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Haiying Shen - One of the best experts on this subject based on the ideXlab platform.

  • An Economical and SLO-Guaranteed Cloud Storage Service Across Multiple Cloud Service Providers
    IEEE Transactions on Parallel and Distributed Systems, 2017
    Co-Authors: Haiying Shen, Haoyu Wang
    Abstract:

    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 (ES3), which determines data allocation and resource reservation schedules with payment cost minimization and SLO guarantee. ES3 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 ES3 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 ES3 in payment cost minimization and SLO guarantee in comparison with previous methods.

  • Minimum-Cost Cloud Storage Service Across Multiple Cloud Providers
    IEEE ACM Transactions on Networking, 2017
    Co-Authors: Haiying Shen
    Abstract:

    Many cloud service providers (CSPs) provide data storage services with datacenters distributed worldwide. These datacenters provide different get/put latencies and unit prices for resource utilization and reservation. Thus, when selecting different CSPs' datacenters, cloud customers of globally distributed applications (e.g., online social networks) face two challenges: 1) how to allocate data to worldwide datacenters to satisfy application service level objective (SLO) requirements, including both data retrieval latency and availability and2) how to allocate data and reserve resources in datacenters belonging to different CSPs to minimize the payment cost. To handle these challenges, we first model the cost minimization problem under SLO constraints using the integer programming. Due to its NP-hardness, we then introduce our heuristic solution, including a dominant-cost-based data allocation algorithm and an optimal resource reservation algorithm. We further propose three enhancement methods to reduce the payment cost and service latency: 1) coefficient-based data reallocation; 2) multicast-based data transferring; and 3) request redirection-based congestion control. We finally introduce an infrastructure to enable the conduction of the algorithms. 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 effectiveness of our algorithms for SLO guaranteed services and customer cost minimization.

  • Minimum-Cost Cloud Storage Service Across Multiple Cloud Providers
    2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS), 2016
    Co-Authors: Haiying Shen
    Abstract:

    Many Cloud Service Providers (CSPs) provide data storage services with datacenters distributed worldwide. These datacenters provide different Get/Put latencies and unit prices for resource utilization and reservation. Thus, when selecting different CSPs' datacenters, cloud customers of globally distributed applications (e.g., online social networks) face two challenges: (i) how to allocate data to worldwide datacenters to satisfy application SLO (service level objective) requirements including both data retrieval latency and availability, and (ii) how to allocate data and reserve resources in datacenters belonging to different CSPs to minimize the payment cost. To handle these challenges, we first model the cost minimization problem under SLO constraints using integer programming. Due to its NP-hardness, we then introduce our heuristic solution, including a dominant-cost based data allocation algorithm and an optimal resource reservation algorithm. We finally introduce an infrastructure to enable the conduction of the algorithms. 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 effectiveness of our algorithms for SLO guaranteed services and customer cost minimization.

Haoyu Wang - One of the best experts on this subject based on the ideXlab platform.

  • An Economical and SLO-Guaranteed Cloud Storage Service Across Multiple Cloud Service Providers
    IEEE Transactions on Parallel and Distributed Systems, 2017
    Co-Authors: Haiying Shen, Haoyu Wang
    Abstract:

    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 (ES3), which determines data allocation and resource reservation schedules with payment cost minimization and SLO guarantee. ES3 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 ES3 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 ES3 in payment cost minimization and SLO guarantee in comparison with previous methods.

Tadeusz Sawik - One of the best experts on this subject based on the ideXlab platform.

  • on the fair optimization of cost and customer service level in a supply chain under disruption risks
    Omega-international Journal of Management Science, 2015
    Co-Authors: Tadeusz Sawik
    Abstract:

