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  • 1
    Language: English
    In: The Journal of Supercomputing, 2012, Vol.61(1), pp.46-66
    Description: There is growing demand on datacenters to serve more clients with reasonable response times, demanding more hardware resources, and higher energy consumption. Energy-aware datacenters have thus been amongst the forerunners to deploy virtualization technology to multiplex their physical machines (PMs) to as many virtual machines (VMs) as possible in order to utilize their hardware resources more effectively and save power. The achievement of this objective strongly depends on how smart VMs are consolidated. In this paper, we show that blind consolidation of VMs not only does not reduce the power consumption of datacenters but it can lead to energy wastage. We present four models, namely the target system model, the application model, the energy model, and the migration model, to identify the performance interferences between processor and disk utilizations and the costs of migrating VMs. We also present a consolidation fitness metric to evaluate the merit of consolidating a number of known VMs on a PM based on the processing and storage workloads of VMs. We then propose an energy-aware scheduling algorithm using a set of objective functions in terms of this consolidation fitness metric and presented power and migration models. The proposed scheduling algorithm assigns a set of VMs to a set of PMs in a way to minimize the total power consumption of PMs in the whole datacenter. Empirical results show nearly 24.9% power savings and nearly 1.2% performance degradation when the proposed scheduling algorithm is used compared to when other scheduling algorithms are used.
    Keywords: Power management ; Virtualization technology ; Workload characterization ; Cloud computing
    ISSN: 0920-8542
    E-ISSN: 1573-0484
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  • 2
    Language: English
    In: Computing, 2013, Vol.95(1), pp.67-88
    Description: Power efficiency is one of the main challenges in large-scale distributed systems such as datacenters, Grids, and Clouds. One can study the scheduling of applications in such large-scale distributed systems by representing applications as a set of precedence-constrained tasks and modeling them by a Directed Acyclic Graph. In this paper we address the problem of scheduling a set of tasks with precedence constraints on a heterogeneous set of Computing Resources (CRs) with the dual objective of minimizing the overall makespan and reducing the aggregate power consumption of CRs. Most of the related works in this area use Dynamic Voltage and Frequency Scaling (DVFS) approach to achieve these objectives. However, DVFS requires special hardware support that may not be available on all processors in large-scale distributed systems. In contrast, we propose a novel two-phase solution called PASTA that does not require any special hardware support. In its first phase, it uses a novel algorithm to select a subset of available CRs for running an application that can balance between lower overall power consumption of CRs and shorter makespan of application task schedules. In its second phase, it uses a low-complexity power-aware algorithm that creates a schedule for running application tasks on the selected CRs. We show that the overall time complexity of PASTA is $$O(p.v^{2})$$ where $$p$$ is the number of CRs and $$v$$ is the number of tasks. By using simulative experiments on real-world task graphs, we show that the makespan of schedules produced by PASTA are approximately 20 % longer than the ones produced by the well-known HEFT algorithm. However, the schedules produced by PASTA consume nearly 60 % less energy than those produced by HEFT. Empirical experiments on a physical test-bed confirm the power efficiency of PASTA in comparison with HEFT too.
    Keywords: DAG scheduling ; Energy-awareness ; High performance computing ; Heterogeneous computing resources
    ISSN: 0010-485X
    E-ISSN: 1436-5057
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  • 3
    Language: English
    In: The Journal of Supercomputing, 2012, Vol.59(1), pp.548-567
    Description: Interprocess communication (IPC) is a well-known technique commonly used by programs running on homogeneous distributed systems. However, it cannot be used readily and efficiently by programs running on heterogeneous distributed systems. This is because it must be given a uniform interface either by a set of middleware or more efficiently properly ported to the kernel of all varieties of open source and closed source proprietary operating systems running on heterogeneous nodes of distributed systems. This is particularly problematic to achieve when the kernel code of closed source operating systems are inaccessible to third parties. We propose an alternative nonproprietary approach to enable the use of IPC in heterogeneous distributed systems by wrapping IPC calls from the kernel of closed source operating systems, and converting them into equivalent IPC calls that are efficiently implemented inside the kernel code of open source operating systems. To show the superiority of our approach, we developed a wrapper for converting MS-Windows IPC calls into equivalent Linux IPC calls and benched our approach on a hybrid computer cluster running both types of operating systems.
