Offloading perception tasks to an edge server enhances extended reality (XR) experiences on resource-constrained mobile devices. However, when serving multiple users, an edge server may face resource contention and not be able to meet the service level objectives (SLOs), leading to degraded user experiences. To improve server scalability, we propose a system that adaptively schedules the inference executions of object detection models based on estimated effectiveness. The perception effectiveness is estimated by the inter-frame similarity and the object distance. We present the initial design of the system and preliminary results demonstrating its feasibility. We describe the next steps to further develop our system.

A digital twin presents promising opportunities and potential benefits for various industrial use cases by enabling simulation and prediction on the virtual representation of the real-world environment. However, the implementation and maintenance costs for the digital twin are prohibitively high, restricting its widespread adoption. To address this issue, we present a framework, GT-Craft, which enables fast prototyping the geospatial-based digital twin at scale. GT-Craft automates the generation of the digital twin by using the streamed geospatial data and the semantic information extracted from deep neural network (DNN) models. As GT-Craft generates digital twins on the Unity game engine, the Unity-based simulators and game applications can seamlessly use the digital twins generated by GT-Craft. The presented framework is compatible with non-Unity-based applications and existing 3D software and simulation tools, e.g., Blender, Apple Reality Composer, and NVIDIA Omniverse, as it supports exporting the generated digital twin in the universal scene description (USD) format, which is an emerging industrial open standard for exchanging and editing 3D contents.

Extended reality (XR) applications require computationally demanding functionalities with low end-to-end latency and high throughput. To enable XR on commodity devices, a number of distributed systems solutions enable offloading of XR workloads on remote servers. However, they make a priori decisions regarding the offloaded functionalities based on assumptions about operating factors, and their benefits are restricted to specific deployment contexts. To realize the benefits of offloading in various distributed environments, we present a distributed stream processing system, FleXR, which is specialized for real-time and interactive workloads and enables flexible distributions of XR functionalities. In building FleXR, we identified and resolved several issues of presenting XR functionalities as distributed pipelines. FleXR provides a framework for flexible distribution of XR pipelines while streamlining development and deployment phases. We evaluate FleXR with three XR use cases in four different distribution scenarios. In the results, the best-case distribution scenario shows up to 50% less end-to-end latency and 3.9x pipeline throughput compared to alternatives.

Real-time light detection and ranging (LiDAR) perceptions, e.g., 3D object detection and simultaneous localization and mapping are computationally intensive to mobile devices of limited resources and often offloaded on the edge. Offloading Li-DAR perceptions requires compressing the raw sensor data, and lossy compression is used for efficiently reducing the data volume. Lossy compression degrades the quality of LiDAR point clouds, and the perception performance is decreased consequently. In this work, we present an interpolation algorithm improving the quality of a LiDAR point cloud to mitigate the perception performance loss due to lossy compression. The algorithm targets the range image (RI) representation of a point cloud and interpolates points at the RI based on depth gradients. Compared to existing image interpolation algorithms, our algorithm shows a better qualitative result when the point cloud is reconstructed from the interpolated RI. With the preliminary results, we also describe the next steps of the current work.

Light detection and ranging (LiDAR) sensors are becoming available on modern mobile devices and provide a 3D sensing capability. This new capability is beneficial for perceptions in various use cases, but it is challenging for resource-constrained mobile devices to use the perceptions in real-time because of their high computational complexity. In this context, edge computing can be used to enable LiDAR online perceptions, but offloading the perceptions on the edge server requires a low-latency, lightweight, and efficient compression due to the large volume of LiDAR point clouds data. This paper presents FLiCR, a fast and lightweight LiDAR point cloud compression method for enabling edge-assisted online perceptions. FLiCR is based on range images (RI) as an intermediate representation (IR), and dictionary coding for compressing RIs. FLiCR achieves its benefits by leveraging lossy RIs, and we show the efficiency of bytestream compression is largely improved with quantization and subsampling. In addition, we identify the limitation of current quality metrics for presenting the entropy of a point cloud, and introduce a new metric that reflects both point-wise and entropy-wise qualities for lossy IRs. The evaluation results show FLiCR is more suitable for edge-assisted real-time perceptions than the existing LiDAR compressions, and we demonstrate the effectiveness of our compression and metric with the evaluations on 3D object detection and LiDAR SLAM.

