This paper evaluates the suitability of the MapReduce model for multi-core and multi-processor systems. MapReduce was created by Google for application development on data-centers with thousands of servers. It allows programmers to write functional-style code that is automatically parallelized and scheduled in a distributed system.We describe Phoenix, an implementation of MapReduce for shared-memory systems that includes a programming API and an efficient runtime system. The Phoenix runtime automatically manages thread creation, dynamic task scheduling, data partitioning, and fault tolerance across processor nodes. We study Phoenix with multi-core and symmetric multiprocessor systems and evaluate its performance potential and error recovery features. We also compare MapReduce code to code written in lower-level APIs such as P-threads. Overall, we establish that, given a careful implementation, MapReduce is a promising model for scalable performance on shared-memory systems with simple parallel code.

Large-scale datacenters (DCs) host tens of thousands of diverse applications each day. However, interference between colocated workloads and the difficulty of matching applications to one of the many hardware platforms available can degrade performance, violating the quality of service (QoS) guarantees that many cloud workloads require. While previous work has identified the impact of heterogeneity and interference, existing solutions are computationally intensive, cannot be applied online, and do not scale beyond a few applications.We present Paragon, an online and scalable DC scheduler that is heterogeneity-and interference-aware. Paragon is derived from robust analytical methods, and instead of profiling each application in detail, it leverages information the system already has about applications it has previously seen. It uses collaborative filtering techniques to quickly and accurately classify an unknown incoming workload with respect to heterogeneity and interference in multiple shared resources. It does so by identifying similarities to previously scheduled applications. The classification allows Paragon to greedily schedule applications in a manner that minimizes interference and maximizes server utilization. After the initial application placement, Paragon monitors application behavior and adjusts the scheduling decisions at runtime to avoid performance degradations. Additionally, we design ARQ, a multiclass admission control protocol that constrains application waiting time. ARQ queues applications in separate classes based on the type of resources they need and avoids long queueing delays for easy-to-satisfy workloads in highly-loaded scenarios. Paragon scales to tens of thousands of servers and applications with marginal scheduling overheads in terms of time or state.We evaluate Paragon with a wide range of workload scenarios, on both small and large-scale systems, including 1,000 servers on EC2. For a 2,500-workload scenario, Paragon enforces performance guarantees for 91% of applications, while significantly improving utilization. In comparison, heterogeneity-oblivious, interference-oblivious, and least-loaded schedulers only provide similar guarantees for 14%, 11%, and 3% of workloads. The differences are more striking in oversubscribed scenarios where resource efficiency is more critical. Fig. 1. Overview of scheduling in a datacenter. Applications vary in their resource requirements, their sensitivity to server configuration and interference, and their performance constraints. The scheduler needs to account for all these factors to assign applications to DC servers.

In this paper, we propose a new shared memory model: Transactional memory Coherence and Consistency (TCC). TCC provides a model in which atomic transactions are always the basic unit of parallel work, communication, memory coherence, and memory reference consistency. TCC greatly simplifies parallel software by eliminating the need for synchronization using conventional locks and semaphores, along with their complexities. TCC hardware must combine all writes from each transaction region in a program into a single packet and broadcast this packet to the permanent shared memory state atomically as a large block. This simplifies the coherence hardware because it reduces the need for small, low-latency messages and completely eliminates the need for conventional snoopy cache coherence protocols, as multiple speculatively written versions of a cache line may safely coexist within the system. Meanwhile, automatic, hardware-controlled rollback of speculative transactions resolves any correctness violations that may occur when several processors attempt to read and write the same data simultaneously. The cost of this simplified scheme is higher interprocessor bandwidth. To explore the costs and benefits of TCC, we study the characteristics of an optimal transaction-based memory system, and examine how different design parameters could affect the performance of real systems. Across a spectrum of applications, the TCC model itself did not limit available parallelism. Most applications are easily divided into transactions requiring only small write buffers, on the order of 4-8 KB. The broadcast requirements of TCC are high, but are well within the capabilities of CMPs and smallscale SMPs with high-speed interconnects.

User-facing, latency-sensitive services, such as websearch, underutilize their computing resources during daily periods of low traffic. Reusing those resources for other tasks is rarely done in production services since the contention for shared resources can cause latency spikes that violate the service-level objectives of latency-sensitive tasks. The resulting under-utilization hurts both the affordability and energy efficiency of large-scale datacenters. With the slowdown in technology scaling caused by the sunsetting of Moore's law, it becomes important to address this opportunity. We present Heracles, a feedback-based controller that enables the safe colocation of best-effort tasks alongside a latency-critical service. Heracles dynamically manages multiple hardware and software isolation mechanisms, such as CPU, memory, and network isolation, to ensure that the latency-sensitive job meets latency targets while maximizing the resources given to best-effort tasks. We evaluate Heracles using production latency-critical and batch workloads from Google and demonstrate average server utilizations of 90% without latency violations across all the load and colocation scenarios that we evaluated. CCS Concepts: r Computer systems organization → Cloud computing; r Software and its engineering → Scheduling

Distributed processing frameworks, such as Yahoo!'s Hadoop and Google's MapReduce, have been successful at harnessing expansive datacenter resources for large-scale data analysis. However, their effect on datacenter energy efficiency has not been scrutinized. Moreover, the filesystem component of these frameworks effectively precludes scale-down of clusters deploying these frameworks (i.e. operating at reduced capacity). This paper presents our early work on modifying Hadoop to allow scale-down of operational clusters. We find that running Hadoop clusters in fractional configurations can save between 9% and 50% of energy consumption, and that there is a tradeoff between performance energy consumption. We also outline further research into the energy-efficiency of these frameworks.