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The planning and operation problems of parking lots of plug-in electric vehicles (PEVs) are studied in this paper. Herein, each distribution company (DISCO) allocates the parking lots to the electrical feeders to minimize the power loss and expected energy not supplied of the system, and consequently minimize the total cost of the planning problem over the given time horizon. In addition, the generation company (GENCO) manages the charging time of PEVs parked in the parking lots to defer the more expensive and pollutant generation units, and as a result maximize its daily profit. In both planning and operation problems, the behavioral model of PEVs’ drivers are modeled with respect to the value of incentive and their distance from the parking lots. To achieve the realistic results in the planning problem of each DISCO, several economic and technical factors including yearly inflation and interest rates, hourly and daily variations of the load demand, yearly load growth of the system, and yearly growth rate of PEVs’ application are considered. The optimization problems of each DISCO and GENCO are solved applying quantum-inspired simulated annealing algorithm and genetic algorithm, respectively. It is demonstrated that the behavioral model of drivers, their driving patterns, and even the type of PEVs can remarkably affect the outcomes of planning and operation problems. It is shown that the optimal allocation of parking lots can minimize every DISCO’s planning cost and optimal charging management of PEVs can increase the GENCO’s daily profit. [ABSTRACT FROM AUTHOR]

In the cloud, multiple tenants share the resource of datacenters and their applications compete with each other for scarce network bandwidth. Current studies have shown that the lack of bandwidth guarantee causes unpredictable network performance, leading to poor application performance. To address this issue, several virtual network abstractions have been proposed which allow the tenants to reserve virtual clusters with specified bandwidth between the Virtual Machines (VMs) in the datacenters. However, all these existing proposals require the tenants to deterministically characterize the bandwidth demands in the abstractions, which can be difficult and result in inefficient bandwidth reservation due to the demand uncertainty. In this paper, we explore a virtual cluster abstraction with stochastic bandwidth characterization to address the bandwidth demand uncertainty. We propose Stochastic Virtual Cluster (SVC), which models the bandwidth demand between VMs in a probabilistic way. Based on SVC, we develop a stochastic framework for virtual cluster allocation, in which the admitted virtual cluster's bandwidth demands are satisfied with a high probability. Efficient VM allocation algorithms are proposed to implement the framework while reducing the possibility of link congestion through minimizing the maximum bandwidth occupancy of a virtual cluster on physical links. Using simulations, we show that SVC achieves the trade-off between the job concurrency and the average job running time, and demonstrate its effectiveness for accommodating cloud application workloads with highly volatile bandwidth demands and its improvement to work-conserving bandwidth enforcement. [ABSTRACT FROM AUTHOR]

It is important for cloud service brokers to provide a multi-cloud storage service to minimize their payment cost to cloud service providers (CSPs) while providing service level objective (SLO) guarantee to their customers. Many multi-cloud storage services have been proposed or payment cost minimization or SLO guarantee. However, no previous works fully leverage the current cloud pricing policies (such as resource reservation pricing) to reduce the payment cost. Also, few works achieve both cost minimization and SLO guarantee. In this paper, we propose a multi-cloud Economical and SLO-guaranteed Storage Service ($ES^3$ ), which determines data allocation and resource reservation schedules with payment cost minimization and SLO guarantee. $ES^3$ incorporates (1) a coordinated data allocation and resource reservation method, which allocates each data item to a datacenter and determines the resource reservation amount on datacenters by leveraging all the pricing policies; (2) a genetic algorithm based data allocation adjustment method, which reduce data Get/Put rate variance in each datacenter to maximize the reservation benefit. We also propose several algorithms to enhance the cost efficient and SLO guarantee performance of $ES^3$ including i) dynamic request redirection, ii) grouped Gets for cost reduction, iii) lazy update for cost-efficient Puts, and iv) concurrent requests for rigid Get SLO guarantee. Our trace-driven experiments on a supercomputing cluster and on real clouds (i.e., Amazon S3, Windows Azure Storage and Google Cloud Storage) show the superior performance of $ES^3$ in payment cost minimization and SLO guarantee in comparison with previous methods. [ABSTRACT FROM AUTHOR]

It is imperative for cloud storage systems to be able to provide deadline guaranteed services according to service level agreements (SLAs) for online services. In spite of many previous works on deadline aware solutions, most of them focus on scheduling work flows or resource reservation in datacenter networks but neglect the server overload problem in cloud storage systems that prevents providing the deadline guaranteed services. In this paper, we introduce a new form of SLAs, which enables each tenant to specify a percentage of its requests it wishes to serve within a specified deadline. We first identify the multiple objectives (i.e., traffic and latency minimization, resource utilization maximization) in developing schemes to satisfy the SLAs. To satisfy the SLAs while achieving the multi-objectives, we propose a Parallel Deadline Guaranteed (PDG) scheme, which schedules data reallocation (through load re-assignment and data replication) using a tree-based bottom-up parallel process. The observation from our model also motivates our deadline strictness clustered data allocation algorithm that maps tenants with the similar SLA strictness into the same server to enhance SLA guarantees. We further enhance PDG in supplying SLA guaranteed services through two algorithms: i) a prioritized data reallocation algorithm that deals with request arrival rate variation, and ii) an adaptive request retransmission algorithm that deals with SLA requirement variation. Our trace-driven experiments on a simulator and Amazon EC2 show the effectiveness of our schemes for guaranteeing the SLAs while achieving the multi-objectives. [ABSTRACT FROM AUTHOR]

