We present strategies for "online" dynamic power management(DPM) based on the notion of the competitive ratio that allows us to compare the effectiveness of algorithms against an optimal strategy. This paper makes two contributions: it provides a theoretical basis for the analysis of DPM strategies for systems with multiple power down states; and provides a competitive algorithm based on probabilistically generated inputs that improves the competitive ratio over deterministic strategies. Experimental results show that our probability-based DPM strategy improves the efficiency of power management over the deterministic DPM strategy by 25%, bringing the strategy to within 23% of the optimal offline DPM.
Dynamic power management (DPM) refers to the problem of judicious application of various low-power techniques based on runtime conditions in an embedded system to minimize the total energy consumption. To be effective, often such decisions take into account the operating conditions and the system-level design goals. DPM has been a subject of intense research in the past decade driven by the need for low power consumption in modern embedded devices. We present a comprehensive overview of two closely related approaches to designing DPM strategies, namely, competitive analysis approach and model checking approach based on adversarial modeling. Although many other approaches exist for solving the system-level DPM problem, these two approaches are closely related and are based on a common theme. This commonality is in the fact that the underlying model is that of a competition between the system and an adversary. The environment that puts service demands on devices is viewed as an adversary, or to be in competition with the system to make it burn more energy, and the DPM strategy is employed by the system to counter that.Index Terms-Competitive analysis, dynamic power management, low-power design, online algorithm, probabilistic model checking, stochastic policy.
Directed Graph based models of a blockchain that capture accounts as nodes and transactions as edges, evolve over time. This temporal nature of a blockchain model enables us to understand the behavior (malicious or benign) of the accounts. Predictive classification of accounts as malicious or benign could help users of the permissionless blockchain platforms to operate in a secure manner. Motivated by this, we introduce temporal features such as burst and attractiveness on top of several already used graph properties such as the node degree and clustering coefficient. Using identified features, we train various Machine Learning (ML) models and identify the algorithm that performs the best in detecting malicious accounts. We then study the behavior of the accounts over different temporal granularities of the dataset before assigning them malicious tags. For the Ethereum blockchain, we identify that for the entire dataset—the ExtraTreesClassifier performs the best among supervised ML algorithms. On the other hand, using cosine similarity on top of the results provided by unsupervised ML algorithms such as K-Means on the entire dataset, we were able to detect 554 more suspicious accounts. Further, using behavior change analysis for accounts, we identify 814 unique suspicious accounts across different temporal granularities.
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