The Experts below are selected from a list of 1749 Experts worldwide ranked by ideXlab platform
Gang Quan - One of the best experts on this subject based on the ideXlab platform.
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Workload Consolidation for cloud data centers with guaranteed qos using request reneging
IEEE Transactions on Parallel and Distributed Systems, 2017Co-Authors: Soamar Homsi, Shuo Liu, Gustavo A Chaparrobaquero, Ou Bai, Shaolei Ren, Gang QuanAbstract:Cloud data centers are widely employed to offer reliable cloud services. However, low resource utilization and high power consumption have been great challenges for cloud providers. Moreover, the rapid increase in demand for affordable cloud services magnifies the obstacles for proficient resource management policies. In this paper, we investigate how to improve resource utilization and power consumption in cloud data centers when delivering services with statistically guaranteed Quality of Service (QoS). We assume that the service provider hosts different types of services, each of which has request classes with different QoS requirements. Different from the traditional approaches that distribute Workloads with different QoS levels on different Virtual Machines (VMs), we introduce an approach to pack requests of the same service type, even with different QoS requirements, into the same VM, and to remove potential failure requests in time to improve resource usage and energy cost. We formally prove that our algorithm can statistically guarantee QoS conditions in terms of deadline miss ratios. We develop a cloud prototype to empirically validate our proposed methods and algorithm. Our experimental results demonstrate that our approach can significantly outperform other traditional approaches in terms of QoS guarantees, power consumption, resource demand and electricity cost.
Albert Y Zomaya - One of the best experts on this subject based on the ideXlab platform.
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Profiling-Based Workload Consolidation and Migration in Virtualized Data Centers
IEEE Transactions on Parallel and Distributed Systems, 2015Co-Authors: Kejiang Ye, Zhaohui Wu, Chen Wang, Bing Bing Zhou, Weisheng Si, Xiaohong Jiang, Albert Y ZomayaAbstract:Improving energy efficiency of data centers has become increasingly important nowadays due to the significant amounts of power needed to operate these centers. An important method for achieving energy efficiency is server Consolidation supported by virtualization. However, server Consolidation may incur significant degradation to Workload performance due to virtual machine (VM) co-location and migration. How to reduce such performance degradation becomes a critical issue to address. In this paper, we propose a profiling-based server Consolidation framework which minimizes the number of physical machines (PMs) used in data centers while maintaining satisfactory performance of various Workloads. Inside this framework, we first profile the performance losses of various Workloads under two situations: running in co-location and experiencing migrations. We then design two modules: (1) Consolidation planning module which, given a set of Workloads, minimizes the number of PMs by an integer programming model, and (2) migration planning module which, given a source VM placement scenario and a target VM placement scenario, minimizes the number of VM migrations by a polynomial time algorithm. Also, based on the Workload performance profiles, both modules can guarantee the performance losses of various Workloads below configurable thresholds. Our experiments for Workload profiling are conducted with real data center Workloads and our experiments on our two modules validate the integer programming model and the polynomial time algorithm.
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data intensive Workload Consolidation for the hadoop distributed file system
Grid Computing, 2012Co-Authors: R Moraveji, Javid Taheri, Mohammad Reza, Nikzad Babaii Rizvandi, Albert Y ZomayaAbstract:Workload Consolidation, sharing physicalresources among multiple Workloads, is a promisingtechnique to save cost and energy in cluster computingsystems. This paper highlights a number of challengesassociated with Workload Consolidation for Hadoop, as oneof the current state-of-the-art data-intensive clustercomputing systems. Through a systematic step-by-stepprocedure, we investigate challenges for efficient serverConsolidation in Hadoop environments. To this end, we firstinvestigate the inter-relationship between last level cache(LLC) contention and throughput degradation forconsolidated Workloads on a single physical serveremploying Hadoop distributed file system (HDFS). We theninvestigate the general case of Consolidation on multiplephysical servers so that their throughput never falls below adesired/predefined utilization level. We use our empiricalresults to model Consolidation as a classic two-dimensionalbin packing problem and then design a computationallyefficient greedy algorithm to achieve minimum throughputdegradation on multiple servers. Results are very promisingand show that our greedy approach is able to achieve nearoptimal solutions in all experimented cases.
Nagarajan Kandasamy - One of the best experts on this subject based on the ideXlab platform.
