Zoltán Ádám Mann

Hardware-software partitioning in embedded system design

One of the most crucial steps in the design of embedded systems is hardware-software partitioning, i.e. deciding which components of the system are implemented in hardware and which ones in software. Different versions of the partitioning problem are defined, corresponding to real-time systems, and cost-constrained systems, respectively. The authors provide a formal mathematic analysis of the complexity of the problems: it is proven that they are NP-hard in the general case, and some efficiently solvable special cases are also presented. An ILP (integer linear programming) based approach is presented that are solving the problem optimally even for quite big systems, and a genetic algorithm (GA) that finds near-optimal solutions for even larger systems. A specialty of the GA is that nonvalid individuals are also allowed, but punished by the fitness function.

Allocation of Virtual Machines in Cloud Data Centers—A Survey of Problem Models and Optimization Algorithms

Data centers in public, private, and hybrid cloud settings make it possible to provision virtual machines (VMs) with unprecedented flexibility. However, purchasing, operating, and maintaining the underlying physical resources incurs significant monetary costs and environmental impact. Therefore, cloud providers must optimize the use of physical resources by a careful allocation of VMs to hosts, continuously balancing between the conflicting requirements on performance and operational costs. In recent years, several algorithms have been proposed for this important optimization problem. Unfortunately, the proposed approaches are hardly comparable because of subtle differences in the used problem models. This article surveys the used problem formulations and optimization algorithms, highlighting their strengths and limitations, and pointing out areas that need further research.

Algorithmic aspects of hardware/software partitioning

One of the most crucial steps in the design of embedded systems is hardware/software partitioning, that is, deciding which components of the system should be implemented in hardware and which ones in software. Most formulations of the hardware/software partitioning problem are NP-hard, so the majority of research efforts on hardware/software partitioning has focused on developing efficient heuristics.This article considers the combinatorial structure behind hardware/software partitioning. Two similar versions of the partitioning problem are defined, one of which turns out to be NP-hard, whereas the other one can be solved in polynomial time. This helps in understanding the real cause of complexity in hardware/software partitioning. Moreover, the polynomial-time algorithm serves as the basis for a highly efficient novel heuristic for the NP-hard version of the problem. Unlike general-purpose heuristics such as genetic algorithms or simulated annealing, this heuristic makes use of problem-specific knowledge, and can thus find high-quality solutions rapidly. Moreover, it has the unique characteristic that it also calculates lower bounds on the optimum solution. It is demonstrated on several benchmarks and also large random examples that the new algorithm clearly outperforms other heuristics that are generally applied to hardware/software partitioning.

Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines in a cloud data center

Dynamic consolidation of virtual machines (VMs) in a cloud data center can be used to minimize power consumption. Beloglazov et al. have proposed the MM (Minimization of Migrations) heuristic for selecting the VMs to migrate from under- or over-utilized hosts, as well as the MBFD (Modified Best Fit Decreasing) heuristic for deciding the placement of the migrated VMs. According to their simulation results, these heuristics work very well in practice. In this paper, we investigate what performance guarantees can be rigorously proven for the heuristics. In particular, we establish that MM is optimal with respect to the number of selected VMs of an over-utilized host and it is a 1.5-approximation with respect to the decrease in utilization. On the other hand, we show that the result of MBFD can be arbitrarily far from the optimum. Moreover, we show that even if both MM and MBFD deliver optimal results, their combination does not necessarily result in optimal VM consolidation, but approximation results can be proven under suitable technical conditions. To the best of our knowledge, these are the first rigorously proven results on the effectiveness of also practically useful heuristic algorithms for the VM consolidation problem. The MM heuristic is proven to be a 3/2-approximation algorithm.The result of the MBFD heuristic can be arbitrarily far from the optimum.If MM and MBFD give optimal results, then their interplay is a 2-approximation algorithm.

Three public enemies: cut, copy, and paste

Many software developers know the feeling of desperately debugging a program only to discover after a sleepless night that the error stemmed from copy-pasted code segments that had become inconsistent in subsequent editing. The problems arising from copied code are not new, and many researchers have investigated how to automatically find copied code segments. Given the extensive use of copy-paste operations and their tendency to cause inconsistencies, there is clearly a pressing need to rethink current editor programs. One solution is to replace cut, copy, and paste with operations that correspond directly to the intended semantics behind their use. With these operations, the user can specify semantic relationships among copied objects, and the editor program can use that information to help in the long-term support of those relationships. It would thus avoid the inconsistencies that currently arise from the use of cut, copy, and paste

CACEV: A Cost and Carbon Emission-Efficient Virtual Machine Placement Method for Green Distributed Clouds.

Distributed clouds have recently attracted many cloud providers and researchers as a topic of intensive interest. High energy costs and carbon emissions are two significant problems in distributed clouds. Due to the geographic distribution of data centers (DCs), there are a variety of resources, energy prices and carbon emission rates to consider in a distributed cloud, which makes the placement of virtual machines (VMs) for cost and carbon efficiency even more critical than in centralized clouds. Most previous work in this field investigated either optimizing cost without considering the amount of produced carbon or vice versa. This paper presents a cost and carbon emission-efficient VM placement method (CACEV) in distributed clouds. CACEV considers geographically varying energy prices and carbon emission rates as well as optimizing both network and server resources at the same time. By combining prediction-based A* algorithm with Fuzzy Sets technique, CACEV makes an intelligent decision to optimize cost and carbon emission for providers. Simulation results show the applicability and performance of CACEV.

Finding optimal hardware/software partitions

Abstract Most previous approaches to hardware/software partitioning considered heuristic solutions. In contrast, this paper presents an exact algorithm for the problem based on branch-and-bound. Several techniques are investigated to speed up the algorithm, including bounds based on linear programming, a custom inference engine to make the most out of the inferred information, advanced necessary conditions for partial solutions, and different heuristics to obtain high-quality initial solutions. It is demonstrated with empirical measurements that the resulting algorithm can solve highly complex partitioning problems in reasonable time. Moreover, it is about ten times faster than a previous exact algorithm based on integer linear programming. The presented methods can also be useful in other related optimization problems.

Improved Bounds on the Complexity of Graph Coloring

The coloring of random graphs has been the subject of intensive research in the last decades. As a result, the asymptotic behaviour of both the chromatic number and the complexity of the color ability problem are quite well understood. However, the asymptotic results give limited help in predicting the behaviour in specific finite cases. In this paper, we consider the application of the usual backtrack algorithm to random graphs, and analyze the expected size of the search tree as a machine-independent measure of algorithm complexity. With a combination of combinatorial, probabilistic and analytical methods, we derive upper and lower bounds for the expected size of the search tree. Our bounds are much tighter than previous results and thus enable accurate prediction of algorithm runtime.

Graph coloring: The more colors, the better?

In this paper, we investigate the algorithmic complexity of deciding colorability, as a function of the number of available colors. Intuitively, one may assume that the problems complexity is highest around the chromatic number of the graph. We give substantial empirical evidence that this intuition is largely true, both for exact and heuristic graph coloring algorithms. We give a rigorous proof that the complexity of a class of exact algorithms is monotonously increasing in the number of available colors in the non-colorable case, and give a counter-example to demonstrate that the analogous claim does not always hold for colorable graphs.

BCAT: A framework for analyzing the complexity of algorithms

This paper presents BCAT (Budapest Complexity Analysis Toolkit), a software package to facilitate research on algorithms and computational complexity. BCAT supports the implementation of computational problems, algorithms to solve the problems, and analyzers to analyze the problems. The paper contains details on the softwares architecture and the related main design decisions, and reports on the first experiences with using the system.