skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Locking Down Science Gateways
The most recent Linux kernels have a new feature for securing applications: Landlock. Like Seccomp before it, Landlock makes it possible for a running process to give up access to resources. For applications running as Science Gateways, we want to have network access while starting up MPI, but we want to take away network access prior to the reading of parameter files in order to prevent malicious exploits of the gateway code. We explore the usefulness of this tool by modifying and locking down two mature scientific codes: The Einstein Toolkit, and Octo- Tiger.  more » « less
Award ID(s):
2004157
PAR ID:
10608123
Author(s) / Creator(s):
;
Publisher / Repository:
Zenodo
Date Published:
Subject(s) / Keyword(s):
Science Gateways security landlock
Format(s):
Medium: X
Location:
Bozeman, MT
Right(s):
Creative Commons Attribution 4.0 International
Sponsoring Org:
National Science Foundation
More Like this
  1. User-associated contents play an increasingly important role in modern network applications. With growing deployments of edge servers, the capacity of content storage in edge clusters significantly increases, which provides great potential to satisfy content requests with much shorter latency. However, the large number of contents also causes the difficulty of searching contents on edge servers in different locations because indexing contents costs huge DRAM on each edge server. In this work, we explore the opportunity of efficiently indexing user-associated contents and propose a scalable content-sharing mechanism for edge servers, called EdgeCut, that significantly reduces content access latency by allowing many edge servers to share their cached contents. We design a compact and dynamic data structure called Ludo Locator that returns the IP address of the edge server that stores the requested user-associated content. We have implemented a prototype of EdgeCut in a real network environment running in a public geo-distributed cloud. The experiment results show that EdgeCut reduces content access latency by up to 50% and reduces cloud traffic by up to 50% compared to existing solutions. The memory cost is less than 50MB for 10 million mobile users. The simulations using real network latency data show EdgeCut’s advantages over existing solutions on a large scale. 
    more » « less
  2. null (Ed.)
    Many recent efforts have demonstrated the performance benefits of running datacenter functions (e.g., NATs, load balancers, monitoring) on programmable switches. However, a key missing piece remains: fault tolerance. This is especially critical as the network is no longer stateless and pure endpoint recovery does not suffice. In this paper, we design and implement RedPlane, a fault-tolerant state store for stateful in-switch applications. This provides in-switch applications consistent access to their state, even if the switch they run on fails or traffic is rerouted to an alternative switch. We address key challenges in devising a practical, provably correct replication protocol and implementing it in the switch data plane. Our evaluations show that RedPlane incurs negligible overhead and enables end-to-end applications to rapidly recover from switch failures. 
    more » « less
  3. Motivated by a wide range of applications, research on agent-based models of contagion propagation over networks has attracted a lot of attention in the literature. Many of the available software systems for simulating such agent-based models require users to download software, build the executable, and set up execution environments. Further, running the resulting executable may require access to high performance computing clusters. Our work describes an open access software system (NetSimS) that works under the “Modeling and Simulation as a Service” (MSaaS) paradigm. It enables users to run simulations by selecting models and parameter values, initial conditions, and networks through a web interface. The system supports a variety of models and networks with millions of nodes and edges. In addition to the simulator, the system includes components that enable users to choose initial conditions for simulations in a variety of ways, to analyze the data generated through simulations, and to produce plots from the data. We describe the components of NetSimS and carry out a performance evaluation of the system. We also discuss two case studies carried out on large networks using the system. NetSimS is a major component within net.science, a cyberinfrastructure for network science. 
    more » « less
  4. Abstract. Flash based solid state drives (SSDs) have established them- selves as a higher-performance alternative to hard disk drives in cloud and mobile environments. Nevertheless, SSDs remain a performance bot- tleneck of computer systems due to their high I/O access latency. A com- mon approach for improving the access latency is prefetching. Prefetch- ing predicts future block accesses and preloads them into main memory ahead of time. In this paper, we discuss the challenges of prefetching in SSDs, explain why prior approaches fail to achieve high accuracy, and present a neural network based prefetching approach that signi cantly outperforms the state-of the-art. To achieve high performance, we ad- dress the challenges of prefetching in very large sparse address spaces, as well as prefetching in a timely manner by predicting ahead of time. We collect I/O trace les from several real-world applications running on cloud servers and show that our proposed approach consistently outper- forms the existing stride prefetchers by up to 800 and prior prefetching approaches based on Markov chains by up to 8. Furthermore, we pro- pose an address mapping learning technique to demonstrate the applica- bility of our approach to previously unseen SSD workloads and perform a hyperparameter sensitivity study. 
    more » « less
  5. null (Ed.)
    Fast networks and the desire for high resource utilization in data centers and the cloud have driven disaggregation. Application compute is separated from storage, but this leads to high overheads when data must move over the network for simple operations on it. Alternatively, systems could allow applications to run application logic within storage via user-defined functions. Unfortunately, this ties provisioning and utilization of storage and compute resources together again. We present a new approach to executing storage-level functions in an in-memory key-value store that avoids this problem by dynamically deciding where to execute functions over data. Users write storage functions that are logically decoupled from storage, but storage servers choose where to run invocations of these functions physically. By using a server-internal cost model and observing function execution, servers choose to directly run inexpensive functions, while preferring to execute functions with high CPU-cost at client machines. We show that with this approach storage servers can reduce network request processing costs, avoid server compute bottlenecks, and improve aggregate storage system throughput. We realize our approach on an in-memory key-value store that executes 3.2 million strict serializable user-defined storage functions per second with 100 us response times. When running a mix of logic from different applications, it provides throughput better than running that logic purely at storage servers (85% more) or purely at clients (10% more). For our workloads, it also reduces latency (up to 2x) and transactional aborts (up to 33%) over pure client-side execution. 
    more » « less