Shouxi Luo

Fast incremental flow table aggregation in SDN

In OpenFlow-based SDN, flow tables are TCAM-hungry and commodity switches suffer from limited concrete flow table size. One method for coping with the limitations is to use aggregation schemes to reduce the number of flow entries required to represent the same forwarding semantics. Unfortunately, the aggregation retards table updates and lengthens the updating time. During which, the data plane is inconsistent with the control plane, forwarding errors such as Reachability Failures, Forwarding Loops, Traffic Isolation and Leakage are prone to occur. Since network updates take place frequently in practice, the aggregation scheme must be efficient enough. In this paper we propose offline FFTA (Fast Flow Table Aggregation) and its online improver iFFTA to shrink the flow table size and to provide practical fast updates. iFFTA is the first online non-prefix aggregation scheme. Extensive experiments demonstrate: (1) FFTA is about 200× faster than the previously published best non-prefix aggregation scheme without loss of compression ratio on offline aggregation; and (2) iFFTA achieves about 3× faster than FFTA on online update incorporations with a loss of an acceptable compression ratio per update. Thus the user could make a combination use of FFTA and iFFTA for table aggregations: call iFFTA usually and recall the efficient FFTA once the switch is running out of concrete flow table space.

Minimizing average coflow completion time with decentralized scheduling

In current data centers, an application (e.g. MapReduce) usually generates a collection of parallel flows sharing a common goal. These flows compose a coflow and only completing them all is meaningful. Accordingly, minimizing the average coflow completion time (CCT) becomes a critical objective for flow scheduling. In this topic, the state-of-the-art centralized method, Varys, achieves a good average CCT; but it has the scalability problem. Alternatively, the only existing decentralized method, Baraat, suffers from the head-of-line blocking problem. To solve these problems, we propose D-CAS, a preemptive, decentralized, coflow-aware scheduling system in this paper. D-CAS pursues coflow-level remaining-time-first (MRTF) principle by leveraging a simple negotiation mechanism between each coflows data senders and receivers. As the MRTF principle is inherently preemptive and proven to be a near-optimal guideline to minimize average CCT, D-CAS avoids the head-of-line blocking problem and gets good performances. Through extensive simulations, we find that D-CAS achieves a performance close to Varys (gap <; 15%) and outperforms Baraat significantly (about 1.4-4×).

Consistency is Not Easy: How to Use Two-Phase Update for Wildcard Rules?

The recent proposed two-phase mechanism is a provable theory to achieve consistent updates for SDN. However, how to make it work for practical rules is important yet unsolved-(1) two-phase mechanism requires that rules in the new configuration after an update are assigned with a distinct version number from rules in the old configuration before an update; but (2) setting rules in each configuration with a distinct version number causes serious rule-space overheads in practice due to the sophisticated “covered” relationships between practical wildcard rules. In this letter, we design a simple yet generic solution for the problem. By using well-designed wildcard-based version number matchings, we simplify the update procedure, make a stream of updates easy to be processed in parallel, and avoid all unwanted rule-space overheads. We think that our mechanism bridges the gap between the theory of two-phase consistent update and the practical issue of how to use it for todays networks.

Practical flow table aggregation in SDN

In OpenFlow-driven SDN, flow tables are TCAM-hungry; commodity switches suffer from limited concrete flow table size. One method for coping with the limitations is to use aggregation schemes to reduce the number of flow entries required to express the same forwarding semantics. Unfortunately, the aggregation of rules would retard table updates and lengthen the updating duration, during which, the data plane is inconsistent with the control plane. Forwarding errors such as Reachability Failures, Forwarding Loops, Traffic Isolation and Leakage are prone to occur. Since network updates take place frequently in practice, the aggregation scheme must be efficient and effective.In this paper, we proposed FFTA (Fast Flow Table Aggregation) and its online companion, iFFTA (incremental FFTA), to make practical flow table aggregation. FFTA is an offline solution performing snapshot aggregation of non-prefix rules by (1) splitting them into prefix-permutable partitions in an aggregation-aware manner, and (2) applying optimal prefix-based aggregation techniques, respectively. When some original rules are updated, iFFTA is triggered to incorporate the update immediately by leveraging the order-independence relationship and structure information of rules. To the best of our knowledge, iFFTA is the first online aggregation scheme for non-prefix rules. We employed public available prefix rules as well as synthetic non-prefix rules generated with real parameters to evaluate their performances. Extensive experiments demonstrated that FFTA significantly outperforms prior art on both efficiency and effectiveness, while iFFTA greatly simplifies the update of aggregated rules with an acceptable loss of compression ratio. Accordingly, users could make a combination use of FFTA and iFFTA in practice: call iFFTA usually and recall FFTA once the switch is running out of concrete flow table space.

Towards Practical and Near-Optimal Coflow Scheduling for Data Center Networks

In current data centers, an application (e.g., MapReduce, Dryad, search platform, etc.) usually generates a group of parallel flows to complete a job. These flows compose a coflow and only completing them all is meaningful to the application. Accordingly, minimizing the average Coflow Completion Time (CCT) becomes a critical objective of flow scheduling. However, achieving this goal in todays Data Center Networks (DCNs) is quite challenging, not only because the schedule problem is theoretically NP-hard, but also because it is tough to perform practical flow scheduling in large-scale DCNs. In this paper, we find that minimizing the average CCT of a set of coflows is equivalent to the well-known problem of minimizing the sum of completion times in a concurrent open shop. As there are abundant existing solutions for concurrent open shop, we open up a variety of techniques for coflow scheduling. Inspired by the best known result, we derive a 2-approximation algorithm for coflow scheduling, and further develop a decentralized coflow scheduling system, D-CAS, which avoids the system problems associated with current centralized proposals while addressing the performance challenges of decentralized suggestions. Trace-driven simulations indicate that D-CAS achieves a performance close to Varys, the state-of-the-art centralized method, and outperforms Baraat, the only existing decentralized method, significantly.

