Optimal Allocation of Tasks and Price of Anarchy of Distributed Optimization in Networked Computing Facilities

TL;DR

This paper investigates optimal task allocation in distributed networked computing, considering diverse server latencies, and analyzes the efficiency loss in decentralized systems through the price of anarchy, supported by algorithms and experiments.

Contribution

It introduces a new model accounting for server location and latency in task allocation, along with exact algorithms and analysis of the price of anarchy in distributed systems.

Findings

Server location significantly impacts task allocation efficiency.

The price of anarchy remains low under normal loads.

Maximum price of anarchy can be efficiently computed.

Abstract

The allocation of computing tasks for networked distributed services poses a question to service providers on whether centralized allocation management be worth its cost. Existing analytical models were conceived for users accessing computing resources with practically indistinguishable (hence irrelevant for the allocation decision) delays, which is typical of services located in the same distant data center. However, with the rise of the edge-cloud continuum, a simple analysis of the sojourn time that computing tasks observe at the server misses the impact of diverse latency values imposed by server locations. We therefore study the optimization of computing task allocation with a new model that considers both distance of servers and sojourn time in servers. We derive exact algorithms to optimize the system and we show, through numerical analysis and real experiments, that differences in server location in the edge-cloud continuum cannot be neglected. By means of algorithmic game theory, we study the price of anarchy of a distributed implementation of the computing task allocation problem and unveil important practical properties such as the fact that the price of anarchy tends to be small -- except when the system is overloaded -- and its maximum can be computed with low complexity.