Configuration space complexity makes the big-data software systems hard to configure well. Consider Hadoop, with over nine hundred parameters, developers often just use thedefaultconfigurations provided with Hadoop distributions. The opportunity costs in lost performance are significant. Popular learning-based approaches to auto-tune software does not scale well for big-data systems because of the high cost of collecting training data. We present a new method based on a combination ofEvolutionary Markov Chain Monte Carlo (EMCMC)sampling and cost reduction techniques to find better-performing configurations for big data systems. For cost reduction, we developed and experimentally tested and validated two approaches: using scaled-up big data jobs as proxies for the objective function for larger jobs and using a dynamic job similarity measure to infer that results obtained for one kind of big data problem will work well for similar problems. Our experimental results suggest that our approach promises to improve the performance of big data systems significantly and that it outperforms competing approaches based on random sampling, basic genetic algorithms (GA), and predictive model learning. Our experimental results support the conclusion that our approach strongly demonstrates the potential to improve the performance of big data systems significantly and frugally.

The runtime performance of a software system often depends on a large number of static parameters, which usually interact in complex ways to carry out system functionality and influence system performance. It's hard to understand such configuration spaces and find good combinations of parameter values to gain available levels of performance. Engineers in practice often just accept the default settings, leading such systems to significantly underperform relative to their potential. This problem, in turn, has impacts on cost, revenue, customer satisfaction, business reputation, and mission effectiveness. To improve the overall performance of the end-to-end systems, we propose to systematically explore (i) how to design new systems towards good performance through design space synthesis and evaluation, and (ii) how to auto-configure an existing system to obtain better performance through heuristic configuration space search. In addition, this research further studies execution traces of a system to predict runtime performance under new configurations.

Producing software systems that achieve acceptable tradeoffs among multiple non-functional properties remains a significant engineering problem. We propose an approach to solving this problem that combines synthesis of spaces of design alternatives from logical specifications and dynamic analysis of each point in the resulting spaces. We hypothesize that this approach has potential to help engineers understand important tradeoffs among dynamically measurable properties of system components at meaningful scales within reach of existing synthesis tools. To test this hypothesis, we developed tools to enable, and we conducted, a set of experiments in the domain of relational databases for object-oriented data models. For each of several data models, we used our approach to empirically test the accuracy of a published suite of metrics to predict tradeoffs based on the static schema structure alone. The results show that exhaustive synthesis and analysis provides a superior view of the tradeoff spaces for such designs. This work creates a path forward toward systems that achieve significantly better tradeoffs among important system properties.

System designers today are focusing less on point solutions for complex systems and more on design spaces, often with a focus on understanding tradeoffs among non-functional properties across such spaces. This shift places a premium on the efficient comparative evaluation of non-functional properties of designs in such spaces. While static analysis of designs will sometimes suffice, often one must run designs dynamically, under comparable loads, to determine properties and tradeoffs. Yet variant designs often present variant interfaces, requiring that common loads be specialized to many interfaces. The main contributions of this paper are a mathematical framework, architecture, and tool for specification-driven synthesis of design spaces and common loads specialized to individual designs for dynamic tradeoff analysis of non-functional properties in large design spaces. To test our approach we used it to run an experiment to test the validity of static metrics for object-relational database mappings, requiring design space and load synthesis for, and dynamic analysis of, hundreds of database designs.

Wireless communication is indispensable to Internet of Things (IoT). Carrier sensing multiple access/collision avoidance (CSMA/CA) is a well-proven wireless random access protocol and allows each node of equal probability in accessing wireless channel, which incurs equal throughput in long term regardless of the channel conditions. To exploit node diversity that refers to the difference of channel condition among nodes, this paper proposes two opportunistic random access mechanisms: overlapped contention and segmented contention, to favor the node of the best channel condition. In the overlapped contention, the contention windows of all nodes share the same ground of zero, but have different upper bounds upon channel condition. In the segmented contention, the contention window upper bound of a better channel condition is smaller than the lower bound of a worse channel condition; namely, their contention windows are segmented without any overlapping. These algorithms are also polished to provide temporal fairness and avoid starving the nodes of poor channel conditions. The proposed mechanisms are analyzed, implemented, and evaluated on a Linux-based testbed and in the NS3 simulator. Extensive comparative experiments show that both opportunistic solutions can significantly improve the network performance in throughput, delay, and jitter over the current CSMA/CA protocol. In particular, the overlapped contention scheme can offer 73.3% and 37.5% throughput improvements in the infrastructure-based and ad hoc networks, respectively.

Cooperative communication can achieve diversity gain and increase the channel capacity. This paper proposes a novel cooperative scheme called Cooperative Binary Relaying and Combining (CBRC) for multi-hop wireless networking systems where the nodes cooperatively demodulate high-order modulated signal symbols with low-order robust modulations. Low-order demodulation schemes make partial decision at each relay node and avoid propagating the errors that can result from high-order demodulation schemes in conventional cooperative relaying. Therefore, CBRC supports high bit rate transmission at high-order modulations over low SNR links. Extensive simulations are conducted to evaluate the bit error rate performance of CBRC and conventional cooperative strategies. The results show that CBRC can significantly improve the performance.