Videos take a lot of time to transport over the network, hence running analytics on the live video on embedded or mobile devices has become an important system driver. Considering such devices, e.g., surveillance cameras or AR/VR gadgets, are resource constrained, although there has been significant work in creating lightweight deep neural networks (DNNs) for such clients, none of these can adapt to changing runtime conditions, e.g., changes in resource availability on the device, the content characteristics, or requirements from the user. In this article, we introduce ApproxNet, a video object classification system for embedded or mobile clients. It enables novel dynamic approximation techniques to achieve desired inference latency and accuracy trade-off under changing runtime conditions. It achieves this by enabling two approximation knobs within a single DNN model rather than creating and maintaining an ensemble of models, e.g., MCDNN [MobiSys-16]. We show that ApproxNet can adapt seamlessly at runtime to these changes, provides low and stable latency for the image and video frame classification problems, and shows the improvement in accuracy and latency over ResNet [CVPR-16], MCDNN [MobiSys-16], MobileNets [Google-17], NestDNN [MobiCom-18], and MSDNet [ICLR-18].

In this work, the main theme is to investigate a novel generalized parametric exponential intuitionistic fuzzy divergence measure along with the study of its detailed properties for its authenticity. The applications of this newly developed generalized intuitionistic fuzzy divergence measure have been provided to multi-attribute decision making (MADM). Numerical verification has been illustrated to demonstrate the proposed method for solving multi-attribute decision making problem under fuzzy environment.

Node downtime and failed jobs in a computing cluster translate into wasted resources and user dissatisfaction. Therefore understanding why nodes and jobs fail in HPC clusters is essential. This paper provides analyses of node and job failures in two university-wide computing clusters at two Tier I US research universities. We analyzed approximately 3.0M job execution data of System A and 2.2M of System B with data sources coming from accounting logs, resource usage for all primary local and remote resources (memory, IO, network), and node failure data. We observe different kinds of correlations of failures with resource usages and propose a job failure prediction model to trigger event-driven checkpointing and avoid wasted work. Additionally, we present user history based resource usage and runtime prediction models. These models have the potential to avoid system related issues such as contention, and improve quality of service such as lower mean queue time, if their predictions are used to make a more informed scheduling decision. As a proof of concept, we simulate an easy backfill scheduler to use predictions of one of these models, i.e., runtime and show the improvements in terms of lower mean queue time. Arising out of these observations, we provide generalizable insights for cluster management to improve reliability, such as, for some execution environments local contention dominates, while for others system-wide contention dominates.

Achieving cost and performance efficiency for cloud-hosted databases requires exploring a large configuration space, including the parameters exposed by the database along with the variety of VM configurations available in the cloud. Even small deviations from an optimal configuration have significant consequences on performance and cost. Existing systems that automate cloud deployment configuration can select near-optimal instance types for homogeneous clusters of virtual machines and for stateless, recurrent data analytics workloads. We show that to find optimal performance-per-$ cloud deployments for NoSQL database applications, it is important to (1) consider heterogeneous cluster configurations, (2) jointly optimize database and VM configurations, and (3) dynamically adjust configuration as workload behavior changes. We present OPTIMUSCLOUD, an online reconfiguration system that can efficiently perform such joint and heterogeneous configuration for dynamic workloads. We evaluate our system with two clustered NoSQL systems: Cassandra and Redis, using three representative workloads and show that OPTIMUSCLOUD provides 40% higher throughput/$ and 4.5x lower 99-percentile latency on average compared to state-of-the-art prior systems, CherryPick, Selecta, and SOPHIA.

The literature of probability, fuzzy and intuitionistic fuzzy divergence measures provides the applications of a variety of divergence measures to different disciplines dealing with real life problems. Many such divergence measures have been generated through different approaches but still there is a scope that better ones can be developed which will provide applications to variety of disciplines. The present communication involving the development of a new intuitionistic measure of divergence for fuzzy distributions is a motivation in this direction. The newly proposed mathematical model is helpful for the study of fault diagnosis of turbine. In the present paper, we have provided an algorithm which can handle the main faults in the turbine along with useful information for future trends and verified the results numerically.

Many video streaming applications require low-latency processing on resource-constrained devices. To meet the latency and resource constraints, developers must often approximate filter computations. A key challenge to successfully tuning approximations is finding the optimal configuration, which may change across and within the input videos because it is content-dependent. Searching through the entire search space for every frame in the video stream is infeasible, while tuning the pipeline offline, on a set of training videos, yields suboptimal results. We present VIDEOCHEF, a system for approximate optimization of video pipelines. VIDEOCHEF finds the optimal configurations of approximate filters at runtime, by leveraging the previously proposed concept of canary inputs—using small inputs to tune the accuracy of the computations and transferring the approximate configurations to full inputs. VIDEOCHEF is the first system to show that canary inputs can be used for complex streaming applications. The two key innovations of VIDEOCHEF are (1) an accurate error mapping from the approximate processing with downsampled inputs to that with full inputs and (2) a directed search that balances the cost of each search step with the estimated reduction in the run time. We evaluate our approach on 106 videos obtained from YouTube, on a set of 9 video processing pipelines with a total of 10 distinct filters. Our results show significant performance improvement over the baseline and the previous approach that uses canary inputs. We also perform a user study that shows that the videos produced by VIDEOCHEF are often acceptable to human subjects.

The existing literature of fuzzy divergence measures provides a variety of such measures scoring one over the other and even having certain drawbacks. The main objective of the present communication is to remove such drawbacks and propose a modified fuzzy divergence measure along with the study of its detailed properties. The applications of this modified version of fuzzy divergence measure have been provided to the fields of medical diagnosis and multi-criteria decision making (MCDM) problem. Numerical verification has been illustrated to demonstrate the proposed method under fuzzy environment.