Xiaohui Gu

CloudScale: elastic resource scaling for multi-tenant cloud systems

Elastic resource scaling lets cloud systems meet application service level objectives (SLOs) with minimum resource provisioning costs. In this paper, we present CloudScale, a system that automates fine-grained elastic resource scaling for multi-tenant cloud computing infrastructures. CloudScale employs online resource demand prediction and prediction error handling to achieve adaptive resource allocation without assuming any prior knowledge about the applications running inside the cloud. CloudScale can resolve scaling conflicts between applications using migration, and integrates dynamic CPU voltage/frequency scaling to achieve energy savings with minimal effect on application SLOs. We have implemented CloudScale on top of Xen and conducted extensive experiments using a set of CPU and memory intensive applications (RUBiS, Hadoop, IBM System S). The results show that CloudScale can achieve significantly higher SLO conformance than other alternatives with low resource and energy cost. CloudScale is non-intrusive and light-weight, and imposes negligible overhead (< 2% CPU in Domain 0) to the virtualized computing cluster.

PRESS: PRedictive Elastic ReSource Scaling for cloud systems

Cloud systems require elastic resource allocation to minimize resource provisioning costs while meeting service level objectives (SLOs). In this paper, we present a novel PRedictive Elastic reSource Scaling (PRESS) scheme for cloud systems. PRESS unobtrusively extracts fine-grained dynamic patterns in application resource demands and adjust their resource allocations automatically. Our approach leverages light-weight signal processing and statistical learning algorithms to achieve online predictions of dynamic application resource requirements. We have implemented the PRESS system on Xen and tested it using RUBiS and an application load trace from Google. Our experiments show that we can achieve good resource prediction accuracy with less than 5% over-estimation error and near zero under-estimation error, and elastic resource scaling can both significantly reduce resource waste and SLO violations.

Adaptive offloading for pervasive computing

Delivering a complex application on a resource-constrained mobile device is challenging. An adaptive offloading system enables dynamic partitioning of the application and efficient offloading of part of its execution to a nearby surrogate. To deliver pervasive services without modifying the application or degrading its fidelity, we propose an adaptive offloading system that includes two key parts: a distributed offloading platform and an offloading inference engine. There are two important decision-making problems for adaptive offloading: adaptive offloading triggering and efficient application partitioning.

QoS-assured service composition in managed service overlay networks

Many value-added and content delivery services are being offered via service level agreements (SLAs). These services can be interconnected to form a service overlay network (SON) over the Internet. Service composition in SON has emerged as a cost-effective approach to quickly creating new services. Previous research has addressed the reliability, adaptability, and compatibility issues for composed services. However little has been done to manage generic quality-of-service (QoS) provisioning for composed services, based on the SLA contracts of individual services. In this paper we present QUEST a QoS assUred composEable Service infrasTructure, to address the problem. QUEST framework provides: (1) initial service composition, which can compose a qualified service path under multiple QoS constraints (e.g., response time, availability). If multiple qualified service paths exist, QUEST chooses the best one according to the load balancing metric; and (2) dynamic service composition, which can dynamically recompose the service path to quickly recover from service outages and QoS violations. Different from the previous work, QUEST can simultaneously achieve QoS assurances and good load balancing in SON.

SpiderNet: an integrated peer-to-peer service composition framework

Service composition is highly desirable in peer-to-peer (P2P) systems where application services are naturally dispersed on distributed peers. However, it is challenging to provide high quality and failure resilient service composition in P2P systems due to the decentralization requirement and dynamic peer arrivals/departures. We present an integrated P2P service composition framework called SpiderNet to address the challenges. At service setup phase, SpiderNet performs a novel bounded composition probing protocol to provide scalable quality-aware and resource-efficient sendee composition in a fully distributed fashion. Moreover, SpiderNet supports directed acyclic graph composition topologies and explores exchangeable composition orders for enhanced service quality. During service runtime, SpiderNet provides proactive failure recovery to overcome dynamic changes (e.g., peer departures) in P2P systems. The proactive failure recovery scheme maintains a small number of dynamically selected backup compositions to achieve quick failure recovery for soft realtime streaming applications. We have implemented a prototype of SpiderNet and conducted extensive experiments using both large-scale simulations and wide-area network testbed. Experimental results show the feasibility and efficiency of the SpiderNet service composition solution for P2P systems.

