D-SPACE4Cloud: A Design Tool for Big Data Applications

The last years have seen a steep rise in data generation worldwide, with the development and widespread adoption of several software projects targeting the Big Data paradigm. Many companies currently engage in Big Data analytics as part of their core business activities, nonetheless there are no tools and techniques to support the design of the underlying hardware configuration backing such systems. In particular, the focus in this report is set on Cloud deployed clusters, which represent a cost-effective alternative to on premises installations. We propose a novel tool implementing a battery of optimization and prediction techniques integrated so as to efficiently assess several alternative resource configurations, in order to determine the minimum cost cluster deployment satisfying QoS constraints. Further, the experimental campaign conducted on real systems shows the validity and relevance of the proposed method

QoS-aware mechanisms for improving cost-efficiency of datacenters

Warehouse Scale Computers (WSCs) promise high cost-efficiency by amortizing power, cooling, and management overheads. WSCs today host a large variety of jobs with two broad performance requirements categories: latency-critical (LC) and best-effort (BE). Ideally, to fully utilize all hardware resources, WSC operators can simply fill all the nodes with computing jobs. Unfortunately, because colocated jobs contend for shared resources, systems with high loads often experience performance degradation, which negatively impacts the Quality of Service (QoS) for LC jobs. In fact, service providers usually over-provision resources to avoid any interference with LC jobs, leading to significant resource inefficiencies. In this dissertation, I explore opportunities across different system-abstraction layers to improve the cost-efficiency of dataceters by increasing resource utilization of WSCs with little or no impact on the performance of LC jobs. The dissertation has three main components. First, I explore opportunities to improve the throughput of multicore systems by reducing the performance variation of LC jobs. The main insight is that by reshaping the latency distribution curve, performance headroom of LC jobs can be effectively converted to improved BE throughput. I develop, implement, and evaluate a runtime system that achieves this goal with existing hardware. I leverage the cache partitioning, per-core frequency scaling, and thread masking of server processors. Evaluation results show the proposed solution enables 30% higher system throughput compared to solutions proposed in prior works while maintaining at least as good QoS for LC jobs. Second, I study resource contention in near-future heterogeneous memory architectures (HMA). This study is motivated by recent developments in non-volatile memory (NVM) technologies, which enable higher storage density at the cost of same performance. To understand the performance and QoS impact of HMAs, I design and implement a performance emulator in the Linux kernel that runs unmodified workloads with high accuracy, low overhead, and complete transparency. I further propose and evaluate multiple data and resource management QoS mechanisms, such as locality-aware page admission, occupancy management, and write buffer jailing. Third, I focus on accelerated machine learning (ML) systems. By profiling the performance of production workloads and accelerators, I show that accelerated ML tasks are highly sensitive to main memory interference due to fine-grained interaction between CPU and accelerator tasks. As a result, memory resource contention can significantly decreases the performance and efficiency gains of accelerators. I propose a runtime system that leverages existing hardware capabilities and show 17% higher system efficiency compared to previous approaches. This study further exposes opportunities for future processor architecturesElectrical and Computer Engineerin

Evaluation of Load Scheduling Strategies for Real-Time Data Warehouse Environments

The demand for so-called living or real-time data warehouses is increasing in many application areas, including manufacturing, event monitoring and telecommunications. In fields like these, users normally expect short response times for their queries and high freshness for the requested data. However, it is truly challenging to meet both requirements at the same time because of the continuous flow of write-only updates and read-only queries as well as the latency caused by arbitrarily complex ETL processes. To optimize the update flow in terms of data freshness maximization and load minimization, we propose two algorithms - local and global scheduling - that operate on the basis of different system information. We want to discuss the benefits and drawbacks of both approaches in detail and derive recommendations regarding the optimal scheduling strategy for any given system setup and workload

Investigating Decision Support Techniques for Automating Cloud Service Selection

The compass of Cloud infrastructure services advances steadily leaving users in the agony of choice. To be able to select the best mix of service offering from an abundance of possibilities, users must consider complex dependencies and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal on investigating an intelligent decision support system for selecting Cloud based infrastructure services (e.g. storage, network, CPU).Comment: Accepted by IEEE Cloudcom 2012 - PhD consortium trac

Ubik: efficient cache sharing with strict qos for latency-critical workloads

Chip-multiprocessors (CMPs) must often execute workload mixes with different performance requirements. On one hand, user-facing, latency-critical applications (e.g., web search) need low tail (i.e., worst-case) latencies, often in the millisecond range, and have inherently low utilization. On the other hand, compute-intensive batch applications (e.g., MapReduce) only need high long-term average performance. In current CMPs, latency-critical and batch applications cannot run concurrently due to interference on shared resources. Unfortunately, prior work on quality of service (QoS) in CMPs has focused on guaranteeing average performance, not tail latency. In this work, we analyze several latency-critical workloads, and show that guaranteeing average performance is insufficient to maintain low tail latency, because microarchitectural resources with state, such as caches or cores, exert inertia on instantaneous workload performance. Last-level caches impart the highest inertia, as workloads take tens of milliseconds to warm them up. When left unmanaged, or when managed with conventional QoS frameworks, shared last-level caches degrade tail latency significantly. Instead, we propose Ubik, a dynamic partitioning technique that predicts and exploits the transient behavior of latency-critical workloads to maintain their tail latency while maximizing the cache space available to batch applications. Using extensive simulations, we show that, while conventional QoS frameworks degrade tail latency by up to 2.3x, Ubik simultaneously maintains the tail latency of latency-critical workloads and significantly improves the performance of batch applications.United States. Defense Advanced Research Projects Agency (Power Efficiency Revolution For Embedded Computing Technologies Contract HR0011-13-2-0005)National Science Foundation (U.S.) (Grant CCF-1318384

An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads

TimeTrader: Exploiting Latency Tail to Save Datacenter Energy for On-line Data-Intensive Applications

Datacenters running on-line, data-intensive applications (OLDIs) consume significant amounts of energy. However, reducing their energy is challenging due to their tight response time requirements. A key aspect of OLDIs is that each user query goes to all or many of the nodes in the cluster, so that the overall time budget is dictated by the tail of the replies' latency distribution; replies see latency variations both in the network and compute. Previous work proposes to achieve load-proportional energy by slowing down the computation at lower datacenter loads based directly on response times (i.e., at lower loads, the proposal exploits the average slack in the time budget provisioned for the peak load). In contrast, we propose TimeTrader to reduce energy by exploiting the latency slack in the sub- critical replies which arrive before the deadline (e.g., 80% of replies are 3-4x faster than the tail). This slack is present at all loads and subsumes the previous work's load-related slack. While the previous work shifts the leaves' response time distribution to consume the slack at lower loads, TimeTrader reshapes the distribution at all loads by slowing down individual sub-critical nodes without increasing missed deadlines. TimeTrader exploits slack in both the network and compute budgets. Further, TimeTrader leverages Earliest Deadline First scheduling to largely decouple critical requests from the queuing delays of sub- critical requests which can then be slowed down without hurting critical requests. A combination of real-system measurements and at-scale simulations shows that without adding to missed deadlines, TimeTrader saves 15-19% and 41-49% energy at 90% and 30% loading, respectively, in a datacenter with 512 nodes, whereas previous work saves 0% and 31-37%.Comment: 13 page