Yanbin Liu

Cloud federation in a layered service model

We show how a layered Cloud service model of software (SaaS), platform (PaaS), and infrastructure (IaaS) leverages multiple independent Clouds by creating a federation among the providers. The layered architecture leads naturally to a design in which inter-Cloud federation takes place at each service layer, mediated by a broker specific to the concerns of the parties at that layer. Federation increases consumer value for and facilitates providing IT services as a commodity. This business model for the Cloud is consistent with broker mediated supply and service delivery chains in other commodity sectors such as finance and manufacturing. Concreteness is added to the federated Cloud model by considering how it works in delivering the Weather Research and Forecasting service (WRF) as SaaS using PaaS and IaaS support. WRF is used to illustrate the concepts of delegation and federation, the translation of service requirements between service layers, and inter-Cloud broker functions needed to achieve federation.

Enabling Interoperability among Meta-Schedulers

Grid computing supports shared access to computing resources from cooperating organizations or institutes in the form of virtual organizations. Resource brokering middleware, commonly known as a meta-scheduler or a resource broker, matches jobs to distributed resources. Recent advances in meta- scheduling capabilities are extended to enable resource matching across multiple virtual organizations. Several architectures have been proposed for interoperating meta-scheduling systems. This paper presents a hybrid approach, combining hierarchical and peer-to-peer architectures for flexibility and extensibility of these systems. A set of protocols are introduced to allow different meta-scheduler instances to communicate over Web Services. Interoperability between three heterogeneous and distributed organizations (namely, BSC, FIU, and IBM), each using different meta-scheduling technologies, is demonstrated under these protocols and resource models.

Looking for an Evolution of Grid Scheduling: Meta-Brokering

A Grid Resource Broker for a Grid domain, or also known as meta-scheduler, is a middleware component used for matching works to available Grid resources from one or more IT organizations. A Grid meta-scheduler usually has its own interfaces for the functionalities it provides and its own job scheduling objectives. This situation causes two main problems: the user uniform access to the Grid is lost, and the scheduling decisions are taken separately while they should be done in coordination. These problems have been observed in different efforts such as the HPC-Europa project but they are still open problems. In this paper we discuss the requirements to achieve a more uniform access to the Grids through a new approach to global brokering. As the results of these discussions on brokering requirements, we propose a meta-brokering design, so called metameta-scheduler design, and discuss how it can be realized as a centralized model for the HPC-Europa project, and as a distributed model for the LA Grid project.

Enabling Interoperability among Grid Meta-Schedulers

The goal of Grid computing is to integrate the usage of computer resources from cooperating partners in the form of Virtual Organizations (VO). One of its key functions is to match jobs to execution resources efficiently. For interoperability between VOs, this matching operation occurs in resource brokering middleware, commonly referred to as the meta-scheduler or meta-broker. In this paper, we present an approach to a meta-scheduler architecture, combining hierarchical and peer-to-peer models for flexibility and extensibility. Interoperability is further promoted through the introduction of a set of protocols, allowing meta-schedulers to maintain sessions and exchange job and resource state using Web Services. Our architecture also incorporates a resource model that enables an efficient resource matching across multiple Virtual Organizations, especially where the compute resources and state are dynamic. Experiments demonstrate these new functional features across three distributed organizations (BSC, FIU, and IBM), that internally use different job scheduling technologies, computing infrastructure and security mechanisms. Performance evaluations through actual system measurements and simulations provide the insights on the architecture’s effectiveness and scalability.

Detection of false sharing using machine learning

False sharing is a major class of performance bugs in parallel applications. Detecting false sharing is difficult as it does not change the program semantics. We introduce an efficient and effective approach for detecting false sharing based on machine learning. We develop a set of mini-programs in which false sharing can be turned on and off. We then run the mini-programs both with and without false sharing, collect a set of hardware performance event counts and use the collected data to train a classifier. We can use the trained classifier to analyze data from arbitrary programs for detection of false sharing. Experiments with the PARSEC and Phoenix benchmarks show that our approach is indeed effective. We detect published false sharing regions in the benchmarks with zero false positives. Our performance penalty is less than 2%. Thus, we believe that this is an effective and practical method for detecting false sharing.

