This paper proposes M3, an end-to-end system that dynamically distributes memory resources among competing applications to maximize their overall performance. Today's data center workloads, can adapt to a wide range of memory sizes, and they are built on complex software stacks.

We present a framework for creating heterogeneous virtualized network function (VNF) service chains from cloud data center resources. Traditionally, these functions are packaged in software images within a catalog of networking applications that can be loaded onto a virtual machine CPU, and can be offered to users as a service. Our framework combines the best of both software and hardware by allowing users to chain traditional software-based VNFs with hardware-based VNFs that the user provides as an IP to generate a bitstream or a pre-generated VNF as part of a library. To accomplish this, our framework first creates the hardware bitstreams and programs the FPGA VNFs, loads any software VNFs requested, and programs the network to daisy chain the VNFs together. Furthermore, this enables an incremental design flow where the user can start by implementing a chain of VNFs in software and incrementally substitute software VNFs for their hardware counterparts. Our paper investigates two case studies to show the ability to switch between hardware and software VNFs in our framework and to demonstrate the benefit of using hardware VNFs. The first study is signature matching at fixed offsets, similar to matching packet headers. In this case study, the CPU can keep up at line-rate using specialized networking drivers. The second case study involves string matching within a packet, which requires scanning through the entire frame. In this case, the CPU performance drops to approximately 20 percent of the input rate, whereas the FPGA can continue to keep up at line-rate.

Complex and unforeseen failures in distributed systems must be diagnosed and replicated in a development environment so that developers can understand the underlying problem and verify the resolution. System logs often form the only source of diagnostic information, and developers reconstruct a failure using manual guesswork. This is an unpredictable and time-consuming process which can lead to costly service outages while a failure is repaired.