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Data-intensive analytics on large clusters is important for modern Internet services. As machines in these clusters have large memories, in-memory caching of inputs is an effective way to speed up these analytics jobs. The key challenge, however, is that these jobs run multiple tasks in parallel and a job is sped up only when inputs of all such parallel tasks are cached. Indeed, a single task whose input is not cached can slow down the entire job. To meet this "All-or-nothing" property, the authors have built PACMan, a caching service that coordinates access to the distributed caches. This coordination is essential to improve job completion times and cluster efficiency.
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