For predictable application performance or fairness in network sharing in clouds, many bandwidth allocation policies have been proposed. However, with these policies, tenants are not incentivized to use idle bandwidth or prevent link congestion, and may even take advantage of the policies to gain unfair bandwidth allocation. Increasing network utilization while avoiding congestion not only benefits cloud provider but also the tenants by improving application performance. In this paper, we propose a new pricing model that sets different unit prices for reserved bandwidth, the bandwidth on congested links and on uncongested links, and makes the unit price for congested links proportional to their congestion degrees. We use game theory model to analyze tenants’ behaviors in our model and the current pricing models, which shows the effectiveness of our model in providing the incentives. With the pricing model, we propose a network sharing policy to achieve both min-guarantee and proportionality, while prevent tenants from earning unfair bandwidth. We further propose methods for each virtual machine to arrange its traffic to reduce its unsatisfied demand and maximize its utility, while increase network utilization. As a result, our solution creates a win-win situation, where tenants strive to increase their benefits in bandwidth sharing, which also concurrently increases the utilities of cloud provider and other tenants. Our simulation and trace-driven experimental results show the effectiveness of our solutions in creating the win-win situation. [ABSTRACT FROM PUBLISHER]

Though a cooperative broadcast scheme has been proposed for fading environments, it has two defects: First, it only handles a packet flow from a single source node in the network, but does not consider the scenario of multiple packet flows simultaneously broadcasted from different source nodes. Second, it only allows a single relay node to forward a packet in each time slot, though multiple relay nodes forwarding in a time slot can significantly reduce broadcast latency. In this paper, we aim achieve low-latency multi-flow broadcast in wireless multi-hop networks with fading channels. To describe the interference among the transmission in different flows, we incorporate the Rayleigh fading model to the signal to noise ratio (SNR) model. Then, we introduce a cooperative diversity scheme which allows multiple relays forwarding in a time slot to reduce broadcast latency. We then formulate an interesting problem: In a fading environment, what is the optimal relay allocation schedule to minimize the broadcast latency? We propose a warm up heuristic algorithm for single-flow cooperative broadcast, based on which, we further propose a heuristic algorithm for multi-flow cooperative broadcast. Simulation results demonstrate that the two algorithms achieve lower broadcast latency than a previous method. [ABSTRACT FROM PUBLISHER]

A large-scale resource sharing system (e.g., collaborative cloud computing and grid computing) creates a virtual supercomputer by providing an infrastructure for sharing tremendous amounts of resources (e.g., computing, storage, and data) distributed over the Internet. A resource information service, which collects resource data and provides resource search functionality for locating desired resources, is a crucial component of the resource sharing system. In addition to resource discovery speed and cost (i.e., efficiency), the ability to accurately locate all satisfying resources (i.e., fidelity) is also an important metric for evaluating service quality. Previously, a number of resource information service systems have been proposed based on Distributed Hash Tables (DHTs) that offer scalable key-based lookup functions. However, these systems either achieve high fidelity at low efficiency, or high efficiency at low fidelity. Moreover, some systems have limited flexibility by only providing exact-matching services or by describing a resource using a pre-defined list of attributes. This paper presents a resource information service that offers high efficiency and fidelity without restricting resource expressiveness, while also providing a similar-matching service. Extensive simulation and PlanetLab experimental results show that the proposed service outperforms other services in terms of efficiency, fidelity, and flexibility; it dramatically reduces overhead and yields significant enhancements in efficiency and fidelity. [ABSTRACT FROM AUTHOR]

Advancements in cloud computing are leading to a promising future for collaborative cloud computing (CCC), where globally-scattered distributed cloud resources belonging to different organizations or individuals (i.e., entities) are collectively used in a cooperative manner to provide services. Due to the autonomous features of entities in CCC, the issues of resource management and reputation management must be jointly addressed in order to ensure the successful deployment of CCC. However, these two issues have typically been addressed separately in previous research efforts, and simply combining the two systems generates double overhead. Also, previous resource and reputation management methods are not sufficiently efficient or effective. By providing a single reputation value for each node, the methods cannot reflect the reputation of a node in providing individual types of resources. By always selecting the highest-reputed nodes, the methods fail to exploit node reputation in resource selection to fully and fairly utilize resources in the system and to meet users’ diverse QoS demands. We propose a CCC platform, called Harmony, which integrates resource management and reputation management in a harmonious manner. Harmony incorporates three key innovations: integrated multi-faceted resource/reputation management, multi-QoS-oriented resource selection, and price-assisted resource/reputation control. The trace data we collected from an online trading platform implies the importance of multi-faceted reputation and the drawbacks of highest-reputed node selection. Simulations and trace-driven experiments on the real-world PlanetLab testbed show that Harmony outperforms existing resource management and reputation management systems in terms of QoS, efficiency and effectiveness. [ABSTRACT FROM PUBLISHER]

In large-scale computational Grids, discovery of heterogeneous resources as a working group is crucial to achieving scalable performance. This paper presents a resource management scheme including a hierarchical cycloid overlay architecture, resource clustering and discovery algorithms for wide-area distributed Grid systems. We establish program/data locality by clustering resources based on their physical proximity and functional matching with user applications. We further develop dynamism-resilient resource management algorithm, cluster-token forwarding algorithm, and deadline-driven resource management algorithms. The advantage of the proposed scheme lies in low overhead, fast and dynamism-resilient multiresource discovery. The paper presents the scheme, new performance metrics, and experimental simulation results. This scheme compares favorably with other resource discovery methods in static and dynamic Grid applications. In particular, it supports efficient resource clustering, reduces communications cost, and enhances resource discovery success rate in promoting large-scale distributed supercomputing applications. [ABSTRACT FROM AUTHOR]