Energy and cost aware scheduling with batch processing for instance-intensive IoT workflows in clouds

Wen, Yiping; Wang, Zhibin; Zhang, Yu; Liu, Jianxun; Cao, Buqing; Chen, Jinjun

doi:10.1016/j.future.2019.05.046

Cited by 37 publications

(18 citation statements)

References 21 publications

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“…The reduction of energy consumption is a topic of considerable interest in the scientific community. Wen et al [16] presented the ECIB algorithm that considers energy and cost variables to program IoT workflows with intensive batch processing in clouds. The main objective of ECIB is to improve energy efficiency and therefore reduce costs of operation.…”

Section: Related Workmentioning

confidence: 99%

Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm

et al. 2021

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In recent years, technological paradigms such as Internet of Things (IoT) and machine learning have become very important due to the benefit that their application represents in various areas of knowledge. It is interesting to note that implementing these two technologies promotes more and better automatic control systems that adjust to each user’s particular preferences in the home automation area. This work presents Smart Home Control, an intelligent platform that offers fully customized automatic control schemes for a home’s domotic devices by obtaining residents’ behavior patterns and applying machine learning to the records of state changes of each device connected to the platform. The platform uses machine learning algorithm C4.5 and the Weka API to identify the behavior patterns necessary to build home devices’ configuration rules. Besides, an experimental case study that validates the platform’s effectiveness is presented, where behavior patterns of smart homes residents were identified according to the IoT devices usage history. The discovery of behavior patterns is essential to improve the automatic configuration schemes of personalization according to the residents’ history of device use.

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Section: Related Workmentioning

confidence: 99%

Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm

et al. 2021

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“…According to Figure 10, the quorums set of the AS-Grid(5 × 10) are as follows: {a:(0, 5,10,15,20,25,30,35,40,45,46,47,48,49), b:(0, 1,6,11,16,21,26,31,36,41,46,47,48,49), c:(0, 1,2,7,12,17,22,27,32,37,42,47,48,49), d:(0, 1,2,3,8,13,18,23,28,33,38,43,48,49), e:(0, 1,…”

Section: The Eqos the Active Ratio And The Qer Of As-grid( × )mentioning

confidence: 99%

“…Similar to the AS-Grid(5× 10), we derive the EQOS of the LPS-Grid(3× 5) and LPS-Grid(4× 6) then expand our results to . As Figure 12 shows, quorum sets of the LPS-Grid(3× 5) are as follows: {a:(0,3,6,9,12,13), b: (1,4,7,10,13,14), c: (2,5,8,11,14,12)} If we assume that there are two nodes that want to select one of the quorum sets randomly and node 1 selects quorum A, then:…”

Section: The Eqos the Active Ratio And The Qer Of Lps-grid( × )mentioning

confidence: 99%

“…For another example, as Figure 13 shows, quorums set of the LPS-Grid(4× 6) are as follows: {a:(0, 4,8,12,16,20,21,22), b: (1,5,9,13,17,21,22,23), c: (2,6,10,14,18,22,23,20), d: (3,7,11,15,19,23,20,21)} Similarly, if we assume that there are two nodes that want to select one of the quorum sets in a random manner and node 1 selects quorum A, then:…”

Section: The Eqos the Active Ratio And The Qer Of Lps-grid( × )mentioning

confidence: 99%

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Power-saving Asynchronous Quorum-based Protocols for Maximal Neighbour Discovery

Imani¹,

Ali²,

Arabnia³

2020

AETiC

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The discovery of neighbouring active nodes is one of the most challenging problems in asynchronous ad hoc networks. Since time synchronization is extremely costly in these networks, application of asynchronous methods like quorum-based protocols have attracted increased interest for their suitability. This is because Quorum-based protocols can guarantee that two nodes with differing clock times have an intersection within at least one timeslot. A higher neighbour discovery rate of active nodes is desired, but it also results in a higher active ratio and consequently and adversely more overall power consumption of the nodes and a shorter network lifetime. There must be a trade-off between extensive neighbour discovery and active ratio in order to design high-performance and efficient protocols. In this paper, two novel asynchronous quorum-based protocols to maximize the neighbour discovery and minimize the active ratio have been designed and presented. A new metric (Quorum Efficiency Ratio: QER) has also been designed to evaluate and compare the performance of quorum-based protocols in terms of their neighbour discovery (the Expected Quorum Overlap Size: EQOS) and the active ratio. The EQOS has been theoretically derived, along with the Active Ratio and the QER values for the proposed novel protocols and the other contemporary protocols. Finally, the proposed methods have been evaluated and compared against the other methods based on the current metrics and the new metric.

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“…Although, cloud computing has the capability of providing a virtual infrastructure for these applications [31], the need for configuration of existing protocols still remain there. As far as the multimedia data is concerned, cost-based models are able to facilitate it [32]. These models will enable accessing the applications from anywhere around the world which is one of the main objective of IoT.…”

Section: Introductionmentioning

confidence: 99%

Interoperability and Data Storage in Internet of Multimedia Things: Investigating Current Trends, Research Challenges and Future Directions

et al. 2020

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Internet of Things (IoT) has widely been accepted as a promising paradigm for connecting a large number of resource-constrained miniature sensor nodes that have the ability to sense the deployed environment. They have found their applications in various aspect of our daily lives. However, these nodes are mostly restricted to sense only the scalar data. Nowadays, multimedia sensor nodes are gaining significant attention due to their ability to collect scalar as well as multi-dimensional data. These nodes are considered as the foundation of Internet of Multimedia Things (IoMT) and are shaping the perception of IoT. Multimedia data have stringent requirements in terms of reliability, latency, storage, bandwidth, and Quality of Service (QoS). To provide seamless and interoperable communication in IoMT, the underlying protocol stacks need to fulfill these stringent requirements. However, the heterogeneous nature of multimedia sensors makes interoperability a challenging task to fulfill. To understand the challenges faced by seamless and interoperable communication in IoMT, we provide a comprehensive review of the existing protocol stacks of IoMT and analyze their feasibility for multimedia streaming applications. Data storage of multimedia applications is another area that requires immediate attention of the research community. For this purpose, we study cloud as an entity to facilitate multimedia applications of IoMT. The instances of multimedia cloud are analyzed and a number of shortcomings are identified that pave the way for edge computing in IoMT. Finally, we present a case study that shows the significance of our work. The case study portrays an in-home patient monitoring system with an interoperable communication among the connected multimedia streaming devices at home, and healthcare practitioners at hospital. The case study also highlights the importance of uninterrupted data storage and retrieval at the network edge and multimedia sensor nodes.

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Energy and cost aware scheduling with batch processing for instance-intensive IoT workflows in clouds

Cited by 37 publications

References 21 publications

Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm

Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm

Power-saving Asynchronous Quorum-based Protocols for Maximal Neighbour Discovery

Interoperability and Data Storage in Internet of Multimedia Things: Investigating Current Trends, Research Challenges and Future Directions

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