Network speed scaling

Vaze, Rahul; Nair, Jayakrishnan

doi:10.1016/j.peva.2020.102145

Cited by 3 publications

(2 citation statements)

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“…An added feature in modern servers is their ability to work at different speeds. This paradigm is called speed scaling [2,6,16,17], where one or more servers with tuneable speed are available, and operating any server at speed s consumes energy at rate P (s), a non-decreasing convex function of s. With speed scaling, the problem is to choose speed of operation so as to minimize the sum of the flow time and energy.…”

Section: Introductionmentioning

confidence: 99%

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Speed Scaling with Multiple Servers Under A Sum Power Constraint

Vaze¹,

Nair²

2021

Preprint

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The problem of scheduling jobs and choosing their respective speeds with multiple servers under a sum power constraint to minimize the flow time + energy is considered. This problem is a generalization of the flow time minimization problem with multiple unit-speed servers, when jobs can be parallelized, however, with a sub-linear, concave speedup function k 1/α , α > 1 when allocated k servers, i.e., jobs experience diminishing returns from being allocated additional servers. When all jobs are available at time 0, we show that a very simple algorithm EQUI, that processes all available jobs at the same speed is 2-competitive, while in the general case, when jobs arrive over time, an LCFS based algorithm is shown to have a constant (dependent only on α) competitive ratio. * We acknowledge support of the Department of Atomic Energy, Government of India, under project no. RTI4001

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Section: Introductionmentioning

confidence: 99%

“…In prior work, speed scaling problem with multiple servers has been considered [2,6,16,17], however, with a fixed number of servers and without any upper bound on the power consumption of any server. For a single server, the flow time + energy problem under a power constraint or upper limit on speed has been solved in [2].…”

Section: Introductionmentioning

confidence: 99%

Speed Scaling with Multiple Servers Under A Sum Power Constraint

Vaze¹,

Nair²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

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Network Speed Scaling

Vaze

Nair

2021

SIGMETRICS Perform. Eval. Rev.

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Speed scaling for a network of servers represented by a directed acyclic graph is considered. Jobs arrive at a source server, with a specified destination server, and are defined to be complete once they are processed by all servers on any feasible path between the source and the corresponding destination. Each server has variable speed, with power consumption function P, a convex increasing function of the speed. The objective is to minimize the sum of the flow time (summed across jobs) and the energy consumed by all the servers, which depends on how jobs are routed, as well as how server speeds are set. Algorithms are derived for both the worst case and stochastic job arrivals setting, whose competitive ratio depends only on the power functions and path diversity in the network, but is independent of the workload.

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