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Jemal H Abawajy - One of the best experts on this subject based on the ideXlab platform.
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adaptive hierarchical Scheduling Policy for enterprise grid computing systems
Journal of Network and Computer Applications, 2009Co-Authors: Jemal H AbawajyAbstract:In an enterprise grid computing environments, users have access to multiple resources that may be distributed geographically. Thus, resource allocation and Scheduling is a fundamental issue in achieving high performance on enterprise grid computing. Most of current job Scheduling systems for enterprise grid computing provide batch queuing support and focused solely on the allocation of processors to jobs. However, since I/O is also a critical resource for many jobs, the allocation of processor and I/O resources must be coordinated to allow the system to operate most effectively. To this end, we present a hierarchical Scheduling Policy paying special attention to I/O and service-demands of parallel jobs in homogeneous and heterogeneous systems with background workload. The performance of the proposed Scheduling Policy is studied under various system and workload parameters through simulation. We also compare performance of the proposed Policy with a static space-time sharing Policy. The results show that the proposed Policy performs substantially better than the static space-time sharing Policy.
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fault tolerant Scheduling Policy for grid computing systems
International Parallel and Distributed Processing Symposium, 2004Co-Authors: Jemal H AbawajyAbstract:Summary form only given. With the momentum gaining for the grid computing systems, the issue of deploying support for integrated Scheduling and fault-tolerant approaches becomes paramount importance. Unfortunately, fault-tolerance has not been factored into the design of most existing grid Scheduling strategies. To this end, we propose a fault-tolerant Scheduling Policy that loosely couples job Scheduling with job replication scheme such that jobs are efficiently and reliably executed. Performance evaluation of the proposed fault-tolerant scheduler against a nonfault-tolerant Scheduling Policy is presented and shown that the proposed Policy performs reasonably in the presence of various types of failures.
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time space sharing distributed job Scheduling Policy in a workstation cluster environment
Parallel Computing in Electrical Engineering, 2000Co-Authors: Jemal H Abawajy, Sivarama P DandamudiAbstract:This paper presents an algorithm for Scheduling communication-intensive parallel applications in workstation clusters environment. The proposed Scheduling Policy combines the best attributes of both space-sharing and time-sharing principles and coexists with local schedulers (e.g., the Windows NT scheduler), which both provides coordinated Scheduling and can generalise to provide a wide range of resource abstractions.
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parallel job Scheduling Policy for workstation cluster environments
Cluster Computing, 2000Co-Authors: Jemal H Abawajy, Sivarama P DandamudiAbstract:As workstation clusters (WC) become more commonly usedfor parallel jobs, there is a growing awareness for the needof job Scheduling policies. There have been a fair number ofstudies on how to schedule parallel applications on parallelsystems and a good survey in the area can be found in [5]. Ithas been shown that the best solution to the processorallocation problem in a distributed multiprocessorenvironment is an adaptive Scheduling Policy that can adjustload distribution based on runtime Scheduling algorithms[2,3]. The main idea of adaptive space-sharing policies isthat the number of processors assigned to a job is acompromise between the user’s request and what the systemcan provide. Note that there are differences in thearchitecture of the multiprocessor systems and WC-baseddistributed systems. For example, the processors in themultiprocessors systems are usually homogenous whereasthose of WC are usually heterogeneous. This change ofarchitectural environment requires important differences inthe decisions made by the system Scheduling Policy. Mostof adaptive Scheduling policies for WC-based systemsprovide only rudimentary facilities for partitioning, i.e.,space sharing, the processors among parallel jobs. Inaddition, parallel applications targeted to WC are typicallyresource-intensive, i.e. they require more resources than areavailable at a single site. However, existing adaptiveScheduling policies cannot accommodate this requirement.This is because they may assign 1 processor to a job in theextreme cases [2] or lead to a
Eytan Modiano - One of the best experts on this subject based on the ideXlab platform.
