Computing while charging

Arslan, Mustafa Y.; Singh, Indrajeet; Singh, Shailendra; Madhyastha, Harsha V.; Sundaresan, Karthikeyan; Krishnamurthy, Srikanth V.

doi:10.1145/2413176.2413199

Cited by 56 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our evaluations, PUE is not considered for IoT and ONU devices, as there is generally no cooling requirements for them [79]. The power usage effectiveness (PUE) is the ratio of the total power consumed by a facility (i.e.…”

Section: Power Usage Effectiveness (Pue)mentioning

confidence: 99%

Energy Efficient Distributed Processing for IoT

et al. 2020

View full text Add to dashboard Cite

In the near future, the number of Internet connected objects is expected to be between 26-50 billion devices. This figure is expected to grow even further due to the production of miniaturized portable devices that are lightweight, energy, and cost-efficient. In this paper, the entire IoT-fog-cloud architecture is modeled, the service placement problem is formulated using Mixed Integer Linear Programming (MILP) and the total power consumption is jointly minimized for processing and networking. We evaluate the distributed processing paradigm for both the un-capacitated and capacitated design settings in order to provide solutions for the long-term and short-term basis, respectively. Furthermore, four aspects of the IoT processing placement problem are examined: 1) IoT services with non-splittable tasks, 2) IoT services with splittable tasks, 3) impact of processing overheads needed for inter-service communication and 4) deployment of special-purpose data centers (SP-DCs) as opposed to the conventional general-purpose data center (GP-DC) in the core network. The results showed that for the capacitated problem, task splitting introduces power savings of up to 86% compared to 46% with non-splittable tasks. Moreover, it is observed that the overheads due to inter-service communication greatly impacts the total number of splits. However much insignificant the overhead factor, the results showed that this is not a trivial matter and hence much attention needs to be paid to this area to make the best use of the resources that are available at the edge of the network.

show abstract

Section: Power Usage Effectiveness (Pue)mentioning

confidence: 99%

Energy Efficient Distributed Processing for IoT

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Condor [8], XtremWeb [9], SETI@home [10], Entropia [11], Farsite [12], BOINC [13], CCOF [14], Kosha [15], OurGrid [16], Alchemi [17],FreeLoader [18], LHC@home [19], Aneka [20], Cohesion [21], EDGeS [22], BitDew [23], unaGrid [24], ATLAS@home [25] Volunteer cloud computing Storage@home [26], Cloud@home [27], Seattle [28], C3 [29], P3R3.O.KOM [30], STACEE [31], UnaCloud [32], Personal Cloud [33], P2PCS [34], SoCVC [35], Fatman [36], AdHoc Cloud [37], SASCloud [38], DIaaS [39], Nebula [40], cuCloud [41] Volunteer mobile computing Mobiscope [42], AnonySense [43], Micro-blog [44], LiveCompare [45], Bubblesensing [46], PRISM [47], CrowdLab [48], CWC [49], Serendipity [50], Mobile Device Clouds [51], CellCloud [52], GEMCloud [53], Femt...…”

Section: Category Platforms and References (Ordered By Publication Date)mentioning

confidence: 99%

Volunteer Down: How COVID-19 Created the Largest Idling Supercomputer on Earth

Kratzke

2020

Future Internet

View full text Add to dashboard Cite

From close to scratch, the COVID-19 pandemic created the largest volunteer supercomputer on earth. Sadly, processing resources assigned to the corresponding Folding@home project cannot be shared with other volunteer computing projects efficiently. Consequently, the largest supercomputer had significant idle times. This perspective paper investigates how the resource sharing of future volunteer computing projects could be improved. Notably, efficient resource sharing has been optimized throughout the last ten years in cloud computing. Therefore, this perspective paper reviews the current state of volunteer and cloud computing to analyze what both domains could learn from each other. It turns out that the disclosed resource sharing shortcomings of volunteer computing could be addressed by technologies that have been invented, optimized, and adapted for entirely different purposes by cloud-native companies like Uber, Airbnb, Google, or Facebook. Promising technologies might be containers, serverless architectures, image registries, distributed service registries, and all have one thing in common: They already exist and are all tried and tested in large web-scale deployments.

show abstract

“…We have discussed various MCC frameworks: (1) commercial clouds with complete task offloading for mobile devices, [2][3][4][5] (2) commercial clouds with partial task offloading for mobile devices, [9][10][11][12] (3) volunteer global cloud with stationary devices, [13][14][15] (4) volunteer global cloud with mobile devices, [18][19][20][21][22] (5) commercial or volunteer global cloud with mobile devices using cloudlets, [23][24][25][26][27][28][29][30][31][32][33][34] and (6) volunteer local cloud with mobile devices. [35][36][37][38][39][40] The major limitation of all the above-mentioned techniques 2,3,7-40 is that a fixed infrastructure is required which acts as a central controller to manage resource sharing. Relieving the network with the restriction of fixed infrastructure requires direct device-to-device communication among the mobile devices.…”

Section: Mobile Cloud and Its Offloading Architecturesmentioning

confidence: 99%

EMCloud: A hierarchical volunteer cloud with explicit mobile devices

Malhotra

Dhurandher

Gupta³

et al. 2018

Int J Communication

View full text Add to dashboard Cite

Summary Today, the world is witnessing an exponential growth in the computational capabilities and storage capabilities of mobile devices. This has inspired application developers to create new and interesting applications for mobile devices. The applications have varied resource requirements and mobile devices are heterogeneous in their configurations. The idea of this research is to design a model for resource sharing among mobile devices so that any type of application can be run on a mobile device with the help of other such devices in close geographical proximity. A novel architecture for managing a group of volunteer nodes with unused or underused resources is designed and algorithms that are required for its operation are developed. The purpose of this research is to shift the focus towards futuristic computing paradigm where seamless resource sharing in the network would be feasible with explicitly all mobile nodes and without any dependency on fixed infrastructures. An extensive evaluation of the proposed Explicit Mobile Cloud (EMCloud) has been undertaken for mobile nodes in the network. It includes experience with a prototype that demonstrates EMCloud's performance. Simulation results demonstrate that the proposed scheme outperforms the state‐of‐the‐art Serendipity, OpenNebula, and Eucalyptus prototypes in terms of success rate and average delay for different network density, mobility, network load, and extent of hierarchy.

show abstract

Computing while charging

Cited by 56 publications

References 26 publications

Energy Efficient Distributed Processing for IoT

Energy Efficient Distributed Processing for IoT

Volunteer Down: How COVID-19 Created the Largest Idling Supercomputer on Earth

EMCloud: A hierarchical volunteer cloud with explicit mobile devices

Contact Info

Product

Resources

About