Energy efficient partition allocation in mixed-criticality systems

Abstract

This paper addresses the problem of energy management of mixed criticality applications in a multi-core partitioned architecture. Instead of focusing on new scheduling algorithms to adjust frequency in order to save energy, we propose a partition to CPU allocation that takes into account not only the different frequencies at which the CPU can operate but the level of criticality of the partitions. The goal is to provide a set of pre-calculated allocations, called profiles, so at run time the system can switch to different modes depending on the battery level. These profiles achieve different levels of energy saving and performance applying different strategies. We also present a comparison in terms of energy saving of the most used bin-packing algorithms for partition allocation. As this is an heuristic, it is not possible to ensure that our results involve the minimum energy consumption. For this reason, we also provide a comparison with a exact method, such as constraint programming.

Positions and covering: A two-stage methodology to obtain optimal solutions for the 2d-bin packing problem

“…Columns 3 to 10 show the execution time (seconds) required by the initial set-covering ILP model (Original Model, without valid inequalities) and the ILP model with different incorporations of valid inequalities. This manner, the fourth column considers the set-covering ILP with the addition of valid inequality (6), and the last column considers the incorporation of the three families (6), (7), and (8). "T.O."…”

“…Bin-packing problems are at the heart of many interesting applications. In [7], an energy management problem in multi-core partitioned architectures requires bin-packing algorithms. In [8], a bioinformatic problem where the sequencing alignment processes is computationally challenging, a distributed and parallel indexing and alignment framework requires a greedy approximation algorithm for solving the bin-packing problem.…”

“…Optimal solutions for the 2D-bin packing problem number of constraints, but they strengthen the convex hull of the solution polyhedra, since for some instances, this combination is better than the others. In terms of the total time required to solve all the instances of Class 1, we point out that the ILP enhanced with valid inequalities (7)& (8) is the less time-consuming. Table 6 shows another aspect of our experimental results.…”

“…The fourth column is the lower bound reported by [26], computed by applying all the lower-bounding procedures from the literature and an exact algorithm for long computing time. The fifth column indicates the optimal number of bins obtained by the P&C methodology with valid inequalities (7) and (8). The rest of…”

Covering, corner-searching and occupying: A three-stage intelligent algorithm for the 2d multishape part packing problem

“…Bin packing problems not only have important applications in industry field but are also at the heart of many interesting fields. Guasque and Ana aim to use bin packing algorithms for partition allocation to save energy [ 4 ]. Danqing Feng, Zhibo Wu et al mention that researchers have solved virtual machine placement problems by using bin packing algorithms to meet users’ flexible demands in cloud computing [ 5 ].…”