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optimizing information freshness in wireless networks under general interference constraints
IEEE ACM Transactions on Networking, 2020Co-Authors: Rajat Talak, Sertac Karaman, Eytan ModianoAbstract:Age of information (AoI) is a recently proposed metric for measuring information freshness. AoI measures the time that elapsed since the last received update was generated. We consider the problem of minimizing average and peak AoI in a wireless networks, consisting of a set of source-destination links, under general interference constraints. When fresh information is always available for transmission, we show that a stationary Scheduling Policy is peak age optimal. We also prove that this Policy achieves average age that is within a factor of two of the optimal average age. In the case where fresh information is not always available, and packet/information generation rate has to be controlled along with Scheduling links for transmission, we prove an important separation principle : the optimal Scheduling Policy can be designed assuming fresh information, and independently, the packet generation rate control can be done by ignoring interference. Peak and average AoI for discrete time G/Ber/1 queue is analyzed for the first time, which may be of independent interest.
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optimizing information freshness in wireless networks under general interference constraints
Mobile Ad Hoc Networking and Computing, 2018Co-Authors: Rajat Talak, Sertac Karaman, Eytan ModianoAbstract:Age of information (AoI) is a recently proposed metric for measuring information freshness. AoI measures the time that elapsed since the last received update was generated. We consider the problem of minimizing average and peak AoI in wireless networks under general interference constraints. When fresh information is always available for transmission, we show that a stationary Scheduling Policy is peak age optimal. We also prove that this Policy achieves average age that is within a factor of two of the optimal average age. In the case where fresh information is not always available, and packet/information generation rate has to be controlled along with Scheduling links for transmission, we prove an important separation principle: the optimal Scheduling Policy can be designed assuming fresh information, and independently, the packet generation rate control can be done by ignoring interference. Peak and average AoI for discrete time G/Ber/1 queue is analyzed for the first time, which may be of independent interest.
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minimizing the age of information in broadcast wireless networks
Prof. Modiano, 2017Co-Authors: Igor Kadota, Elif Uysalbiyikoglu, Rahul Singh, Eytan ModianoAbstract:We consider a wireless broadcast network with a base station sending time-sensitive information to a number of clients. The Age of Information (AoI), namely the amount of time that elapsed since the most recently delivered packet was generated, captures the freshness of the information. We formulate a discrete-time decision problem to find a Scheduling Policy that minimizes the expected weighted sum AoI of the clients in the network. To the best of our knowledge, this is the first work to provide a Scheduling Policy that optimizes AoI in a wireless network with unreliable channels. The results are twofold: first, we show that a Greedy Policy, which transmits the packet with highest current age, is optimal for the case of symmetric networks. Then, for the general network case, we establish that the problem is indexable and obtain the Whittle Index in closed-form. Numerical results are presented to demonstrate the performance of the policies.
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The Impact of Queue Length Information on Buffer Overflow in Parallel Queues
IEEE Transactions on Information Theory, 2013Co-Authors: Krishna Jagannathan, Eytan ModianoAbstract:We consider a system consisting of N parallel queues, served by one server. Time is slotted, and the server serves one of the queues in each time slot, according to some Scheduling Policy. We first characterize the exponent of the buffer overflow probability and the most likely overflow trajectories under the Longest Queue First (LQF) Scheduling Policy. Under statistically identical arrivals to each queue, we show that the buffer overflow exponents can be simply expressed in terms of the total system occupancy exponent of m parallel queues, for some m ≤ N. We next turn our attention to the rate of queue length information needed to operate a Scheduling Policy, and its relationship to the buffer overflow exponents. It is known that queue length blind policies such as processor sharing and random Scheduling perform worse than the queue aware LQF Policy, when it comes to buffer overflow probability. However, we show that the overflow exponent of the LQF Policy can be preserved with arbitrarily infrequent queue length updates.
