System-Level Support for Composition of Applications

ABSTRACT Current HPC system software lacks support for emerging application deployment scenarios that combine one or more simulations with in situ analytics, sometimes called multi-component or multi-enclave applications. This paper presents an initial design study, implementation, and evaluation of mechanisms supporting composite multi-enclave applications in the Hobbes exascale operating system. These mechanisms include virtualization techniques isolating application custom enclaves while using the vendor-supplied host operating system and high-performance inter-VM communication mechanisms. Our initial single-node performance evaluation of these mechanisms on multi-enclave science applications, both real and proxy, demonstrate the ability to support multi-enclave HPC job composition with minimal performance overhead

Bidding for Highly Available Services with Low Price in Spot Instance Market

ABSTRACT Amazon EC2 has built the Spot Instance Marketplace and offers a new type of virtual machine instances called as spot instances. These instances are less expensive but considered failure-prone. Despite the underlying hardware status, if the bidding price is lower than the market price, such an instance will be terminated. Distributed systems can be built from the spot instances to reduce the cost while still tolerating instance failures. For example, embarrassingly parallel jobs can use the spot instances by re-executing failed tasks. The bidding framework for such jobs simply selects the spot price as the bid. However, highly available services like lock service or storage service cannot use the similar techniques for availability consideration. The spot instance failure model is different to that of normal instances (fixed failure probability in traditional distributed model). This makes the bidding strategy more complex to keep service availability for such systems. We formalize this problem and propose an availability and cost aware bidding framework. Experiment results show that our bidding framework can reduce the costs of a distributed lock service and a distributed storage service by 81.23% and 85.32% respectively while still keeping availability level the same as it is by using on-demand instances

A smartwater metering deployment based on the fog computing paradigm

In this paper, we look into smart water metering infrastructures that enable continuous, on-demand and bidirectional data exchange between metering devices, water flow equipment, utilities and end-users. We focus on the design, development and deployment of such infrastructures as part of larger, smart city, infrastructures. Until now, such critical smart city infrastructures have been developed following a cloud-centric paradigm where all the data are collected and processed centrally using cloud services to create real business value. Cloud-centric approaches need to address several performance issues at all levels of the network, as massive metering datasets are transferred to distant machine clouds while respecting issues like security and data privacy. Our solution uses the fog computing paradigm to provide a system where the computational resources already available throughout the network infrastructure are utilized to facilitate greatly the analysis of fine-grained water consumption data collected by the smart meters, thus significantly reducing the overall load to network and cloud resources. Details of the system's design are presented along with a pilot deployment in a real-world environment. The performance of the system is evaluated in terms of network utilization and computational performance. Our findings indicate that the fog computing paradigm can be applied to a smart grid deployment to reduce effectively the data volume exchanged between the different layers of the architecture and provide better overall computational, security and privacy capabilities to the system

Big data workflows: Locality-aware orchestration using software containers

The emergence of the Edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the performance penalties from data transfers among remote data centres. Existing Big Data processing solutions provide limited support for handling data locality and are inefficient in processing small and frequent events specific to the Edge environments. This article proposes a novel architecture and a proof-of-concept implementation for software container-centric Big Data workflow orchestration that puts data locality at the forefront. The proposed solution considers the available data locality information, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare the proposed solution with Argo Workflows and demonstrate a significant performance improvement in the execution speed for processing the same data units. Finally, we carry out experiments with the proposed solution under different configurations and analyze individual aspects affecting the performance of the overall solution.publishedVersio

Big data workflows: Locality-aware orchestration using software containers

The emergence of the Edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the performance penalties from data transfers among remote data centres. Existing Big Data processing solutions provide limited support for handling data locality and are inefficient in processing small and frequent events specific to the Edge environments. This article proposes a novel architecture and a proof-of-concept implementation for software container-centric Big Data workflow orchestration that puts data locality at the forefront. The proposed solution considers the available data locality information, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare the proposed solution with Argo Workflows and demonstrate a significant performance improvement in the execution speed for processing the same data units. Finally, we carry out experiments with the proposed solution under different configurations and analyze individual aspects affecting the performance of the overall solution.publishedVersio

PIM-Enclave: Bringing Confidential Computation Inside Memory

Demand for data-intensive workloads and confidential computing are the prominent research directions shaping the future of cloud computing. Computer architectures are evolving to accommodate the computing of large data better. Protecting the computation of sensitive data is also an imperative yet challenging objective; processor-supported secure enclaves serve as the key element in confidential computing in the cloud. However, side-channel attacks are threatening their security boundaries. The current processor architectures consume a considerable portion of its cycles in moving data. Near data computation is a promising approach that minimizes redundant data movement by placing computation inside storage. In this paper, we present a novel design for Processing-In-Memory (PIM) as a data-intensive workload accelerator for confidential computing. Based on our observation that moving computation closer to memory can achieve efficiency of computation and confidentiality of the processed information simultaneously, we study the advantages of confidential computing \emph{inside} memory. We then explain our security model and programming model developed for PIM-based computation offloading. We construct our findings into a software-hardware co-design, which we call PIM-Enclave. Our design illustrates the advantages of PIM-based confidential computing acceleration. Our evaluation shows PIM-Enclave can provide a side-channel resistant secure computation offloading and run data-intensive applications with negligible performance overhead compared to baseline PIM model