OpenVX is an open standard for cross platform acceleration of computer vision applications. It was created to address the challenge of implementing efficient, portable and easy to use vision processing algorithms by separating application specification and implantation. It offers a set of basic, widely used vision kernels that accelerator vendors are supposed to provide. This work presents a framework for turning a high-level OpenVX graph specification into an efficient FPGA implementation.

Total energy minimization in data centers (including both computing and cooling energy) requires modeling the interactions between computing decisions (such as load distribution) and heat transfer in the room, since load acts as heat sources whose distribution in space affects cooling energy. This paper presents the first closed-form analytic optimal solution for load distribution in a machine rack that minimizes the sum of computing and cooling energy. We show that by considering actuation knobs on both computing and cooling sides, it is possible to reduce energy cost comparing to state of the art solutions that do not offer holistic energy optimization. The above can be achieved while meeting both throughput requirements and maximum CPU temperature constraints. Using a thorough evaluation on a real test bed of 20 machines, we demonstrate that our simple model adequately captures the thermal behavior and energy consumption of the system. We further show that our approach saves more energy compared to the state of the art in the field.

In this paper, we present an automated on-line service for troubleshooting performance problems in server clusters caused by unintended vicious cycles. The tool complements a large volume of prior performance troubleshooting and diagnostic literature for server farms that identifies problems arising due to resource bottlenecks or failed components. We show that unintended interactions between components in large-scale systems can cause performance problems even in the absence of bottlenecks or failures. Our tool leverages discriminative sequence mining to identify anomalous sequences of events that are candidates for blame for the performance problem. The tool looks for patterns consistent with "vicious cycles" or unstable behavior, as such patterns, when present, are most likely to be problematic. It highlights candidates that are semantically conflicting, such as those arising when different performance management mechanisms make adjustments in conflicting directions. Our approach offers two key advantages in performance troubleshooting. First, it does not require detailed prior knowledge of the underlying system to diagnose the problem. Second, contrary to simple statistical techniques, such as correlation analysis, that work well for continuous variables, our scheme can also identify chains of events (labels) that may explain the root cause of a problem. Our service is deployed on a web server testbed of 17 machines. To make the comparison of our scheme to prior work more concrete, we first reproduce two real-life problem scenarios reported in earlier literature, then explore a third, new case study. In all cases, our tool reports the patterns that explain the cause of the problem without requiring detailed a priori knowledge.

In this paper, we propose a new H.264/AVC based intra codec for near lossless coding. The proposed algorithm is composed of two parts: line-based intra prediction and modified context-based adaptive binary arithmetic coding (CABAC). Experimental results show that the proposed method provides about 8.95% bit savings, compared to the current H.264/AVC FRExt high profile.

The depth map represents three-dimensional (3D) data and is used for depth image-based rendering (DIBR) to synthesize virtual views. Since the quality of synthesized virtual views highly depends on the quality of the depth map, we encode the depth map by lossless coding mode. However, context-based adaptive binary arithmetic coding (CABAC) for the H.264/AVC standard does not guarantee the best coding performance for lossless depth map coding because CABAC was originally designed for lossy coding. In this paper, we propose an improved coding method of CABAC for lossless depth map coding considering the statistical properties of residual data from lossless depth map coding. Experimental results show that the proposed CABAC method provides approximately 4.3% bit saving, compared to the original CABAC in H.264/AVC.

