A large literature is available on quantization for communication efficiency in distributed learning. However, these studies often overlook the enhancement of privacy through quantization. This paper aims to fill this research gap by undertaking a systematic literature review on the use of quantization in distributed learning for privacy enhancement. We explore peer-reviewed literature that utilizes quantization for privacy-preserving purposes. Our analysis identifies the limitations and challenges of current approaches. It also highlights the need to integrate quantization techniques for dual objectives (privacy and communication efficiency) in distributed learning frameworks.

This paper introduces the Open-source Secure Hardware Design and Analysis (OSHDA) toolchain for the logic locking of finite-state machines (FSMs) at the behavioral level. OSHDA's FSM obfuscation method is based on the recently developed State Permutation Logic Locking (SPeLL) algorithm which obfuscates the behavioral transition graph of the FSM, thus avoiding the use of dummy states and reducing exposure to reverse engineering attacks. In addition to implementing the SPeLL algorithm, the toolchain implements a full logic synthesis flow, including the evaluation of the gate-level SPeLL hardware overhead for both FPGA and ASIC designs. In particular, OSHDA enables the automation of trade-off analysis between the strength of SPeLL security and its hardware overhead. The paper further describes attempted attacks on SPeLL using state-of-the-art de-obfuscation tools and identifies research gaps in behavioral de-obfuscation that must be addressed before one can successfully de-obfuscate SPeLL. OSHDA comes with its own scripting subsystem for augmenting its analysis, adding de-obfuscation methods, and integrating physical design tools. Finally, OSHDA is deployed as a hardware security microservice using the Docker framework.

The growing adoption of microservice architectures (MSAs) has led to major research and development efforts to address their challenges and improve their performance, reliability, and robustness. Important aspects of MSA that are not sufficiently covered in the open literature include efficient cloud resource allocation and optimal power management. Other aspects of MSA remain widely scattered in the literature, including cost analysis, service level agreements (SLAs), and demand-driven scaling. In this article, we examine recent cloud frameworks for containerized microservices with a focus on efficient resource utilization using auto-scaling. We classify these frameworks on the basis of their resource allocation models and underlying hardware resources. We highlight current MSA trends and identify workload-driven resource sharing within microservice meshes and SLA streamlining as two key areas for future microservice research.

One major advantage of microservice cloud architectures is the agility with which microservices can be replicated to help improve the overall quality of service and meet service-level contracts. Their challenge is to carefully balance the horizontal microservice replicas with the vertical resources of CPU, memory, and IO that are allocated to each microservice. The objective of such balancing act is, of course, to avoid both service bottlenecks and resource wastage. In this paper, we present OSμS, a new open-source microservice prototyping platform that has been developed and instrumented from the ground up with the objective of collecting fine-grained, non-proprietary metrology on microservice mesh performance. We will illustrate the use of OSμS for developing and evaluating machine-learning algorithms for the horizontal and vertical autoscaling of microservice architectures. A hybrid algorithm based on decision-tree learning will be implemented on OSμS and compared with the academic state of the art and existing cloud-provider solutions. The advantages of such algorithm in improving horizontal and vertical resource utilization will be highlighted.

The popularity of cloud computing services for delivering and accessing infrastructure on demand has significantly increased over the last few years. Concurrently, the usage of FPGAs to accelerate compute-intensive applications has become more widespread in different computational domains due to their ability to achieve high throughput and predictable latency while providing programmability and improved energy efficiency. Computationally intensive applications such as big data analytics, machine learning, and video processing have been accelerated by FPGAs. With the exponential workload increase in data centers, major cloud service providers have made FPGAs and their capabilities available as cloud services. However, enabling FPGAs in the cloud is not a trivial task due to incompatibilities with existing cloud infrastructure and operational challenges related to abstraction, virtualization, partitioning, and security. In this article, we survey recent frameworks for offering FPGA hardware acceleration as a cloud service, classify them based on their virtualization mode, tenancy model, communication interface, software stack, and hardware infrastructure. We further highlight current FPGAaaS trends and identify FPGA resource sharing, security, and microservicing as important areas for future research.

In this paper, we propose a web-based microservice for programming FPGAs remotely in the cloud without requiring additional hardware or third party software. Docker and Docker-compose are used to implement a scalable, secure environment for FPGA design and operation. The paper further describes in detail the architectural design, user interface, tool flow, and hardware back-end of the FPGA cloud service. The Docker files are made publicly available at GitHub.