Abstract-Tasks in modern data-parallel clusters have highly diverse resource requirements along CPU, memory, disk and network. We present Tetris, a multi-resource cluster scheduler that packs tasks to machines based on their requirements of all resource types. Doing so avoids resource fragmentation as well as over-allocation of the resources that are not explicitly allocated, both of which are drawbacks of current schedulers. Tetris adapts heuristics for the multidimensional bin packing problem to the context of cluster schedulers wherein task arrivals and machine availability change in an online manner and wherein task's resource needs change with time and with the machine that the task is placed at. In addition, Tetris improves average job completion time by preferentially serving jobs that have less remaining work. We observe that fair allocations do not o er the best performance and the above heuristics are compatible with a large class of fairness policies; hence, we show how to simultaneously achieve good performance and fairness. Tracedriven simulations and deployment of our Apache YARN prototype on a node cluster show gains of over in makespan and job completion time while achieving nearly perfect fairness.
Network functions virtualization (NFV) together with softwaredefined networking (SDN) has the potential to help operators satisfy tight service level agreements, accurately monitor and manipulate network traffic, and minimize operating expenses. However, in scenarios that require packet processing to be redistributed across a collection of network function (NF) instances, simultaneously achieving all three goals requires a framework that provides efficient, coordinated control of both internal NF state and network forwarding state. To this end, we design a control plane called OpenNF. We use carefully designed APIs and a clever combination of events and forwarding updates to address race conditions, bound overhead, and accommodate a variety of NFs. Our evaluation shows that OpenNF offers efficient state control without compromising flexibility, and requires modest additions to NFs.
Network functions virtualization (NFV) together with softwaredefined networking (SDN) has the potential to help operators satisfy tight service level agreements, accurately monitor and manipulate network traffic, and minimize operating expenses. However, in scenarios that require packet processing to be redistributed across a collection of network function (NF) instances, simultaneously achieving all three goals requires a framework that provides efficient, coordinated control of both internal NF state and network forwarding state. To this end, we design a control plane called OpenNF. We use carefully designed APIs and a clever combination of events and forwarding updates to address race conditions, bound overhead, and accommodate a variety of NFs. Our evaluation shows that OpenNF offers efficient state control without compromising flexibility, and requires modest additions to NFs.
Increasingly, middleboxes are being deployed as software components and, with the advent of software defined networking, can be deployed at arbitrary locations. However, existing approaches for controlling the operations of middleboxes continue to be rudimentary and ad hoc. As such, a variety of dynamic network control scenarios that are crucial to enhancing the security, availability and performance of enterprise applications cannot be realized today.In this paper, we ask: what is the right way to exercise unified control over the actions of middlebox that enables sophisticated dynamic network control scenarios? Inspired by SDN, we argue that a software-defined middlebox networking (SDMBN) framework-which provides fine-grained, programmatic control over all MB state in concert with control over the network-is the answer to this question. Thus, we present the design and implementation of OpenMB. OpenMB consists of slightly modified middleboxes that expose a southbound API for importing/exporting middlebox state, a middlebox controller that implements a northbound API to define how state can be accessed or placed, and scenario-specific control applications that orchestrate middlebox and network changes in tandem.
Remote, in-memory key-value (RInK) stores such as Memcached [6] and Redis [7] are widely used in industry and are an active area of academic research. Coupled with stateless application servers to execute business logic and a databaselike system to provide persistent storage, they form a core component of popular data center service architectures. We argue that the time of the RInK store has come and gone: their domain-independent APIs (e.g., PUT/GET) push complexity back to the application, leading to extra (un)marshalling overheads and network hops. Instead, data center services should be built using stateful application servers or custom in-memory stores with domain-specific APIs, which offer higher performance than RInKs at lower cost. Such designs have been avoided because they are challenging to implement without appropriate infrastructure support. Given recent advances in auto-sharding [8,9], we argue it is time to revisit these decisions. In this paper, we evaluate the potential performance improvements of stateful designs, propose a new abstraction, the linked, in-memory key-value (LInK) store, to enable developers to easily implement stateful services, and discuss areas for future research.
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