Guaranteeing deadlines for inter-datacenter transfers

Abstract

Inter-datacenter wide area networks (inter-DC WAN) carry a significant amount of data transfers that require to be completed within certain time periods, or deadlines. However, very little work has been done to guarantee such deadlines. The crux is that the current inter-DC WAN lacks an interface for users to specify their transfer deadlines and a mechanism for provider to ensure the completion while maintaining high WAN utilization.This paper addresses the problem by introducing a Deadline-based Network Abstraction (DNA) for inter-DC WANs. DNA allows users to explicitly specify the amount of data to be delivered and the deadline by which it has to be completed. The malleability of DNA provides flexibility in resource allocation. Based on this, we develop a system called Amoeba that implements DNA. Our simulations and testbed experiments show that Amoeba, by harnessing DNA's malleability, accommodates 15% more user requests with deadlines, while achieving 60% higher WAN utilization than prior solutions.

DCRoute: Speeding up Inter-Datacenter Traffic Allocation while Guaranteeing Deadlines

“…Many applications hosted on these datacenters need to transfer data to their peers in other datacenters for the purpose of data replication and synchronization [5], [6]. The aim is to improve fault-tolerance and user quality of service by making multiple copies of data and getting data closer to end users.…”

“…Previous work in the context of inter-datacenter traffic scheduling either fails to consider the negative effects of packet reordering caused by multiplexing packets over different paths (AMOEBA [6]) or cannot guarantee that admitted requests will actually complete transmission before the deadlines specified by customers (B4 [9], SWAN [8], TEMPUS [7]). In addition, AMOEBA and TEMPUS which are the state-of-the-art techniques in this area, model the allocation problem as large linear programs (LP), with possibly hundreds of thousands of variables, solving which incurs large memory and CPU overhead and can take a long time.…”

“…As in [6], [8], we assume the timeline is divided into properly sized timeslots over which the transmission rate is constant. Using a slotted timeline allows for schedules with variable transmission rates over time.…”

“…Similar to previous work [6], we assume arriving requests are put into a queue and processed in the order of arrival and that there is no preemption: once a request is allocated, it cannot be unallocated from the system. At each moment, we have two parameters t now and t end which represent current timeslot and the latest deadline among all active requests, respectively.…”

“…One of the ways to speed up this process is to limit the number of possible paths between every 2. An example of "As Late As Possible" allocation pair of nodes [7], for example, using k-shortest paths [6]. While solving the LP, another speedup method is to limit the number of considered active requests based on some criterion [6] such as having a common link with the new request.…”