The Experts below are selected from a list of 228 Experts worldwide ranked by ideXlab platform

Rajiv Ranjan - One of the best experts on this subject based on the ideXlab platform.

  • spot pricing in the Cloud Ecosystem a comparative investigation
    arXiv: Distributed Parallel and Cluster Computing, 2017
    Co-Authors: He Zhang, Liam Obrien, Shu Jiang, You Zhou, Maria Kihl, Rajiv Ranjan
    Abstract:

    Background: Spot pricing is considered as a significant supplement for building a full-fledged market economy for the Cloud Ecosystem. However, it seems that both providers and consumers are still hesitating to enter the Cloud spot market. The relevant academic community also has conflicting opinions about Cloud spot pricing in terms of revenue generation. Aim: This work aims to systematically identify, assess, synthesize and report the published evidence in favor of or against spot-price scheme compared with fixed-price scheme of Cloud computing, so as to help relieve the aforementioned conflict. Method: We employed the systematic literature review (SLR) method to collect and investigate the empirical studies of Cloud spot pricing indexed by major electronic libraries. Results: This SLR identified 61 primary studies that either delivered discussions or conducted experiments to perform comparison between spot pricing and fixed pricing in the Cloud domain. The reported benefits and limitations were summarized to facilitate cost-benefit analysis of being a Cloud spot pricing player, while four types of theories were distinguished to help both researchers and practitioners better understand the Cloud spot market. Conclusions: This SLR shows that the academic community strongly advocates the emerging Cloud spot market. Although there is still a lack of practical and easily deployable market-driven mechanisms, the overall findings of our work indicate that spot pricing plays a promising role in the sustainability of Cloud resource exploitation.

  • spot pricing in the Cloud Ecosystem a comparative investigation
    Journal of Systems and Software, 2016
    Co-Authors: He Zhang, Liam Obrien, Shu Jiang, You Zhou, Maria Kihl, Rajiv Ranjan
    Abstract:

    Background: Spot pricing is considered as a significant supplement for building a full-fledged market economy for the Cloud Ecosystem. However, it seems that both providers and consumers are still hesitating to enter the Cloud spot market. The relevant academic community also has conflicting opinions about Cloud spot pricing in terms of revenue generation. Aim: This work aims to systematically identify, assess, synthesize and report the published evidence in favor of or against spot-price scheme compared with fixed-price scheme of Cloud computing, so as to help relieve the aforementioned conflict. Method: We employed the systematic literature review (SLR) method to collect and investigate the empirical studies of Cloud spot pricing indexed by major electronic libraries. Results: This SLR identified 61 primary studies that either delivered discussions or conducted experiments to perform comparison between spot pricing and fixed pricing in the Cloud domain. The reported benefits and limitations were summarized to facilitate cost-benefit analysis of being a Cloud spot pricing player, while four types of theories were distinguished to help both researchers and practitioners better understand the Cloud spot market. Conclusions: This SLR shows that the academic community strongly advocates the emerging Cloud spot market. Although there is still a lack of practical and easily deployable market-driven mechanisms, the overall findings of our work indicate that spot pricing plays a promising role in the sustainability of Cloud resource exploitation. (Less)

Dan C Marinescu - One of the best experts on this subject based on the ideXlab platform.

  • Energy-Aware Load Balancing and Application Scaling for the Cloud Ecosystem
    IEEE Transactions on Cloud Computing, 2017
    Co-Authors: Ashkan Paya, Dan C Marinescu
    Abstract:

    In this paper, we introduce an energy-aware operation model used for load balancing and application scaling on a Cloud. The basic philosophy of our approach is defining an energy-optimal operation regime and attempting to maximize the number of servers operating in this regime. Idle and lightly-loaded servers are switched to one of the sleep states to save energy. The load balancing and scaling algorithms also exploit some of the most desirable features of server consolidation mechanisms discussed in the literature.

