Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial

Improving DBMS performance through diverse redundancy

Database replication is widely used to improve both fault tolerance and DBMS performance. Non-diverse database replication has a significant limitation - it is effective against crash failures only. Diverse redundancy is an effective mechanism of tolerating a wider range of failures, including many non-crash failures. However it has not been adopted in practice because many see DBMS performance as the main concern. In this paper we show experimental evidence that diverse redundancy (diverse replication) can bring benefits in terms of DBMS performance, too. We report on experimental results with an optimistic architecture built with two diverse DBMSs under a load derived from TPC-C benchmark, which show that a diverse pair performs faster not only than non-diverse pairs but also than the individual copies of the DBMSs used. This result is important because it shows potential for DBMS performance better than anything achievable with the available off-the-shelf servers

Rethinking State-Machine Replication for Parallelism

State-machine replication, a fundamental approach to designing fault-tolerant services, requires commands to be executed in the same order by all replicas. Moreover, command execution must be deterministic: each replica must produce the same output upon executing the same sequence of commands. These requirements usually result in single-threaded replicas, which hinders service performance. This paper introduces Parallel State-Machine Replication (P-SMR), a new approach to parallelism in state-machine replication. P-SMR scales better than previous proposals since no component plays a centralizing role in the execution of independent commands---those that can be executed concurrently, as defined by the service. The paper introduces P-SMR, describes a "commodified architecture" to implement it, and compares its performance to other proposals using a key-value store and a networked file system

Dependability Evaluation of Middleware Technology for Large-scale Distributed Caching

Distributed caching systems (e.g., Memcached) are widely used by service providers to satisfy accesses by millions of concurrent clients. Given their large-scale, modern distributed systems rely on a middleware layer to manage caching nodes, to make applications easier to develop, and to apply load balancing and replication strategies. In this work, we performed a dependability evaluation of three popular middleware platforms, namely Twemproxy by Twitter, Mcrouter by Facebook, and Dynomite by Netflix, to assess availability and performance under faults, including failures of Memcached nodes and congestion due to unbalanced workloads and network link bandwidth bottlenecks. We point out the different availability and performance trade-offs achieved by the three platforms, and scenarios in which few faulty components cause cascading failures of the whole distributed system.Comment: 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE 2020