    Keywords: Hybrid cluster ; Wrapper ; Open source ; Closed source ; Interprocess communication (IPC) ; Kernel level ; User level ; Message ; Remote procedure call
    ISSN: 0920-8542
    E-ISSN: 1573-0484
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  • 4
    Language: English
    In: The Journal of Supercomputing, 2010, Vol.52(2), pp.149-170
    Description: Traditional Peer-to-Peer (P2P) systems were restricted to sharing of files on the Internet. Although some of the more recent P2P distributed systems have tried to support transparent sharing of other types of resources, like computer processing power, but none allow and support sharing of all types of resources available on the Internet. This is mainly because the resource management part of P2P systems are custom designed in support of specific features of only one type of resource, making simultaneous access to all types of resources impractical. Another shortcoming of existing P2P systems is that they follow a client/server model of resource sharing that makes them structurally constrained and dependent on dedicated servers (resource managers). Clients must get permission from a limited number of servers to share or access resources, and resource management mechanisms run on these servers. Because resource management by servers is not dynamically reconfigurable, such P2P systems are not scalable to the ever growing extent of Internet. We present an integrated framework for sharing of all types of resources in P2P systems by using a dynamic structure for managing four basic types of resources, namely process , file , memory , and I/O , in the same way they are routinely managed by operating systems. The proposed framework allows P2P systems to use dynamically reconfigurable resource management mechanisms where each machine in the P2P system can at the same time serve both as a server and as a client. The pattern of requests for shared resources at a given time identifies which machines are currently servers and which ones are currently clients. The client server pattern changes with changes in the pattern of requests for distributed resources. Scalable P2P systems with dynamically reconfigurable structures can thus be built using our proposed resource management mechanisms. This dynamic structure also allows for the interoperability of different P2P systems.
    Keywords: P2P distributed systems ; Integrated resource management ; Resource sharing ; Framework ; Operating system
    ISSN: 0920-8542
    E-ISSN: 1573-0484
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  • 5
    Language: English
    In: Future Generation Computer Systems, October 2013, Vol.29(8), pp.2057-2066
    Description: The use of virtualization technology (VT) has become widespread in modern datacenters and Clouds in recent years. In spite of their many advantages, such as provisioning of isolated execution environments and migration, current implementations of VT do not provide effective performance isolation between virtual machines (VMs) running on a physical machine (PM) due to workload interference of VMs. Generally, this interference is due to contention on physical resources that impacts performance in different workload configurations. To investigate the impacts of this interference, we formalize the concept of interference for a consolidated multi-tenant virtual environment. This formulation, represented as a mathematical model, can be used by schedulers to estimate the interference of a consolidated virtual environment in terms of the processing and networking workloads of running VMs, and the number of consolidated VMs. Based on the proposed model, we present a novel batch scheduler that reduces the interference of running tenant VMs by pausing VMs that have a higher impact on proliferation of the interference. The scheduler achieves this by selecting a set of VMs that produce the least interference using a 0–1 knapsack problem solver. The selected VMs are allowed to run and other VMs are paused. Users are not troubled by the pausing and resumption of VMs for a short time because the scheduler has been designed for the execution of batch type applications such as scientific applications. Evaluation results on the makespan of VMs executed under the control of our scheduler have shown nearly 33% improvement in the best case and 7% improvement in the worst case compared to the case in which all VMs are running concurrently. In addition, the results show that our scheduling algorithm outperforms serial and random scheduling of VMs as well. ► We propose a VM interference model in terms of VM workloads and the number of VMs. ► We present a VM batch scheduler, working on the basis of pausing/resuming VMs. ► The proposed scheduling algorithm is mapped to a 0–1 knapsack problem. ► The results of our scheduler are compared to random, concurrent and serial schedulers. ► The results show that our scheduler outperforms other schedulers.
    Keywords: Virtualization Technology ; Consolidated Multi-Tenant Virtual Environments ; Batch Scheduling ; Performance Isolation ; Workload Interference ; Knapsack Algorithm ; Computer Science
    ISSN: 0167-739X
    E-ISSN: 1872-7115
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  • 6
    Language: English
    In: Computing, 2012, Vol.94(11), pp.833-856
    Description: The deployment of sensors without enough coverage can result in unreliable outputs in wireless sensor networks (WSNs). Thus sensing coverage is one of the most important quality of service factors in WSNs. A useful metric for quantifying the coverage reliability is the coverage rate that is the area covered by sensor nodes in a region of interest. The network sink can be informed about locations of all nodes and calculate the coverage rate centrally. However, this approach creates huge load on the network nodes that had to send their location information to the sink. Thus, a distributed approach is required to calculate the coverage rate. This paper is among the very first to provide a localized approach to calculate the coverage rate. We provide two coverage rate calculation (CRC) protocols, namely distributed exact coverage rate calculation (DECRC) and distributed probabilistic coverage rate calculation (DPCRC). DECRC calculates the coverage rate precisely using the idealized disk graph model. Precise calculation of the coverage rate is a unique property of DECRC compared to similar works that have used the disk graph model. In contrast, DPCRC uses a more realistic model that is probabilistic coverage model to determine an approximate coverage rate. DPCRC is in fact an extended version of DECRC that uses a set of localized techniques to make it a low cost protocol. Simulation results show significant overall performance improvement of CRC protocols compared to related works.
