Concurrency control poses significant challenges when composing computations over multiple data-structures (objects) with different concurrency-control implementations. We formalize the usually desired requirements (serializability, abort-safety, deadlock-safety, and opacity) as well as stronger versions of these properties that enable composition. We show how to compose protocols satisfying these properties so that the resulting combined protocol also satisfies these properties. Our approach generalizes well-known protocols (such as two-phase-locking and two-phase-commit) and leads to new protocols. We apply this theory to show how we can safely compose optimistic and pessimistic concurrency control. For example, we show how we can execute a transaction that accesses two objects, one controlled by an STM and another by locking.

Concurrency control poses significant challenges when composing computations over multiple data-structures (objects) with different concurrency-control implementations. We formalize the usually desired requirements (serializability, abort-safety, deadlock-safety, and opacity) as well as stronger versions of these properties that enable composition. We show how to compose protocols satisfying these properties so that the resulting combined protocol also satisfies these properties. Our approach generalizes well-known protocols (such as two-phase-locking and two-phase-commit) and leads to new protocols. We apply this theory to show how we can safely compose optimistic and pessimistic concurrency control. For example, we show how we can execute a transaction that accesses two objects, one controlled by an STM and another by locking.

Companies o en have very limited information about the applications running in their datacenter or public/private cloud environments. As this can harm e ciency, performance, and security, many network administrators work hard to manually assign actionable description to (virtual) machines.is paper presents and evaluates N etSlicer , a machine-learning approach that enables an automated grouping of nodes into applications and their tiers. Our solution is based solely on the available network layer data which is used as part of a novel graph clustering algorithm, tailored toward the datacenter use case and accounting also for observed port numbers. For the sake of this paper, we also performed an extensive empirical measurement study, collecting actual workloads from di erent production datacenters (data to be released together with this paper). We nd that our approach features a high accuracy.