Workload shaping for QoS and power efficiency of storage systems

The growing popularity of hosted storage services and shared storage infrastructure in data centers is driving the recent interest in resource management and QoS in storage systems. The bursty nature of storage workloads raises significant performance and provisioning challenges, leading to increased resource requirements, management costs, and energy consumption. We present a novel dynamic workload shaping framework to handle bursty server workloads, where the arrival stream is dynamically decomposed to isolate its bursty, and then rescheduled to exploit available slack. An optimal decomposition algorithm RTT and a recombination algorithm Miser make up the scheduling framework. We evaluate this framework using several real world storage workloads traces. The results show that workload shaping: (i) reduces the server capacity requirements and power consumption dramatically while affecting QoS guarantees minimally, (ii) provides better response time distributions over non-decomposed traditional scheduling methods, and (iii) decomposition can be used to provide more accurate capacity estimates for multiplexing several clients on a shared server

Efficient caching algorithms for memory management in computer systems

As disk performance continues to lag behind that of memory systems and processors, fully utilizing memory to reduce disk accesses is a highly effective effort to improve the entire system performance. Furthermore, to serve the applications running on a computer in distributed systems, not only the local memory but also the memory on remote servers must be effectively managed to minimize I/O operations. The critical challenges in an effective memory cache management include: (1) Insightfully understanding and quantifying the locality inherent in the memory access requests; (2) Effectively utilizing the locality information in replacement algorithms; (3) Intelligently placing and replacing data in the multi-level caches of a distributed system; (4) Ensuring that the overheads of the proposed schemes are acceptable.;This dissertation provides solutions and makes unique and novel contributions in application locality quantification, general replacement algorithms, low-cost replacement policy, thrashing protection, as well as multi-level cache management in a distributed system. First, the dissertation proposes a new method to quantify locality strength, and accurately to identify the data with strong locality. It also provides a new replacement algorithm, which significantly outperforms existing algorithms. Second, considering the extremely low-cost requirements on replacement policies in virtual memory management, the dissertation proposes a policy meeting the requirements, and considerably exceeding the performance existing policies. Third, the dissertation provides an effective scheme to protect the system from thrashing for running memory-intensive applications. Finally, the dissertation provides a multi-level block placement and replacement protocol in a distributed client-server environment, exploiting non-uniform locality strengths in the I/O access requests.;The methodology used in this study include careful application behavior characterization, system requirement analysis, algorithm designs, trace-driven simulation, and system implementations. A main conclusion of the work is that there is still much room for innovation and significant performance improvement for the seemingly mature and stable policies that have been broadly used in the current operating system design

Synthesizing Representative I/O Workloads Using Iterative Distillation

Storage systems designers are still searching for better methods of obtaining representative I/O workloads to drive studies of I/O systems. Traces of production workloads are very accurate, but inflexible and difficult to obtain. (Privacy and performance concerns discourage most system administrators from collecting such traces and making them available to the public.) The use of synthetic workloads addresses these limitations; however, synthetic workloads are accurate only if they share certain key properties with the production workload on which they are based (e.g., mean request size, read percentage). Unfortunately, we do not know which properties are "key" for a given workload and storage system. We have developed a tool, the Distiller, that automatically identifies the key properties (more formally called attribute-values) of the workload. These attribute-values can then be used to generate a synthetic workload representative of the production workload. This paper presents the design and evaluation of the Distiller. We demonstrate how the Distiller finds representative synthetic workloads for simple artificial workloads and three production workload traces