This paper develops a software service for dynamic performance optimization and control in performance-sensitive systems. The next generation of performance-sensitive systems is expected to be more distributed and dynamic. They will have multiple "knobs” that affect performance and resource allocation. However, relying on the conglomeration of independent knob controls can become increasingly suboptimal. The problem lies in performance composability or lack thereof; a challenge that arises because individual optimizations in performance-sensitive systems generally do not compose well when combined. Performance adaptation in such systems needs to be carefully designed and implemented by holistically considering performance composability in order to achieve desired system performance. A flexible supporting software layer is therefore needed to easily apply different holistic performance management techniques. In this paper, we develop a software service, called OptiTuner, that monitors the current performance and the resource availability in performance-sensitive systems and allows easy implementation of different performance management schemes based on theoretical concepts of constrained optimization and feedback control. In order to show the efficacy of OptiTuner, we apply it to implement three holistic energy minimization techniques in a real-time web server farm comprising 18 machines. Using an industry standard e-Business benchmark, TPC-W, we demonstrate that the three approaches save up to 40 percent of total energy cost compared to the baseline approaches that do not holistically optimize the cost.

Since H.264/AVC was designed mainly for lossy video coding, the entropy coding methods in H.264/AVC are not appropriate for lossless video coding. Based on statistical differences of residual data in lossy and lossless coding, we develop efficient level and zero coding methods. Therefore, we design an improved context-based adaptive variable length coding (CAVLC) scheme for lossless intra coding by modifying the relative entropy coding parts in H.264/AVC. Experimental results show that the proposed method provides approximately 6.8% bit saving, compared with the H.264/AVC FRExt high profile.

The depth map, which represents three-dimensional (3D) information, is used to synthesize virtual views in the depth image-based rendering (DIBR) method. Since the quality of synthesized virtual views highly depends on the quality of depth map, we encode the depth map under the lossless coding mode. The original context-based adaptive binary arithmetic coding (CABAC) that was originally designed for lossy texture coding cannot provide the best coding performance for lossless depth map coding due to the statistical differences of residual data in lossy and lossless depth map coding. In this letter, we propose an enhanced CABAC coding mechanism for lossless depth map coding based on the statistics of residual data. Experimental results show that the proposed CABAC method provides approximately 4% bit saving compared to the original CABAC in H.264/AVC.

Context-based adaptive variable length coding (CAVLC) for the H.264/advanced video coding (AVC) standard was originally designed for lossy video coding, and as such does not yield adequate performance for lossless video coding. In this paper, we propose an improved CAVLC for lossless intra-coding by considering the statistical differences in residual data between lossy and lossless coding. From experimental results, we confirm that the proposed method provides approximately 9% bit saving in terms of a compression ratio compared with the current H.264/AVC fidelity range extensions high profile.

The context-based adaptive variable length coder (CAVLC) in H.264/AVC is not appropriate for lossless video coding because it was designed for lossy video coding. Since statistical characteristics of residual data in lossy and lossless coding are quite different, we design a new VLC table for the number of non-zero coefficients and an adaptive scheme for VLC table selection in level coding for lossless intra coding. Experimental results show that the proposed CAVLC scheme provides approximately 10% bit saving, compared to the original CAVLC scheme in H.264/AVC.

The newly emergent cloud computing environments host hundreds to thousands of services on a shared resource pool. The sharing is enhanced by virtualization technologies allowing multiple services to run in different virtual machines (VMs) on a single physical node. Resource over-booking allows more services with time-varying demands to be consolidated reducing operational costs. In the past, researchers have studied dynamic control mechanisms for allocating CPU to virtual machines, when CPU is over-booked with respect to the sum of the peak demands from all the VMs. However, runtime re-allocation of memory among multiple VMs has not been widely studied, except on VMware platforms. In this paper, we present a case study where feedback control is used for dynamic memory allocation to Xen virtual machines in a consolidated environment. We illustrate how memory behaves differently from CPU in terms of its relationship to application-level performance, such as response times. We have built a prototype of a joint resource control system for allocating both CPU and memory resources to co-located VMs in real time. Experimental results show that our solution allows all the hosted applications to achieve the desired performance in spite of their time-varying CPU and memory demands, whereas a solution without memory control incurs significant service level violations.

Context-based adaptive variable length coding (CAVLC) of H.264/AVC was originally designed for quantized transform coefficients in lossy video coding, and as such does not yield adequate performance in lossless video coding. In this paper, we propose an improved CAVLC for lossless intra coding. Considering statistical differences of residual data in lossy and lossless coding, we design a new CAVLC encoding algorithm. Experimental results show that the proposed method provides approximately 7.6% bit savings, compared to the current H.264/AVC FRExt high profile.

