Seeing Shapes in Clouds: On the Performance-Cost trade-off for Heterogeneous Infrastructure-as-a-Service

In the near future FPGAs will be available by the hour, however this new Infrastructure as a Service (IaaS) usage mode presents both an opportunity and a challenge: The opportunity is that programmers can potentially trade resources for performance on a much larger scale, for much shorter periods of time than before. The challenge is in finding and traversing the trade-off for heterogeneous IaaS that guarantees increased resources result in the greatest possible increased performance. Such a trade-off is Pareto optimal. The Pareto optimal trade-off for clusters of heterogeneous resources can be found by solving multiple, multi-objective optimisation problems, resulting in an optimal allocation of tasks to the available platforms. Solving these optimisation programs can be done using simple heuristic approaches or formal Mixed Integer Linear Programming (MILP) techniques. When pricing 128 financial options using a Monte Carlo algorithm upon a heterogeneous cluster of Multicore CPU, GPU and FPGA platforms, the MILP approach produces a trade-off that is up to 110% faster than a heuristic approach, and over 50% cheaper. These results suggest that high quality performance-resource trade-offs of heterogeneous IaaS are best realised through a formal optimisation approach.Comment: Presented at Second International Workshop on FPGAs for Software Programmers (FSP 2015) (arXiv:1508.06320

Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

Mapping tree-shaped workflows on systems with different memory sizes and processor speeds

Directed acyclic graphs are commonly used to model scientific workflows, by expressing dependencies between tasks, as well as the resource requirements of the workflow. As a special case, rooted directed trees occur in several applications, for instance in sparse matrix computations. Since typical workflows are modeled by large trees, it is crucial to schedule them efficiently, so that their execution time (or makespan) is minimized. Furthermore, it is usually beneficial to distribute the execution on several compute nodes, hence increasing the available memory, and allowing us to parallelize parts of the execution. To exploit the heterogeneity of modern clusters in this context, we investigate the partitioning and mapping of tree‐shaped workflows on two types of target architecture models: in AM1, each processor can have a different memory size, and in AM2, each processor can also have a different speed (in addition to a different memory size). We design a three‐step heuristic for AM1, which adapts and extends previous work for homogeneous clusters [Gou C, Benoit A, Marchal L. Partitioning tree‐shaped task graphs for distributed platforms with limited memory. IEEE Trans Parallel Dist Syst 2020; 31(7): 1533–1544]. The changes we propose concern the assignment to processors (accounting for the different memory sizes) and the availability of suitable processors when splitting or merging subtrees. For AM2, we extend the heuristic for AM1 with a two‐phase local search approach. Phase A is a swap‐based hill climber, while (the optional) Phase B is inspired by iterated local search. We evaluate our heuristics for AM1 and AM2 with extensive simulations, and we demonstrate that exploiting the heterogeneity in the cluster significantly reduces the makespan, compared to the state of the art for homogeneous processors.Peer Reviewe

Radio Resource Management Optimization For Next Generation Wireless Networks

The prominent versatility of today’s mobile broadband services and the rapid advancements in the cellular phones industry have led to a tremendous expansion in the wireless market volume. Despite the continuous progress in the radio-access technologies to cope with that expansion, many challenges still remain that need to be addressed by both the research and industrial sectors. One of the many remaining challenges is the efficient allocation and management of wireless network resources when using the latest cellular radio technologies (e.g., 4G). The importance of the problem stems from the scarcity of the wireless spectral resources, the large number of users sharing these resources, the dynamic behavior of generated traffic, and the stochastic nature of wireless channels. These limitations are further tightened as the provider’s commitment to high quality-of-service (QoS) levels especially data rate, delay and delay jitter besides the system’s spectral and energy efficiencies. In this dissertation, we strive to solve this problem by presenting novel cross-layer resource allocation schemes to address the efficient utilization of available resources versus QoS challenges using various optimization techniques. The main objective of this dissertation is to propose a new predictive resource allocation methodology using an agile ray tracing (RT) channel prediction approach. It is divided into two parts. The first part deals with the theoretical and implementational aspects of the ray tracing prediction model, and its validation. In the second part, a novel RT-based scheduling system within the evolving cloud radio access network (C-RAN) architecture is proposed. The impact of the proposed model on addressing the long term evolution (LTE) network limitations is then rigorously investigated in the form of optimization problems. The main contributions of this dissertation encompass the design of several heuristic solutions based on our novel RT-based scheduling model, developed to meet the aforementioned objectives while considering the co-existing limitations in the context of LTE networks. Both analytical and numerical methods are used within this thesis framework. Theoretical results are validated with numerical simulations. The obtained results demonstrate the effectiveness of our proposed solutions to meet the objectives subject to limitations and constraints compared to other published works