Systems & networking
Researching and inventing new technologies for next-generation infrastructures and platforms for sensors, devices and the cloud.
Highlights
Researchers seek to simplify the complex in cloud computing
Read more about Researchers seek to simplify the complex in cloud computing
High performance container networking
Read more about High performance container networking
Ultra-dense data storage and extreme parallelism with electronic-molecular systems
Read more about Ultra-dense data storage and extreme parallelism with electronic-molecular systems
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4,757 results found
Publication
Timing-Based Browsing Privacy Vulnerabilities Via Site Isolation
Chromium’s site isolation ensures that different sites are rendered by different processes, which is a vision that academic researchers set forth over a decade ago. The journey from academic prototypes to the commercial availability represents a holistic rethinking about the security architecture for modern browsers. In this paper, we emphasize that the timing issues under site isolation need a thorough study. Specifically, we show that site isolation enables a realistic timing attack, which allows the…
Publication
Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers
With the widespread deployment of video analytics applications, edge compute servers are preferred for the analytics of the videos (for reasons of bandwidth and privacy). DNNs are deployed on the edge servers for inference, but when out in the field, they suffer from data drift where the live video data is significantly different than the training data. Continuous learning techniques are designed to handle data drift by periodically retraining the models on new data. Our…
Event
The 3rd Workshop on Hot Topics in Video Analytics and Intelligent Edges
The 3rd Workshop on Hot Topics in Video Analytics and Intelligent Edges will be hosted in conjunction with ACM MobiCom 2021 on October 25, 2021.
Publication
Piper: Multidimensional Planner for DNN Parallelization
The rapid increase in sizes of state-of-the-art DNN models and in the compute and memory requirements of training them has led to the development of many execution schemes such as data parallelism, pipelined model parallelism, tensor (intra-layer) model parallelism, and various memory-saving optimizations. However, no prior work has tackled the highly complex problem of finding the optimal partitioning of the DNN computation graph across many accelerators while combining the modes of parallelism and optimizations above.…
Publication
Spider: A Multi-Hop Millimeter-Wave Network for Live Video Analytics
Massive video analytics systems, comprised of many densely deployed cameras and supporting edge servers, are driving innovation in many areas including smart retail stores and security monitoring. To support such systems the challenge lies in collecting video footage in a way that maximizes end-to-end application goals, and scales this performance as camera density increases to meet application needs. This paper presents Spider, a multi-hop, millimeter-wave (mmWave) wireless relay network design that meets these needs. To…
Publication
Efficient Large-Scale Language Model Training on GPU Clusters
Large language models have led to state-of-the-art accuracies across a range of tasks. However, training these models efficiently is challenging for two reasons: a) GPU memory capacity is limited, making it impossible to fit large models on even a multi-GPU server, and b) the number of compute operations required to train these models can result in unrealistically long training times. Consequently, new methods of model parallelism such as tensor and pipeline parallelism have been proposed.…
Publication
Provisioning Differentiated Last-Level Cache Allocations to VMs in Public Clouds
Public cloud providers offer access to hardware resources and users rent resources by choosing among many VM sizes. While users choose the CPU core count and main memory size per VM, they cannot specify last-level cache (LLC) requirements. LLC is typically shared among all cores of a modern CPU causing cache contention and performance interference among co-located VMs. Consequently, a user’s only way to avoid this interference is purchasing a full-server VM to prevent co-tenants.…
Publication
Faa$T: A Transparent Auto-Scaling Cache for Serverless Applications
Function-as-a-Service (FaaS) has become an increasingly popular way for users to deploy their applications without the burden of managing the underlying infrastructure. However, existing FaaS platforms rely on remote storage to maintain state, limiting the set of applications that can be run efficiently. Recent caching work for FaaS platforms has tried to address this problem, but has fallen short: it disregards the widely different characteristics of FaaS applications, does not scale the cache based on…
Publication
Faster and Cheaper Serverless Computing on Harvested Resources
Serverless computing is becoming increasingly popular due to its ease of programming, fast elasticity, and fine-grained billing. However, the serverless provider still needs to provision, manage, and pay the IaaS provider for the virtual machines (VMs) hosting its platform. This ties the cost of the serverless platform to the cost of the underlying VMs. One way to significantly reduce cost is to use spare resources, which cloud providers rent at a massive discount. Harvest VMs…
Publication
Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP
Resource allocation problems in many computer systems can be formulated as mathematical optimization problems. However, finding exact solutions to these problems using off-the-shelf solvers is often intractable for large problem sizes with tight SLAs, leading system designers to rely on cheap, heuristic algorithms. We observe, however, that many allocation problems are granular: they consist of a large number of clients and resources, each client requests a small fraction of the total number of resources, and…