Exploiting Power-of-Choices for Load Balancing in Fog Computing

Power-of-random choices is a well-known phenomenon exploited in load balancing algorithms to achieve an extraordinary improvement at low cost. These algorithms are particularly suitable for the Fog computing model, since they don't require coordination among different fog nodes when they decide to mutually share their resources. In this paper, we propose LL(F, T ) a power-of-random choices based distributed peer-to-peer load balancing algorithm running on a set of autonomous cooperating fog nodes, where F is the protocol fan-out and T a threshold. Nodes implement a random choice over F fog nodes when their current load is above T. Through a mathematical analysis and preliminary simulations we show that tuning T very close to the node saturation condition, this algorithm achieves practically the same performance of its classical implementation requiring a single global scheduler, without the need for each and every job execution to be preceded by a time costly probing phase, a clear benefit for the low delay requirement of fog applications.

[1]  Ravi Mazumdar,et al.  Mean-Field Analysis of Loss Models with Mixed-Erlang Distributions under Power-of-d Routing , 2017, 2017 29th International Teletraffic Congress (ITC 29).

[2]  Yi Lu,et al.  Asymptotic independence of queues under randomized load balancing , 2012, Queueing Syst. Theory Appl..

[3]  Thirupathaiah Vasantam,et al.  Mean-Field Analysis of Loss Models with Mixed-Erlang Distributions under Power-of-d Routing , 2017, ITC 2017.

[4]  Dimitra I. Kaklamani,et al.  A Cooperative Fog Approach for Effective Workload Balancing , 2017, IEEE Cloud Computing.

[5]  Perry Cheng,et al.  The serverless trilemma: function composition for serverless computing , 2017, Onward!.

[6]  Ramesh K. Sitaraman,et al.  The power of two random choices: a survey of tech-niques and results , 2001 .

[7]  Leandros Maglaras,et al.  Security and Privacy in Fog Computing: Challenges , 2017, IEEE Access.

[8]  Weisong Shi,et al.  The Promise of Edge Computing , 2016, Computer.

[9]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[10]  Patrick Wendell,et al.  Sparrow: distributed, low latency scheduling , 2013, SOSP.

[11]  Fabrice Guillemin,et al.  The Power of Randomized Routing in Heterogeneous Loss Systems , 2015, 2015 27th International Teletraffic Congress.

[12]  C. Graham Chaoticity on path space for a queueing network with selection of the shortest queue among several , 2000, Journal of Applied Probability.

[13]  Rajesh K. Gupta,et al.  CoolSpots: reducing the power consumption of wireless mobile devices with multiple radio interfaces , 2006, MobiSys '06.

[14]  Michael Mitzenmacher,et al.  The Power of Two Choices in Randomized Load Balancing , 2001, IEEE Trans. Parallel Distributed Syst..

[15]  Ravi Mazumdar,et al.  Insensitivity of the mean-field Limit of Loss Systems Under Power-of-d Routing , 2017, ArXiv.

[16]  Roberto Beraldi,et al.  A Power-of-Two Choices Based Algorithm for Fog Computing , 2020, IEEE Transactions on Cloud Computing.

[17]  R. Srikant,et al.  Power of d Choices for Large-Scale Bin Packing , 2015, SIGMETRICS.

[18]  Jonathan Fürst,et al.  Towards Adaptive Actors for Scalable IoT Applications at the Edge , 2018, Open J. Internet Things.

[19]  Félix García Carballeira,et al.  Reducing Randomization in the Power of Two Choices Load Balancing Algorithm , 2017, 2017 International Conference on High Performance Computing & Simulation (HPCS).