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queue length asymptotics for generalized max weight Scheduling in the presence of heavy tailed traffic
International Conference on Computer Communications, 2011Co-Authors: Krishna Jagannathan, Eytan Modiano, Mihalis G Markakis, John N TsitsiklisAbstract:We investigate the asymptotic behavior of the steady-state queue length distribution under generalized max-weight Scheduling in the presence of heavy-tailed traffic. We consider a system consisting of two parallel queues, served by a single server. One of the queues receives heavy-tailed traffic, and the other receives light-tailed traffic. We study the class of throughput optimal max-weight-α Scheduling policies, and derive an exact asymptotic characterization of the steady-state queue length distributions. In particular, we show that the tail of the light queue distribution is heavier than a power-law curve, whose tail coefficient we obtain explicitly. Our asymptotic characterization also shows that the celebrated max-weight Scheduling Policy leads to the worst possible tail of the light queue distribution, among all non-idling policies. Motivated by the above ‘negative’ result regarding the max-weight-α Policy, we analyze a log-max-weight (LMW) Scheduling Policy. We show that the LMW Policy guarantees an exponentially decaying light queue tail, while still being throughput optimal.
Wei Chen - One of the best experts on this subject based on the ideXlab platform.
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joint channel and queue aware Scheduling for latency sensitive mobile edge computing with power constraints
IEEE Transactions on Wireless Communications, 2020Co-Authors: Di Han, Wei Chen, Yuguang FangAbstract:Mobile edge computing (MEC) is a promising technique to improve the quality of computation experience for mobile devices by providing computation resources in their close proximity. However, the design of Scheduling policies for MEC systems inevitably encounters a challenging optimization problem that should take both transmissions and computations into consideration. In particular, how to jointly schedule transmissions and computations should adapt to the cross-layer system dynamics, i.e., random task arrivals and channel state variations. We formulate this Scheduling problem as a joint optimization problem for both transmissions and computations in order to minimize the power consumption of mobile devices, while meeting the latency requirement. With given distributions of the system dynamics, Markov decision process (MDP) is used to model the system operations. Based on this model, the power-optimal Scheduling Policy can be obtained by converting the joint optimization problem to linear programming (LP) by using variable substitutions and thus the optimal power-latency tradeoff can be achieved. When the distribution information of the system dynamics is unknown, we exploit the Lyapunov optimization to present a low complexity Scheduling Policy. Our theoretical analysis and extensive simulation studies show that our approach can offer a good tradeoff between power consumption and latency.
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Joint Queue-Aware and Channel-Aware Delay Optimal Scheduling of Arbitrarily Bursty Traffic Over Multi-State Time-Varying Channels
IEEE Transactions on Communications, 2019Co-Authors: Meng Wang, Wei Chen, Anthony EphremidesAbstract:This paper is motivated by the observation that the average queueing delay can be decreased by sacrificing power efficiency in wireless communications. In this sense, we naturally wonder what the minimum queueing delay is when the available power is limited and how to achieve the minimum queueing delay. To answer these two questions in the scenario where randomly arriving packets are transmitted over multi-state wireless fading channel, a probabilistic cross-layer Scheduling Policy is proposed in this paper, and characterized by a constrained Markov decision process. Using the steady-state probability of the underlying Markov chain, we are able to derive the mathematical expressions of the concerned metrics, namely, the average queueing delay and the average power consumption. To describe the delay-power tradeoff, we formulate a non-linear programming problem, which, however, is very challenging to solve. By analyzing its structure, this optimization problem can be converted into an equivalent linear programming problem via variable substitution, which allows us to derive the optimal delay-power tradeoff as well as the optimal Scheduling Policy. The optimal Scheduling Policy turns out to be dual-threshold-based, which means transmission decisions should be made based on the optimal thresholds imposed on the queue length and the channel state.
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delay optimal Scheduling of arbitrarily bursty traffic over multi state time varying channels
Global Communications Conference, 2016Co-Authors: Meng Wang, Juan Liu, Wei ChenAbstract:An important challenge in the Internet of Things (IoT) is to provide real-time services on low-energy-supply devices. In this paper, we study joint queue-aware and channel-aware Scheduling of arbitrarily bursty traffic over multi-state time-varying channels, where the bursty packet arrival in the network layer, the backlogged queue in the data link layer, and the power adaptive transmission with fixed modulation in the physical layer are jointly considered from a cross-layer perspective. To achieve minimum queueing delay given a power constraint, a probabilistic cross-layer Scheduling Policy is proposed, and characterized by a Markov chain model. To describe the delay-power tradeoff, we formulate a non-linear optimization problem, which however is very challenging to solve. To handle with this issue, we convert the optimization problem into an equivalent Linear Programming (LP) problem, which allows us to obtain the optimal threshold-based Scheduling Policy with an optimal threshold imposed on the queue length in accordance with each channel state.
