By leveraging virtual machine (VM) technology which provides performance and fault isolation, cloud resources can be provisioned on demand in a fine grained, multiplexed manner rather than in monolithic pieces. By integrating volunteer computing into cloud architectures, we envision a gigantic self-organizing cloud (SOC) being formed to reap the huge potential of untapped commodity computing power over the Internet. Toward this new architecture where each participant may autonomously act as both resource consumer and provider, we propose a fully distributed, VM-multiplexing resource allocation scheme to manage decentralized resources. Our approach not only achieves maximized resource utilization using the proportional share model (PSM), but also delivers provably and adaptively optimal execution efficiency. We also design a novel multiattribute range query protocol for locating qualified nodes. Contrary to existing solutions which often generate bulky messages per request, our protocol produces only one lightweight query message per task on the Content Addressable Network (CAN). It works effectively to find for each task its qualified resources under a randomized policy that mitigates the contention among requesters. We show the SOC with our optimized algorithms can make an improvement by 15-60 percent in system throughput than a P2P Grid model. Our solution also exhibits fairly high adaptability in a dynamic node-churning environment.

/pdf/dynamic-optimization-of-multiattribute-resource-allocation-1gjndegjx0.pdf

Dynamic Optimization of Multiattribute Resource Allocation in Self-Organizing Clouds

Internet-based Virtual Computing Environment

DHT is a widely used building block for scalable P2P systems. However, as uniform hashing employed in DHTs destroys data locality, it is not a trivial task to support complex queries (e.g., range queries and k-nearest-neighbor queries) in DHT-based P2P systems. In order to support efficient processing of such complex queries, a popular solution is to build indexes on top of the DHT. Unfortunately, existing over-DHT indexing schemes suffer from either query inefficiency or high maintenance cost. In this paper, we propose LIGhtweight Hash Tree (LIGHT)-a query-efficient yet low-maintenance indexing scheme. LIGHT employs a novel naming mechanism and a tree summarization strategy for graceful distribution of its index structure. We show through analysis that it can support various complex queries with near-optimal performance. Extensive experimental results also demonstrate that, compared with state of the art over-DHT indexing schemes, LIGHT saves 50-75 percent of index maintenance cost and substantially improves query performance in terms of both response time and bandwidth consumption. In addition, LIGHT is designed over generic DHTs and hence can be easily implemented and deployed in any DHT-based P2P system.

/pdf/light-a-query-efficient-yet-low-maintenance-indexing-scheme-3izf3v86yw.pdf

LIGHT: A Query-Efficient Yet Low-Maintenance Indexing Scheme over DHTs

Distributed systems such as Peer-to-Peer overlays have been shown to efficiently support the processing of range queries over large numbers of participating hosts. In such systems, uneven load allocation has to be effectively tackled in order to minimize overloaded peers and optimize their performance. In this work, we detect the two basic methodologies used to achieve load-balancing: Iterative key redistribution between neighbors and node migration. We identify these two key mechanisms and describe their relative advantages and disadvantages. Based on this analysis, we propose NIXMIG, a hybrid method that adaptively utilizes these two extremes to achieve both fast and cost-effective load-balancing in distributed systems that support range queries. We theoretically prove its convergence and as a case study, we offer an implementation on top of a Skip Graph, where we thoroughly validate our findings in a variety of static, dynamic and realistic workloads. We compare NIXMIG with an existing load-balancing algorithm proposed by Karger and Ruhl [1] and our experimental analysis shows that, NIXMIG can be as much as three times faster, requiring only one sixth and one third of message and item exchanges, respectively, to bring the system to a balanced state.

/pdf/fast-and-cost-effective-online-load-balancing-in-distributed-3du430ov1w.pdf

Fast and Cost-Effective Online Load-Balancing in Distributed Range-Queriable Systems

The scale of global data center market has been explosive in recent years. As the market grows, the demand for fast provisioning of the virtual resources to support elastic, manageable, and economical computing over the cloud becomes high. Fast provisioning of large-scale virtual machines (VMs), in particular, is critical to guarantee quality of service (QoS). In this paper, we systematically review the existing VM provisioning schemes and classify them in three main categories. We discuss the features and research status of each category, and introduce two recent solutions, VMThunder and VMThunder+, both of which can provision hundreds of VMs in seconds.

Large-scale virtual machines provisioning in clouds: challenges and approaches

With the increasing popularity of the peer-to-peer (P2P) computing paradigm, many general range query schemes for distributed hash table (DHT)-based P2P systems have been proposed. Although those schemes can provide range query capability without modifying the underlying DHTs, they have the query delay depending on both the scale of the system and the size of the query space or the specific query, and thus cannot guarantee to return the query results in a bounded delay. In this paper, we propose Armada, an efficient range query processing scheme to support delay-bounded single-attribute and multiple-attribute range queries. It is the first delay-bounded general range query scheme on constant-degree DHTs, and can return the results for any range query within 2logN hops in a P2P system with N peers. Results of analysis and simulations show that the average delay in Armada is less than logN, and the average message cost of single-attribute range queries is about logN+2n 2 (n is the number of peers that intersect with the query). These results are very close to the lower bounds on delay and message cost of range queries over constant-degree DHTs.

/pdf/efficient-range-query-processing-in-peer-to-peer-systems-cqdk4uci0h.pdf

Efficient Range Query Processing in Peer-to-Peer Systems

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Efficient Range Query Processing in Peer-to-Peer Systems