  • energy aware load balancing policies for the Cloud Ecosystem
    arXiv: Distributed Parallel and Cluster Computing, 2014
    Co-Authors: Ashkan Paya, Dan C Marinescu
    Abstract:

    The energy consumption of computer and communication systems does not scale linearly with the workload. A system uses a significant amount of energy even when idle or lightly loaded. A widely reported solution to resource management in large data centers is to concentrate the load on a subset of servers and, whenever possible, switch the rest of the servers to one of the possible sleep states. We propose a reformulation of the traditional concept of load balancing aiming to optimize the energy consumption of a large-scale system: {\it distribute the workload evenly to the smallest set of servers operating at an optimal energy level, while observing QoS constraints, such as the response time.} Our model applies to clustered systems; the model also requires that the demand for system resources to increase at a bounded rate in each reallocation interval. In this paper we report the VM migration costs for application scaling.

He Zhang - One of the best experts on this subject based on the ideXlab platform.

  • spot pricing in the Cloud Ecosystem a comparative investigation
    arXiv: Distributed Parallel and Cluster Computing, 2017
    Co-Authors: He Zhang, Liam Obrien, Shu Jiang, You Zhou, Maria Kihl, Rajiv Ranjan
    Abstract:

    Background: Spot pricing is considered as a significant supplement for building a full-fledged market economy for the Cloud Ecosystem. However, it seems that both providers and consumers are still hesitating to enter the Cloud spot market. The relevant academic community also has conflicting opinions about Cloud spot pricing in terms of revenue generation. Aim: This work aims to systematically identify, assess, synthesize and report the published evidence in favor of or against spot-price scheme compared with fixed-price scheme of Cloud computing, so as to help relieve the aforementioned conflict. Method: We employed the systematic literature review (SLR) method to collect and investigate the empirical studies of Cloud spot pricing indexed by major electronic libraries. Results: This SLR identified 61 primary studies that either delivered discussions or conducted experiments to perform comparison between spot pricing and fixed pricing in the Cloud domain. The reported benefits and limitations were summarized to facilitate cost-benefit analysis of being a Cloud spot pricing player, while four types of theories were distinguished to help both researchers and practitioners better understand the Cloud spot market. Conclusions: This SLR shows that the academic community strongly advocates the emerging Cloud spot market. Although there is still a lack of practical and easily deployable market-driven mechanisms, the overall findings of our work indicate that spot pricing plays a promising role in the sustainability of Cloud resource exploitation.

  • spot pricing in the Cloud Ecosystem a comparative investigation
    Journal of Systems and Software, 2016
    Co-Authors: He Zhang, Liam Obrien, Shu Jiang, You Zhou, Maria Kihl, Rajiv Ranjan
    Abstract:

    Background: Spot pricing is considered as a significant supplement for building a full-fledged market economy for the Cloud Ecosystem. However, it seems that both providers and consumers are still hesitating to enter the Cloud spot market. The relevant academic community also has conflicting opinions about Cloud spot pricing in terms of revenue generation. Aim: This work aims to systematically identify, assess, synthesize and report the published evidence in favor of or against spot-price scheme compared with fixed-price scheme of Cloud computing, so as to help relieve the aforementioned conflict. Method: We employed the systematic literature review (SLR) method to collect and investigate the empirical studies of Cloud spot pricing indexed by major electronic libraries. Results: This SLR identified 61 primary studies that either delivered discussions or conducted experiments to perform comparison between spot pricing and fixed pricing in the Cloud domain. The reported benefits and limitations were summarized to facilitate cost-benefit analysis of being a Cloud spot pricing player, while four types of theories were distinguished to help both researchers and practitioners better understand the Cloud spot market. Conclusions: This SLR shows that the academic community strongly advocates the emerging Cloud spot market. Although there is still a lack of practical and easily deployable market-driven mechanisms, the overall findings of our work indicate that spot pricing plays a promising role in the sustainability of Cloud resource exploitation. (Less)

Antonio Puliafito - One of the best experts on this subject based on the ideXlab platform.

  • A sustainable energy-aware resource management strategy for IoT Cloud federation
    2015 IEEE International Symposium on Systems Engineering (ISSE), 2015
    Co-Authors: Maurizio Giacobbe, Antonio Celesti, Massimo Villari, Maria Fazio, Antonio Puliafito
    Abstract:

