The measurable properties of the artifacts or objects in the computer, management, or finance disciplines are extrinsic, not inherent — dependent on their problem definitions and solution instantiations. The processes of problem definition, solution instantiation, and measurement are entangled. Only after the instantiation can the solutions to the problem be measured. Definition, instantiation, and measurement have complex mutual influences. Meanwhile, the technology inertia brings instantiation bias — trapped into a subspace or even a point at a high-dimension solution space. These daunting challenges, which emerging computing aggravates, make metrology cannot work for benchmark communities. It is pressing to establish independent benchmark science and engineering.

This article presents a unifying benchmark definition, a conceptual framework, and a traceable and supervised learning-based benchmarking methodology, laying the foundation for benchmark science and engineering. I also discuss BenchCouncil’s plans for emerging and future computing. The ongoing projects include defining the challenges of intelligence, instinct, quantum computers, Metaverse, planet-scale computers, and reformulating data centers, artificial intelligence for science, and CPU benchmark suites. Also, BenchCouncil will collaborate with ComputerCouncil on open-source computer systems for planet-scale computing, AI for science systems, and Metaverse.

Scientific research communities are embracing AI-based solutions to target tractable scientific tasks and improve research work flows. However, the development and evaluation of such solutions are scattered across multiple disciplines. We formalize the problem of scientific AI benchmarking, and propose a system called SAIBench in the hope of unifying the efforts and enabling low-friction on-boarding of new disciplines. The system approaches this goal with SAIL, a domain-specific language to decouple research problems, AI models, ranking criteria, and software/hardware configuration into reusable modules. We show that this approach is flexible and can adapt to problems, AI models, and evaluation methods defined in different perspectives. The project homepage is https://www.computercouncil.org/SAIBench .

With the rapid expansion of Internet of Things (IoT), relevant files are stored and transmitted at the network edge by employing data deduplication to eliminate redundant data for the best accessibility. Although deduplication improves storage and network efficiency, it decreases security strength and performance. Existing schemes usually adopt message-locked encryption (MLE) to encrypt data, which is vulnerable to brute-force attacks. Meanwhile, these schemes utilize proof-of-ownership (PoW) to prevent duplicate-faking attacks, while they suffer from replay attacks or incur large computation overheads. This paper proposes SE-PoW, an efficient and location-aware hybrid encrypted deduplication scheme with a dual-level security-enhanced Proof-of-Ownership in edge computing. Specifically, SE-PoW firstly encrypts files with an inter-edge server-aided randomized convergent encryption (RCE) method and then protects blocks with an intra-edge edge-aided MLE method to balance security and system efficiency. To resist duplicate-faking attacks and replay attacks, SE-PoW performs the dual-level PoW algorithm. Then it combines the verification of a cuckoo filter and the homomorphism of algebraic signatures in sequence to enhance security and improve ownership checking efficiency. Security analysis demonstrates that SE-PoW ensures data security and resists the mentioned attacks. Evaluation results show that SE-PoW reduces up to 61.9% upload time overheads compared with the state-of-the-art schemes.

Server consolidation is one of the techniques used to increase energy efficiency in datacentres. Nevertheless, the server consolidation has an inherent trade-off between performance degradation and energy consumption which has to be quantified to be managed. In this paper, the index is proposed to quantify the mentioned trade-off. We validated de use of the index through real experimentation. Also, these observations lead us to propose the second contribution, which focuses on the consolidation overhead. We proposed a general method to quantify this overhead and be able to manage its effect on performance degradation. To sum up, this paper improved the management of energy efficiency in datacentres’ servers through the index and the server consolidation determination method.

In future data centers, applications will make heavy use of far memory (including disaggregated memory pools and NVM). The access latency of far memory is more widely distributed than that of local memory accesses. This makes the efficiency of traditional out-of-order load/store mechanism in most general-purpose processors decrease in this scenario. Therefore, this work proposes an in-core asynchronous memory access unit to fully utilize the far memory resources.