    Abstract This paper presents a new decision-making problem of a fair optimization with respect to the two equally important conflicting objective functions: cost and customer service level, in the presence of supply chain disruption risks. Given a set of customer orders for products, the decision maker needs to select suppliers of parts required to complete the orders, allocate the demand for parts among the selected suppliers, and schedule the orders over the planning horizon, to equitably optimize expected cost and expected customer service level. The supplies of parts are subject to independent random local and regional disruptions. The fair decision-making aims at achieving the normalized expected cost and customer service level values as much close to each other as possible. The obtained combinatorial stochastic optimization problem is formulated as a stochastic mixed integer program with the ordered weighted averaging aggregation of the two conflicting objective functions. Numerical examples and computational results, in particular comparison with the weighted-sum aggregation of the two objective functions are presented and some managerial insights are reported. The findings indicate that for the minimum cost objective the cheapest supplier is usually selected, and for the maximum service level objective a subset of most reliable and most expensive suppliers is usually chosen, whereas the equitably efficient supply portfolio usually combines the most reliable and the cheapest suppliers. While the minimum cost objective function leads to the largest expected unfulfilled demand and the expected production schedule for the maximum service level follows the customer demand with the smallest expected unfulfilled demand, the equitably efficient solution ensures a reasonable value of expected unfulfilled demand.

Christian Schropfer - One of the best experts on this subject based on the ideXlab platform.

  • service level enforcement in web services based systems
    International Journal of Web and Grid Services, 2009
    Co-Authors: Vladimir Stantchev, Christian Schropfer
    Abstract:

    Web-services-based systems present challenges for service-level enforcement. Contrary to typical enterprise architectures where we are in control of all services and can easily specify and monitor service levels, here we need self-adapting approaches. This article proposes an approach for the assurance of service levels. The steps of the approach are: the formalisation of requirements using a Service Level objective (SLO), the formalisation of web service properties in a service-level capability statement and adaptive actions based on the translucent replication of services. The evaluation is based on a reference process model in Business Processing Modelling Notation (BPMN) and subsequent mapping at the infrastructure level. This mapping includes the definition, implementation and deployment of web services that cover the functional requirements, orchestration and coordination of these services using Business Process Execution Language (BPEL), as well as the the runtime monitoring and assurance of Nonfunctional Properties (NFPs) within a business process management framework.

  • techniques for service level enforcement in web services based systems
    Information Integration and Web-based Applications & Services, 2008
    Co-Authors: Vladimir Stantchev, Christian Schropfer
    Abstract:

    Web-services based systems present challenges for service level enforcement. Contrary to typical enterprise architectures where we are in control of all services and can easily specify and monitor service levels, here we need self-adapting approaches. The paper proposes techniques for service level enforcement in the context of a four step approach. The proposed techniques are: formalization of requirements using a service level objective, formalization of Web Service properties in a service level capability statement, and adaptive actions based on translucent replication of services. For an experimental evaluation we deployed a Web Service benchmark and our replication framework in Amazon's Elastic Compute Cloud. It provided continuous meeting of specified service levels.

Vladimir Stantchev - One of the best experts on this subject based on the ideXlab platform.

  • service level enforcement in web services based systems
    International Journal of Web and Grid Services, 2009
    Co-Authors: Vladimir Stantchev, Christian Schropfer
    Abstract:

    Web-services-based systems present challenges for service-level enforcement. Contrary to typical enterprise architectures where we are in control of all services and can easily specify and monitor service levels, here we need self-adapting approaches. This article proposes an approach for the assurance of service levels. The steps of the approach are: the formalisation of requirements using a Service Level objective (SLO), the formalisation of web service properties in a service-level capability statement and adaptive actions based on the translucent replication of services. The evaluation is based on a reference process model in Business Processing Modelling Notation (BPMN) and subsequent mapping at the infrastructure level. This mapping includes the definition, implementation and deployment of web services that cover the functional requirements, orchestration and coordination of these services using Business Process Execution Language (BPEL), as well as the the runtime monitoring and assurance of Nonfunctional Properties (NFPs) within a business process management framework.

  • techniques for service level enforcement in web services based systems
    Information Integration and Web-based Applications & Services, 2008
    Co-Authors: Vladimir Stantchev, Christian Schropfer
    Abstract:

    Web-services based systems present challenges for service level enforcement. Contrary to typical enterprise architectures where we are in control of all services and can easily specify and monitor service levels, here we need self-adapting approaches. The paper proposes techniques for service level enforcement in the context of a four step approach. The proposed techniques are: formalization of requirements using a service level objective, formalization of Web Service properties in a service level capability statement, and adaptive actions based on translucent replication of services. For an experimental evaluation we deployed a Web Service benchmark and our replication framework in Amazon's Elastic Compute Cloud. It provided continuous meeting of specified service levels.