ComFlux: External Composition and Adaptation of Pervasive Applications

Technology is becoming increasingly pervasive. At present, the system components working together to provide functionality, be they purely software or with a physical element, tend to operate within silos, bound to a particular application or usage. This is counter to the wider vision of pervasive computing, where a potentially limitless number of applications can be realised through the dynamic and seamless interactions of system components. We believe this application composition should be externally controlled, driven by policy and subject to access control. We present ComFlux, our open source middleware, and show through a number of designs and implementations, how it supports this functionality with acceptable overhead

Enabling efficient application monitoring in cloud data centers using SDN

Software Defined Networking (SDN) not only enables agility through the realization of part of the network functionality in software but also facilitates offering advanced features at the network layer. Hence, SDN can support a wide range of middleware services; network performance monitoring is an example of these services that are already deployed in practice. In this paper, we exploit the use of SDNs to efficiently provide application monitoring functionality. The recent rise of complex cloud applications has made performance monitoring a major issue. We show that many performance indicators can be inferred from messages exchanged among application components. By analyzing these messages, we argue that the overhead of performance monitoring could be effectively moved from the end hosts into the SDN middleware of the cloud infrastructure which enables more flexible placement of logging functionality. This paper explores several approaches for supporting application monitoring through SDN. In particular, we combine selective forwarding in SDN to enable message filtering and reformatting, and propose a customized port sniffing technique. We describe the implementation of the approach within the standard SDN software, namely OVS. We further provide a comprehensive performance evaluation to analyze advantages and disadvantages of our approach, and highlight the trade-offs

Effective Elastic Scaling of Deep Learning Workloads

The increased use of deep learning (DL) in academia, government and industry has, in turn, led to the popularity of on-premise and cloud-hosted deep learning platforms, whose goals are to enable organizations utilize expensive resources effectively, and to share said resources among multiple teams in a fair and effective manner. In this paper, we examine the elastic scaling of Deep Learning (DL) jobs over large-scale training platforms and propose a novel resource allocation strategy for DL training jobs, resulting in improved job run time performance as well as increased cluster utilization. We begin by analyzing DL workloads and exploit the fact that DL jobs can be run with a range of batch sizes without affecting their final accuracy. We formulate an optimization problem that explores a dynamic batch size allocation to individual DL jobs based on their scaling efficiency, when running on multiple nodes. We design a fast dynamic programming based optimizer to solve this problem in real-time to determine jobs that can be scaled up/down, and use this optimizer in an autoscaler to dynamically change the allocated resources and batch sizes of individual DL jobs. We demonstrate empirically that our elastic scaling algorithm can complete up to $\approx 2 \times$ as many jobs as compared to a strong baseline algorithm that also scales the number of GPUs but does not change the batch size. We also demonstrate that the average completion time with our algorithm is up to $\approx 10 \times$ faster than that of the baseline

Authenticated Key-Value Stores with Hardware Enclaves

Authenticated data storage on an untrusted platform is an important computing paradigm for cloud applications ranging from big-data outsourcing, to cryptocurrency and certificate transparency log. These modern applications increasingly feature update-intensive workloads, whereas existing authenticated data structures (ADSs) designed with in-place updates are inefficient to handle such workloads. In this paper, we address this issue and propose a novel authenticated log-structured merge tree (eLSM) based key-value store by leveraging Intel SGX enclaves. We present a system design that runs the code of eLSM store inside enclave. To circumvent the limited enclave memory (128 MB with the latest Intel CPUs), we propose to place the memory buffer of the eLSM store outside the enclave and protect the buffer using a new authenticated data structure by digesting individual LSM-tree levels. We design protocols to support query authentication in data integrity, completeness (under range queries), and freshness. The proof in our protocol is made small by including only the Merkle proofs at selective levels. We implement eLSM on top of Google LevelDB and Facebook RocksDB with minimal code change and performance interference. We evaluate the performance of eLSM under the YCSB workload benchmark and show a performance advantage of up to 4.5X speedup.Comment: eLSM, Enclave, key-value store, ADS, 18 page