Virtual machine consolidation in cloud data centers using ACO metaheuristic C3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Authors: Ferdaus, Md Hasanul , Murshed, Manzur , Calheiros, Rodrigo , Buyya, Rajkumar
- Date: 2014
- Type: Text , Conference paper
- Relation: 20th International Conference on Parallel Processing, Euro-Par 2014 Vol. 8632 LNCS, p. 306-317
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- Description: In this paper, we propose the AVVMC VM consolidation scheme that focuses on balanced resource utilization of servers across different computing resources (CPU, memory, and network I/O) with the goal of minimizing power consumption and resource wastage. Since the VM consolidation problem is strictly NP-hard and computationally infeasible for large data centers, we propose adaptation and integration of the Ant Colony Optimization (ACO) metaheuristic with balanced usage of computing resources based on vector algebra. Our simulation results show that AVVMC outperforms existing methods and achieves improvement in both energy consumption and resource wastage reduction.
Network-aware virtual machine placement and migration in cloud data centres
- Authors: Ferdaus, Md Hasanul , Murshed, Manzur , Clalheiros, Rodrigo , Buyya, Rajkumar
- Date: 2015
- Type: Text , Book chapter
- Relation: Emerging research in cloud distributed computing systems (Advances in systems analysis, software engineering, and high performance computing (ASASEHPC) book series) Chapter 2 p. 42-91
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- Description: With the pragmatic realization of computing as a utility, Cloud Computing has recently emerged as a highly successful alternative IT paradigm. Cloud providers are deploying large-scale data centers across the globe to meet the Cloud customers’ compute, storage, and network resource demands. Efficiency and scalability of these data centers, as well as the performance of the hosted applications’ highly depend on the allocations of the data center resources. Very recently, network-aware Virtual Machine (VM) placement and migration is developing as a very promising technique for the optimization of compute-network resource utilization, energy consumption, and network traffic minimization. This chapter presents the relevant background information and a detailed taxonomy that characterizes and classifies the various components of VM placement and migration techniques, as well as an elaborate survey and comparative analysis of the state of the art techniques. Besides highlighting the various aspects and insights of the network-aware VM placement and migration strategies and algorithms proposed by the research community, the survey further identifies the benefits and limitations of the existing techniques and discusses on the future research directions.
Exploiting user provided information in dynamic consolidation of virtual machines to minimize energy consumption of cloud data centers
- Authors: Khan, Anit , Paplinski, Andrew , Khan, Abdul , Murshed, Manzur , Buyya, Rajkumar
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 3rd International Conference on Fog and Mobile Edge Computing, FMEC 2018; Barcelona, Spain; 23rd-26th April 2018; p. 105-114
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- Description: Dynamic consolidation of Virtual Machines (VMs) can effectively enhance the resource utilization and energy-efficiency of the Cloud Data Centers (CDC). Existing research on Cloud resource reservation and scheduling signify that Cloud Service Users (CSUs) can play a crucial role in improving the resource utilization by providing valuable information to Cloud service providers. However, utilization of CSUs' provided information in minimization of energy consumption of CDC is a novel research direction. The challenges herein are twofold. First, finding the right benign information to be received from a CSU which can complement the energy-efficiency of CDC. Second, smart application of such information to significantly reduce the energy consumption of CDC. To address those research challenges, we have proposed a novel heuristic Dynamic VM Consolidation algorithm, RTDVMC, which minimizes the energy consumption of CDC through exploiting CSU provided information. Our research exemplifies the fact that if VMs are dynamically consolidated based on the time when a VM can be removed from CDC-a useful information to be received from respective CSU, then more physical machines can be turned into sleep state, yielding lower energy consumption. We have simulated the performance of RTDVMC with real Cloud workload traces originated from more than 800 PlanetLab VMs. The empirical figures affirm the superiority of RTDVMC over existing prominent Static and Adaptive Threshold based DVMC algorithms.
An algorithm for network and data-aware placement of multi-tier applications in cloud data centers
- Authors: Ferdaus, Md Hasanul , Murshed, Manzur , Calheiros, Rodrigo , Buyya, Rajkumar
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Network and Computer Applications Vol. 98, no. (2017), p. 65-83
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- Description: Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses network-aware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the state-of-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%. © 2017 Elsevier Ltd
Workload-aware incremental repartitioning of shared-nothing distributed databases for scalable OLTP applications
- Authors: Kamal, Joarder , Murshed, Manzur , Buyya, Rajkumar
- Date: 2016
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 56, no. March (2016), p. 421-436
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- Description: On-line Transaction Processing (OLTP) applications often rely on shared-nothing distributed databases that can sustain rapid growth in data volume. Distributed transactions (DTs) that involve data tuples from multiple geo-distributed servers can adversely impact the performance of such databases, especially when the transactions are short-lived and these require immediate responses. The. k-way min-cut graph clustering based database repartitioning algorithms can be used to reduce the number of DTs with acceptable level of load balancing. Web applications, where DT profile changes over time due to dynamically varying workload patterns, frequent database repartitioning is needed to keep up with the change. This paper addresses this emerging challenge by introducing incremental repartitioning. In each repartitioning cycle, DT profile is learnt online and. k-way min-cut clustering algorithm is applied on a special sub-graph representing all DTs as well as those non-DTs that have at least one tuple in a DT. The latter ensures that the min-cut algorithm minimally reintroduces new DTs from the non-DTs while maximally transforming existing DTs into non-DTs in the new partitioning. Potential load imbalance risk is mitigated by applying the graph clustering algorithm on the finer logical partitions instead of the servers and relying on random one-to-one cluster-to-partition mapping that naturally balances out loads. Inter-server data-migration due to repartitioning is kept in check with two special mappings favouring the current partition of majority tuples in a cluster-the many-to-one version minimising data migrations alone and the one-to-one version reducing data migration without affecting load balancing. A distributed data lookup process, inspired by the roaming protocol in mobile networks, is introduced to efficiently handle data migration without affecting scalability. The effectiveness of the proposed framework is evaluated on realistic TPC-C workloads comprehensively using graph, hypergraph, and compressed hypergraph representations used in the literature. To compare the performance of any incremental repartitioning framework without any bias of the external min-cut algorithm due to graph size variations, a transaction generation model is developed that can maintain a target number of unique transactions in any arbitrary observation window, irrespective of new transaction arrival rate. The overall impact of DTs at any instance is estimated from the exponential moving average of the recurrence period of unique transactions to avoid transient fluctuations. The effectiveness and adaptability of the proposed incremental repartitioning framework for transactional workloads have been established with extensive simulations on both range partitioned and consistent hash partitioned databases. © 2015 Elsevier B.V.
