Autotuning of Exascale Applications With Anomalies Detection

The execution of complex distributed applications in exascale systems faces many challenges, as it involves empirical evaluation of countless code variations and application runtime parameters over a heterogeneous set of resources. To mitigate these challenges, the research field of autotuning has gained momentum. The autotuning automates identifying the most desirable application implementation in terms of code variations and runtime parameters. However, the complexity and size of the exascale systems make the autotuning process very difficult, especially considering the number of parameter variations that have to be identified. Therefore, we introduce a novel approach for autotuning exascale applications based on a genetic multi-objective optimization algorithm integrated within the ASPIDE exascale computing framework. The approach considers multi-dimensional search space with support for pluggable objective functions, including execution time and energy requirements. Furthermore, the autotuner employs a machine learning-based event detection approach to detect events and anomalies during application execution, such as hardware failures or communication bottlenecks.

A Survey on Security Mechanisms for NoC-based Many-Core SoCs

The adoption of many-cores systems introduces the concern for data protection as a critical design requirement due to the resource sharing and the simultaneous executions of several applications on the platform. A secure application that processes sensitive data may have its security harmed by a malicious process. The literature contains several proposals to protect many-cores against attacks, focusing on the protection of the application execution or the access to shared memories. However, there is a gap to be fulfilled: a solution covering the entire application lifetime, including its admission, execution, and peripheral's access. This survey discusses three security-related issues: the secure admission of applications, the prevention of resource sharing during their execution, and the safe access to external devices. This survey concludes with an evaluation of the studied methods, pointing out directions and research opportunities.

Resource-Aware Topology Adaptation in P2P Overlay Adhoc Network

With the emergence of wireless devices, service delivery for mobile as hoc networks (MANET) has started to attract a lot of attention recently. We believe that overlay networks, particularly peer-to-peer (P2P) systems, is a good abstraction for application design and deployment over ad hoc networks. The principal benefit of this approach is that application states are only maintained by the nodes involved in the application execution and all other nodes only perform networking related functions. We propose a P2P system for MANET, RAON, which performs query forwarding and overlay topology adaptation based on link instability and power constraints. We evaluated and compared the performance of RAON with an existing P2P system, Gia. Our simulation results show that RAON improves the success rate and delay of query search as compared to Gia. It, however, achieves this at the expense of higher energy consumption.

Resource-Aware Topology Adaptation in P2P Overlay Adhoc Network

With the emergence of wireless devices, service delivery for mobile as hoc networks (MANET) has started to attract a lot of attention recently. We believe that overlay networks, particularly peer-to-peer (P2P) systems, is a good abstraction for application design and deployment over ad hoc networks. The principal benefit of this approach is that application states are only maintained by the nodes involved in the application execution and all other nodes only perform networking related functions. We propose a P2P system for MANET, RAON, which performs query forwarding and overlay topology adaptation based on link instability and power constraints. We evaluated and compared the performance of RAON with an existing P2P system, Gia. Our simulation results show that RAON improves the success rate and delay of query search as compared to Gia. It, however, achieves this at the expense of higher energy consumption.

A Reusable Characterization of the Memory System Behavior of SPEC2017 and SPEC2006

The SPEC CPU Benchmarks are used extensively for evaluating and comparing improvements to computer systems. This ubiquity makes characterization critical for researchers to understand the bottlenecks the benchmarks do and do not expose and where new designs should and should not be expected to show impact. However, in characterization there is a tradeoff between accuracy and reusability: The more precisely we characterize a benchmark’s performance on a given system, the less usable it is across different micro-architectures and varying memory configurations. For SPEC, most existing characterizations include system-specific effects (e.g., via performance counters) and/or only look at aggregate behavior (e.g., averages over the full application execution). While such approaches simplify characterization, they make it difficult to separate the applications’ intrinsic behavior from the system-specific effects and/or lose the diverse phase-based behaviors. In this work we focus on characterizing the applications’ intrinsic memory behaviour by isolating them from micro-architectural configuration specifics. We do this by providing a simplified generic system model that evaluates the applications’ memory behavior across multiple cache sizes, with and without prefetching, and over time. The resulting characterization can be reused across a range of systems to understand application behavior and allow us to see how frequently different behaviors occur. We use this approach to compare the SPEC 2006 and 2017 suites, providing insight into their memory system behaviour beyond previous system-specific and/or aggregate results. We demonstrate the ability to use this characterization in different contexts by showing a portion of the SPEC 2017 benchmark suite that could benefit from giga-scale caches, despite aggregate results indicating otherwise.

Cost-efficient service selection and execution and blockchain-enabled serverless network for internet of medical things

<abstract><p>These days, healthcare applications on the Internet of Medical Things (IoMT) network have been growing to deal with different diseases via different sensors. These healthcare sensors are connecting to the various healthcare fog servers. The hospitals are geographically distributed and offer different services to the patients from any ubiquitous network. However, due to the full offloading of data to the insecure servers, two main challenges exist in the IoMT network. (i) Data security of workflows healthcare applications between different fog healthcare nodes. (ii) The cost-efficient and QoS efficient scheduling of healthcare applications in the IoMT system. This paper devises the Cost-Efficient Service Selection and Execution and Blockchain-Enabled Serverless Network for Internet of Medical Things system. The goal is to choose cost-efficient services and schedule all tasks based on their QoS and minimum execution cost. Simulation results show that the proposed outperform all existing schemes regarding data security, validation by 10%, and cost of application execution by 33% in IoMT.</p></abstract>