Obfuscating LLVM Intermediate Representation Source Code with NSGA-II

in 15th Intl. Conf. on Computational Intelligence in Security for Information Systems (CISIS'22) (2022, September)

With the generalisation of distributed computing paradigms to sustain the surging demands for massive processing and data-analytic capabilities, the protection of the intellectual property tied to the ... [more ▼]

With the generalisation of distributed computing paradigms to sustain the surging demands for massive processing and data-analytic capabilities, the protection of the intellectual property tied to the executed programs transferred onto these remote shared platforms becomes critical. A more and more popular solution to this problem consists in applying obfuscating techniques, in particular at the source code level. Informally, the goal of obfuscation is to conceal the purpose of a program or its logic without altering its functionality, thus preventing reverse-engineering on the program even with the help of computing resources. This allows to protect software against plagiarism, tampering, or finding vulnerabilities that could be used for different kinds of attacks. The many advantages of code obfuscation, together with its low cost, makes it a popular technique. This paper proposes a novel methodology for source code obfuscation relying on the reference LLVM compiler infrastructure that can be used together with other traditional obfuscation techniques, making the code more robust against reverse engineering attacks. The problem is defined as a Multi-Objective Combinatorial Optimization (MOCO) problem, where the goal is to find sequences of LLVM optimizations that lead to highly obfuscated versions of the original code. These transformations are applied to the back-end pseudo- assembly code (i.e., LLVM Intermediate Representation), thus avoiding any further optimizations by the compiler. Three different problem flavours are defined and solved with popular NSGA-II genetic algorithm. The promising results show the potential of the proposed technique. [less ▲]

The sandpile scheduler: How self-organized criticality may lead to dynamic load-balancing

in Cluster Computing (2014)

This paper studies a self-organized criticality model called sandpile for dynamically load-balancing tasks arriving in the form of Bag-of-Tasks in large-scale decentralized system. The sandpile is ... [more ▼]

This paper studies a self-organized criticality model called sandpile for dynamically load-balancing tasks arriving in the form of Bag-of-Tasks in large-scale decentralized system. The sandpile is designed as a decentralized agent system characterizing a cellular automaton, which works in a critical state at the edge of chaos. Depending on the state of the cellular automaton, different responses may occur when a new task is assigned to a resource: it may change nothing or generate avalanches that reconfigure the state of the system. The abundance of such avalanches is in power-law relation with their sizes, a scale-invariant behavior that emerges without requiring tuning or control parameters. That means that large—catastrophic—avalanches are very rare but small ones occur very often. Such emergent pattern can be efficiently adapted for non-clairvoyant scheduling, where tasks are load balanced in computing resources trying to maximize the performance but without assuming any knowledge on the tasks features. The algorithm design is experimentally validated showing that the sandpile is able to find near-optimal schedules by reacting differently to different conditions of workloads and architectures. [less ▲]

Cellular genetic algorithms without additional parameters

in Journal of Supercomputing (2013), 63(3), 816-835

Cellular genetic algorithms (cGAs) are a kind of genetic algorithms (GAs) with decentralized population in which interactions among individuals are restricted to close ones. The use of decentralized ... [more ▼]

Cellular genetic algorithms (cGAs) are a kind of genetic algorithms (GAs) with decentralized population in which interactions among individuals are restricted to close ones. The use of decentralized populations in GAs allows to keep the population diversity for longer, usually resulting in a better exploration of the search space and, therefore, in a better performance of the algorithm. However, it supposes the need of several new parameters that have a major impact on the behavior of the algorithm. In the case of cGAs, these parameters are the population and neighborhood shapes. We propose in this work two innovative cGAs with new adaptive techniques that allow removing the neighborhood and population shape from the algorithm’s configuration. As a result, the new adaptive cGAs are highly competitive (statistically) with all the compared cGAs in terms of the average solutions found in the continuous and combinatorial domains, while finding, in general, the best solutions for the considered problems, and with less computational effort. [less ▲]

A Parallel Multi-objective Local Search for AEDB Protocol Tuning

in IEEE International Parallel and Distributed Processing Simposium (2013)

List scheduling heuristics for virtual machine mapping in cloud systems

in VI Latin American Symposium on High Performance Computing (HPCLatam) (2013)

This article introduces the formulation of the VirtualMachine Planning Problem in cloud computing systems. It deals with the efficient allocation of a set of virtual machine requests from customers into ... [more ▼]

This article introduces the formulation of the VirtualMachine Planning Problem in cloud computing systems. It deals with the efficient allocation of a set of virtual machine requests from customers into the available pre-booked resources the broker has in a number of cloud providers, maximizing the broker profit. Eight list scheduling heuristics are proposed to solve the problem, by taking into account different criteria for mapping request to available virtual machines. The experimental evaluation analyzes the profit, makespan, and flowtime results of the proposed methods over a set of 400 problem instances that account for realistic workloads and scenarios using real data from cloud providers. [less ▲]

Energy-Aware Scheduling on Multicore Heterogeneous Grid Computing Systems

in Journal of Grid Computing (2013), 11

We address a multicriteria nonpreemptive energy-aware scheduling problem for computationalGrid systems. This work introduces a new formulation of the scheduling problem for multicore heterogeneous ... [more ▼]

We address a multicriteria nonpreemptive energy-aware scheduling problem for computationalGrid systems. This work introduces a new formulation of the scheduling problem for multicore heterogeneous computational Grid systems in which the minimization of the energy consumption, along with the makespan metric, is considered. We adopt a two-level model, in which a meta-broker agent (level 1) receives all user tasks and schedules them on the available resources, belonging to different local providers (level 2). The computing capacity and energy consumption of resources are taken from real multi-core processors from the main current vendors. Twenty novel list scheduling methods for the problem are proposed, and a comparative analysis of all of them over a large set of problem instances is presented. Additionally, a scalability study is performed in order to analyze the contribution of the best new bi-objective list scheduling heuristics when the problem dimension grows. We conclude after the experimental analysis that accurate trade-off schedules are computed by using the new proposed methods. [less ▲]

Finding Scalable Configurations for AEDB Broadcasting Protocol using Multi-objective Evolutionary

in Cluster Computing (2012), 16