Towards trainable synthesis for optimized circuit deployment on FPGA

Field Programmable Gate Arrays (FPGAs) utilize multiple programmable elements and non-programmable blocks. After synthesizing an input Hardware Design Language (HDL) design into a circuit, optimizations are used to discover a satisfactory deployment on a target FPGA. HDLs' compound operations, such as addition, can be implemented in various ways and thus, multiple but functionally equivalent circuits can be synthesized. To leverage this, we propose a methodology that first enables configurable synthesis of compound operations. Second, it trains the system using a set of HDL files and architectures to optimize target performance objectives, such as critical path length and power. We prototyped our technique in the open source Verilog-To-Routing (VTR) tool. We subsequently produced two configuration files targeting different deployment objectives; experimental results with the VTR Verilog benchmarks revealed significant improvements

Towards embedding data provenance in files

Data provenance (keeping track of who did what, where, when and how) boasts of various attractive use cases for distributed systems, such as intrusion detection, forensic analysis and secure information dependability. This potential, however, can only be realized if provenance is accessible by its primary stakeholders: the end-users. Existing provenance systems are designed in a ‘all-or-nothing’ fashion, making provenance inaccessible, difficult to extract and crucially, not controlled by its key stakeholders. To mitigate this, we propose that provenance be separated into system, data-specific and file-metadata provenance. Furthermore, we expand data-specific provenance as changes at a fine-grain level, or provenance-per-change, that is recorded alongside its source. We show that with the use of delta-encoding, provenance-per-change is viable, asserting our proposed architecture to be effectively realizable

Insights into WebAssembly: Compilation performance and shared code caching in node.js

Alongside JavaScript, V8 and Node.js have become essential components of contemporary web and cloud applications. With the addition of WebAssembly to the web, developers finally have a fast platform for performance-critical code. However, this addition also introduces new challenges to client and server applications. New application architectures, such as serverless computing, require instantaneous performance without long startup times. In this paper, we investigate the performance of WebAssembly compilation in V8 and Node.js, and present the design and implementation of a multi-process shared code cache for Node.js applications. We demonstrate how such a cache can significantly increase application performance, and reduce application startup time, CPU usage, and memory footprint

Node.js scalability investigation in the cloud

Node.js has gained popularity in cloud development due to its asynchronous, non-blocking and event-driven nature. However, scalability issues can limit the number of concurrent requests while achieving an acceptable level of performance. To the best of our knowledge, no cloud-based benchmarks or metrics focusing on Node.js scalability exist. This paper presents the design and implementation of Ibenchjs, a scalability-oriented benchmarking framework, and a set of sample test applications. We deploy Ibenchjs in a local and isolated cloud to collect and report scalability-related measurements and issues of Node.js as well as performance bottlenecks. Our findings include: 1) the scaling performance of the tested Node.js test applications was sub-linear; 2) no improvements were measured when more CPUs were added without modifying the number of Node.js instances; and 3) leveraging cloud scaling solutions significantly outperformed Node.js-module-based scaling

Toward sustainable serverless computing

Although serverless computing generally involves executing short-lived “functions,” the increasing migration to this computing paradigm requires careful consideration of energy and power requirements. serverless computing is also viewed as an economically-driven computational approach, often influenced by the cost of computation, as users are charged for per-subsecond use of computational resources rather than the coarse-grained charging that is common with virtual machines and containers. To ensure that the startup times of serverless functions do not discourage their use, resource providers need to keep these functions hot, often by passing in synthetic data. We describe the real power consumption characteristics of serverless, based on execution traces reported in the literature, and describe potential strategies (some adopted from existing VM and container-based approaches) that can be used to reduce the energy overheads of serverless execution. Our analysis is, purposefully, biased toward the use of machine learning workloads because: (1) workloads are increasingly being used widely across different applications; (2) functions that implement machine learning algorithms can range in complexity from long-running (deep learning) versus short-running (inference only), enabling us to consider serverless across a variety of possible execution behaviors. The general findings are easily translatable to other domains.PostprintPeer reviewe

Maintaining SLOs of cloud-native applications via self-adaptive resource sharing

With changing workloads, cloud service providers can leverage vertical container scaling (adding/removing resources) so that Service Level Objective (SLO) violations are minimized and spare resources are maximized. In this paper, we investigate a solution to the self-adaptive problem of vertical elasticity for co-located containerized applications. First, the system learns performance models that relate SLOs to workload, resource limits and service level indicators. Second, it derives limits that meet SLOs and minimize resource consumption via a combination of optimization and restricted brute-force search. Third, it vertically scales containers based on the derived limits. We evaluated our technique on a Kubernetes private cloud of 8 nodes with three deployed applications. The results registered two SLO violations out of 16 validation tests; acceptably low derivation times facilitate realistic deployment. Violations are primarily attributed to application specifics, such as garbage collection, which require further research to be circumvented

A comparison of verilog synthesis frontends

A crucial consideration in choosing a frontend synthesis tool is the quality of the synthesised result. This kind of benchmarking is critical to choosing a fit-for-purpose tool. However, to the best of the authors’ knowledge, the only comparison of Odin II, the front-end of Verilog-to-Routing, and another synthesis tool was focused primarily on Odin II and Yosys’ performance with respect to commercial counterparts in the Xilinx ISE tool. Further, such an evaluation is to improve confidence in research findings utilising these tools. The quality of results for a poorly optimised research tool may not reflect the performance of real-world applications, adding uncertainty to any findings and requiring extra work from the researcher to obtain valid results. We compare Odin II and Yosys targeting the Xilinx Artix-7 architecture provided by SymbiFlow

Energy digital twin technology for industrial energy management: Classification, challenges and future

Digitalisation of the process and energy industries through energy digital twin technology promises step-improvements in energy management and optimisation, better servicing and maintenance, energy-efficient design and evolution of existing sites, and integration with locally and regionally generated renewable energy. This systematic and critical review aims to accelerate the understanding, classification, and application of energy digital twin technology. It adds to the literature by developing an original multi-dimensional digital twin classification framework, summarising the applications of energy digital twins throughout a site's lifecycle, and constructing a proposal of how to apply the technology to industrial sites and local areas to enable a reduction in carbon and other environmental footprints. The review concludes by identifying key challenges that face uptake of energy digital twins and a framework to apply the energy digital twins

The weakest link: Revealing and modeling the architectural patterns of microservice applications

Cloud microservice applications comprise interconnected services packed into containers. Such applications generate complex communication patterns among their microservices. Studying such patterns can support assuring various quality attributes, such as autoscaling for satisfying performance, availability and scalability, or targeted penetration testing for satisfying security and correctness. We study the structure of containerized microservice applications via providing the methodology and the results of a structural graphbased analysis of 103 Docker Compose deployment files from opensourced Github repositories. Our findings indicate the dominance of a power-law distribution of microservice interconnections. Further analysis highlights the suitability of the Barabási-Albert model for generating large random graphs that model the architecture of real microservice applications. The exhibited structures and their usage for engineering microservice applications are discussed