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delay optimal Scheduling of arbitrarily bursty traffic over multi state time varying channels
arXiv: Information Theory, 2016Co-Authors: Meng Wang, Juan Liu, Wei ChenAbstract:In this paper, we study joint queue-aware and channel-aware Scheduling of arbitrarily bursty traffic over multi-state time-varying channels, where the bursty packet arrival in the network layer, the backlogged queue in the data link layer, and the power adaptive transmission with fixed modulation in the physical layer are jointly considered from a cross-layer perspective. To achieve minimum queueing delay given a power constraint, a probabilistic cross-layer Scheduling Policy is proposed, and characterized by a Markov chain model. To describe the delay-power tradeoff, we formulate a non-linear optimization problem, which however is very challenging to solve. To handle with this issue, we convert the optimization problem into an equivalent Linear Programming (LP) problem, which allows us to obtain the optimal threshold-based Scheduling Policy with an optimal threshold imposed on the queue length in accordance with each channel state.
Anthony Ephremides - One of the best experts on this subject based on the ideXlab platform.
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Joint Queue-Aware and Channel-Aware Delay Optimal Scheduling of Arbitrarily Bursty Traffic Over Multi-State Time-Varying Channels
IEEE Transactions on Communications, 2019Co-Authors: Meng Wang, Wei Chen, Anthony EphremidesAbstract:This paper is motivated by the observation that the average queueing delay can be decreased by sacrificing power efficiency in wireless communications. In this sense, we naturally wonder what the minimum queueing delay is when the available power is limited and how to achieve the minimum queueing delay. To answer these two questions in the scenario where randomly arriving packets are transmitted over multi-state wireless fading channel, a probabilistic cross-layer Scheduling Policy is proposed in this paper, and characterized by a constrained Markov decision process. Using the steady-state probability of the underlying Markov chain, we are able to derive the mathematical expressions of the concerned metrics, namely, the average queueing delay and the average power consumption. To describe the delay-power tradeoff, we formulate a non-linear programming problem, which, however, is very challenging to solve. By analyzing its structure, this optimization problem can be converted into an equivalent linear programming problem via variable substitution, which allows us to derive the optimal delay-power tradeoff as well as the optimal Scheduling Policy. The optimal Scheduling Policy turns out to be dual-threshold-based, which means transmission decisions should be made based on the optimal thresholds imposed on the queue length and the channel state.
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stability properties of constrained queueing systems and Scheduling policies for maximum throughput in multihop radio networks
IEEE Transactions on Automatic Control, 1992Co-Authors: Leandros Tassiulas, Anthony EphremidesAbstract:The stability of a queueing network with interdependent servers is considered. The dependency among the servers is described by the definition of their subsets that can be activated simultaneously. Multihop radio networks provide a motivation for the consideration of this system. The problem of Scheduling the server activation under the constraints imposed by the dependency among servers is studied. The performance criterion of a Scheduling Policy is its throughput that is characterized by its stability region, that is, the set of vectors of arrival and service rates for which the system is stable. A Policy is obtained which is optimal in the sense that its stability region is a superset of the stability region of every other Scheduling Policy, and this stability region is characterized. The behavior of the network is studied for arrival rates that lie outside the stability region. Implications of the results in certain types of concurrent database and parallel processing systems are discussed. >
Krishna Jagannathan - One of the best experts on this subject based on the ideXlab platform.
