Computer Science

We introduce the "continuized" Nesterov acceleration, a close variant of Nesterov acceleration whose variables are indexed by a continuous time parameter. The two variables continuously mix following a linear ordinary differential equation and take gradient steps at random times. This continuized variant benefits from the best of the continuous and the discrete frameworks: as a continuous process, one can use differential calculus to analyze convergence and obtain analytical expressions for the parameters; but a discretization of the continuized process can be computed exactly with convergence rates similar to those of Nesterov original acceleration. We show that the discretization has the same structure as Nesterov acceleration, but with random parameters.

Edge Computing (EC) offers an infrastructure that acts as the mediator between the Cloud and the Internet of Things (IoT). The goal is to reduce the latency that we enjoy when relying on Cloud. IoT devices interact with their environment to collect data relaying them towards the Cloud through the EC. Various services can be provided at the EC for the immediate management of the collected data. One significant task is the management of missing values. In this paper, we propose an ensemble based approach for data imputation that takes into consideration the spatio-temporal aspect of the collected data and the reporting devices. We propose to rely on the group of IoT devices that resemble to the device reporting missing data and enhance its data imputation process. We continuously reason on the correlation of the reported streams and efficiently combine the available data. Our aim is to `aggregate' the local view on the appropriate replacement with the `opinion' of the group. We adopt widely known similarity techniques and a statistical modelling methodology to deliver the final outcome. We provide the description of our model and evaluate it through a high number of simulations adopting various experimental scenarios.

Loop scheduling techniques aim to achieve load-balanced executions of scientific applications. Dynamic loop self-scheduling (DLS) libraries for distributed-memory systems are typically MPI-based and employ a centralized chunk calculation approach (CCA) to assign variably-sized chunks of loop iterations. We present a distributed chunk calculation approach (DCA) that supports various types of DLS techniques. Using both CCA and DCA, twelve DLS techniques are implemented and evaluated in different CPU slowdown scenarios. The results show that the DLS techniques implemented using DCA outperform their corresponding ones implemented with CCA, especially in extreme system slowdown scenarios.

Code offloading refers to partitioning software and migrating the mobile codes to other computational entities for processing. Often when a large number of mobile codes need to be distributed to many heterogenous hosts, this can easily lead to poor system performance if one host gets too many mobile codes to process while others are almost idle. To resolve such situation, we proposed a proposed a load balancing algorithm to ensure fairness in the distribution of the mobile codes. The algorithm is based on the popular Weighted Least-Connections (WLC) scheduling algorithm while taking into consideration the dynamic recalculation of the hosts weights and system attributes such as CPU idle rate and memory idle rate which the WLC algorithm does not take into consideration. Using simulation, various number of mobile codes were distributed to the hosts/servers and the proposed algorithm outperform existing Least-Connections and Weighted Least-Connections scheduling algorithms thus improving system efficiency.

Serverless computing with cloud functions is quickly gaining adoption, but constrains programmers with its limited support for state management. We introduce a shared file system for cloud functions. It offers familiar POSIX semantics while taking advantage of distinctive aspects of cloud functions to achieve scalability and performance beyond what traditional shared file systems can offer. We take advantage of the function-grained fault tolerance model of cloud functions to proceed optimistically using local state, safe in the knowledge that we can restart if cache reads or lock activity cannot be reconciled upon commit. The boundaries of cloud functions provide implicit commit and rollback points, giving us the flexibility to use transaction processing techniques without changing the programming model or API. This allows a variety of stateful sever-based applications to benefit from the simplicity and scalability of serverless computing, often with little or no modification.

Application partitioning and code offloading are being researched extensively during the past few years. Several frameworks for code offloading have been proposed. However, fewer works attempted to address issues occurred with its implementation in pervasive environments such as frequent network disconnection due to high mobility of users. Thus, in this paper, we proposed a fault tolerant algorithm that helps in consolidating the efficiency and robustness of application partitioning and offloading frameworks. To permit the usage of different fault tolerant policies such as replication and checkpointing, the devices are grouped into high and low reliability clusters. Experimental results shown that the fault tolerant algorithm can easily adapt to different execution conditions while incurring minimum overhead.

Betweenness centrality (BC) is an important graph analytical application for large-scale graphs. While there are many efforts for parallelizing betweenness centrality algorithms on multi-core CPUs and many-core GPUs, in this work, we propose a novel fine-grained CPU-GPU hybrid algorithm that partitions a graph into CPU and GPU partitions, and performs BC computations for the graph on both the CPU and GPU resources simultaneously with very small number of CPU-GPU communications. The forward phase in our hybrid BC algorithm leverages the multi-source property inherent in the BC problem. We also perform a novel hybrid and asynchronous backward phase that performs minimal CPU-GPU synchronizations. Evaluations using a large number of graphs with different characteristics show that our hybrid approach gives 80% improvement in performance, and 80-90% less CPU-GPU communications than an existing hybrid algorithm based on the popular Bulk Synchronous Paradigm (BSP) approach.

Byzantine fault tolerant protocols enable state replication in the presence of crashed, malfunctioning, or actively malicious processes. Designing such protocols without the assistance of verification tools, however, is remarkably error-prone. In an adversarial environment, performance and flexibility come at the cost of complexity, making the verification of existing protocols extremely difficult. We take a different approach and propose a formally verified consensus protocol designed for a specific use case: secure logging. Our protocol allows each node to propose entries in a parallel subroutine, and guarantees that correct nodes agree on the set of all proposed entries, without leader election. It is simple yet practical, as it can accommodate the workload of a logging system such as Certificate Transparency. We show that it is optimal in terms of both required rounds and tolerable faults. Using Isabelle/HOL, we provide a fully machine-checked security proof based upon the Heard-Of model, which we extend to support signatures. We also present and evaluate a prototype implementation.

As the Data Science field continues to mature, and we collect more data, the demand to store and analyze them will continue to increase. This increase in data availability and demand for analytics will put a strain on data centers and compute clusters-with implications for both energy costs and emissions. As the world battles a climate crisis, it is prudent for organizations with data centers to have a framework for combating increasing energy costs and emissions to meet demand for analytics work. In this paper, I present a generalized framework for organizations to audit data centers energy efficiency to understand the resources required to operate a given data center and effective steps organizations can take to improve data center efficiency and lower the environmental impact.

The Cloud infrastructure offers to end users a broad set of heterogenous computational resources using the pay-as-you-go model. These virtualized resources can be provisioned using different pricing models like the unreliable model where resources are provided at a fraction of the cost but with no guarantee for an uninterrupted processing. However, the enormous gamut of opportunities comes with a great caveat as resource management and scheduling decisions are increasingly complicated. Moreover, the presented uncertainty in optimally selecting resources has also a negatively impact on the quality of solutions delivered by scheduling algorithms. In this paper, we present a dynamic scheduling algorithm (i.e., the Uncertainty-Driven Scheduling - UDS algorithm) for the management of scientific workflows in Cloud. Our model minimizes both the makespan and the monetary cost by dynamically selecting reliable or unreliable virtualized resources. For covering the uncertainty in decision making, we adopt a Fuzzy Logic Controller (FLC) to derive the pricing model of the resources that will host every task. We evaluate the performance of the proposed algorithm using real workflow applications being tested under the assumption of different probabilities regarding the revocation of unreliable resources. Numerical results depict the performance of the proposed approach and a comparative assessment reveals the position of the paper in the relevant literature.