Arrange your network updates as you wish

Updating network configurations responding to dynamic changes is still a tricky task in SDN. During the update process, in-flight packets might misuse different versions of rules, and “hot” links could be overloaded due to the unplanned update order. As for the problem of misusing rule, recently proposed suggestions like two-phase mechanism and Customizable Consistency Generator (CCG) have provided generic and customizable solutions. Yet, there does not exist an approach that is flexible to avoid the transient congestion on hot links respecting to diverse user requirements like guaranteeing update deadline, managing transient throughput loss, etc.; controllers urgently need one. In this paper, we propose CUP, Customizable Update Planner, to seek the solution. Different from prior approaches that adopt fixed designs for a single purpose like optimizing the update speed (e.g., Dionysus) or avoiding congestions (e.g., zUpdate, SWAN), CUP introduces generic linear programming models to formulate user-specified requirements and the update planning problem. By solving these customized models, CUP is able to plan network updates according to a large fraction of user requirements, such as guaranteeing deadlines, prioritizing operation orders, managing throughput loss, etc., while avoiding transient congestion. We prototype CUP on Ryu and employ it to arrange updates for networks built upon Mininet. Results confirm the flexibility of CUP while indicating that it always obtains the “best” update plans following the users wish.

Fast lossless traffic migration for SDN updates

Migration of traffic from one configuration to another is common in SDNs due to node/link failures, network maintenance, policy reconfiguration, intrusion detection, network upgrades, and etc. When the network devices are informed by the controller to execute the traffic migration, its difficult even impossible to force all network devices to perform the update action in a strict synchronized way. Thus the network is likely to see transient overlapped traffic from both the new configuration and the old one. This kind of overlap may cause overload to those hot spots. This paper reveals the transient congestion problem during traffic migration in an SDN update. According to the observation, its feasible for the controller to schedule ingress nodes to perform the migration in an order thus the transient congestion is avoided. This scheduling problem is formulated as a Mixed Integer Linear Program (MIP) model. If feasible orders exist for ingress network nodes to perform the migration, the MIP can always find the order that achieves the minimum steps in all possibilities. A heuristic method (named ATOMIP (ATOmic-MIP)) is proposed to speed-up the solving of this MIP. Evaluation based on network topologies observed from real ISPs shows that the lossless migration happens in sub-seconds.

Decentralized deadline-aware coflow scheduling for datacenter networks

This paper presents D2-CAS, a novel decentralized coflow scheduling system, to minimize the rate of deadline missed coflow for datacenter networks. To design D2-CAS, we first formulate the deadline-missed coflow minimization problem and show its equivalence with the well-known problem of minimizing the late jobs in a concurrent open shop, which is NP-hard in ordinary sense. Inspired by Moore-Hodgsos algorithm (MHA), the optimal solution for minimizing late jobs on a single machine, we design an efficient coflow schedule algorithm, CS-MHA, and further propose its decentralized implementation, D2-CAS. Basically, each sender in D2-CAS periodically runs a part of CS-MHA to get a local suggestion for flow priority assignment, and then multiple senders of a coflow negotiate for an orchestrated priority by leveraging their common data receivers. Via delivering each coflows packets with its negotiated priority, senders finally carry out efficient deadline-aware coflow scheduling in a decentralized fashion. To the best of our knowledge, this is the first paper that theoretically investigates the deadline-aware coflow scheduling problem, while D2-CAS is the first decentralized solution. Real parameter driven simulations imply that, with the simple yet efficient mechanism, D2-CAS greatly outperforms all existing solutions on reducing the deadline-missed coflows (e.g., outperforms Varys more than 2x).

Information-agnostic coflow scheduling with optimal demotion thresholds

Previous coflow scheduling proposals improve the coflow completion time (CCT) over per-flow scheduling based on prior information of coflows, which makes them hard to apply in practice. State-of-art information-agnostic coflow scheduling solution Aalo adopts Discretized Coflow-aware Least-Attained-Service (D-CLAS) to gradually demote coflows from the highest priority class into several lower priority classes when their sent-bytes-count exceeds several predefined demotion thresholds. However, current design standards of these demotion thresholds are crude because they do not analyze the impacts of different demotion thresholds on the average coflow delay. In this paper, we model the D-CLAS system by an M/G/1 queue and formulate the average coflow delay as a function of the demotion thresholds. In addition, we prove the valley-like shape of the function and design the Down-hill searching (DHS) algorithm. The DHS algorithm locates a set of optimal demotion thresholds which minimizes the average coflow delay in the system. Real-data-center-trace driven simulations indicate that DHS improves average CCT up to 6.20× over Aalo.

Dynamic topology management in optical datacenter networks

In this paper, we study how to manage the topology reconfiguration in OSA-based datacenter networks (DCNs). Though an OSA-based DCN can change its topology to adapt to the traffic matrix and improve the network scalability, it requires too much time (10ms) to reconfigure the topology, which may not only incur a great amount of traffic loss in high throughput low latency DCNs, but also bring much performance degradation to the delay sensitive flows. Therefore, a progressive topology reconfiguration scheme is required to reduce the traffic loss and guarantee the performance of delay sensitive flows. To this end, we first formulate the problem as a mathematical model, and then analyze its feasibility and complexity. Based on these analyses, topology management algorithm (TMA) is proposed to calculate the topology reconfiguration scheme that can maintain the topology connectivity during reconfiguration. By simulation, we find that TMA can reduce the traffic loss during topology reconfiguration by up to 50% in most of the cases and reconfigure topology without traffic loss in some cases.