HALO: Post-Link Heap-Layout Optimisation

Today, general-purpose memory allocators dominate the landscape of dynamic memory management. While these so- lutions can provide reasonably good behaviour across a wide range of workloads, it is an unfortunate reality that their behaviour for any particular workload can be highly suboptimal. By catering primarily to average and worst-case usage patterns, these allocators deny programs the advantages of domain-specific optimisations, and thus may inadvertently place data in a manner that hinders performance, generating unnecessary cache misses and load stalls. To help alleviate these issues, we propose HALO: a post-link profile-guided optimisation tool that can improve the layout of heap data to reduce cache misses automatically. Profiling the target binary to understand how allocations made in different contexts are related, we specialise memory-management routines to allocate groups of related objects from separate pools to increase their spatial locality. Unlike other solutions of its kind, HALO employs novel grouping and identification algorithms which allow it to create tight-knit allocation groups using the entire call stack and to identify these efficiently at runtime. Evaluation of HALO on contemporary out-of-order hardware demonstrates speedups of up to 28% over jemalloc, out-performing a state-of-the-art data placement technique from the literature

Service Abstractions for Scalable Deep Learning Inference at the Edge

Deep learning driven intelligent edge has already become a reality, where millions of mobile, wearable, and IoT devices analyze real-time data and transform those into actionable insights on-device. Typical approaches for optimizing deep learning inference mostly focus on accelerating the execution of individual inference tasks, without considering the contextual correlation unique to edge environments and the statistical nature of learning-based computation. Specifically, they treat inference workloads as individual black boxes and apply canonical system optimization techniques, developed over the last few decades, to handle them as yet another type of computation-intensive applications. As a result, deep learning inference on edge devices still face the ever increasing challenges of customization to edge device heterogeneity, fuzzy computation redundancy between inference tasks, and end-to-end deployment at scale. In this thesis, we propose the first framework that automates and scales the end-to-end process of deploying efficient deep learning inference from the cloud to heterogeneous edge devices. The framework consists of a series of service abstractions that handle DNN model tailoring, model indexing and query, and computation reuse for runtime inference respectively. Together, these services bridge the gap between deep learning training and inference, eliminate computation redundancy during inference execution, and further lower the barrier for deep learning algorithm and system co-optimization. To build efficient and scalable services, we take a unique algorithmic approach of harnessing the semantic correlation between the learning-based computation. Rather than viewing individual tasks as isolated black boxes, we optimize them collectively in a white box approach, proposing primitives to formulate the semantics of the deep learning workloads, algorithms to assess their hidden correlation (in terms of the input data, the neural network models, and the deployment trials) and merge common processing steps to minimize redundancy

Quality of Service Aware Data Stream Processing for Highly Dynamic and Scalable Applications

Huge amounts of georeferenced data streams are arriving daily to data stream management systems that are deployed for serving highly scalable and dynamic applications. There are innumerable ways at which those loads can be exploited to gain deep insights in various domains. Decision makers require an interactive visualization of such data in the form of maps and dashboards for decision making and strategic planning. Data streams normally exhibit fluctuation and oscillation in arrival rates and skewness. Those are the two predominant factors that greatly impact the overall quality of service. This requires data stream management systems to be attuned to those factors in addition to the spatial shape of the data that may exaggerate the negative impact of those factors. Current systems do not natively support services with quality guarantees for dynamic scenarios, leaving the handling of those logistics to the user which is challenging and cumbersome. Three workloads are predominant for any data stream, batch processing, scalable storage and stream processing. In this thesis, we have designed a quality of service aware system, SpatialDSMS, that constitutes several subsystems that are covering those loads and any mixed load that results from intermixing them. Most importantly, we natively have incorporated quality of service optimizations for processing avalanches of geo-referenced data streams in highly dynamic application scenarios. This has been achieved transparently on top of the codebases of emerging de facto standard best-in-class representatives, thus relieving the overburdened shoulders of the users in the presentation layer from having to reason about those services. Instead, users express their queries with quality goals and our system optimizers compiles that down into query plans with an embedded quality guarantee and leaves logistic handling to the underlying layers. We have developed standard compliant prototypes for all the subsystems that constitutes SpatialDSMS

Designing for practice-based context-awareness in ubiquitous e-health environments