Dynamic virtual machine consolidation algorithms for energy-efficient cloud resource management : A review
- Authors: Khan, Md Anit , Paplinski, Andrew , Khan, Abdul , Murshed, Manzur , Buyya, Rajkumar
- Date: 2017
- Type: Text , Book chapter
- Relation: Sustainable Cloud and Energy Services : Principles and Practice Chapter 6 p. 135-165
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- Description: As envisioned by Leonard Kleinrock [1], Cloud computing has transformed the dream of “computing as a utility” into reality, so much so it has turned out as the latest computing paradigm [2]. Cloud computing is called as Service-on-demand, as Cloud Service Providers (CSPs) assure users about potentially unlimited amount of resources that can be chartered on demand. It is also known as elastic computing, since Cloud Service Users (CSUs) can dynamically scale, expand, or shrink their rented resources anytime and expect to pay for the exact tenure of resource usage under Service Level Agreements (SLA). Through such flexibilities and financial benefits, CSPs have been attracting millions of clients who are simultaneously sharing the underlying computing and storage resources that are collectively known as Cloud data centers.
Workload-aware incremental repartitioning of shared-nothing distributed databases for scalable cloud applications
- Authors: Kamal, Joarder , Murshed, Manzur , Buyya, Rajkumar
- Date: 2014
- Type: Text , Conference paper
- Relation: 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC) p. 213-222
- Full Text: false
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- Description: Cloud applications often rely on shared-nothing distributed databases that can sustain rapid growth in data volume. Distributed transactions (DTs) that involve data tuples from multiple geo-distributed servers can adversely impact the performance of such databases, especially when the transactions are short-lived in and require immediate response. The k-way min-cut graph clustering algorithm has been found effective to reduce the number of DTs with acceptable level of load balancing. Benefits of such a static partitioning scheme, however, is short-lived in Cloud applications with dynamically varying workload patterns where DT profile changes over time. This paper addresses this emerging challenge by introducing incremental repartitioning. In each repartitioning cycle, DT profile is learnt online and k-way min-cut clustering algorithm is applied on a special sub-graph representing all DTs as well as those non-DTs that have at least one tuple in a DT. The latter ensures that the min-cut algorithm minimally reintroduces new DTs from the non-DTs while maximally transforming existing DTs into non-DTs in the new partitioning. Potential load imbalance risk is mitigated by applying the graph clustering algorithm on the finer logical partitions instead of the servers and relying on random one-to-one cluster-to-partition mapping that naturally balances out loads. Inter-server data-migration due to repartitioning is kept in check with two special mappings favouring the current partition of majority tuples in a cluster -- the many-to-one version minimising data migrations alone and the one-to-one version reducing data migration without affecting load balancing. A distributed data lookup process, inspired by the roaming protocol in mobile networks, is introduced to efficiently handle data migration without affecting scalability. The effectiveness of the proposed framework is evaluated on realistic TPC-C workloads comprehensively using graph, hyper graph, and compressed hyper graph representations used in the literature. Simulation results convincingly support incremental repartitioning against static partitioning.
Multi-objective dynamic virtual machine consolidation algorithm for cloud data centers with highly energy proportional servers and heterogeneous workload
- Authors: Khan, Md Anit , Paplinski, Andrew , Khan, Abdul , Murshed, Manzur , Buyya, Rajkumar
- Date: 2022
- Type: Text , Book chapter
- Relation: New Frontiers in Cloud Computing and Internet of Things Chapter 3 p. 69-106
- Full Text: false
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- Description: Present Dynamic VM Consolidation (DVMC) algorithms assume that optimal energy efficiency can be achieved via maximum load on Physical Machines (PMs). Such assumption has become invalid with the advent of the highly energy proportional PMs. Additionally, these algorithms consider only varying resource demand, ignoring dissimilarity of workload finishing time, aka the VM Release Time (VMRT), whereas both aspects are strongly associated with energy consumption. Consequently, traditional algorithms fail to proffer optimal performance under real Cloud scenarios. Although minimization of VM migration brings massive benefit for Cloud Data Center (CDC), it is complete opposite of what is needed to minimize energy consumption through DVMC. As such, our proposed multi-objective Stochastic Release Time aware DVMC (SRTDVMC) algorithm is unique in addressing concomitant minimization of energy consumption and VM migration in the presence of state-of-the-art PMs and heterogeneous workloads. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Classification of methods to reduce clinical alarm signals for remote patient monitoring : a critical review
- Authors: Arora, Teena , Balasubramanian, Venki , Stranieri, Andrew , Shenhan, Mai , Buyya, Rajkumar , Islam, Sardar
- Date: 2023
- Type: Text , Book chapter
- Relation: Cloud Computing in Medical Imaging Chapter 10 p. 173-194
- Full Text: false
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