Rong N. Chang

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.

GoCast: gossip-enhanced overlay multicast for fast and dependable group communication

We study dependable group communication for large-scale and delay-sensitive mission critical applications. The goal is to design a protocol that imposes low loads on bottleneck network links and provides both stable throughput and fast delivery of multicast messages even in the presence of frequent node and link failures. To this end, we propose our GoCast protocol. GoCast builds a resilient overlay network that is proximity aware and has balanced node degrees. Multicast messages propagate rapidly through an efficient tree embedded in the overlay. In the background, nodes exchange message summaries (gossips) with their overlay neighbors and pick up missing messages due to disruptions in the tree-based multicast. Our simulation based on real Internet data shows that, compared with a traditional gossip-based multicast protocol, GoCast can reduce the delivery delay of multicast messages by a factor of 8.9 when no node fails or a factor of 2.3 when 20% nodes fail.

ASAP: A Self-Adaptive Prediction System for Instant Cloud Resource Demand Provisioning

The promise of cloud computing is to provide computing resources instantly whenever they are needed. The state-of-art virtual machine (VM) provisioning technology can provision a VM in tens of minutes. This latency is unacceptable for jobs that need to scale out during computation. To truly enable on-the-fly scaling, new VM needs to be ready in seconds upon request. In this paper, We present an online temporal data mining system called ASAP, to model and predict the cloud VM demands. ASAP aims to extract high level characteristics from VM provisioning request stream and notify the provisioning system to prepare VMs in advance. For quantification issue, we propose Cloud Prediction Cost to encodes the cost and constraints of the cloud and guide the training of prediction algorithms. Moreover, we utilize a two-level ensemble method to capture the characteristics of the high transient demands time series. Experimental results using historical data from an IBM cloud in operation demonstrate that ASAP significantly improves the cloud service quality and provides possibility for on-the-fly provisioning.

Cloud Analytics for Capacity Planning and Instant VM Provisioning

The popularity of cloud service spurs the increasing demands of virtual resources to the service vendors. Along with the promising business opportunities, it also brings new technique challenges such as effective capacity planning and instant cloud resource provisioning. In this paper, we describe our research efforts on improving the service quality for the capacity planning and instant cloud resource provisioning problem. We first formulate both of the two problems as a generic cost-sensitive prediction problem. Then, considering the highly dynamic environment of cloud, we propose an asymmetric and heterogeneous measure to quantify the prediction error. Finally, we design an ensemble prediction mechanism by combining the prediction power of a set of prediction techniques based on the proposed measure. To evaluate the effectiveness of our proposed solution, we design and implement an integrated prototype system to help improve the service quality of the cloud. Our system considers many practical situations of the cloud system, and is able to dynamically adapt to the changing environment. A series of experiments on the IBM Smart Cloud Enterprise (SCE) trace data demonstrate that our method can significantly improve the service quality by reducing the resource provisioning time while maintaining a low cloud overhead.

A Generic SLA Semantic Model for the Execution Management of E-business Outsourcing Contracts

It is imperative for a competitive e-business outsourcing service provider to manage the execution of its service level agreement (SLA) contracts in business terms (e.g., minimizing financial penalties for service-level violations, maximizing service-level measurement based customer satisfaction metrics, etc.). In order to do that, the provider must possess a generic means of capturing and managing the SLA contract data (e.g., quality measurement data sources, service-level evaluation rules, etc.) as well as the relationships between them and internal service-level management (SLM) data (e.g., resource management data, system configuration data, etc.). This paper presents the design rationale of a generic SLA semantic model (including a set of semantic elements and relationships) based on an in-depth analysis of nine real e-business outsourcing SLA contracts/templates comprising over 100 service-level guarantees and intents. Our development experience with a state-of-the-art SLA contract execution manager (named SAM) suggests the semantic model is practical and useful.

Low traffic overlay networks with large routing tables

The routing tables of Distributed Hash Tables (DHTs) can vary from size O(1) to O(n). Currently, what is lacking is an analytic framework to suggest the optimal routing table size for a given workload. This paper (1) compares DHTs with O(1) to O(n) routing tables and identifies some good design points; and (2) proposes protocols to realize the potential of those good design points.We use total traffic as the uniform metric to compare heterogeneous DHTs and emphasize the balance between maintenance cost and lookup cost. Assuming a node on average processes 1,000 or more lookups during its entire lifetime, our analysis shows that large routing tables actually lead to both low traffic and low lookup hops. These good design points translate into one-hop routing for systems of medium size and two-hop routing for large systems.Existing one-hop or two-hop protocols are based on a hierarchy. We instead demonstrate that it is possible to achieve completely decentralized one-hop or two-hop routing, i.e., without giving up being peer-to-peer. We propose 1h-Calot for one-hop routing and 2h-Calot for two-hop routing. Assuming a moderate lookup rate, compared with DHTs that use O(log n) routing tables, 1h-Calot and 2h-Calot save traffic by up to 70% while resolving lookups in one or two hops as opposed to O(log n) hops.

