Parallel Running and Comparing the Behavior of Two Identical Network Servers In this paper, we design, implement, and evaluate the functions and performance of a parallel running and comparing system that is built on OpenVswitch software switch. Parallel running is a very important method to change from an existing system to a new system. The conversion usually occurs when the technology of the old system is outdated and a new system needs to be installed to replace the old one. During parallel running, both the old and new systems are running side by side and their behavior are compared against each other until the users are certain that the new and old systems behave exactly the same. At that time, the old system will be removed and the new system will totally replace the old system. In this paper, we use the OpenVswitch to design and implement a comparing system for parallel running. Our system has many useful features and can be applied to real missioncritical systems.

KVSwitch: An Innetwork Load Balancer for KeyValue Stores Todayxe2x80x99s cloudbased online services are underpinned by distributed keyvalue stores (KVSs). Keys and values are distributed across backend servers in such scaleout systems. One primary reallife performance bottleneck occurs when storage servers suffer from load imbalance under skewed workloads. In this paper, we present KVSwitch, a centralized selfmanaging load balancer that leverages the power and flexibility of emerging programmable switches. The balance is achieved through dynamically predicting the hot items and creating replication strategies according to KVS loading. To overcome the challenges in realizing KVSwitch given the limitations of the switch hardware, we decompose KVSwitchxe2x80x99s functions and carefully design them for the heterogeneous processors inside the switch. We prototype KVSwitch in a Tofino switch. Experimental results show that our solution can effectively keep the KVS servers balanced even under highly skewed workloads. Furthermore, KVSwitch only replicates 70% of hot items and consumes 9.88% of server memory rather than simply replicating all hot items to each server.