    The advent of both Cloud computing and Internet of Things (IoT) is changing the way to conceive distributed systems. Nowadays, we can talk about IoT Cloud to indicate a new type of distributed system consisting of a set of smart IoT devices or sensors interconnected with a remote Cloud infrastructure, platform, or software. Energy sustainability in IoT Cloud providers offers new tempting business opportunities for organizations, but at the same time it raises new challenges. In this paper, a flexible IoT Cloud federation energy management strategy is presented for optimizing the allocation of geographically localized smart sensors. We define the concept of IoT Cloud Federation as a mesh of IoT Cloud providers that are interconnected to provide a universal decentralized sensing and actuating environment where everything is driven by constraints and agreements in a ubiquitous infrastructure. In particular, a dynamic algorithm able to improve energy sustainability in a federated IoT Cloud Ecosystem is discussed. In addition, we analyze a use-case driven strategy that allows both IoT Cloud providers and brokers to determine the paths to reach possible destination IoT devices in which computational resources should be dynamically migrated in order to push down the energy consumption due to IoT distributed applications.

  • an approach to reduce energy costs through virtual machine migrations in Cloud federation
    International Symposium on Computers and Communications, 2015
    Co-Authors: Maurizio Giacobbe, Antonio Celesti, Massimo Villari, Maria Fazio, Antonio Puliafito
    Abstract:

    Cloud federation offers new business models to enforce more flexible energy management strategies. Independent Cloud providers are exclusively bounded to the specific energy supplier powering its Data Centers. The situation radically change if we consider a federation of cooperating Cloud providers. In such a context a proper migration of virtual machines among providers can lead to a global energy cost-saving strategy. In this paper, we present an approach to reduce energy cost in a federated Cloud Ecosystem. More specifically, we propose an algorithm that allows providers to determine a map of possible destinations for cost-evaluation. Furthermore, we introduce an additional algorithm to determine the optimum energy cost migration path, and, consequently, the best Cloud Data Center where virtual machines should be migrated in order to push down energy costs.

  • how to enhance Cloud architectures to enable cross federation towards interoperable storage providers
    IEEE International Conference on Cloud Engineering, 2015
    Co-Authors: Maria Fazio, Antonio Celesti, Massimo Villari, Antonio Puliafito
    Abstract:

    Small/medium Cloud storage providers can hardly compete with the biggest Cloud players such as Google, Amazon, Dropbox, etc. As a consequence, the Cloud storage market depends on such mega-providers and each small/medium provider cannot face alone the challenge of Big Data storage. A possible solution consists in establishing stronger partnerships among small-medium providers where they can borrow/lend resources each other, according to the rules of the federated Cloud Ecosystem they belong to. According to such an approach, the challenge consists in creating federated Cloud Ecosystems able to compete with mega-provides and one of the major problems for the achievement of such an Ecosystem is the management of inter-domain communications. In this paper, we propose an architecture addressing such an issue. In particular, we present and test a solution integrating the CLEVER Message Oriented Middleware (MOM) with the Hadoop Distribute File System (HDFS), i.e., one of the major massive storage solutions currently available on the market.

Ashkan Paya - One of the best experts on this subject based on the ideXlab platform.

  • Energy-Aware Load Balancing and Application Scaling for the Cloud Ecosystem
    IEEE Transactions on Cloud Computing, 2017
    Co-Authors: Ashkan Paya, Dan C Marinescu
    Abstract:

    In this paper, we introduce an energy-aware operation model used for load balancing and application scaling on a Cloud. The basic philosophy of our approach is defining an energy-optimal operation regime and attempting to maximize the number of servers operating in this regime. Idle and lightly-loaded servers are switched to one of the sleep states to save energy. The load balancing and scaling algorithms also exploit some of the most desirable features of server consolidation mechanisms discussed in the literature.

  • energy aware load balancing policies for the Cloud Ecosystem
    arXiv: Distributed Parallel and Cluster Computing, 2014
    Co-Authors: Ashkan Paya, Dan C Marinescu
    Abstract:

    The energy consumption of computer and communication systems does not scale linearly with the workload. A system uses a significant amount of energy even when idle or lightly loaded. A widely reported solution to resource management in large data centers is to concentrate the load on a subset of servers and, whenever possible, switch the rest of the servers to one of the possible sleep states. We propose a reformulation of the traditional concept of load balancing aiming to optimize the energy consumption of a large-scale system: {\it distribute the workload evenly to the smallest set of servers operating at an optimal energy level, while observing QoS constraints, such as the response time.} Our model applies to clustered systems; the model also requires that the demand for system resources to increase at a bounded rate in each reallocation interval. In this paper we report the VM migration costs for application scaling.