Data-Driven Intelligent Scheduling For Long Running Workloads In Large-Scale Datacenters

Cloud computing is becoming a fundamental facility of society today. Large-scale public or private cloud datacenters spreading millions of servers, as a warehouse-scale computer, are supporting most business of Fortune-500 companies and serving billions of users around the world. Unfortunately, modern industry-wide average datacenter utilization is as low as 6% to 12%. Low utilization not only negatively impacts operational and capital components of cost efficiency, but also becomes the scaling bottleneck due to the limits of electricity delivered by nearby utility. It is critical and challenge to improve multi-resource efficiency for global datacenters. Additionally, with the great commercial success of diverse big data analytics services, enterprise datacenters are evolving to host heterogeneous computation workloads including online web services, batch processing, machine learning, streaming computing, interactive query and graph computation on shared clusters. Most of them are long-running workloads that leverage long-lived containers to execute tasks. We concluded datacenter resource scheduling works over last 15 years. Most previous works are designed to maximize the cluster efficiency for short-lived tasks in batch processing system like Hadoop. They are not suitable for modern long-running workloads of Microservices, Spark, Flink, Pregel, Storm or Tensorflow like systems. It is urgent to develop new effective scheduling and resource allocation approaches to improve efficiency in large-scale enterprise datacenters. In the dissertation, we are the first of works to define and identify the problems, challenges and scenarios of scheduling and resource management for diverse long-running workloads in modern datacenter. They rely on predictive scheduling techniques to perform reservation, auto-scaling, migration or rescheduling. It forces us to pursue and explore more intelligent scheduling techniques by adequate predictive knowledges. We innovatively specify what is intelligent scheduling, what abilities are necessary towards intelligent scheduling, how to leverage intelligent scheduling to transfer NP-hard online scheduling problems to resolvable offline scheduling issues. We designed and implemented an intelligent cloud datacenter scheduler, which automatically performs resource-to-performance modeling, predictive optimal reservation estimation, QoS (interference)-aware predictive scheduling to maximize resource efficiency of multi-dimensions (CPU, Memory, Network, Disk I/O), and strictly guarantee service level agreements (SLA) for long-running workloads. Finally, we introduced a large-scale co-location techniques of executing long-running and other workloads on the shared global datacenter infrastructure of Alibaba Group. It effectively improves cluster utilization from 10% to averagely 50%. It is far more complicated beyond scheduling that involves technique evolutions of IDC, network, physical datacenter topology, storage, server hardwares, operating systems and containerization. We demonstrate its effectiveness by analysis of newest Alibaba public cluster trace in 2017. We are the first of works to reveal the global view of scenarios, challenges and status in Alibaba large-scale global datacenters by data demonstration, including big promotion events like Double 11 . Data-driven intelligent scheduling methodologies and effective infrastructure co-location techniques are critical and necessary to pursue maximized multi-resource efficiency in modern large-scale datacenter, especially for long-running workloads

The coastal sustainability standard: A management systems approach to ICZM

This paper presents a systems-based appraisal methodology that has been designed specifically to consider the effectiveness of Integrated Coastal Zone Management (ICZM) initiatives. Since ICZM is defined in terms of achieving sustainable development, any such initiative must therefore be capable of meeting the multiple and often conflicting objectives inherent in this ubiquitous concept. The methodology outlined here is designed to critically review ICZM in order to pinpoint areas of management weakness and determine the likely ‘success’ of the process. It represents an example of a management system, incorporates both qualitative and quantitative information, and is proposed as a ‘Coastal Sustainability Standard’ (CoSS). Initial field testing of the methodology has proved successful and shown that the approach holds some efficacy as a means of assessment

Efficient systems for the securities transaction industry : a framework for the European Union

This paper provides a framework for the securities transaction industry in the EU to understand the functions performed, the institutions involved and the parameters concerned that shape market and ownership structure. Of particular interest are microeconomic incentives of the industry players that can be in contradiction to social welfare. We evaluate the three functions and the strategic parameters - the boundary decision, the communication standard employed and the governance implemented - along the lines of three efficiency concepts. By structuring the main factors that influence these concepts and by describing the underlying trade-offs among them, we provide insight into a highly complex industry. Applying our framework, the paper describes and analyzes three consistent systems for the securities transaction industry. We point out that one of the systems, denoted as 'contestable monopolies', demonstrates a superior overall efficiency while it might be the most sensitive in terms of configuration accuracy and thus difficult to achieve and sustain

Towards Power- and Energy-Efficient Datacenters

As the Internet evolves, cloud computing is now a dominant form of computation in modern lives. Warehouse-scale computers (WSCs), or datacenters, comprising the foundation of this cloud-centric web have been able to deliver satisfactory performance to both the Internet companies and the customers. With the increased focus and popularity of the cloud, however, datacenter loads rise and grow rapidly, and Internet companies are in need of boosted computing capacity to serve such demand. Unfortunately, power and energy are often the major limiting factors prohibiting datacenter growth: it is often the case that no more servers can be added to datacenters without surpassing the capacity of the existing power infrastructure. This dissertation aims to investigate the issues of power and energy usage in a modern datacenter environment. We identify the source of power and energy inefficiency at three levels in a modern datacenter environment and provides insights and solutions to address each of these problems, aiming to prepare datacenters for critical future growth. We start at the datacenter-level and find that the peak provisioning and improper service placement in multi-level power delivery infrastructures fragment the power budget inside production datacenters, degrading the compute capacity the existing infrastructure can support. We find that the heterogeneity among datacenter workloads is key to address this issue and design systematic methods to reduce the fragmentation and improve the utilization of the power budget. This dissertation then narrow the focus to examine the energy usage of individual servers running cloud workloads. Especially, we examine the power management mechanisms employed in these servers and find that the coarse time granularity of these mechanisms is one critical factor that leads to excessive energy consumption. We propose an intelligent and low overhead solution on top of the emerging finer granularity voltage/frequency boosting circuit to effectively pinpoints and boosts queries that are likely to increase the tail distribution and can reap more benefit from the voltage/frequency boost, improving energy efficiency without sacrificing the quality of services. The final focus of this dissertation takes a further step to investigate how using a fundamentally more efficient computing substrate, field programmable gate arrays (FPGAs), benefit datacenter power and energy efficiency. Different from other types of hardware accelerations, FPGAs can be reconfigured on-the-fly to provide fine-grain control over hardware resource allocation and presents a unique set of challenges for optimal workload scheduling and resource allocation. We aim to design a set coordinated algorithms to manage these two key factors simultaneously and fully explore the benefit of deploying FPGAs in the highly varying cloud environment.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144043/1/hsuch_1.pd

Understanding policy volatility in Sudan:

"In this paper we present the findings of a qualitative investigation into some dimensions and implications of policy volatility in the realms of natural resource (NR) governance and devolution in contemporary Sudan, with particular reference to Greater Kordofan. Our goal is to map out some aspects of the interplay between volatility, disempowerment processes affecting both state agents and the rural population, and certain problems of governance that are characteristic but not unique to Sudan. In particular, we argue that volatility is a dimension of poor governance worthy of investigation in its own right, as it is a primary ingredient of what we may call a “self-disempowering state,” where adaptive learning in policy processes is impeded and successful devolution faces particularly complex obstacles. The policy domain that we consider for analysis includes laws, regulations and policies enacted under the label of “Decentralization, Land Allocation and Land Use,” as well as large development projects supporting the decentralization or devolution of NR management to local communities in the region." from Authors' AbstractPolicy Volatility, Devolution, Communities, Governance, Rural population., Decentralization, Natural resource management, Land allocation, Land use, Greater Kordofan,