C-RAM: Breaking Mobile Device Memory Barriers Using the Cloud

Mobile applications are constrained by the available memory of mobile devices. We present C-RAM, a system that uses cloud-based memory to extend the memory of mobile devices. It splits application state and its associated computation between a mobile device and a cloud node to allow applications to consume more memory, while minimising the performance impact. C-RAM thus enables developers to realise new applications or port legacy desktop applications with a large memory footprint to mobile platforms without explicitly designing them to account for memory limitations. To handle network failures with partitioned application state, C-RAM uses a new snapshot-based fault tolerance mechanism in which changes to remote memory objects are periodically backed up to the device. After failure, or when network usage exceeds a given limit, the device rolls back execution to continue from the last snapshot. C-RAM supports local execution with an application state that exceeds the available device memory through a user-level virtual memory: objects are loaded on-demand from snapshots in flash memory. Our C-RAM prototype supports Objective-C applications on the unmodified iOS platform. With C-RAM, applications can consume 10× more memory than the device capacity, with a negligible impact on application performance. In some cases, C-RAM even achieves a significant speed-up in execution time (up to 9.7×)

Edge Reduce: Eliminating Mobile Network Traffic Using Application-Specific Edge Proxies

Mobile carriers are struggling to cope with the surge in smartphone traffic, which reflects badly on end users who often experience poor connectivity in densely populated urban environments. Data transfers between mobile client applications and their Internet backend services contribute significantly to the contention in radio access networks (RANs). Client applications, however, typically transfer unnecessary data because (i) backend service APIs do not support a fine-grained specification of the data actually required by clients and (ii) clients aggressively prefetch data that is never used. We describe EDGEREDUCE, an automated approach for reducing the data transmitted from backend services to a mobile device. Based on source-level program analysis, EDGEREDUCE generates application-specific proxies for mobile client applications that execute part of the application logic at the network edge to filter data returned by backend API calls and only send used data to the client. EDGEREDUCE also permits the tuning of aggressive prefetching strategies: proxies replace large prefetched objects such as images by futures, whose access by the client triggers the retrieval of the object on-demand. We show that EDGEREDUCE reduces the RAN traffic for real-world iOS client applications by up to 8×, with only a modest increase in response time

Evaluating DHT-Based Service Placement for Stream Based Overlays

Stream-based overlay networks (SBONs) are one approach to implementing large-scale stream processing systems. A fundamental consideration in an SBON is that of service placement, which determines the physical location of in-network processing services or operators, in such a way that network resources are used efficiently. Service placement consists of two components: node discovery, which selects a candidate set of nodes on which services might be placed, and node selection, which chooses the particular node to host a service. By viewing the placement problem as the composition of these two processes we can trade-off quality and efficiency between them. A bad discovery scheme can yield a good placement, but at the cost of an expensive selection mechanism. Recent work on operator placement [3, 9] proposes to leverage routing paths in a distributed hash table (DHT) to obtain a set of candidate nodes for service placement. We evaluate the appropriateness of using DHT routing paths for service placement in an SBON, when aiming to minimize network usage. For this, we consider two DHT-based algorithms for node discovery, which use either the union or intersection of DHT routing paths in the SBON, and compare their performance to other techniques. We show that current DHT-based schemes are actually rather poor node discovery algorithms, when minimizing network utilization. An efficient DHT may not traverse enough hops to obtain a sufficiently large candidate set for placement. The union of DHT routes may result in a low-quality set of discovered nodes that requires an expensive node selection algorithm. Finally, the intersection of DHT routes relies on route convergence, which prevents the placement of services with a large fan-in.Engineering and Applied Science

Towards Enabling Hyper-Responsive Mobile Apps Through Network Edge Assistance

Poor Internet performance currently undermines the efficiency of hyper-responsive mobile apps such as augmented reality clients and online games, which require low-latency access to real-time backend services. While edge-assisted execution, i.e. moving entire services to the edge of an access network, helps eliminate part of the communication overhead involved, this does not scale to the number of users that share an edge infrastructure. This is due to a mismatch between the scarce availability of resources in access networks and the aggregate demand for computational power from client applications. Instead, this paper proposes a hybrid edge-assisted deployment model in which only part of a service executes on LTE edge servers. We provide insights about the conditions that must hold for such a model to be effective by investigating in simulation different deployment and application scenarios. In particular, we show that using LTE edge servers with modest capabilities, performance can improve significantly as long as at most 50% of client requests are processed at the edge. Moreover, we argue that edge servers should be installed at the core of a mobile network, rather than the mobile base station: the difference in performance is negligible, whereas the latter choice entails high deployment costs. Finally, we verify that, for the proposed model, the impact of user mobility on TCP performance is low

KungFu: Making Training in Distributed Machine Learning Adaptive

When using distributed machine learning (ML) systems to train models on a cluster of worker machines, users must con-figure a large number of parameters: hyper-parameters (e.g. the batch size and the learning rate) affect model convergence; system parameters (e.g. the number of workers and their communication topology) impact training performance. In current systems, adapting such parameters during training is ill-supported. Users must set system parameters at deployment time, and provide fixed adaptation schedules for hyper-parameters in the training program. We describe Kung Fu, a distributed ML library for Tensor-Flow that is designed to enable adaptive training. Kung Fu allows users to express high-level Adaptation Policies(APs)that describe how to change hyper- and system parameters during training. APs take real-time monitored metrics (e.g. signal-to-noise ratios and noise scale) as input and trigger control actions (e.g. cluster rescaling or synchronisation strategy updates). For execution, APs are translated into monitoring and control operators, which are embedded in the data flowgraph. APs exploit an efficient asynchronous collective communication layer, which ensures concurrency and consistency of monitoring and adaptation operation