Self-Adaptive Cloud Capacity Planning

The popularity of cloud service spurs the increasing demands of cloud resources to the cloud service providers. Along with the new business opportunities, the pay-as-you-go model drastically changes the usage pattern and brings technology challenges to effective capacity planning. In this paper, we propose a new method for cloud capacity planning with the goal of fully utilizing the physical resources, as we believe this is one of the emerging problems for cloud providers. To solve this problem, we present an integrated system with intelligent cloud capacity prediction. Considering the unique characteristics of the cloud service that virtual machines are provisioned and de-provisioned frequently to meet the business needs, we propose an asymmetric and heterogeneous measure for modeling the over-estimation, and under-estimation of the capacity. To accurately forecast the capacity, we first divide the change of cloud capacity demand into provisioning and de-provisioning components, and then estimate the individual components respectively. The future provisioning demand is predicted by an ensemble time-series prediction method, while the future de-provisioning is inferred based on the life span distribution and the number of active virtual machines. Our proposed solution is simple and computational efficient, which make it practical for development and deployment. Our solution also has the advantages for generating interpretable predictions. The experimental results on the IBM Smart Cloud Enterprise trace data demonstrate the effectiveness, accuracy and efficiency of our solution.

Cloud Service Reliability Enhancement via Virtual Machine Placement Optimization

With rapid adoption of the cloud computing model, many enterprises have begun deploying cloud-based services. Failures of virtual machines (VMs) in clouds have caused serious quality assurance issues for those services. VM replication is a commonly used technique for enhancing the reliability of cloud services. However, when determining the VM redundancy strategy for a specific service, many state-of-the-art methods ignore the huge network resource consumption issue that could be experienced when the service is in failure recovery mode. This paper proposes a redundant VM placement optimization approach to enhancing the reliability of cloud services. The approach employs three algorithms. The first algorithm selects an appropriate set of VM-hosting servers from a potentially large set of candidate host servers based upon the network topology. The second algorithm determines an optimal strategy to place the primary and backup VMs on the selected host servers with k-fault-tolerance assurance. Lastly, a heuristic is used to address the task-to-VM reassignment optimization problem, which is formulated as finding a maximum weight matching in bipartite graphs. The evaluation results show that the proposed approach outperforms four other representative methods in network resource consumption in the service recovery stage.

Fresco: a Web services based framework for configuring extensible SLA management systems

A service level agreement (SLA) is a service contract that includes the evaluation criteria for agreed service quality standards. Since agreeable specifications on the evaluation criteria cannot be limited in practice, competitive SLA management products must be extensible in terms of their support for contract-specific SLA compliance evaluations. While the need of running and managing those software products as services increases, we have found that developing a good solution for configuring them as per contractual terms is a challenging task. This paper presents the Fresco framework, which facilitates configuring extensible SLA management systems using Web services. An XML-based specification of SLA management related data called SCOL will also be presented to show how the framework supports contract-specific SLA terms and contract-specific extensions of the deployed SLA management software. The paper furthermore shows how the Fresco system uses a template-based approach to communicate with other Web services applications with support for various input and output formats. Our experience with implementing the Fresco framework for a leading commercial SLA management software product demonstrates that the framework facilitates the creation of effective and efficient solutions for configuring extensible SLA management systems.

A Distributed Service Management Infrastructure for Enterprise Data Centers Based on Peer-to-Peer Technology

This paper presents a distributed service management infrastructure called BISE. One distinguishing feature of BISE is its adoption of the peer-to-peer (P2P) model in support of realtime service managements. BISE offers significant advantages over existing systems in scalability, resilience, and manageability. Current P2P algorithms are mainly developed for the file-sharing applications running on desktops, which have characteristics dramatically different from enterprise data centers. This difference led us to design our own P2P algorithms specifically optimized for enterprise environments. Based on these algorithms, we implemented a P2P substrate called BiseWeaver (25,000 lines of Java code) as the core of BISE. Our evaluation on a set of distributed machines shows that BiseWeaver is efficient and robust, and provides timely monitoring data in support of proactive SLA management