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on the design of decentralized control architectures for Workload Consolidation in large scale server clusters
International Conference on Autonomic Computing, 2012Co-Authors: Rui Wang, Nagarajan KandasamyAbstract:This paper develops a fully decentralized control architecture to address the Workload Consolidation problem in large-scale server clusters wherein the cluster's processing capacity is dynamically tuned to satisfy the service level agreements (SLAs) associated with the incoming Workload while consolidating the Workload onto the fewest number of servers. In a decentralized setting, this problem is decomposed into simpler subproblems, each of which is mapped to a server and solved by a controller assigned to that server. Though control loops on different servers run independently of each other, they are implicitly coupled via the shared high-level performance goal and interactions between controllers may result in undesired system behavior such as SLA violations and frequent switching of cores on and off. Using the proposed architecture as the reference, we analyze how the organization of individual controllers within the control structure affects its overall performance for large clusters of up to thousand servers. Our studies indicate that the control structure, when organized as a causal system in which a precedence relation exists among the individual controllers, achieves a high degree of SLA satisfaction (> 98%) while significantly reducing the corresponding switching cost.
Soamar Homsi - One of the best experts on this subject based on the ideXlab platform.
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Workload Consolidation for cloud data centers with guaranteed qos using request reneging
IEEE Transactions on Parallel and Distributed Systems, 2017Co-Authors: Soamar Homsi, Shuo Liu, Gustavo A Chaparrobaquero, Ou Bai, Shaolei Ren, Gang QuanAbstract:Cloud data centers are widely employed to offer reliable cloud services. However, low resource utilization and high power consumption have been great challenges for cloud providers. Moreover, the rapid increase in demand for affordable cloud services magnifies the obstacles for proficient resource management policies. In this paper, we investigate how to improve resource utilization and power consumption in cloud data centers when delivering services with statistically guaranteed Quality of Service (QoS). We assume that the service provider hosts different types of services, each of which has request classes with different QoS requirements. Different from the traditional approaches that distribute Workloads with different QoS levels on different Virtual Machines (VMs), we introduce an approach to pack requests of the same service type, even with different QoS requirements, into the same VM, and to remove potential failure requests in time to improve resource usage and energy cost. We formally prove that our algorithm can statistically guarantee QoS conditions in terms of deadline miss ratios. We develop a cloud prototype to empirically validate our proposed methods and algorithm. Our experimental results demonstrate that our approach can significantly outperform other traditional approaches in terms of QoS guarantees, power consumption, resource demand and electricity cost.
Hussein T. Mouftah - One of the best experts on this subject based on the ideXlab platform.
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Energy-Efficient Information and Communication Infrastructures in the Smart Grid: A Survey on Interactions and Open Issues
IEEE Communications Surveys and Tutorials, 2015Co-Authors: Melike Erol-kantarci, Hussein T. MouftahAbstract:Smart grid has modernized the way electricity is generated, transported, distributed, and consumed by integrating advanced sensing, communications, and control in the day-to-day operation of the grid. Electricity is a core utility for the functioning of society and for the services provided by information and communication technologies (ICTs). Several concepts of the smart grid, such as dynamic pricing, distributed generation, and demand management, have significantly impacted the operation of ICT services, in particular, communication networks and data centers. Ongoing energy-efficiency and operational expenditures reduction efforts in communication networks and data centers have gained another dimension with those smart grid concepts. In this paper, we provide a comprehensive survey on the smart grid-driven approaches in energy-efficient communications and data centers, and the interaction between smart grid and information and communication infrastructures. Although the studies on smart grid, energy-efficient communications, and green data centers have been separately surveyed in previous studies, to this end, research that falls in the intersection of those fields has not been properly classified and surveyed yet. We start our survey by providing background information on the smart grid and continue with surveying smart grid-driven approaches in energy-efficient communication systems, followed by energy, cost and emission minimizing approaches in data centers, and the corresponding cloud network infrastructure. We discuss the open issues in smart grid-driven approaches in ICTs and point some important research directions such as the distributed renewable energy generation capability-coupled communication infrastructures, optimum energy-efficient network design for the smart grid environment, the impact of green communication techniques on the reliability and latency requirements of smart grid data, Workload Consolidation with smart grid-awareness, and many more.
