Volume 34 - 2020 - Special Issue CARI 2020

Special issue dedicated to CARI 2020

1. Parallel Hybridization for SAT: An Efficient Combination of Search Space Splitting and Portfolio

Konan Tchinda, Rodrigue ; Tayou Djamegni, Clémentin.
Search space splitting and portfolio are the two main approaches used in parallel SAT solving. Each of them has its strengths but also, its weaknesses. Decomposition in search space splitting can help improve speedup on satisfiable instances while competition in portfolio increases robustness. Many parallel hybrid approaches have been proposed in the literature but most of them still cope with load balancing issues that are the cause of a non-negligible overhead. In this paper, we describe a new parallel hybridization scheme based on both search space splitting and portfolio that does not require the use of load balancing mechanisms (such as dynamic work stealing).

2. Big Steps Towards Query Eco-Processing - Thinking Smart

Dembele, Simon Pierre ; Bellatreche, Ladjel ; Ordonez, Carlos ; Gmati, Nabil ; Roche, Mathieu ; Nguyen-Huu, Tri ; Debreu, Laurent.
Computers and electronic machines in businesses consume a significant amount of electricity, releasing carbon dioxide (CO2), which contributes to greenhouse gas emissions. Energy efficiency is a pressing concern in IT systems, ranging from mobile devices to large servers in data centers, in order to be more environmentally responsible. In order to meet the growing demands in the awareness of excessive energy consumption, many initiatives have been launched on energy efficiency for big data processing covering electronic components, software and applications. Query optimizers are one of the most power consuming components of a DBMS. They can be modified to take into account the energetical cost of query plans by using energy-based cost models with the aim of reducing the power consumption of computer systems. In this paper, we study, describe and evaluate the design of three energy cost models whose values of energy sensitive parameters are determined using the Nonlinear Regression and the Random Forests techniques. To this end, we study in depth the operating principle of the selected DBMS and present an analysis comparing the performance time and energy consumption of typical queries in the TPC benchmark. We perform extensive experiments on a physical testbed based on PostreSQL, MontetDB and Hyrise systems using workloads generatedusing our chosen benchmark to validate our proposal.