The Role of Grid Computing Technologies in Cloud Computing

The fields of Grid, Utility and Cloud Computing have a set of common objectives in harnessing shared resources to optimally meet a great variety of demands cost-effectively and in a timely manner Since Grid Computing started its technological journey about a decade earlier than Cloud Computing, the Cloud can benefit from the technologies and experience of the Grid in building an infrastructure for distributed computing. Our comparison of Grid and Cloud starts with their basic characteristics and interaction models with clients, resource consumers and providers. Then the similarities and differences in architectural layers and key usage patterns are examined. This is followed by an in depth look at the technologies and best practices that have applicability from Grid to Cloud computing, including scheduling, service orientation, security, data management, monitoring, interoperability, simulation and autonomic support. Finally, we offer insights on how these techniques will help solve the current challenges faced by Cloud computing.

Effectiveness Assessment of Solid-State Drive Used in Big Data Services

Big data poses challenges to the technologies required to process data of high volume, velocity, variety, and veracity. Among the challenges, the storage and computing required by big data analytics is usually huge, and as a result big data capabilities are often provisioned in cloud and delivered in the form of Web-based services. Solid-state drive (SSD) is widely used nowadays as an elementary hardware feature in cloud infrastructure for big data services. For example, Amazon Web Service (AWS) offers EC2 instances with SSD storage, and its key-value data store, DynamoDB, is backed up by SSD for superior performance. Compared to hard disk drive (HDD), SSD prevails in both access latency and bandwidth. In the foreseeable future, SSD would be readily available on commodity servers though its capacity would be neither large enough nor cost effective to accommodate big data on its own. Therefore, it is essential to investigate how to efficiently leverage SSD as one layer in a storage hierarchy in addition to HDD. In this paper, we investigate the effectiveness of using SSD in three workloads, namely standalone Hadoop MapReduce jobs, Hive jobs, and HBase queries. Firstly, we device an approach to enable Hadoop Distributed File System (HDFS) having a SSD-HDD storage hierarchy. Secondly, we investigate the IO involved in different phases of Hadoop jobs and design different schemes to place data discriminatively in the aforementioned storage hierarchy. Afterward, the effectiveness of different schemes are evaluated with respect to job run time. Finally, we summarize best practices of data placement for examined workloads in a SSD-HDD storage hierarchy.

A distributed job scheduling and flow management system

Grid computing, as a specific model of distributed systems, has sparked recent interest in managing job execution among distributed resource domains. Introduction of the meta-scheduler is a key feature in grid evolution, and the next step is to achieve collaborative interactions between meta-schedulers within and external to organizational boundaries to achieve scalability, balanced resource utilization, and location transparency to job submitters. This paper details a distributed system design that consists of a collaborative meta-scheduling framework, and an expanded resource model with schedulers and data as resources. With this framework, we also explore job scheduling and data management issues, and investigate job flow and meta-scheduling interactions for new applications that require job execution beyond simple sequential and conditional control.

Efficiency Assessment of Parallel Workloads on Virtualized Resources

In cloud computing, virtual containers on physical resources are provisioned to requesting users. Resource providers may pack as many containers as possible onto each of their physical machines, or may pack specific types and quantities of virtual containers based on user or system QoS objectives. Such elastic provisioning schemes for resource sharing may present major challenges to scientific parallel applications that require task synchronization during execution. Such elastic schemes may also inadvertently lower utilization of computing resources. In this paper, we describe the elasticity constraint effect and ripple effect that cause a negative impact to application response time and system utilization. We quantify the impact using real workload traces through simulation. Then, we demonstrate that some resource scheduling techniques can be effective in mitigating the impacts. We find that a tradeoff is needed among the elasticity of virtual containers, the complexity of scheduling algorithms, and the response time of applications.

Enabling Autonomic Meta-Scheduling in Grid Environments

Grid computing supports workload execution on computing resources that are shared across a set of collaborative organizations. At the core of workload management for grid computing is a software component, called meta-scheduler or grid resource broker, that provides a virtual layer on top of heterogeneous grid middleware, schedulers, and resources. Meta-schedulers typically enable end-users and applications to compete over distributed shared resources through the use of one or more instances of the same meta-scheduler, in a centralized or distributed manner, respectively. We propose an approach to enabling autonomic meta-scheduling through the use of a new communication protocol that -if adopted by different meta-schedulers or by the applications using them- can improve the workload execution while avoiding potential chaos, which can be resulted from blind competition over resources. This can be made possible by allowing the meta- schedulers and/or their applications to engage in a process to negotiate their roles (e.g., consumer, provider, or both), scheduling policies, service-level agreement, etc. To show the feasibility of our approach, we developed a prototype that enables some preliminary autonomic management among three different meta-schedulers, namely, GridWay, eNANOS, andTDWB.