1 research outputs found
CARMA: Contention-aware Auction-based Resource Management in Architecture
As the number of resources on chip multiprocessors (CMPs) increases, the
complexity of how to best allocate these resources increases drastically.
Because the higher number of applications makes the interaction and impacts of
various memory levels more complex. Also, the selection of the objective
function to define what \enquote{best} means for all applications is
challenging. Memory-level parallelism (MLP) aware replacement algorithms in
CMPs try to maximize the overall system performance or equalize each
application's performance degradation due to sharing. However, depending on the
selected \enquote{performance} metric, these algorithms are not efficiently
implemented, because these centralized approaches mostly need some further
information regarding about applications' need. In this paper, we propose a
contention-aware game-theoretic resource management approach (CARMA) using
market auction mechanism to find an optimal strategy for each application in a
resource competition game. The applications learn through repeated interactions
to choose their action on choosing the shared resources. Specifically, we
consider two cases: (i) cache competition game, and (ii) main processor and
co-processor congestion game. We enforce costs for each resource and derive
bidding strategy. Accurate evaluation of the proposed approach show that our
distributed allocation is scalable and outperforms the static and traditional
approaches.Comment: 13 pages, 13 figure