A Comparison of Fair Sharing Algorithms for Regulating Search as a Service API

Providers of a Search as a Service (SaaS) environment must ensure that their users will not monopolize the service or use more than their fair share of resources. Fair sharing algorithms have long been used in computer networking to balance access to a router or switch, and some of these algorithms have also been applied to the control of queries submitted to search engine APIs. If a search query’s execution cost can be reliably estimated, fair sharing algorithms can be applied to the input of a SaaS API to ensure everyone has equitable access to the search engine. The novelty of this paper lies in presenting a Single-Server Max-Min Fair Deficit Round Robin algorithm, a modified version of the Multi-Server Max-Min Fair Deficit Round Robin algorithm. The Single-Server Max-Min Fair Deficit Round Robin algorithm is compared to three other fair sharing algorithms, token-bucket, Deficit Round Robin (DRR), and Peng and Plale’s [1] Modified Deficit Round Robin (MDRR) in terms of three different usage scenarios, balanced usage, unbalanced usage as well as an idle client usage, to determine which is the most suitable fair sharing algorithm for use in regulating traffic to a SaaS API. This research demonstrated that the Single-Server Max-Min Fair DRR algorithm provided the highest throughput of traffic to the search engine while also maintaining a fair balance of resources among clients by re-allocating unused throughput to clients with saturated queues so a max-min allocation was achieved.

Regulating Scheduler (RSC): A Novel Solution for IEEE 802.1 Time Sensitive Network (TSN)

Emerging applications such as industrial automation, in-vehicle, professional audio-video, and wide area electrical utility networks require strict bounds on the end-to-end network delay. Solutions so far to such a requirement are either impractical or ineffective. Flow based schedulers suggested in a traditional integrated services (IntServ) framework are O(N) or O(log N), where N is the number of flows in the scheduler, which can grow to tens of thousands in a core router. Due to such complexity, class-based schedulers are adopted in real deployments. The class-based systems, however, cannot provide bounded delays in networks with cycle, since the maximum burst grows infinitely along the cycled path. Attaching a regulator in front of a scheduler to limit the maximum burst is considered as a viable solution. International standards, such as IEEE 802.1 time sensitive network (TSN) and IETF deterministic network (DetNet) are adopting this approach as a standard. The regulator in TSN and DetNet, however, requires flow state information, therefore contradicts to the simple class-based schedulers. This paper suggests non-work conserving fair schedulers, called ‘regulating schedulers’ (RSC), which function as a regulator and a scheduler at the same time. A deficit round-robin (DRR) based RSC, called nw-DRR, is devised and proved to be both a fair scheduler and a regulator. Despite the lower complexity, the input port-based nw-DRR is shown to perform better than the current TSN approach, and to bind the end-to-end delay within a few milliseconds in realistic network scenarios.