title: Balancing performance and energy consumption of bagging ensembles for the classification of data streams in edge computing

authors: Cassales, Guilherme

subjects: computer science

abstract: In recent years, the Edge Computing (EC) paradigm has emerged as an enabling factor for developing technologies like the Internet of Things (IoT) and 5G networks, bridging the gap between Cloud Computing services and end-users, supporting low latency, mobility, and location awareness to delay-sensitive applications. An increasing number of solutions in EC have employed machine learning (ML) methods to perform data classification and other information processing tasks on continuous and evolving data streams. Usually, such solutions have to cope with vast amounts of data that come as data streams while balancing energy consumption, latency, and the predictive performance of the algorithms. Ensemble methods achieve remarkable predictive performance when applied to evolving data streams due to several models and the possibility of selective resets. This work investigates a strategy that introduces short intervals to defer the processing of mini-batches. Well balanced, our strategy can improve the performance (i.e., delay, throughput) and reduce the energy consumption of bagging ensembles to classify data streams. The experimental evaluation involved six state-of-art ensemble algorithms (OzaBag, OzaBag Adaptive Size Hoeffding Tree, Online Bagging ADWIN, Leveraging Bagging, Adaptive RandomForest, and Streaming Random Patches) applying five widely used machine learning benchmark datasets with varied characteristics on three computer platforms. As a result, our strategy can significantly reduce energy consumption in 96% of the experimental scenarios evaluated. Despite the trade-offs, it is possible to balance them to avoid significant loss in predictive performance.

text: Balancing performance and energy consumption of bagging ensembles for the classification of data streams in edge computing In recent years, the Edge Computing (EC) paradigm has emerged as an enabling factor for developing technologies like the Internet of Things (IoT) and 5G networks, bridging the gap between Cloud Computing services and end-users, supporting low latency, mobility, and location awareness to delay-sensitive applications. An increasing number of solutions in EC have employed machine learning (ML) methods to perform data classification and other information processing tasks on continuous and evolving data streams. Usually, such solutions have to cope with vast amounts of data that come as data streams while balancing energy consumption, latency, and the predictive performance of the algorithms. Ensemble methods achieve remarkable predictive performance when applied to evolving data streams due to several models and the possibility of selective resets. This work investigates a strategy that introduces short intervals to defer the processing of mini-batches. Well balanced, our strategy can improve the performance (i.e., delay, throughput) and reduce the energy consumption of bagging ensembles to classify data streams. The experimental evaluation involved six state-of-art ensemble algorithms (OzaBag, OzaBag Adaptive Size Hoeffding Tree, Online Bagging ADWIN, Leveraging Bagging, Adaptive RandomForest, and Streaming Random Patches) applying five widely used machine learning benchmark datasets with varied characteristics on three computer platforms. As a result, our strategy can significantly reduce energy consumption in 96% of the experimental scenarios evaluated. Despite the trade-offs, it is possible to balance them to avoid significant loss in predictive performance.

year: 2022

title: Searching for satisfaction

authors: Grant, Bevan C.

subjects: leisure

abstract: Consumerism is the way of life in the 21st century. Everything is for sale, principally to those with the resources – which is where the profit lies. But it's a competitive market so people from all walks of life are being coerced to part with their discretionary dollars. Leisure is like most other aspects of life - a commodity, a range of goods and services for sale - so it is no surprise that economic viability attracts more attention than social capital.

text: Searching for satisfaction Consumerism is the way of life in the 21st century. Everything is for sale, principally to those with the resources – which is where the profit lies. But it's a competitive market so people from all walks of life are being coerced to part with their discretionary dollars. Leisure is like most other aspects of life - a commodity, a range of goods and services for sale - so it is no surprise that economic viability attracts more attention than social capital.

year: 2007

title: Piahana Hou: a Ngati Rahiri rangatira

authors: Hart, Philip

abstract: Piahana Hou, as the half-brother of Karauna Hou, had an illustrious ancestry, but to Pakeha he was known as, briefly, the skipper of a river steamer and, for most of his life, as a labourer. His interests in several blocks of land enabled him to obtain money through selling some of his holdings, but he never became prosperous. He had minimal involvement in mining at Te Aroha.
Piahana came to prominence not as a rangatira but as a drunk, who was even involved in a bar fight at Te Aroha. Despite this weakness, his high status meant that hundreds attended his splendid tangi. Unlike some other rangatira, he did not use his status to play an important role in either Maori or Pakeha society.

text: Piahana Hou: a Ngati Rahiri rangatira Piahana Hou, as the half-brother of Karauna Hou, had an illustrious ancestry, but to Pakeha he was known as, briefly, the skipper of a river steamer and, for most of his life, as a labourer. His interests in several blocks of land enabled him to obtain money through selling some of his holdings, but he never became prosperous. He had minimal involvement in mining at Te Aroha.
Piahana came to prominence not as a rangatira but as a drunk, who was even involved in a bar fight at Te Aroha. Despite this weakness, his high status meant that hundreds attended his splendid tangi. Unlike some other rangatira, he did not use his status to play an important role in either Maori or Pakeha society.

year: 2016