Datacenters have become increasingly important to host a diverse range of cloud applications with mixed workloads. Traditional applications hosted by datacenters are throughput-oriented without delay requirements, but newer generations of cloud applications, such as web search, recommendations, and social networking, typically employ a tree-based Partition-Aggregate structure, which may incur bursts of traffic. As a result, flows in these applications have stringent latency requirements, i.e., flow deadlines need to be met in order to achieve a satisfactory user experience. To meet these flow deadlines, research efforts in the recent literature have attempted to redesign flow and congestion control protocols that are specific to datacenter networks. In this paper, we focus on the new array of deadline-sensitive flow control protocols, thoroughly investigate their underlying design principles, analyze the evolution of their designs, and evaluate the tradeoffs involved in their design choices.

In the current era of cloud computing, data stored in the cloud is being generated at a tremendous speed, and thus the cloud storage system has become one of the key components in cloud computing. By storing a substantial amount of data in commodity disks inside the data center that hosts the cloud, the cloud storage system must consider one question very carefully: how do we store data reliably with a high efficiency in terms of both storage overhead and data integrity? Though it is easy to store replicated data to tolerate a certain amount of data losses, it suffers from a very low storage efficiency. Conventional erasure coding techniques, such as Reed-Solomon codes, are able to achieve a much lower storage cost with the same level of tolerance against disk failures. However, it incurs much higher repair costs, not to mention an even higher access latency. In this sense, designing new coding techniques for cloud storage systems has gained a significant amount of attention in both academia and the industry. In this paper, we examine the existing results of coding techniques for cloud storage systems. Specifically, we present these coding techniques into two categories: regenerating codes and locally repairable codes. These two kinds of codes meet the requirements of cloud storage along two different axes: optimizing bandwidth and I/O overhead. We present an overview of recent advances in these two categories of coding techniques. Moreover, we introduce the main ideas of some specific coding techniques at a high level, and discuss their motivations and performance.