gateway called UTGATE where some of the discussed higher-level features have been implemented. Our proof-of-concept design demonstrates an IoT-based health monitoring system with enhanced overall system energy efficiency, performance, interoperability, security, and reliability.

HARAQ: Congestion-Aware Learning Model for Highly Adaptive Routing Algorithm in On-Chip Networks

The occurrence of congestion in on-chip networks can severely degrade the performance due to increased message latency. In mesh topology, minimal methods can propagate messages over two directions at each switch. When shortest paths are congested, sending more messages through them can deteriorate the congestion condition considerably. In this paper, we present an adaptive routing algorithm for on-chip networks that provide a wide range of alternative paths between each pair of source and destination switches. Initially, the algorithm determines all permitted turns in the network including 180-degree turns on a single channel without creating cycles. The implementation of the algorithm provides the best usage of all allowable turns to route messages more adaptively in the network. On top of that, for selecting a less congested path, an optimized and scalable learning method is utilized. The learning method is based on local and global congestion information and can estimate the latency from each output channel to the destination region.

Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things

Current developments in ICTs such as in Internet-of-Things (IoT) and CyberPhysical Systems (CPS) allow us to develop healthcare solutions with more intelligent and prediction capabilities both for daily life (home/office) and in-hospitals. In most of IoT-based healthcare systems, especially at smart homes or hospitals, a bridging point (i.e.,gateway) is needed between sensor infrastructure network and the Internet. The gateway at the edge of the network often just performs basic functions such as translating between the protocols used in the Internet and sensor networks. These gateways have beneficial knowledge and constructive control over both the sensor network and the data to be transmitted through the Internet. In this paper, we exploit the strategic position of such gateways at the edge of the network to offer several higher-level services such as local storage, real-time local data processing, embedded data mining, etc., presenting thus a Smart e-Health Gateway. We then propose to exploit the concept of Fog Computing in Healthcare IoT systems by forming a Geo-distributed intermediary layer of intelligence between sensor nodes and Cloud. By taking responsibility for handling some burdens of the sensor network and a remote healthcare center, our Fog-assisted system architecture can cope with many challenges in ubiquitous healthcare systems such as mobility, energy efficiency, scalability, and reliability issues. A successful implementation of Smart e-Health Gateways can enable massive deployment of ubiquitous health monitoring systems especially in clinical environments. We also present a prototype of a Smart e-Health Gateway called UT-GATE where some of the discussed higher-level features have been implemented. We also implement an IoT-based Early Warning Score (EWS) health monitoring to practically show the efficiency and relevance of our system on addressing a medical case study. Our proof-of-concept design demonstrates an IoT-based health monitoring system with enhanced overall system intelligence, energy efficiency, mobility, performance, interoperability, security, and reliability.

CATRA- congestion aware trapezoid-based routing algorithm for on-chip networks

Congestion occurs frequently in Networks-on-Chip when the packets demands exceed the capacity of network resources. Congestion-aware routing algorithms can greatly improve the network performance by balancing the traffic load in adaptive routing. Commonly, these algorithms either rely on purely local congestion information or take into account the congestion conditions of several nodes even though their statuses might be out-dated for the source node, because of dynamically changing congestion conditions. In this paper, we propose a method to utilize both local and non-local network information to determine the optimal path to forward a packet. The non-local information is gathered from the nodes that not only are more likely to be chosen as intermediate nodes in the routing path but also provide up-to-date information to a given node. Moreover, to collect and deliver the non-local information, a distributed propagation system is presented.

Fog Computing in Healthcare Internet of Things: A Case Study on ECG Feature Extraction

Internet of Things technology provides a competent and structured approach to improve health and wellbeing of mankind. One of the feasible ways to offer healthcare services based on IoT is to monitor humans health in real-time using ubiquitous health monitoring systems which have the ability to acquire bio-signals from sensor nodes and send the data to the gateway via a particular wireless communication protocol. The real-time data is then transmitted to a remote cloud server for real-time processing, visualization, and diagnosis. In this paper, we enhance such a health monitoring system by exploiting the concept of fog computing at smart gateways providing advanced techniques and services such as embedded data mining, distributed storage, and notification service at the edge of network. Particularly, we choose Electrocardiogram (ECG) feature extraction as the case study as it plays an important role in diagnosis of many cardiac diseases. ECG signals are analyzed in smart gateways with features extracted including heart rate, P wave and T wave via a flexible template based on a lightweight wavelet transform mechanism. Our experimental results reveal that fog computing helps achieving more than 90% bandwidth efficiency and offering low-latency real time response at the edge of the network.

LiRCUP: Linear Regression Based CPU Usage Prediction Algorithm for Live Migration of Virtual Machines in Data Centers

Virtualization is a vital technology of cloud computing which enables the partition of a physical host into several Virtual Machines (VMs). The number of active hosts can be reduced according to the resources requirements using live migration in order to minimize the power consumption in this technology. However, the Service Level Agreement (SLA) is essential for maintaining reliable quality of service between data centers and their users in the cloud environment. Therefore, reduction of the SLA violation level and power costs are considered as two objectives in this paper. We present a CPU usage prediction method based on the linear regression technique. The proposed approach approximates the short-time future CPU utilization based on the history of usage in each host. It is employed in the live migration process to predict over-loaded and under-loaded hosts. When a host becomes over-loaded, some VMs migrate to other hosts to avoid SLA violation. Moreover, first all VMs migrate from a host while it becomes under-loaded. Then, the host switches to the sleep mode for reducing power consumption. Experimental results on the real workload traces from more than a thousand Planet Lab VMs show that the proposed technique can significantly reduce the energy consumption and SLA violation rates.