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The Impact of Queue Length Information on Buffer Overflow in Parallel Queues
IEEE Transactions on Information Theory, 2013Co-Authors: Krishna Jagannathan, Eytan ModianoAbstract:We consider a system consisting of N parallel queues, served by one server. Time is slotted, and the server serves one of the queues in each time slot, according to some Scheduling Policy. We first characterize the exponent of the buffer overflow probability and the most likely overflow trajectories under the Longest Queue First (LQF) Scheduling Policy. Under statistically identical arrivals to each queue, we show that the buffer overflow exponents can be simply expressed in terms of the total system occupancy exponent of m parallel queues, for some m ≤ N. We next turn our attention to the rate of queue length information needed to operate a Scheduling Policy, and its relationship to the buffer overflow exponents. It is known that queue length blind policies such as processor sharing and random Scheduling perform worse than the queue aware LQF Policy, when it comes to buffer overflow probability. However, we show that the overflow exponent of the LQF Policy can be preserved with arbitrarily infrequent queue length updates.
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queue length asymptotics for generalized max weight Scheduling in the presence of heavy tailed traffic
International Conference on Computer Communications, 2011Co-Authors: Krishna Jagannathan, Eytan Modiano, Mihalis G Markakis, John N TsitsiklisAbstract:We investigate the asymptotic behavior of the steady-state queue length distribution under generalized max-weight Scheduling in the presence of heavy-tailed traffic. We consider a system consisting of two parallel queues, served by a single server. One of the queues receives heavy-tailed traffic, and the other receives light-tailed traffic. We study the class of throughput optimal max-weight-α Scheduling policies, and derive an exact asymptotic characterization of the steady-state queue length distributions. In particular, we show that the tail of the light queue distribution is heavier than a power-law curve, whose tail coefficient we obtain explicitly. Our asymptotic characterization also shows that the celebrated max-weight Scheduling Policy leads to the worst possible tail of the light queue distribution, among all non-idling policies. Motivated by the above ‘negative’ result regarding the max-weight-α Policy, we analyze a log-max-weight (LMW) Scheduling Policy. We show that the LMW Policy guarantees an exponentially decaying light queue tail, while still being throughput optimal.
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queue length asymptotics for generalized max weight Scheduling in the presence of heavy tailed traffic
arXiv: Networking and Internet Architecture, 2010Co-Authors: Krishna Jagannathan, Eytan Modiano, Mihalis G Markakis, John N TsitsiklisAbstract:We investigate the asymptotic behavior of the steady-state queue length distribution under generalized max-weight Scheduling in the presence of heavy-tailed traffic. We consider a system consisting of two parallel queues, served by a single server. One of the queues receives heavy-tailed traffic, and the other receives light-tailed traffic. We study the class of throughput optimal max-weight-alpha Scheduling policies, and derive an exact asymptotic characterization of the steady-state queue length distributions. In particular, we show that the tail of the light queue distribution is heavier than a power-law curve, whose tail coefficient we obtain explicitly. Our asymptotic characterization also contains an intuitively surprising result - the celebrated max-weight Scheduling Policy leads to the worst possible tail of the light queue distribution, among all non-idling policies. Motivated by the above negative result regarding the max-weight-alpha Policy, we analyze a log-max-weight (LMW) Scheduling Policy. We show that the LMW Policy guarantees an exponentially decaying light queue tail, while still being throughput optimal.
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The impact of queue length information on buffer overflow in parallel queues
2009 47th Annual Allerton Conference on Communication Control and Computing (Allerton), 2009Co-Authors: Krishna Jagannathan, Eytan ModianoAbstract:We consider a system consisting of N parallel queues, served by one server. Time is slotted, and the server serves one of the queues in each time slot, according to some Scheduling Policy. In the first part of the paper, we characterize the buffer overflow exponents and the likeliest overflow trajectories under the Longest Queue First (LQF) Scheduling Policy. Under statistically identical arrivals to each queue, we show that the buffer overflow exponent can be simply expressed in terms of the total system occupancy exponent of m parallel queues, for some m ¿ N. We next turn our attention to the rate of queue length information needed to operate a Scheduling Policy, and its relationship to the buffer overflow exponents. It is known that LQF Scheduling has superior overflow exponents compared to queue blind policies such as processor sharing (PS) and random Scheduling. However, we show that the overflow exponent of the LQF Policy can be preserved under arbitrarily infrequent queue length information.