Energy-Aware Development and Labeling for Mobile Applications

Today, mobile devices such as smart phones and tablets have become ubiquitous and are used everywhere. Millions of software applications can be purchased and installed on these devices, customizing them to personal interests and needs. However, the frequent use of mobile devices has let a new problem become omnipresent: their limited operation time, due to their limited energy capacities. Although energy consumption can be considered as being a hardware problem, the amount of energy required by today’s mobile devices highly depends on their current workloads, being highly influenced by the software running on them. Thus, although only hardware modules are consuming energy, operating systems, middleware services, and mobile applications highly influence the energy consumption of mobile devices, depending on how efficient they use and control hardware modules. Nevertheless, most of today’s mobile applications totally ignore their influence on the devices’ energy consumption, leading to energy wastes, shorter operation times, and thus, frustrated application users. A major reason for this energy-unawareness is the lack for appropriate tooling for the development of energy-aware mobile applications. As many mobile applications are today behaving energy-unaware and various mobile applications providing similar services exist, mobile application users aim to optimize their devices by installing applications being known as energy-saving or energy-aware; meaning that they consume less energy while providing the same services as their competitors. However, scarce information on the applications’ energy usage is available and, thus, users are forced to install and try many applications manually, before finding the applications fulfilling their personal functional, non-functional, and energy requirements. This thesis addresses the lack of tooling for the development of energy-aware mobile applications and the lack of comparability of mobile applications in terms of energy-awareness with the following two contributions: First, it proposes JouleUnit, an energy profiling and testing framework using unit-tests for the execution of application workloads while profiling their energy consumption in parallel. By extending a well-known testing concept and providing tooling integrated into the development environment Eclipse, JouleUnit requires a low learning curve for the integration into existing development and testing processes. Second, for the comparability of mobile applications in terms of energy efficiency, this thesis proposes an energy benchmarking and labeling service. Mobile applications belonging to the same usage domain are energy-profiled while executing a usage-domain specific benchmark in parallel. Thus, their energy consumption for specific use cases can be evaluated and compared afterwards. To abstract and summarize the profiling results, energy labels are derived that summarize the applications’ energy consumption over all evaluated use cases as a simple energy grade, ranging from A to G. Besides, users can decide how to weigh specific use cases for the computation of energy grades, as it is likely that different users use the same applications differently. The energy labeling service has been implemented for Android applications and evaluated for three different usage domains (being web browsers, email clients, and live wallpapers), showing that different mobile applications indeed differ in their energy consumption for the same services and, thus, their comparison is both possible and sensible. To the best of my knowledge, this is the first approach providing mobile application users comparable energy consumption information on mobile applications without installing and testing them on their own mobile devices

Performance modelling with adaptive hidden Markov models and discriminatory processor sharing queues

In modern computer systems, workload varies at different times and locations. It is important to model the performance of such systems via workload models that are both representative and efficient. For example, model-generated workloads represent realistic system behaviour, especially during peak times, when it is crucial to predict and address performance bottlenecks. In this thesis, we model performance, namely throughput and delay, using adaptive models and discrete queues. Hidden Markov models (HMMs) parsimoniously capture the correlation and burstiness of workloads with spatiotemporal characteristics. By adapting the batch training of standard HMMs to incremental learning, online HMMs act as benchmarks on workloads obtained from live systems (i.e. storage systems and financial markets) and reduce time complexity of the Baum-Welch algorithm. Similarly, by extending HMM capabilities to train on multiple traces simultaneously it follows that workloads of different types are modelled in parallel by a multi-input HMM. Typically, the HMM-generated traces verify the throughput and burstiness of the real data. Applications of adaptive HMMs include predicting user behaviour in social networks and performance-energy measurements in smartphone applications. Equally important is measuring system delay through response times. For example, workloads such as Internet traffic arriving at routers are affected by queueing delays. To meet quality of service needs, queueing delays must be minimised and, hence, it is important to model and predict such queueing delays in an efficient and cost-effective manner. Therefore, we propose a class of discrete, processor-sharing queues for approximating queueing delay as response time distributions, which represent service level agreements at specific spatiotemporal levels. We adapt discrete queues to model job arrivals with distributions given by a Markov-modulated Poisson process (MMPP) and served under discriminatory processor-sharing scheduling. Further, we propose a dynamic strategy of service allocation to minimise delays in UDP traffic flows whilst maximising a utility function.Open Acces