Traffic often needs to be split over multiple equivalent backend servers, links, paths, or middleboxes. For example, in a load-balancing system, switches distribute requests of online services to backend servers. Hash-based approaches like Equal-Cost Multi-Path (ECMP) have low accuracy due to hash collision and incur significant churn during update. In a Software-Defined Network (SDN) the accuracy of traffic splits can be improved by crafting a set of wildcard rules for switches that better match the actual traffic distribution. The drawback of existing SDN-based traffic-splitting solutions is poor scalability as they generate too many rules for small rule-tables on switches. In this paper, we propose Niagara, an SDN-based traffic-splitting scheme that achieves accurate traffic splits while being extremely efficient in the use of rule-table space available on commodity switches. Niagara uses an incremental update strategy to minimize the traffic churn given an update. Experiments demonstrate that Niagara (1) achieves nearly optimal accuracy using only 1.2%--37% of the rule space of the current state-of-art, (2) scales to tens of thousands of services with the constrained rule-table capacity and (3) offers nearly minimum churn.

Designers of data centers and Web servers aim to make on-demand allocation of resources to clients in order to lower the deployment cost of hosted services. Moreover, they must also minimize operating costs, such as energy consumption, by matching service-capacity demand with resource supply. However, since the term "capacity" is typically defined vaguely or inadequately, it is difficult to assess resource needs and, hence, servers, which are several times larger than needed at runtime, are usually deployed. The time-varying nature of the workload model further complicates the problem and necessitates an online capacity-estimation solution. To address this overprovisioning problem, we first define the capacity of a server cluster as the sustainable throughput subject to a request retransmission ratio constraint and then analyze different approaches to capacity estimation in a running system. Various capacity-estimation mechanisms, such as offline benchmarking and CPU-utilization evaluation, are discussed and compared with our queue-monitoring method. We employ several different data-collection methods (application instrumentation, user-space tools, simple network management protocol (SNMP), and kernel modules) to compare their effects on estimation accuracy. Of these, queue monitoring is found to provide a good and stable estimate of server capacity. To validate this finding, we propose a simple cluster- resizing mechanism and evaluate the energy-conservation performance. A good combination of data collection and online capacity estimation is found to make significantly more energy savings than traditional approaches (that is, static estimation and scheduled capacity). Our experimental results show that more than 40 percent of energy can be saved for regular daily usage patterns without any prior knowledge of the workload and that long start-up and shutdown delays affect energy savings considerably.

Interposition-based system enhancements for multitiered servers are difficult to build because important system context is typically lost at application and machine boundaries. For example, resource quotas and user identities do not propagate easily between cooperating services that execute on different hosts or that communicate with each other via intermediary services. Application-transparent system enhancement is difficult to achieve when such context information is obscured by complex service interaction patterns. We propose a basic mechanism for sharing contextual information across the tiers of multitier computations to support system enhancement for multitier servers and applications.This article introduces generic, cluster-wide context as a new, configurable abstraction for the OS. System administrator- or application-specified context tracking rules determine how context is associated with system processes, sockets, messages, how it is relayed along the interapplication communication channels, and how it is to be interpreted by system interpositions, thus realizing Stateful Distributed Interposition.

Adaptive packet filters (APFs) are motivated by the proliferation of distributed servers and the lack of quality-of-service (QoS) management solutions for them. APFs merge packet-filtering and server load monitoring into a novel load-sensitive packet-filtering abstraction for overload protection and QoS differentiation. They integrate well into network protocol stacks and firewalls, scale to large server farms while remaining completely transparent to the applications. Experimental results of our prototype implementation demonstrate APFs' efficacy in providing QoS differentiation and overload protection with minimal overheads.