Very recently a new passive circuit element called memristor has been extensively investigated by researchers, which can be used for a variety of applications. This two-terminal device having few nanometer dimensions has been experimentally shown to possess both memory and resistor properties. This has also received great attention due to the fact that these devices can very easily be integrated on CMOS subsystems. Most of the logic design works in this context are based on material implication operation which can be very efficiently implemented using memristors. In this paper we propose an efficient realization of 2-to-1 multiplexer using memristors, and hence present a synthesis methodology that represents a given Boolean function as a Reduced Ordered Binary Decision Diagram (ROBDD) and then maps the same to memristor implementation. Index Terms-Memristor, logic design, multiplexer, binary decision diagram

Datacenters suffer from resource utilization inefficiencies due to the conflicting goals of service owners and platform providers. Service owners intending to maintain Service Level Objectives (SLO) for themselves typically request a conservative amount of resources. Platform providers want to increase operational efficiency to reduce capital and operating costs. Achieving both operational efficiency and SLO for individual services at the same time is challenging due to the diversity in service workload characteristics, resource usage patterns that are dependent on input load, heterogeneity in platform compute, memory, I/O, and network architecture, and resource bundling (e.g., compute and memory on a single node). This paper presents a tunable approach to resource allocation that accounts for both dynamic service resource needs and platform heterogeneity. In addition, an online K-Means-based service classification method is used in conjunction with an offline sensitivity component. Our tunable approach allows trading resource utilization efficiency for absolute SLO guarantees based on the service owners' sensitivity to its SLO. We evaluate our tunable resource allocator at scale in a private cloud environment with mostly latency-critical workloads. When tuning for operational efficiency, we demonstrate up to ∼50% reduction in required machines; ∼40% reduction in Total-Cost-of-Ownership (TCO); and ∼60% reduction in CPU and memory fragmentation, but at the cost of increasing the number of tasks experiencing degradation of SLO by up to ∼25% compared to the baseline. When tuning for SLO, by introducing interference-aware colocation, we can tune the solver to reduce tasks experiencing degradation of SLO by up to ∼22% compared to the baseline, but at an additional cost of ∼30% in terms of the number of hosts. We highlight this trade-off between TCO and SLO violations, and offer tuning based on the requirements of the platform owners.

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State-of-the-art systems, whether in servers or desktops, provide ample computational and storage resources to allow multiple simultaneously executing potentially parallel applications. However, performance tends to be unpredictable, being a function of algorithmic design, resource allocation choices, and hardware resource limitations. In this article, we introduce MAPPER, a manager of application performance via parallel efficiency regulation. MAPPER uses a privileged daemon to monitor (using hardware performance counters) and coordinate all participating applications by making two coupled decisions: the degree of parallelism to allow each application to improve system efficiency while guaranteeing quality of service (QoS), and which specific CPU cores to schedule applications on. The QoS metric may be chosen by the application and could be in terms of execution time, throughput, or tail latency, relative to the maximum performance achievable on the machine. We demonstrate that using a normalized parallel efficiency metric allows comparison across and cooperation among applications to guarantee their required QoS. While MAPPER may be used without application or runtime modification, use of a simple interface to communicate application-level knowledge improves MAPPER’s efficacy. Using a QoS guarantee of 85% of the IPC achieved with a fair share of resources on the machine, MAPPER achieves up to 3.3 \( \times \) speedup relative to unmodified Linux and runtime systems, with an average improvement of 17% in our test cases. At the same time, MAPPER violates QoS for only 2% of the applications (compared to 23% for Linux), while placing much tighter bounds on the worst case. MAPPER relieves hardware bottlenecks via task-to-CPU placement and allocates more CPU contexts to applications that exhibit higher parallel efficiency while guaranteeing QoS, resulting in both individual application performance predictability and overall system efficiency.

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