Web applications have been increasingly deployed on the Internet. How to effectively allocate system resources to meet the Service Level Objectives (SLOs) is a challenging problem for Web application providers. In this article, we propose a scheme for automated performance control of Web applications via dynamic resource allocations. The scheme uses a queueing model predictor and an online adaptive feedback loop that enforces admission control of the incoming requests to ensure the desired response time target is met. The proposed Queueing-Model-Based Adaptive Control approach combines both the modeling power of queueing theory and the self-tuning power of adaptive control. Therefore, it can handle both modeling inaccuracies and load disturbances in a better way. To evaluate the proposed approach, we built a multi-tiered Web application testbed with open-source components widely adopted in industry. Experimental studies conducted on the testbed demonstrated the effectiveness of the proposed scheme.

Sensor networks introduce a new application domain and set of challenges in distributed computing including new network-level programming languages, global system abstractions, and general-purpose communication protocols. These challenges are brought about by the tight integration of computation, communication, and distributed real-time interaction with the physical world. With the growing interest in interconnecting different sensor networks across a wide-area communication infrastructure, an overarching challenge becomes one of arriving at an agreed-upon global sensor network architecture that ensures interoperability. Unlike the Internet, where a layered communication stack (namely, the TCP/IP stack) defines the network architecture, a sensor network architecture must unify not only communication interfaces but also programming interfaces, since network communication and computation functions are tightly intertwined. In that sense, the sensor network architecture refers to a layered stack of distributed computing abstractions. This paper presents an architecture and key considerations in designing and interconnecting local and global sensor networks. Candidate protocols and middleware instantiations are described from the authors' ongoing work that meet the discussed considerations

The increased complexity of performance-sensitive software systems leads to increased use of automated adaptation policies in lieu of manual performance tuning. Composition of adaptive components into larger adaptive systems, however, presents challenges that arise from potential incompatibilities among the respective adaptation policies. Consequently, unstable or poorly-tuned feedback loops may result that cause performance deterioration. This paper (i) presents a mechanism, called adaptation graph analysis, for identifying potential incompatibilities between composed adaptation policies and (ii) illustrates a general design methodology for co-adaptation that resolves such incompatibilities. Our results are demonstrated by a case study on energy minimization in multi-tier Web server farms subject to soft real-time constraints. Two independently efficient energy saving policies (an on/off policy that switches machines off when not needed and a dynamic voltage scaling policy) are shown to conflict leading to increased energy consumption when combined. Our adaptation graph analysis predicts the problem, and our co-adaptation design methodology finds a solution that improves performance. Experimental results from a 17-server farm running the industry standard TPC-W e-commerce benchmark show that co-adaptation renders a cut-down in energy consumption by more than 50%, when workload is not high, while maintaining latency within acceptable bounds. The paper serves as a proof of concept of the proposed conflict-identification and resolution methodology and an invitation to further investigate a science for composing adaptive systems.

How to effectively allocate system resources to meet service level objectives (SLOs) is a challenging problem for Web services providers. In this paper, we propose a scheme for autonomous performance control of Web applications. It uses a queueing model predictor and an online adaptive feedback loop that enforces admission control of the incoming requests to ensure the desired response time target is met. The proposed queueing-model-based adaptive control approach combines both the modeling power of queueing theory and self-tuning power of adaptive control. Therefore, it can handle both modeling inaccuracies and load disturbances in a better way. To evaluate the proposed approach, we built a multi-tiered Web application testbed with open-source components widely used in industry. Experimental studies conducted on the testbed demonstrated the effectiveness of the proposed approach

The rapid advancement and deployment of Web applications call for a precise yet simple model for capacity planning and analysis purposes. The most widely deployed Web application architecture is the 3-tiered system, which is composed of a front-end Web server, an application server and a backend database server. In this paper, we present an analytical model of the 3-tiered Web application architecture. We show by using queueing network theory, we can model the 3-tiered Web application architecture accurately. A test-bed is built to measure model parameters based on industry standard server components and TPC-W benchmark. Validation results show that the proposed model predicts performance measures such as response time and throughput accurately.