Existing approaches for supporting context-aware knowledge sharing in ubiquitous healthcare give little attention to practice-based structures of knowledge representation. They guide knowledge re-use at an abstract level and hardly incorporate details of actionable tasks and processes necessary for accomplishing work in a real-world context. This paper presents a context-aware model for supporting clinical knowledge sharing across organizational and geographical boundaries in ubiquitous e-health. The model draws on activity and situation awareness theories as well as the Belief-Desire Intention and Case-based Reasoning techniques in intelligent systems with the goal of enabling clinicians in disparate locations to gain a common representation of relevant situational information in each other's work contexts based on the notion of practice. We discuss the conceptual design of the model, present a formal approach for representing practice as context in a ubiquitous healthcare environment, and describe an application scenario and a prototype system to evaluate the proposed approach

Parallel evaluation strategies for lazy data structures in Haskell

Conventional parallel programming is complex and error prone. To improve programmer productivity, we need to raise the level of abstraction with a higher-level programming model that hides many parallel coordination aspects. Evaluation strategies use non-strictness to separate the coordination and computation aspects of a Glasgow parallel Haskell (GpH) program. This allows the specification of high level parallel programs, eliminating the low-level complexity of synchronisation and communication associated with parallel programming. This thesis employs a data-structure-driven approach for parallelism derived through generic parallel traversal and evaluation of sub-components of data structures. We focus on evaluation strategies over list, tree and graph data structures, allowing re-use across applications with minimal changes to the sequential algorithm. In particular, we develop novel evaluation strategies for tree data structures, using core functional programming techniques for coordination control, achieving more flexible parallelism. We use non-strictness to control parallelism more flexibly. We apply the notion of fuel as a resource that dictates parallelism generation, in particular, the bi-directional flow of fuel, implemented using a circular program definition, in a tree structure as a novel way of controlling parallel evaluation. This is the first use of circular programming in evaluation strategies and is complemented by a lazy function for bounding the size of sub-trees. We extend these control mechanisms to graph structures and demonstrate performance improvements on several parallel graph traversals. We combine circularity for control for improved performance of strategies with circularity for computation using circular data structures. In particular, we develop a hybrid traversal strategy for graphs, exploiting breadth-first order for exposing parallelism initially, and then proceeding with a depth-first order to minimise overhead associated with a full parallel breadth-first traversal. The efficiency of the tree strategies is evaluated on a benchmark program, and two non-trivial case studies: a Barnes-Hut algorithm for the n-body problem and sparse matrix multiplication, both using quad-trees. We also evaluate a graph search algorithm implemented using the various traversal strategies. We demonstrate improved performance on a server-class multicore machine with up to 48 cores, with the advanced fuel splitting mechanisms proving to be more flexible in throttling parallelism. To guide the behaviour of the strategies, we develop heuristics-based parameter selection to select their specific control parameters

Generic Distribution Support for Programming Systems

This dissertation provides constructive proof, through the implementation of a middleware, that distribution transparency is practical, generic, and extensible. Fault tolerant distributed services can be developed by using the failure detection abilities of the middleware. By generic we mean that the middleware can be used for many different programming languages and paradigms. Distribution for each kind of language entity is done in terms of consistency protocols, which guarantee that the semantics of the entities are preserved in a distributed setting. The middleware allows new consistency protocols to be added easily. The efficiency of the middleware and the ease of integration are shown by coupling the middleware to a programming system, which encompasses the object oriented, the functional, and the concurrent-declarative programming paradigms. Our measurements show that the distribution middleware is competitive with the most popular distributed programming systems (JavaRMI, .NET, IBM CORBA)

TEMPOS: QoS Management Middleware for Edge Cloud Computing FaaS in the Internet of Things

Several classes of advanced Internet of Things (IoT) applications, e.g., in the industrial manufacturing domain, call for Quality of Service (QoS) management to guarantee/control performance indicators, even in presence of many sources of "stochastic noise" in real deployment environments, from scarcely available bandwidth in a time window to concurrent usage of virtualized processing resources. This paper proposes a novel IoT-oriented middleware that i) considers and coordinates together different aspects of QoS monitoring, control, and management for different kinds of virtualized resources (from networking to processing) in a holistic way, and ii) specifically targets deployment environments where edge cloud resources are employed to enable the Serverless paradigm in the cloud continuum. The reported experimental results show how it is possible to achieve the desired QoS differentiation by coordinating heterogeneous mechanisms and technologies already available in the market. This demonstrates the feasibility of effective QoS-aware management of virtualized resources in the cloud-to-things continuum when considering a Serverless provisioning scenario, which is completely original in the related literature to the best of our knowledge