Mobile multi-robot teams deployed for monitoring or search-and-rescue missions in urban disaster areas can greatly improve the quality of vital data collected on-site. Analysis of such data can identify hazards and save lives. Unfortunately, such real deployments at scale are cost prohibitive and robot failures lead to data loss. Moreover, scaled-down deployments do not capture significant levels of interaction and communication complexity. To tackle this problem, we propose novel mobility and failure generation frameworks that allow realistic simulations of mobile robot networks for large scale disaster scenarios. Furthermore, since data replication techniques can improve the survivability of data collected during the operation, we propose an adaptive, scalable data replication technique that achieves high data survivability with low overhead. Our technique considers the anticipated robot failures and robot heterogeneity to decide how aggressively to replicate data. In addition, it considers survivability priorities, with some data requiring more effort to be saved than others. Using our novel simulation generation frameworks, we compare our adaptive technique with flooding and broadcast-based replication techniques and show that for failure rates of up to 60 it ensures better data survivability with lower communication costs.
Studies that have considered node heterogeneity assume that some nodes are more stable and more resourceful than others. CLEAR @cite_6 deploys a super-peer architecture that exploits relatively stable peers having maximum remaining battery power and processing capacity among their regional neighbors to determine a near-optimal reallocation period based on mobile host schedules. @cite_35 , resourceful nodes serve as cores to enable core-aided routing. Replication schemes examined include copy-to-core'', where both regular nodes and core nodes are carriers of messages to the destination, and dump-to-core'', where the regular nodes delete the messages, leaving the cores to deal with the delivery. Such nodes, due to their extended resources, acquire a similar role to the and presented in this work. However, unlike in our work, no differentiation was made with respect to their mobility or failures exhibited and how they affect the replication effort.
@cite_35 @cite_6
2023682886 2148058923 2199558228 2099567938
The complexity of current and emerging architectures provides users with options about how best to use the available resources, but makes predicting performance challenging. In this work a benchmark-driven model is developed for a simple shallow water code on a Cray XE6 system, to explore how deployment choices such as domain decomposition and core affinity affect performance. The resource sharing present in modern multi-core architectures adds various levels of heterogeneity to the system. Shared resources often includes cache, memory, network controllers and in some cases floating point units (as in the AMD Bulldozer), which mean that the access time depends on the mapping of application tasks, and the core's location within the system. Heterogeneity further increases with the use of hardware-accelerators such as GPUs and the Intel Xeon Phi, where many specialist cores are attached to general-purpose cores. This trend for shared resources and non-uniform cores is expected to continue into the exascale era. The complexity of these systems means that various runtime scenarios are possible, and it has been found that under-populating nodes, altering the domain decomposition and non-standard task to core mappings can dramatically alter performance. To find this out, however, is often a process of trial and error. To better inform this process, a performance model was developed for a simple regular grid-based kernel code, shallow. The code comprises two distinct types of work, loop-based array updates and nearest-neighbour halo-exchanges. Separate performance models were developed for each part, both based on a similar methodology. Application specific benchmarks were run to measure performance for different problem sizes under different execution scenarios. These results were then fed into a performance model that derives resource usage for a given deployment scenario, with interpolation between results as necessary. We develop a new tractable model for K-tier heterogeneous cellular networks (HetNets), where each base station (BS) is powered solely by a self-contained energy harvesting module. The BSs across tiers differ in terms of the energy harvesting rate, energy storage capacity, transmit power and deployment density. Since a BS may not always have enough energy, it may need to be kept OFF and allowed to recharge while nearby users are served by neighboring BSs that are ON. We show that the fraction of time a k^ th tier BS can be kept ON, termed availability ρ_k, is a fundamental metric of interest. Using tools from random walk theory, fixed point analysis and stochastic geometry, we characterize the set of K-tuples (ρ_1,ρ_2,…ρ_K), termed the availability region, that is achievable by general uncoordinated operational strategies, where the decision to toggle the current ON OFF state of a BS is taken independently of the other BSs. If the availability vector corresponding to the optimal system performance, e.g., in terms of rate, lies in this availability region, there is no performance loss due to the presence of unreliable energy sources. As a part of our analysis, we model the temporal dynamics of the energy level at each BS as a birth-death process, derive the energy utilization rate, and use hitting stopping time analysis to prove that there exists a fundamental limit on ρ_k that cannot be surpassed by any uncoordinated strategy. The continuous increase in volume, variety and velocity of Big Data exposes datacenter resource scaling to an energy utilization problem. Traditionally, datacenters employ x86-64 (big) server nodes with power usage of tens to hundreds of Watts. But lately, low-power (small) systems originally developed for mobile devices have seen significant improvements in performance. These improvements could lead to the adoption of such small systems in servers, as announced by major industry players. In this context, we systematically conduct a performance study of Big Data execution on small nodes in comparison with traditional big nodes, and present insights that would be useful for future development. We run Hadoop MapReduce, MySQL and in-memory Shark workloads on clusters of ARM big. LITTLE boards and Intel Xeon server systems. We evaluate execution time, energy usage and total cost of running the workloads on self-hosted ARM and Xeon nodes. Our study shows that there is no one size fits all rule for judging the efficiency of executing Big Data workloads on small and big nodes. But small memory size, low memory and I O bandwidths, and software immaturity concur in canceling the lower-power advantage of ARM servers. We show that I O-intensive MapReduce workloads are more energy-efficient to run on Xeon nodes. In contrast, database query processing is always more energy-efficient on ARM servers, at the cost of slightly lower throughput. With minor software modifications, CPU-intensive MapReduce workloads are almost four times cheaper to execute on ARM servers. The widespread availability of mobile wireless devices offers growing opportunities for the formation of temporary networks with only intermittent connectivity. These intermittently-connected networks (ICNs) typically lack stable end-to-end paths. In order to improve the delivery rates of the networks, new store-carry-and-forward protocols have been proposed which often use message replication as a forwarding mechanism. Message replication is effective at improving delivery, but given the limited resources of ICN nodes, such as buffer space, bandwidth and energy, as well as the highly dynamic nature of these networks, replication can easily overwhelm node resources. In this work we propose a novel node-based replication management algorithm which addresses buffer congestion by dynamically limiting the replication a node performs during each encounter. The insight for our algorithm comes from a stochastic model of message delivery in ICNs with constrained buffer space. We show through simulation that our algorithm is effective, nearly tripling delivery rates in some scenarios, and imposes little overhead.