    Keywords: Wireless sensor networks ; Coverage rate ; Boundary detection ; Disk graph model ; Probabilistic coverage
    ISSN: 0010-485X
    E-ISSN: 1436-5057
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  • 7
    Language: English
    In: The Journal of Supercomputing, 2014, Vol.68(3), pp.1538-1555
    Description: Resource overloading causes one of the main challenges in computing environments. In this case, a new resource should be discovered to transfer the extra load. However, this results in drastic performance degradation. Thus, it is of high importance to discover the appropriate resource at first. So far, several resource discovery mechanisms have been introduced to overcome this challenge, a majority of which neglect the fact that this important decision should be made in cooperation with other units existing in a computing environment. One of the units is load balancing. In this paper, we propose a model for communication between resource discovery and load balancing units in a computing environment. Based on the model, resource discovery and load balancing decisions are made cooperatively considering the behavior of running processes and resources capacities. These considerations make decisions more precise. In addition, the model presents the loosest type of coupling between resource discovery and load balancing units, i.e., message coupling. This feature provides a better scalability in size for the model. Comparative results show that the proposed model increases scalability in size by 7 to 15 %, cuts message transmission rate by 15 % and improves hit rate by 51 %.
    Keywords: Computing environments ; Communication model ; Resource discovery ; Load balancing ; Scalability ; Message transmission rate ; Hit rate
    ISSN: 0920-8542
    E-ISSN: 1573-0484
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  • 8
    Language: English
    In: The Journal of Supercomputing, 2018, Vol.74(6), pp.2353-2384
    Description: Although virtualization technology is recently applied to next-generation distributed high-performance computing systems, theoretical aspects of scheduling jobs on these virtualized environments are not sufficiently studied, especially in online and non-clairvoyant cases. Virtualization of computing resources results in interference and virtualization overheads that negatively impact the load balancing objectives on commonly used cluster of multi-core physical machines. We present a technique for non-clairvoyant online scheduling of globally synchronized jobs, each of which spawns tasks to execute compute-intensive works. Our technique considers both load balancing of physical cores and per job synchronization cost minimization. We show that in the presence of arbitrary virtualization overheads, interference effects and synchronization cost, the problem can be reduced to an online unrelated parallel machine scheduling, which is solved using routing of virtual circuits. We present a new opportunity cost model to reduce the problem to the routing of virtual circuits and prove the effectiveness of our scheduling technique using mathematical analysis and simulative experiments.
    Keywords: Job scheduling ; Virtual clusters ; Synchronization ; Load balancing ; Non-clairvoyant
    ISSN: 0920-8542
    E-ISSN: 1573-0484
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  • 9
    Language: English
    In: The Journal of Supercomputing, 2014, Vol.67(1), pp.1-30
    Description: High Performance Cluster Computing Systems (HPCSs) represent the best performance because their configuration is customized regarding the features of the problem to be solved at design time. Therefore, if the problem has static nature and features, the best customized configuration can be done. New generations of scientific and industrial problems usually have dynamic nature and behavior. A drawback of this dynamicity is that the customized HPCSs face challenges at runtime, and consequently show the worse performance. The reason for this might be due to the fact that dynamic problems are not adapted to configuration of the HPCS. Hence, requests of the dynamic problem are not in the direction of the HPCS configuration. The main proposed solutions for this challenge are dynamic load balancing or using reconfigurable platforms. In this paper, a vector algebra-based model for HPCS reconfiguration at runtime is presented and named AMRC. This model determines the element causing the dynamic behavior and analyzes the reason regarding both software and hardware at runtime. Some results of the presented model show that by defining a general state vector whose direction is toward reaching high performance computing and whose weight is based on the initial features and explicit requirements of the problem, as well as by defining a vector for each process in the problem at runtime, we can trace changes in the directions and uncover the reason for them.
    Keywords: High performance cluster computing ; Reconfiguration ; Dynamic problems ; Vector algebra model
    ISSN: 0920-8542
    E-ISSN: 1573-0484
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  • 10
    Language: English
    In: Journal of Computer and System Sciences, May 2017, Vol.85, pp.1-17
    Description: This paper presents a high performance technique for virtualization-unaware scheduling of compute-intensive synchronized (i.e., tightly-coupled) jobs in virtualized high performance computing systems. Online tightly-coupled jobs are assigned/reassigned to clustered virtual machines based on synchronization costs. Virtual machines are in turn assigned/reassigned to clustered physical machines based on CPU load. Our analytical study shows that it is possible to minimize the performance and scalability degradation of high performance computing applications such as ExaScale and PetaScale systems and applications that are recommended to use virtualization technology to achieve higher degree of performability, namely higher utilization, energy efficiency, portability, flexibility and configurability.
    Keywords: High Performance ; Virtual Machine ; Cluster ; Tightly-Coupled ; Scheduling ; Opportunity Cost ; Synchronized Jobs ; Scalability ; Engineering ; Computer Science
    ISSN: 0022-0000
    E-ISSN: 1090-2724
    Source: ScienceDirect Journals (Elsevier)
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