MIND-IT: Server Selection in the Edge-Cloud Continuum Under Non-Negligible Path Latencies

Today's networks include decentralized intelligence, scattered over multiple computing facilities, for the execution of computation-intensive tasks that are necessary for both network management and service provision to end users. This raises a number of questions regarding task allocation, in terms of both the task placement algorithm, which can be either centralized or distributed, and the resulting task placement in the edge-cloud continuum. Existing analytical models that tackle these issues were conceived for network scenarios that include only cloud computing facilities. In such settings, performance predictions can be derived assuming that all users are located at practically indistinguishable (hence irrelevant for the allocation decision) distances in terms of delay from the cloud server. With the diffusion of edge servers, the role of path latencies becomes relevant, and just considering the response times (i.e., waiting plus service times) at the servers is no longer sufficient. Thus, we propose a model, called Minimum Delay Independent of Traffic and service (MIND-IT), for the optimization of task allocation. MIND-IT considers both the path latencies to reach the edge/cloud servers and their response times, derives explicit performance metrics and provides optimization algorithms. We show, through numerical analysis and real experiments over the Internet, that differences in path latencies to reach edge/cloud servers cannot be neglected. By means of algorithmic game theory, we also study the inefficiency of distributed and selfish task allocation solutions, unveiling important properties such as the fact that their performance loss with respect to centralized optimization is generally limited, except when the system is overloaded, and that worst-case guarantees can be computed with low complexity.