The extreme latency and throughput requirements of modern web applications are driving the use of distributed inmemory object caches such as Memcached. While extant caching systems scale-out seamlessly, their use in the cloud -with its unique cost and multi-tenancy dynamicspresents unique opportunities and design challenges.In this paper, we propose MBal, a high-performance inmemory object caching framework with adaptive Multiphase load Balancing, which supports not only horizontal (scale-out) but vertical (scale-up) scalability as well. MBal is able to make efficient use of available resources in the cloud through its fine-grained, partitioned, lockless design. This design also lends itself naturally to provide adaptive load balancing both within a server and across the cache cluster through an event-driven, multi-phased load balancer. While individual load balancing approaches are being leveraged in in-memory caches, MBal goes beyond the extant systems and offers a holistic solution wherein the load balancing model tracks hotspots and applies different strategies based on imbalance severity -key replication, server-local or cross-server coordinated data migration. Performance evaluation on an 8-core commodity server shows that compared to a state-of-the-art approach, MBal scales with number of cores and executes 2.3× and 12× more queries/second for GET and SET operations, respectively.
Python-written data analytics applications can be modeled as and compiled into a directed acyclic graph (DAG) based workflow, where the nodes are fine-grained tasks and the edges are task dependencies. Such analytics workflow jobs are increasingly characterized by short, fine-grained tasks with large fan-outs. These characteristics make them well-suited for a new cloud computing model called serverless computing or Function-as-a-Service (FaaS), which has become prevalent in recent years. The auto-scaling property of serverless computing platforms accommodates short tasks and bursty workloads, while the pay-per-use billing model of serverless computing providers keeps the cost of short tasks low.In this paper, we thoroughly investigate the problem space of DAG scheduling in serverless computing. We identify and evaluate a set of techniques to make DAG schedulers serverless-aware. These techniques have been implemented in WUKONG, a serverless, DAG scheduler attuned to AWS Lambda. WUKONG provides decentralized scheduling through a combination of static and dynamic scheduling. We present the results of an empirical study in which WUKONG is applied to a range of microbenchmark and real-world DAG applications. Results demonstrate the efficacy of WUKONG in minimizing the performance overhead introduced by AWS Lambda -WUKONG achieves competitive performance compared to a serverful DAG scheduler, while improving the performance of real-world DAG jobs by as much as 3.1× at larger scale.
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