Distributed multimedia service composition with statistical QoS assurances

Service composition allows multimedia services to be automatically composed from atomic service components based on dynamic service requirements. Previous work falls short for distributed multimedia service composition in terms of scalability, flexibility and quality-of-service (QoS) management. In this paper, we present a fully decentralized service composition framework, called SpiderNet, to address the challenges. SpiderNet provides statistical multiconstrained QoS assurances and load balancing for service composition. Moreover, SpiderNet supports directed acyclic graph composition topologies and exchangeable composition orders. We have implemented a prototype of SpiderNet and conducted experiments on both wide-area networks and a simulation testbed. Our experimental results show the feasibility and efficiency of the SpiderNet service composition framework.

Towards a distributed platform for resource-constrained devices

Many visions of the future predict a world with pervasive computing, where computing services and resources permeate the environment. In these visions, people will want to execute a service on any available device without worrying about whether the service has been tailored for the device. We believe that it will be difficult to create services that can execute well on the wide variety of devices that are being developed because of problems with diversity and resource constraints. We believe that these problems can be greatly reduced by using an ad-hoc distributed platform to transparently off-load portions of a service from a resource-constrained device to a nearby server. We implemented a preliminary prototype and emulator to study this approach. Our experiments show the beneficial use of nearby resources to relieve both memory and processing constraints, when it is appropriate to do so. We believe that this approach will reduce the burden on developers by masking more device details.

Adaptive offloading inference for delivering applications in pervasive computing environments

Pervasive computing allows a user to access an application on heterogeneous devices continuously and consistently. However it is challenging to deliver complex applications on resource-constrained mobile devices, such as cellular telephones and PDA. Different approaches, such as application-based or system-based adaptations, have been proposed to address the problem. However existing solutions often require degrading application fidelity. We believe that this problem can be overcome by dynamically partitioning the application and offloading part of the application execution to a powerful nearby surrogate. This will enable pervasive application delivery to be realized without significant fidelity degradation or expensive application rewriting. Because pervasive computing environments are highly dynamic, the runtime offloading system needs to adapt to both application execution patterns and resource fluctuations. Using the fuzzy control model, we have developed an offloading inference engine to adaptively solve two key decision-making problems during runtime offloading: (1) timely triggering of adaptive offloading, and (2) intelligent selection of an application partitioning policy. Extensive trace-driven evaluations show the effectiveness of the offloading inference engine.

SecureMR: A Service Integrity Assurance Framework for MapReduce

MapReduce has become increasingly popular as a powerful parallel data processing model. To deploy MapReduce as a data processing service over open systems such as service oriented architecture, cloud computing, and volunteer computing, we must provide necessary security mechanisms to protect the integrity of MapReduce data processing services. In this paper, we present SecureMR, a practical service integrity assurance framework for MapReduce. SecureMR consists of five security components, which provide a set of practical security mechanisms that not only ensure MapReduce service integrity as well as to prevent replay and Denial of Service (DoS) attacks, but also preserve the simplicity, applicability and scalability of MapReduce. We have implemented a prototype of SecureMR based on Hadoop, an open source MapReduce implementation. Our analytical study and experimental results show that SecureMR can ensure data processing service integrity while imposing low performance overhead.

PREPARE: Predictive Performance Anomaly Prevention for Virtualized Cloud Systems

Virtualized cloud systems are prone to performance anomalies due to various reasons such as resource contentions, software bugs, and hardware failures. In this paper, we present a novel Predictive Performance Anomaly Prevention (PREPARE) system that provides automatic performance anomaly prevention for virtualized cloud computing infrastructures. PREPARE integrates online anomaly prediction, learning-based cause inference, and predictive prevention actuation to minimize the performance anomaly penalty without human intervention. We have implemented PREPARE on top of the Xen platform and tested it on the NCSUs Virtual Computing Lab using a commercial data stream processing system (IBM System S) and an online auction benchmark (RUBiS). The experimental results show that PREPARE can effectively prevent performance anomalies while imposing low overhead to the cloud infrastructure.